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Electro-optic acquisition system for ECG wearable sensor applications
Sensors and Actuators A 203 (2013) 316–323
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Sensors and Actuators A: Physical journal homepage: www.elsevier.com/locate/sna
Electro-optic acquisition system for ECG wearable sensor applications M.S. Fernandes, J.H. Correia, P.M. Mendes ∗ DEI – Universidade do Minho, Campus Azurem, 4800-058 Guimaraes, Portugal
a r t i c l e
i n f o
Article history: Received 24 February 2013 Received in revised form 3 September 2013 Accepted 3 September 2013 Available online 13 September 2013 Keywords: ECG monitoring Biomedical sensors Photonic sensors Wearable electronics
a b s t r a c t In the past few years a remarkable progress has been made in the design of sensors for wearable devices. Nevertheless, the existent devices are difficult to integrate, mainly due to the quantity of electrical interconnections and components required at the sensing places. This leads to the need for a new generation of wearable sensors. Photonic sensors have been presented in the medical field as a valuable alternative where power supply, electromagnetic interference and integration constitute a challenge. Therefore, this paper presents an electro-optic (EO) acquisition system designed for wearable Electrocardiogram (ECG) monitoring devices. The system includes a Lithium Niobate Mach-Zehnder Interferometer (MZI) modulator as the sensing element, and an optoelectronic signal conversion and processing stage. Tests were made in order to evaluate system performance in terms of signal amplification and sensitivity, frequency response and linearity. Obtained results have shown a suitable sensor’s sensitivity of 20 V and a frequency response of 60 dB flat from 0.2 to 40 Hz. Clinical sensor’s performance was tested considering the requirements for evaluation of ECG. Results were compared with clinical standard electronics. Clear ECGs can be obtained with the EO sensor, showing adequate and reliable clinical performance. This EO sensor can be integrated into wearable materials being a suitable alternative for clinical cardiac monitoring. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Over the next few years, the market for wearable sensors is expected to grow to more than 400 million devices [1]. This is a result of the growing need to a remote and ambulatory monitoring, in or out of the healthcare environment, and support for disabled, rehabilitating or chronically ill patients. In particular, wearable bioelectric sensors are of extreme importance since they contribute to the knowledge of patient health condition, envisioning different physiological functions such as heart, muscles and brain activity. Electrocardiogram (ECG) is one of the most widely used signals for non-invasive diagnosis of different cardiac diseases. In fact, the major estimated cause of death in 2009 (US) is related with heart diseases [2]. Therefore, it is important to develop tools and devices to prevent the progress and appearance of this type of disorders. In most of clinical settings, ECGs are obtained through multiple and independent bench instruments. Consequently, the continuous monitoring on active subjects, while keeping their mobility, is almost impossible. Unobtrusive wearable ECG systems are essential to achieve the objective of making the leap between continuous monitoring and comfortable and easy to use intelligent garments [3]. However, not every sensor can be used in a wearable context and a set of attributes must be considered. These include physical
∗ Corresponding author. Tel.: +351253510190. E-mail address: [email protected] (P.M. Mendes). 0924-4247/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.sna.2013.09.008
attributes such as small size and weight, as well as performance requirements as durability, reliability and low-power consumption. In addition, an electrical output should be produced in order to facilitate further digital processing, storage and communication [3,4]. Properties such integration and easy-of-use, are mandatory when designing wearable systems. In this way, the device can be used as any regular cloth, requiring only intuitive adjustments. A number of wearable devices emerged in the past few years [5–7]. Despite its effectiveness and performance, this type of sensors is usually of difficult integration due to the amount of wires and electrical components needed. This leads to the development of complex interconnection technologies to provide an interface between electronic components and wearable materials. Electrical wires are more susceptible to interference from the surrounding environment, as well as to muscle movements. On the other hand, optical sensors, since based on light modulation, eliminate the need for local electrical powering and are less influenced by electromagnetic fields [8]. This allows their use in a number of medical applications where its electric counterparts fail, such as for example during Magnetic Resonance Imaging (MRI) exams or laser radiation procedures. In addition, optical sensors have a compact design, high-level of integration into several materials and are resistant to harsh environments and conditions [8–10]. A few studies reported the use of optical-based sensors for bioelectric activity recording [11,12]. Despite showing suitable bioelectric signal acquisition, these sensors do not propose a complete bioelectric signal acquisition system, which is of extreme importance for clinical applications.
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Fig. 1. ECG acquisition wearable garment using photonic sensor technology.
The bioelectric acquisition system herein described consists on a photonic sensor including the electronic circuitry for signal conversion and processing. This solution provides a real-time monitoring solution of Electrocardiograms (ECG) for wearable applications. The technology used is based on the electro-optic (EO) principle and uses a Lithium Niobate (LiBnO3 ) Mach-Zehnder Interferometer (MZI) modulator as the sensing component. The optic and electric components of the photonic sensor will be characterized in a conventional EO setup. 2. Electro-optic ECG acquisition system This work proposes a wearable ECG monitoring device based on EO acquisition technology, comprising the following functions: sensing, acquisition and processing. Through the EO effect, a time-varying applied field, the ECG, changes the refractive index of a crystalline substrate, by which light passes. This will produce a phase shift, that, when combined with an interferometer, results in light intensity modulation [13]. Therefore, the sensing component is an EO modulator (Fig. 1) with MZI geometry. The system will consist on a network of optical fibers and components, placed at specific recording locations. The proposed concept relies on the optical components integration into the wearable material, achieving a smart, functional and multi-sensing material. This is possible resorting to integration technologies, such as the one proposed in [8,14], maximizing the comfort when wearing such devices since the patient will be able to use it as a daily ordinary garment. Through the use of this integration technique, different optical fiber configurations and components can be adopted and added to the design. In consequence, contributes to the development of sensors targeting other bioelectric signals, due to similar properties (amplitude, frequency range).
Therefore, Electromyogram (EMG), Electroencephalogram (EEG) or Electroocculogram (EOG) could be also obtained with this photonic sensor. In addition, a recent work [14] have developed optical sensors for strain, temperature and hand movements. Combining these sensors, it is possible to envisage the design of a multi-parameter bioelectric monitoring suit, covering most of the physiological signals of interest, targeting different clinical scenarios. Fig. 1 shows the concept of the proposed ECG acquisition system, considering a wearable monitoring T-shirt application. As shown in Fig. 1, the proposed device consists on a common piece of cloth, such as a T-shirt, with embedded optical sensors–optical module. Sensor location can be customized according to the type of information that is required to extract. In fact, the electrical view of the heart depends on the arrangement and number of sensors used. Through the implementation of all-fiber schemes, such as the ones developed in [15,16], it is possible to achieve this customization as well as to increase the sensor network’s density. Fig. 1 shows an example of a possible ECG wearable monitoring T-shirt, where the ideal location to place the sensor would be on the chest or shoulders, according to pre-cordial or limb leads. Each sensor has an output fiber that will be routed to a centralized acquisition and processing block – electrical module. This module can be small enough, in order to be attached to the textile as a plug-in unit. In this way, the wearable material will only comprise optical components. The photonic ECG acquisition system presents a few advantages facing the conventional systems: no need for electrical power supply and electrical components on the wearable material at the sensing spot, and immunity to electromagnetic interference. This results in less electrical interconnections, easy integration into wearable materials and possibility of operating on specific environment like MRI or laser therapy rooms.
Fig. 2. Main functional stages of the photonic EO sensor.
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ECG+
MZI Electrodes
Modulated output light
Waveguide path 1
Input light Waveguide path 2
ECGFig. 3. Waveguide geometry of a Lithium Niobate Mach-Zehnder Interferometer modulator.
3. Photonic sensor design and implementation The design of acquisition systems for bioelectric signals relies on similar instrumentation and methodologies as other signal requiring transduction. However, properties such as small amplitudes and specific bandwidths make these signals susceptible to interference or artifacts. In particular, the ECG waveform has several components such as waves, intervals and complexes that are evaluated and classified according size, duration and tracing. Most of the clinical important frequency components are in the range of 0.3–40 Hz [17]. These frequencies allow to determine the patient’s type of cardiac rhythm as well as atrial and ventricular function. However, bioelectric signals have components in the same frequency band, resulting in interference for the signal being measured [18]. In addition, since human body is a good antenna, it couples the electromagnetic interferences typical of most clinical conditions. The most common artifacts are the power lines interference, which include the 50/60 Hz component and its harmonics, as well as 100 Hz arising from fluorescent lighting and other equipment [19]. The effect of these interferences in bioelectric signals often compromises the clinical quality of the data acquired. Therefore, to simultaneously ensure the bioelectric signal integrity and attenuation of interference and artifacts, special attention must be taken when designing the EO acquisition and processing unit. Fig. 2 shows the main functional stages of the proposed ECG EO sensor. Desirable features for an ECG acquisition system include high input impedance to minimize signal distortion, low output impedance, high differential gain, high common-mode rejection ratio and adequate frequency response [19]. The challenge when using optical sensors in physiological clinical monitoring resides on the nature of the bioelectric signals, since most of the available EO sensors are used for high frequency and voltage measurements. Nevertheless, with careful selection of optical components, and using dual-drive MZI modulators, it is possible to guarantee proper bioelectric signal acquisition. In fact, the possibility of performing differential measurements is one of the most attractive characteristics offered by this type of EO modulators, since bioelectric signals are acquired as differential potentials according to different electrode placements [18]. 3.1. Photonic setup The optical setup consists on three components: a light source, a dual-drive LiBnO3 MZI modulator and a photodiode. The properties of the light source used, like wavelength and optical power, must match the requirements and specifications of the MZI modulator used. The interferometer design of choice is the MZI, which divides light into two paths or waveguides, and produces different
modulations on each one. Fig. 3 shows the geometry of the MZI modulator, where electrodes depicted are attached to the crystal substrate. Another set of electrodes or antennas are required to provide an interface with the subject skin and the MZI internal electrode plates. As shown in Fig. 3, the ECG signal is used to change the refractive index of the crystalline substrate, changing the optical length of one or both paths. An interference pattern is created, which results in intensity modulation. If setting the modulator operation point at a linear region, i.e., the quadrature point, it is possible to perform reliable bioelectric measurements. A bias voltage is usually applied to set the MZI modulator at this point, which is the linear segment of the response curve. Nevertheless, when using dual-drive configuration, i.e., differential measurements, the linear region is usually centered at 0 V. By doing this, the modulator does not require bias voltage, and the bioelectric signal can be applied to both of the waveguide legs. The linear transfer function of the MZI modulator, taking into account the conversion of the current signal (iph ) to an ECG, is expressed as: ECG RGTIA Pin IL (t) = [ECG+ (t) − ECG (t)] 2V iph
(1)
The responsivity, R (A/W), is the proportionality factor between the modulated optical power and the converted electrical current, (iph ). The input optical power, (Pin ), transimpedance amplifier gain, (GTIA ), as well as the MZI modulator parameters – insertion loss, (IL), and half-wave voltage (V ) also influence the transfer function of the modulator and obtained ECG. The insertion loss refers to the optical power lost within the modulator, during wave propagation. The half-wave voltage is one of the most important characteristics of an EO modulator, since it determines sensor’s sensitivity. This voltage parameter defines the necessary voltage to produce a phase shift of . This parameter determines the sensitivity of the sensor, since the interferometry measurements deal with optical power variations, induced by a phase change. Therefore, the selection of the EO modulator must take into account the preference for a low V . Regarding MZI dimensions, it varies according to sensitivity required, and to configuration of the overall sensor. Reported MZI modulators can reach lengths of m. An example can be found in the work developed by Xueying Wang et al. (2010), where a modulator with a length of 42.6 m and drive voltage of 1.25 V was presented [20]. The LiNbO3 is the material of choice for the MZI modulators due to its combination of high EO coefficient, low optical loss, stability (thermal, chemical and mechanical) and compatibility to common integrated-circuit processing technology [21]. We propose the use of a z-cut orientation, which involves placing the electrodes such
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Fig. 5. Circuit schematic of twin-t notch filter.
Another common problem when dealing with high-gain TIA is a phenomenon called gain peaking that induces an unstable behavior. Gain peaking is characterized by a ringing effect on the output voltage of the circuit [22]. To solve this, a bypass capacitor (Cf ) is connected in parallel to the feedback resistor, as shown in Fig. 4. There is a relationship to prevent gain peaking, by which an optimum Cf is calculated to a specific GTIA , expressed by [19]: Cf =
Fig. 4. Transimpedance amplifier circuit schematic, with resistive tee-network for gain compensation.
that the waveguides are below them and the electric field applied is perpendicular to the z-cut surface as shown in Fig. 3. The design of these devices is simplified whereas a good thermal stability is ensured [21]. After modulation, light follows to a photodiode where the incident optical power is converted to an electrical current. In order to remove the need for an external power source and improve the sensitivity of the device, the photodiode should be use in the photovoltaic mode [22]. 3.2. Electronic setup The electronic module includes: a transimpedance amplifier (TIA), a notch, and band-pass filter. The received current from the photodiode is both converted to a voltage and amplified by a TIA. Since the acquired signal consists of the desired bioelectric signal and unwanted components (e.g. power line interference signals, noise, etc.), a filtering stage is required – notch and band-pass filters. The purpose of the TIA is to convert the current from the photodiode, (iph ), into a voltage signal, (vout ), providing a hightransimpedance gain, (GTIA ), over a wide bandwidth. Since current variations have a value of few microamperes, a high-gain TIA is required to be able to amplify and convert such small electrical currents into a readable voltage. Fig. 4 shows the circuit schematic of the in-house TIA. The feedback resistor, (Rf ), provides amplification during current-to-voltage conversion. Giving the small amplitudes of ECG, higher feedback resistors are needed. Nevertheless, this causes the TIA to be more readily driven into saturation, since it also amplifies the input DC current signal. In addition, it also can induce unintended biasing voltage on the following signal processing stages [22]. As a result, an additional block is added – DC suppression block – which goal is to act as a high-pass filter. The low-cut frequency is selected according to the frequency components of interest of the ECG.
C c
2
1+
C 1+4 i Cc
,
(2)
where Ci combines the three input capacitances of the circuit (photodiode, op-amp common mode and differential) and Cc can be considered a virtual capacitor. This component is determined by the relationship of Rf and the gain-bandwidth product of the op amp (fc ): Cc = 1/2Rf fc [22]. The characteristic transfer function of the designed TIA is: TF(s) =
ECG s(1/Cf ) , (s) = iph −s2 + S(1/Rf Cf ) + (1/R1 R2 Cf C1 )
(3)
where natural frequency is expressed as fn = (1/ R1 R2 Cf C1 ). The selection of the op amp was made considering its input capacitance, both differential and common mode, and the gainbandwidth product. These values contribute to the selection of the right capacitor to bypass the feedback resistor, i.e. Cf . A CMOS op amp with low bias current (OPA2337, Burr-Brown) can be used, with input differential and common mode capacitance, 2 and 4 pF, respectively. Therefore, according to these values and using (2), the minimum recommended Cf value is 1 pF, for an Rf of 4.7 M. However, to reduce band-pass range of the TIA, a capacitor of 66 pF was used. DC suppression block components as R1 and C1 where set at 10 M and 1 F respectively. A notch filter with an active twin-tee network topology is used to remove AC power lines interference. This filter is composed by two T-networks: one having one resistor and two capacitors, whereas the other has two resistors and one capacitor. Fig. 5 shows the circuit topology of the twin-tee notch filter designed. With this topology it is possible to achieve higher Q filters by using an operation amplifier configured as a voltage follower. The characteristic transfer function of this filter is: TF(s) =
s2 + (1/R2 C 2 ) s2 + (4s/RC) + (1/RC)
(4)
where the notch frequency is determined by fn = (1/2RC). The notch frequency should be centered at 50 Hz for European applications, and at 60 Hz if used in the United States. To remove other interferences that may corrupt the ECG, a bandpass filter is included in the acquisition system. The higher cut-off frequency can be set at 40 Hz, since at this frequency an enhanced ECG signal can be easily obtained. In addition, considering a lower rate of 30 beats per minute (frequency of 0.6 Hz), the reasonable
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Fig. 6. Circuit schematic of Butterworth band-pass filter.
lower cut-off frequency should be 0.20 Hz [14]. The topology of the band-pass filter circuit implemented is shown in Fig. 6. The transfer function of the filter is obtained by the product of the transfer functions of both cascaded components:
TF(s) =
s2 s2 + (2s/R2 C1 ) + (1/R1 R2 C12 )
×
1/R2 C2 C3 s2 + (2s/R3 C2 ) + (1/R32 C2 C3 )
(5)
where the low-pass cut-off frequency is defined as fip = (1/C1
R R ), and the high pass cut-off frequency as fhp = 1 2
(1/R3 C2 C3 ). The IC’s chosen for the active band-pass and notch filters were the low-noise high-speed JFET-Input operational amplifiers (TL071CP, Texas Instruments). 4. Experiments and results In this section, tests on the performance of several key elements of our ECG acquisition system are presented. In addition, the quality of the ECG waveforms obtained and a comparison with a standard purely electronic acquisition system is exposed. These results are critical and important for the validation of the full system operation and for future clinical testing and applications. In order to perform the preliminary analysis, an experimental setup was used, consisting on the components described in section III. Fig. 7 shows the setup used to validate the proposed sensor, based on high-speed optical communication components. From (1) we can understand that in order to increase the overall sensitivity, a higher Pin , low V , and high GTIA should be used.
There’s a tradeoff between Pin and GTIA , since to overcompensate the DC levels introduced by a higher optical power, complex TIA design is required. The selection of the EO components used in the experimental setup, was therefore made regarding these constrains. A C-band broadband ASE (Amplified Spontaneous Emission) light source (LXASE-CN-14-NO, Luxpert) generates an optical signal with a power of 7 dBm and a wavelength of 1546 nm. Light is guided through a FC/APC optical fiber to a dual drive LiBnO3 MZI modulator (FTM7921ER, Fujitsu). The selection of this modulator was made considering a small V (3.2 V) and insertion loss (6.3 dB). The photonic stage was stored inside an isolated metal box, as shown in Fig. 7a. The output optical signal was detected by an InGas photodiode (FGA04, Thorlabs) with a responsivity of 0.9 A/W (@1546 nm) and a capacitance of 5 pF (@zero bias). The resultant DC currents were 1.27 mA, and the modulated signal had an average value of 10 A. The processing unit consisted of an in-house TIA, notch and band-pass filter, previously described (Fig. 7b), and was isolated using a faraday cage. A 9 V-lithium battery powers the acquisition electronics, preventing unwanted periodic oscillations (ripple), whose amplitudes are often higher than typical ECG amplitudes.
4.1. Performance assessment for bioelectric signal acquisition The performance assessment of systems for ECG signal acquisition is commonly focused on the verification of the system ability to provide a signal where the waveforms components required for health monitoring must be present [5,17]. This is usually done since the signal source, the bioelectric signal that is measured, which results from electrical heart activity, is different
Fig. 7. Experimental setup used: (left) LiNbO3 MZI modulator; (right) acquisition and processing electronics.
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Fig. 8. (left) MZI modulator transfer curve, highlighting the quadrature region; (right) EO stage frequency response (MZI modulator and TIA, included).
for different subjects, and changes with time, physiological factors, and position of electrode type and position placement. Moreover, the acquisition of an ECG may have two purposes. I may be used for clinical purposes (usually for diagnostics), where a clinical ECG is required, or it can be used for well being purposes (more and more required with the advent of Ambient Assisted Living devices), where not all the ECG waveforms components are required. To assess the performance of the proposed EO system, the ECG data was obtained using healthy male subjects, with normal bioelectric activity. It was used a differential electrode placement (sintered Ag/AgCl ring electrodes), according to the Lead I of Einthoven’s triangle, which measures the signal between an electrode in the right arm (RA) and one on the left arm (LA). To assess the suitability of the proposed EO system, data was also collected using a fully electrical acquisition system, based on the INA129 instrumentation amplifier. The INA129 based system was developed and tested previously against a commercial setup from BrainVision, QuickAmp. Moreover, and usually not required, once the waveforms have enough information for medical inspection, the systems’ performance was also evaluated in the standard way, where the sensitivity, gain and bandwidth where determined and compared with a commercial performing system.
4.2. Circuit performance The MZI modulator transfer function was experimentally determined in order confirm the quadrature region. Fig. 8a shows that it is possible to operate the MZI modulator in the quadrature region (maximum sensitivity) without applying a bias voltage, i.e. Vbias = 0 V. The average measured value for insertion loss was 6.3 dB. Frequency response of the EO stage, i.e. MZI modulator and TIA, was determined using a 20 mVpp sinusoidal signal with a frequency sweep from 0.1 Hz to 1 kHz. Results are depicted in Fig. 8b, showing a stable and flat operation point at 60 dB in the frequency range of interest. The 3 dB frequency is centered at 0.3 Hz, which is suitable for ECG recordings. The overall sensitivity of the EO system was tested using a sine-wave generator, and results have shown a minimum detected voltage of 20 V. The resulting experimental gain of the TIA was 1 × 105 V/A. The performance summary and the comparison between behavioral simulation and measured results are shown in Table 1. The actual specifications of the setup were close to the theoretical estimations shown in Table 1. However, because of process variations, as well as mismatch between rated and the actual
Table 1 Summary of notch and band-pass filter performance (S: simulations; E: experimental). Gain (dB)
Notch BPF
Frequency (Hz)
S
E
S
E
−39 0
−33.9 0
50 0.20–40.1
49.89 0.25–39
specifications of the passive and active components, the circuit setup produced slightly different gains and frequency responses. The frequency ranges of the components include the frequency components of interest of the ECG, i.e. 0.2–40 Hz [17]. Current and power consumption were determined for each approach: EO and purely electric. The measured values are shown in Table 2, considering a supply voltage of ±15 V. The obtained results substantiate the objective of achieving low power consumption systems. In this way, it contributes not only to a prolonged lifetime, but also to system miniaturization since batteries usually occupy more than 50% of the system volume. The bottleneck of the photonic sensor is related with the use of optical power sources that have higher power consumption. Nevertheless, with available ultra-bright LEDs that can achieve power consumptions of few mW, it is possible to reduce overall power consumption of the photonic system, while achieving similar performances over electronic counterparts. 4.3. EO detection of ECG To test the bioelectric signal acquisition capability of the photonic sensor, an ECG signal was obtained from a human subject. A differential recording was made by placing the electrodes according to the Lead II of Einthoven’s triangle, which measures the signal between an electrode in the right arm (RA) and one on the left arm (LA). During the recording session, the overall system gain was set to 1000 V/V. Both electrodes were connected to the MZI modulator, resulting in almost twice the modulation depth. To Table 2 Measured current and power consumption. EO setup
Current (mA) Power (mW)
Electric setup
TIA
Filters
INA
Filters
0.8 12
1.2 18.24
0.6 9.12
1.2 18.24
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Fig. 9. Comparison of ECG signals obtained using two acquisition systems. (a) Standard electrical acquisition system with an instrumentation amplifier, after filtering, and (b) raw signal; (c) the proposed EO sensor, after filtering, and (d) raw signal.
compare these results with a conventional bioelectric acquisition, the modulator was replaced by an instrumentation amplifier. An INA129 (Burr Brown, Texas Instruments) with an input capacitance of 2 pF was used with a gain set to 1000 V/V. The electrodes and the filtering stage were the same for both recordings. Fig. 9 shows the ECG signals obtained for both MZI modulator and instrumentation amplifier. ECGs obtained with both procedures have shown good resolution and quality for accurate detection of typical wave components such as P wave (atrial depolarization), QRS complex (ventricular depolarization), T wave (ventricular repolarization) and PR
interval. In particular, the clear and prominent R-peak presented in both raw signals allows to detect the heart rate more accurately. This parameter is often determined by analyzing the QRS complex, i.e., determining the number of QRS events in a specific period of time, and multiply it to achieve the number of beats per minute [23]. Therefore, assessment of ECG patterns provides useful information about the status of the heart, where typical deviations from normal include: abnormalities in rate, rhythm and cardiac myopathies [23]. In addition, resorting to developed algorithms, such as the one proposed in [24], it is possible to extract information about respiration function.
Fig. 10. Comparison of ECG power spectrum obtained using two acquisition systems. (a) Standard electrical acquisition system with an instrumentation amplifier, after filtering, and (b) raw signal; (c) the proposed EO sensor, after filtering, and (d) raw signal.
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Signals obtained have interference components predominant at 50 Hz, which can be seen in spectrum power charts from each acquired signal, shown in Fig. 10. Spectral components are similar for each approach, showing a higher spectrum power for low frequencies (0–40 Hz). Overall filtering stage has shown good performance in terms of amplifying the knowledge-content frequencies while attenuating unwanted interference components. 5. Conclusions A photonic bioelectric sensor for wearable applications based on EO techniques is proposed. The technology used is based on the EO techniques and uses a Lithium Niobate (LiBnO3 ) Mach-Zehnder Interferometer (MZI) modulator as the sensing component. The optic and electric components of the photonic sensor were characterized in a conventional high-speed optical communication setup. The ability to perform bioelectric measurements was validated by acquiring an ECG with the photonic setup designed. It was demonstrated that the clinical performance of the sensor was adequate, showing good resolution and frequency response. It was found that the bandwidth of the overall sensor is larger then the required. Standard systems use a bandwidth of 35 Hz, and the presented system has a bandwidth of 39 Hz. It is important to highlight that this bandwidth is not limited by the EO transducer, but was set by the electronics, which may be changed by design. Finally, the system overall gain was set to 1000 V/V, a value that may be changed by design. Standard gains are in the 1000–10,000 V/V, depending on the acquired biopotential and electrode impedance. The main difference from standard systems is the fact that this gain, usually, is programmable to accommodate different types of electrodes. The results were compared with clinical conventional electronics and have shown comparable performances. The proposed sensor can be developed for acquisition of the remaining bioelectric signals such as EEG, EMG and EOG. This opens the way to explore an all-optic multi-parameter wearable suit, covering other physiological parameters as for example temperature and pressure. Acknowledgments We would like to acknowledge the MIT-Portugal Program and Portuguese Foundation for Science and Technology for supporting this work (PTDC/EEA-TEL/65286/2006). References [1] Wearable Wireless Sensors, ABI Research, 2010, Research Reports. ˜ H.C. Kung, Deaths: preliminary [2] K.D. Kochanek, J. Xu, S.L. Murphy, A.M. Minino, data for 2009, National Vital Statistics Reports 59 (4) (2011). [3] G. Constantine, D.I. Fotiadis, Wearable devices in healthcare, in: B.G. Silverman, A. Jain, A. Ichalkaranje, L.C. Jain (Eds.), Intelligent Paradigms for Healthcare Enterprises, Springer Berlin, Berlin, Germany, 2005, pp. 237–264. [4] J.M. Winters, Y. Wang, Wearable sensors and telerehabilitation, IEEE Engineering in Medicine and Biology Magazine 22 (3) (2003) 56–65. [5] R.F. Yazicioglu, T. Torfs, P. Merken, et al., Ultra-low-power biopotential interfaces and their applications in wearable and implantable systems, Microelectronics Journal 40 (September (9)) (2009) 1313–1321. [6] Y. Chuo, M. Marzencki, B. Hung, et al., Mechanically flexible wireless multisensor platform for human physical activity and vitals monitoring, IEEE Transactions on Biomedical Circuits and Systems 4 (October (5)) (2010) 281–294. [7] Y.D. Lee, W.Y. Chung, Wireless sensor network based wearable smart shirt for ubiquitous health and activity monitoring, Sensors and Actuators B-Chemical 140 (July (2)) (2009) 390–395.
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[8] M. El-Sherif, Smart fabrics: integrating fiber optic sensors and information networks, Studies in Health Technology and Informatics 108 (2004) 317–323. [9] M.A. El-Sherif, J.M. Yuan, A. MacDiarmid, Fiber optic sensors and smart fabrics, Journal of Intelligent Material Systems and Structures 11 (5) (2000) 407–414. [10] K.T.V. Grattan, B.T. Meggitt, Optical Fiber Sensor Technology: Fundamentals – Optoelectronics, Imaging and Sensing, Springer, 2000. [11] S.A. Kingsley, S. Sriram, A. Pollick, et al., Photrodes(TM) for physiological sensing, Optical Fibers and Sensors for Medical Applications IV 5317 (2004) 158–166. [12] A. Sasaki, A. Furuya, M. Shinagawa, Study of semiconductor electro-optic modulators for sensing extremely-low-frequency electrical signals, Sensors and Actuators A-Physical 151 (April (1)) (2009) 1–8. [13] C. Shun Lien, Physics of Photonic Devices, 2nd ed., John Wiley & Sons, 2009. [14] A.F. Silva, F. Goncalves, L.A. Ferreira, F.M. Araujo, P.M. Mendes, J.H. Correia, A smart skin PVC foil based on FBG sensors for monitoring strain and temperature, IEEE Transactions on Industrial Electronics 58 (7) (2010) 2728–2735. [15] O. Frazão, S.F.O. Silva, J. Viegas, J.M. Baptista, J.L. Santos, J. Kobelke, K. Schuster, All fiber Mach–Zehnder interferometer based on suspended twin-core fiber, Photonics Technology Letters, IEEE 22 (7) (2010) 1300–1302. [16] N. Kumar, M.R. Shenoy, B.P. Pal, Flattop all-fiber wavelength interleaver for DWDM transmission: design analysis, parameter optimization, fabrication and characterization recipe, Optics Communications 281 (25) (2008) 5156–5164. [17] V.K. Murthy, T. MGrove, G.A. Harvey, L.J. Haywood, Clinical usefulness of ECG frequency spectrum analysis, in: Proc Annu Symp Comput Appl Med Care, 1978, pp. 610–612. [18] J.G. Webster, J.W. Clark, Medical Instrumentation: Application and Design, 2nd ed., Wiley, 1995. [19] D. Prutchi, M. Norris, Design and Development of Medical Electronic Instrumentation – A Practical Perspective of the Design, Construction and Test of Medical Devices, John Wiley & Sons, 2005. [20] X. Wang, H. Tian, Y. Ji, Photonic crystal slow light Mach–Zehnder interferometer modulator for optical interconnects, Journal of Optics 12 (2010). [21] E.L. Wooten, et al., A review of lithium niobate modulators for fiber-optic communications systems, IEEE Journal of Selected Topics in Quantum Electronics 6 (1) (2000) 69–82. [22] G. Graeme, Photodiode Amplifiers: Op Amp Solutions, McGraw-Hill Professional, 1996. [23] L. Sherwood, Human Physiology: From Cells to Systems, Cengage Learning, 2008. [24] J. Boyle, N. Bidargaddi, A. Sarela, M. Karunanithi, Automatic detection of respiration rate from ambulatory single-lead ECG, IEEE Transactions on Information Technology in Biomedicine 13 (6) (2009) 890–896.
Biographies Mariana Fernandes is a consultant of Sociedade Portuguesa de Inovac¸ão (SPI) working as a consultant in Research, Development and Innovation (RDI), focused on national and international projects funded by the European Union (R & D and RDI). Mariana Fernandes has a PhD in Bioengineering Systems from the MIT-Portugal Program (2008–2011), and a Biomedical Engineering Integrated Master Degree (2002–2007) from the University of Minho (Braga, Portugal). In 2009, Mariana Fernandes went to the Massachusetts Institute of Technology (MIT) as a PhD Visiting Student to the Research Laboratory of Electronics (RLE), under the supervision of Prof. Rajeev Ram. Her current fields of interest include areas such as photonic sensors, wearable devices, electronics, electrophysiology, medical sensors. Jose Higino Correia graduated in Physical Engineering from University of Coimbra, Portugal in 1990. He obtained in 1999 a PhD degree at the Laboratory for Electronic Instrumentation, Delft University of Technology, The Netherlands, working in the field of microsystems for optical spectral analysis. Presently, he is a Full Professor in Department of Industrial Electronics, University of Minho, Portugal. He was the General-Chairman of Eurosensors 2003 and MME 2007, Guimarães, Portugal. His professional interests are in micromachining and microfabrication technology for mixed-mode systems, solid-state integrated sensors, microactuators and microsystems. P.M. Mendes graduated in 1995, and obtained his MSc degree in Electrical Engineering, in 1999, both from University of Coimbra, Portugal. He obtained his PhD degree in Industrial Electronics, from University of Minho, in 2005. He is an Associate Professor at University of Minho, and a researcher at the Algoritmi Center. His interests are in the field of the project, fabrication, and characterization of RF microdevices for wireless microsystems. P.M. Mendes is Member of the European Microwave Association, of the IEEE Antennas and Propagation Society, and of the IEEE Engineering in Medicine and Biology Society.
Contents lists available at ScienceDirect
Sensors and Actuators A: Physical journal homepage: www.elsevier.com/locate/sna
Electro-optic acquisition system for ECG wearable sensor applications M.S. Fernandes, J.H. Correia, P.M. Mendes ∗ DEI – Universidade do Minho, Campus Azurem, 4800-058 Guimaraes, Portugal
a r t i c l e
i n f o
Article history: Received 24 February 2013 Received in revised form 3 September 2013 Accepted 3 September 2013 Available online 13 September 2013 Keywords: ECG monitoring Biomedical sensors Photonic sensors Wearable electronics
a b s t r a c t In the past few years a remarkable progress has been made in the design of sensors for wearable devices. Nevertheless, the existent devices are difficult to integrate, mainly due to the quantity of electrical interconnections and components required at the sensing places. This leads to the need for a new generation of wearable sensors. Photonic sensors have been presented in the medical field as a valuable alternative where power supply, electromagnetic interference and integration constitute a challenge. Therefore, this paper presents an electro-optic (EO) acquisition system designed for wearable Electrocardiogram (ECG) monitoring devices. The system includes a Lithium Niobate Mach-Zehnder Interferometer (MZI) modulator as the sensing element, and an optoelectronic signal conversion and processing stage. Tests were made in order to evaluate system performance in terms of signal amplification and sensitivity, frequency response and linearity. Obtained results have shown a suitable sensor’s sensitivity of 20 V and a frequency response of 60 dB flat from 0.2 to 40 Hz. Clinical sensor’s performance was tested considering the requirements for evaluation of ECG. Results were compared with clinical standard electronics. Clear ECGs can be obtained with the EO sensor, showing adequate and reliable clinical performance. This EO sensor can be integrated into wearable materials being a suitable alternative for clinical cardiac monitoring. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Over the next few years, the market for wearable sensors is expected to grow to more than 400 million devices [1]. This is a result of the growing need to a remote and ambulatory monitoring, in or out of the healthcare environment, and support for disabled, rehabilitating or chronically ill patients. In particular, wearable bioelectric sensors are of extreme importance since they contribute to the knowledge of patient health condition, envisioning different physiological functions such as heart, muscles and brain activity. Electrocardiogram (ECG) is one of the most widely used signals for non-invasive diagnosis of different cardiac diseases. In fact, the major estimated cause of death in 2009 (US) is related with heart diseases [2]. Therefore, it is important to develop tools and devices to prevent the progress and appearance of this type of disorders. In most of clinical settings, ECGs are obtained through multiple and independent bench instruments. Consequently, the continuous monitoring on active subjects, while keeping their mobility, is almost impossible. Unobtrusive wearable ECG systems are essential to achieve the objective of making the leap between continuous monitoring and comfortable and easy to use intelligent garments [3]. However, not every sensor can be used in a wearable context and a set of attributes must be considered. These include physical
∗ Corresponding author. Tel.: +351253510190. E-mail address: [email protected] (P.M. Mendes). 0924-4247/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.sna.2013.09.008
attributes such as small size and weight, as well as performance requirements as durability, reliability and low-power consumption. In addition, an electrical output should be produced in order to facilitate further digital processing, storage and communication [3,4]. Properties such integration and easy-of-use, are mandatory when designing wearable systems. In this way, the device can be used as any regular cloth, requiring only intuitive adjustments. A number of wearable devices emerged in the past few years [5–7]. Despite its effectiveness and performance, this type of sensors is usually of difficult integration due to the amount of wires and electrical components needed. This leads to the development of complex interconnection technologies to provide an interface between electronic components and wearable materials. Electrical wires are more susceptible to interference from the surrounding environment, as well as to muscle movements. On the other hand, optical sensors, since based on light modulation, eliminate the need for local electrical powering and are less influenced by electromagnetic fields [8]. This allows their use in a number of medical applications where its electric counterparts fail, such as for example during Magnetic Resonance Imaging (MRI) exams or laser radiation procedures. In addition, optical sensors have a compact design, high-level of integration into several materials and are resistant to harsh environments and conditions [8–10]. A few studies reported the use of optical-based sensors for bioelectric activity recording [11,12]. Despite showing suitable bioelectric signal acquisition, these sensors do not propose a complete bioelectric signal acquisition system, which is of extreme importance for clinical applications.
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Fig. 1. ECG acquisition wearable garment using photonic sensor technology.
The bioelectric acquisition system herein described consists on a photonic sensor including the electronic circuitry for signal conversion and processing. This solution provides a real-time monitoring solution of Electrocardiograms (ECG) for wearable applications. The technology used is based on the electro-optic (EO) principle and uses a Lithium Niobate (LiBnO3 ) Mach-Zehnder Interferometer (MZI) modulator as the sensing component. The optic and electric components of the photonic sensor will be characterized in a conventional EO setup. 2. Electro-optic ECG acquisition system This work proposes a wearable ECG monitoring device based on EO acquisition technology, comprising the following functions: sensing, acquisition and processing. Through the EO effect, a time-varying applied field, the ECG, changes the refractive index of a crystalline substrate, by which light passes. This will produce a phase shift, that, when combined with an interferometer, results in light intensity modulation [13]. Therefore, the sensing component is an EO modulator (Fig. 1) with MZI geometry. The system will consist on a network of optical fibers and components, placed at specific recording locations. The proposed concept relies on the optical components integration into the wearable material, achieving a smart, functional and multi-sensing material. This is possible resorting to integration technologies, such as the one proposed in [8,14], maximizing the comfort when wearing such devices since the patient will be able to use it as a daily ordinary garment. Through the use of this integration technique, different optical fiber configurations and components can be adopted and added to the design. In consequence, contributes to the development of sensors targeting other bioelectric signals, due to similar properties (amplitude, frequency range).
Therefore, Electromyogram (EMG), Electroencephalogram (EEG) or Electroocculogram (EOG) could be also obtained with this photonic sensor. In addition, a recent work [14] have developed optical sensors for strain, temperature and hand movements. Combining these sensors, it is possible to envisage the design of a multi-parameter bioelectric monitoring suit, covering most of the physiological signals of interest, targeting different clinical scenarios. Fig. 1 shows the concept of the proposed ECG acquisition system, considering a wearable monitoring T-shirt application. As shown in Fig. 1, the proposed device consists on a common piece of cloth, such as a T-shirt, with embedded optical sensors–optical module. Sensor location can be customized according to the type of information that is required to extract. In fact, the electrical view of the heart depends on the arrangement and number of sensors used. Through the implementation of all-fiber schemes, such as the ones developed in [15,16], it is possible to achieve this customization as well as to increase the sensor network’s density. Fig. 1 shows an example of a possible ECG wearable monitoring T-shirt, where the ideal location to place the sensor would be on the chest or shoulders, according to pre-cordial or limb leads. Each sensor has an output fiber that will be routed to a centralized acquisition and processing block – electrical module. This module can be small enough, in order to be attached to the textile as a plug-in unit. In this way, the wearable material will only comprise optical components. The photonic ECG acquisition system presents a few advantages facing the conventional systems: no need for electrical power supply and electrical components on the wearable material at the sensing spot, and immunity to electromagnetic interference. This results in less electrical interconnections, easy integration into wearable materials and possibility of operating on specific environment like MRI or laser therapy rooms.
Fig. 2. Main functional stages of the photonic EO sensor.
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ECG+
MZI Electrodes
Modulated output light
Waveguide path 1
Input light Waveguide path 2
ECGFig. 3. Waveguide geometry of a Lithium Niobate Mach-Zehnder Interferometer modulator.
3. Photonic sensor design and implementation The design of acquisition systems for bioelectric signals relies on similar instrumentation and methodologies as other signal requiring transduction. However, properties such as small amplitudes and specific bandwidths make these signals susceptible to interference or artifacts. In particular, the ECG waveform has several components such as waves, intervals and complexes that are evaluated and classified according size, duration and tracing. Most of the clinical important frequency components are in the range of 0.3–40 Hz [17]. These frequencies allow to determine the patient’s type of cardiac rhythm as well as atrial and ventricular function. However, bioelectric signals have components in the same frequency band, resulting in interference for the signal being measured [18]. In addition, since human body is a good antenna, it couples the electromagnetic interferences typical of most clinical conditions. The most common artifacts are the power lines interference, which include the 50/60 Hz component and its harmonics, as well as 100 Hz arising from fluorescent lighting and other equipment [19]. The effect of these interferences in bioelectric signals often compromises the clinical quality of the data acquired. Therefore, to simultaneously ensure the bioelectric signal integrity and attenuation of interference and artifacts, special attention must be taken when designing the EO acquisition and processing unit. Fig. 2 shows the main functional stages of the proposed ECG EO sensor. Desirable features for an ECG acquisition system include high input impedance to minimize signal distortion, low output impedance, high differential gain, high common-mode rejection ratio and adequate frequency response [19]. The challenge when using optical sensors in physiological clinical monitoring resides on the nature of the bioelectric signals, since most of the available EO sensors are used for high frequency and voltage measurements. Nevertheless, with careful selection of optical components, and using dual-drive MZI modulators, it is possible to guarantee proper bioelectric signal acquisition. In fact, the possibility of performing differential measurements is one of the most attractive characteristics offered by this type of EO modulators, since bioelectric signals are acquired as differential potentials according to different electrode placements [18]. 3.1. Photonic setup The optical setup consists on three components: a light source, a dual-drive LiBnO3 MZI modulator and a photodiode. The properties of the light source used, like wavelength and optical power, must match the requirements and specifications of the MZI modulator used. The interferometer design of choice is the MZI, which divides light into two paths or waveguides, and produces different
modulations on each one. Fig. 3 shows the geometry of the MZI modulator, where electrodes depicted are attached to the crystal substrate. Another set of electrodes or antennas are required to provide an interface with the subject skin and the MZI internal electrode plates. As shown in Fig. 3, the ECG signal is used to change the refractive index of the crystalline substrate, changing the optical length of one or both paths. An interference pattern is created, which results in intensity modulation. If setting the modulator operation point at a linear region, i.e., the quadrature point, it is possible to perform reliable bioelectric measurements. A bias voltage is usually applied to set the MZI modulator at this point, which is the linear segment of the response curve. Nevertheless, when using dual-drive configuration, i.e., differential measurements, the linear region is usually centered at 0 V. By doing this, the modulator does not require bias voltage, and the bioelectric signal can be applied to both of the waveguide legs. The linear transfer function of the MZI modulator, taking into account the conversion of the current signal (iph ) to an ECG, is expressed as: ECG RGTIA Pin IL (t) = [ECG+ (t) − ECG (t)] 2V iph
(1)
The responsivity, R (A/W), is the proportionality factor between the modulated optical power and the converted electrical current, (iph ). The input optical power, (Pin ), transimpedance amplifier gain, (GTIA ), as well as the MZI modulator parameters – insertion loss, (IL), and half-wave voltage (V ) also influence the transfer function of the modulator and obtained ECG. The insertion loss refers to the optical power lost within the modulator, during wave propagation. The half-wave voltage is one of the most important characteristics of an EO modulator, since it determines sensor’s sensitivity. This voltage parameter defines the necessary voltage to produce a phase shift of . This parameter determines the sensitivity of the sensor, since the interferometry measurements deal with optical power variations, induced by a phase change. Therefore, the selection of the EO modulator must take into account the preference for a low V . Regarding MZI dimensions, it varies according to sensitivity required, and to configuration of the overall sensor. Reported MZI modulators can reach lengths of m. An example can be found in the work developed by Xueying Wang et al. (2010), where a modulator with a length of 42.6 m and drive voltage of 1.25 V was presented [20]. The LiNbO3 is the material of choice for the MZI modulators due to its combination of high EO coefficient, low optical loss, stability (thermal, chemical and mechanical) and compatibility to common integrated-circuit processing technology [21]. We propose the use of a z-cut orientation, which involves placing the electrodes such
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Fig. 5. Circuit schematic of twin-t notch filter.
Another common problem when dealing with high-gain TIA is a phenomenon called gain peaking that induces an unstable behavior. Gain peaking is characterized by a ringing effect on the output voltage of the circuit [22]. To solve this, a bypass capacitor (Cf ) is connected in parallel to the feedback resistor, as shown in Fig. 4. There is a relationship to prevent gain peaking, by which an optimum Cf is calculated to a specific GTIA , expressed by [19]: Cf =
Fig. 4. Transimpedance amplifier circuit schematic, with resistive tee-network for gain compensation.
that the waveguides are below them and the electric field applied is perpendicular to the z-cut surface as shown in Fig. 3. The design of these devices is simplified whereas a good thermal stability is ensured [21]. After modulation, light follows to a photodiode where the incident optical power is converted to an electrical current. In order to remove the need for an external power source and improve the sensitivity of the device, the photodiode should be use in the photovoltaic mode [22]. 3.2. Electronic setup The electronic module includes: a transimpedance amplifier (TIA), a notch, and band-pass filter. The received current from the photodiode is both converted to a voltage and amplified by a TIA. Since the acquired signal consists of the desired bioelectric signal and unwanted components (e.g. power line interference signals, noise, etc.), a filtering stage is required – notch and band-pass filters. The purpose of the TIA is to convert the current from the photodiode, (iph ), into a voltage signal, (vout ), providing a hightransimpedance gain, (GTIA ), over a wide bandwidth. Since current variations have a value of few microamperes, a high-gain TIA is required to be able to amplify and convert such small electrical currents into a readable voltage. Fig. 4 shows the circuit schematic of the in-house TIA. The feedback resistor, (Rf ), provides amplification during current-to-voltage conversion. Giving the small amplitudes of ECG, higher feedback resistors are needed. Nevertheless, this causes the TIA to be more readily driven into saturation, since it also amplifies the input DC current signal. In addition, it also can induce unintended biasing voltage on the following signal processing stages [22]. As a result, an additional block is added – DC suppression block – which goal is to act as a high-pass filter. The low-cut frequency is selected according to the frequency components of interest of the ECG.
C c
2
1+
C 1+4 i Cc
,
(2)
where Ci combines the three input capacitances of the circuit (photodiode, op-amp common mode and differential) and Cc can be considered a virtual capacitor. This component is determined by the relationship of Rf and the gain-bandwidth product of the op amp (fc ): Cc = 1/2Rf fc [22]. The characteristic transfer function of the designed TIA is: TF(s) =
ECG s(1/Cf ) , (s) = iph −s2 + S(1/Rf Cf ) + (1/R1 R2 Cf C1 )
(3)
where natural frequency is expressed as fn = (1/ R1 R2 Cf C1 ). The selection of the op amp was made considering its input capacitance, both differential and common mode, and the gainbandwidth product. These values contribute to the selection of the right capacitor to bypass the feedback resistor, i.e. Cf . A CMOS op amp with low bias current (OPA2337, Burr-Brown) can be used, with input differential and common mode capacitance, 2 and 4 pF, respectively. Therefore, according to these values and using (2), the minimum recommended Cf value is 1 pF, for an Rf of 4.7 M. However, to reduce band-pass range of the TIA, a capacitor of 66 pF was used. DC suppression block components as R1 and C1 where set at 10 M and 1 F respectively. A notch filter with an active twin-tee network topology is used to remove AC power lines interference. This filter is composed by two T-networks: one having one resistor and two capacitors, whereas the other has two resistors and one capacitor. Fig. 5 shows the circuit topology of the twin-tee notch filter designed. With this topology it is possible to achieve higher Q filters by using an operation amplifier configured as a voltage follower. The characteristic transfer function of this filter is: TF(s) =
s2 + (1/R2 C 2 ) s2 + (4s/RC) + (1/RC)
(4)
where the notch frequency is determined by fn = (1/2RC). The notch frequency should be centered at 50 Hz for European applications, and at 60 Hz if used in the United States. To remove other interferences that may corrupt the ECG, a bandpass filter is included in the acquisition system. The higher cut-off frequency can be set at 40 Hz, since at this frequency an enhanced ECG signal can be easily obtained. In addition, considering a lower rate of 30 beats per minute (frequency of 0.6 Hz), the reasonable
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Fig. 6. Circuit schematic of Butterworth band-pass filter.
lower cut-off frequency should be 0.20 Hz [14]. The topology of the band-pass filter circuit implemented is shown in Fig. 6. The transfer function of the filter is obtained by the product of the transfer functions of both cascaded components:
TF(s) =
s2 s2 + (2s/R2 C1 ) + (1/R1 R2 C12 )
×
1/R2 C2 C3 s2 + (2s/R3 C2 ) + (1/R32 C2 C3 )
(5)
where the low-pass cut-off frequency is defined as fip = (1/C1
R R ), and the high pass cut-off frequency as fhp = 1 2
(1/R3 C2 C3 ). The IC’s chosen for the active band-pass and notch filters were the low-noise high-speed JFET-Input operational amplifiers (TL071CP, Texas Instruments). 4. Experiments and results In this section, tests on the performance of several key elements of our ECG acquisition system are presented. In addition, the quality of the ECG waveforms obtained and a comparison with a standard purely electronic acquisition system is exposed. These results are critical and important for the validation of the full system operation and for future clinical testing and applications. In order to perform the preliminary analysis, an experimental setup was used, consisting on the components described in section III. Fig. 7 shows the setup used to validate the proposed sensor, based on high-speed optical communication components. From (1) we can understand that in order to increase the overall sensitivity, a higher Pin , low V , and high GTIA should be used.
There’s a tradeoff between Pin and GTIA , since to overcompensate the DC levels introduced by a higher optical power, complex TIA design is required. The selection of the EO components used in the experimental setup, was therefore made regarding these constrains. A C-band broadband ASE (Amplified Spontaneous Emission) light source (LXASE-CN-14-NO, Luxpert) generates an optical signal with a power of 7 dBm and a wavelength of 1546 nm. Light is guided through a FC/APC optical fiber to a dual drive LiBnO3 MZI modulator (FTM7921ER, Fujitsu). The selection of this modulator was made considering a small V (3.2 V) and insertion loss (6.3 dB). The photonic stage was stored inside an isolated metal box, as shown in Fig. 7a. The output optical signal was detected by an InGas photodiode (FGA04, Thorlabs) with a responsivity of 0.9 A/W (@1546 nm) and a capacitance of 5 pF (@zero bias). The resultant DC currents were 1.27 mA, and the modulated signal had an average value of 10 A. The processing unit consisted of an in-house TIA, notch and band-pass filter, previously described (Fig. 7b), and was isolated using a faraday cage. A 9 V-lithium battery powers the acquisition electronics, preventing unwanted periodic oscillations (ripple), whose amplitudes are often higher than typical ECG amplitudes.
4.1. Performance assessment for bioelectric signal acquisition The performance assessment of systems for ECG signal acquisition is commonly focused on the verification of the system ability to provide a signal where the waveforms components required for health monitoring must be present [5,17]. This is usually done since the signal source, the bioelectric signal that is measured, which results from electrical heart activity, is different
Fig. 7. Experimental setup used: (left) LiNbO3 MZI modulator; (right) acquisition and processing electronics.
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Fig. 8. (left) MZI modulator transfer curve, highlighting the quadrature region; (right) EO stage frequency response (MZI modulator and TIA, included).
for different subjects, and changes with time, physiological factors, and position of electrode type and position placement. Moreover, the acquisition of an ECG may have two purposes. I may be used for clinical purposes (usually for diagnostics), where a clinical ECG is required, or it can be used for well being purposes (more and more required with the advent of Ambient Assisted Living devices), where not all the ECG waveforms components are required. To assess the performance of the proposed EO system, the ECG data was obtained using healthy male subjects, with normal bioelectric activity. It was used a differential electrode placement (sintered Ag/AgCl ring electrodes), according to the Lead I of Einthoven’s triangle, which measures the signal between an electrode in the right arm (RA) and one on the left arm (LA). To assess the suitability of the proposed EO system, data was also collected using a fully electrical acquisition system, based on the INA129 instrumentation amplifier. The INA129 based system was developed and tested previously against a commercial setup from BrainVision, QuickAmp. Moreover, and usually not required, once the waveforms have enough information for medical inspection, the systems’ performance was also evaluated in the standard way, where the sensitivity, gain and bandwidth where determined and compared with a commercial performing system.
4.2. Circuit performance The MZI modulator transfer function was experimentally determined in order confirm the quadrature region. Fig. 8a shows that it is possible to operate the MZI modulator in the quadrature region (maximum sensitivity) without applying a bias voltage, i.e. Vbias = 0 V. The average measured value for insertion loss was 6.3 dB. Frequency response of the EO stage, i.e. MZI modulator and TIA, was determined using a 20 mVpp sinusoidal signal with a frequency sweep from 0.1 Hz to 1 kHz. Results are depicted in Fig. 8b, showing a stable and flat operation point at 60 dB in the frequency range of interest. The 3 dB frequency is centered at 0.3 Hz, which is suitable for ECG recordings. The overall sensitivity of the EO system was tested using a sine-wave generator, and results have shown a minimum detected voltage of 20 V. The resulting experimental gain of the TIA was 1 × 105 V/A. The performance summary and the comparison between behavioral simulation and measured results are shown in Table 1. The actual specifications of the setup were close to the theoretical estimations shown in Table 1. However, because of process variations, as well as mismatch between rated and the actual
Table 1 Summary of notch and band-pass filter performance (S: simulations; E: experimental). Gain (dB)
Notch BPF
Frequency (Hz)
S
E
S
E
−39 0
−33.9 0
50 0.20–40.1
49.89 0.25–39
specifications of the passive and active components, the circuit setup produced slightly different gains and frequency responses. The frequency ranges of the components include the frequency components of interest of the ECG, i.e. 0.2–40 Hz [17]. Current and power consumption were determined for each approach: EO and purely electric. The measured values are shown in Table 2, considering a supply voltage of ±15 V. The obtained results substantiate the objective of achieving low power consumption systems. In this way, it contributes not only to a prolonged lifetime, but also to system miniaturization since batteries usually occupy more than 50% of the system volume. The bottleneck of the photonic sensor is related with the use of optical power sources that have higher power consumption. Nevertheless, with available ultra-bright LEDs that can achieve power consumptions of few mW, it is possible to reduce overall power consumption of the photonic system, while achieving similar performances over electronic counterparts. 4.3. EO detection of ECG To test the bioelectric signal acquisition capability of the photonic sensor, an ECG signal was obtained from a human subject. A differential recording was made by placing the electrodes according to the Lead II of Einthoven’s triangle, which measures the signal between an electrode in the right arm (RA) and one on the left arm (LA). During the recording session, the overall system gain was set to 1000 V/V. Both electrodes were connected to the MZI modulator, resulting in almost twice the modulation depth. To Table 2 Measured current and power consumption. EO setup
Current (mA) Power (mW)
Electric setup
TIA
Filters
INA
Filters
0.8 12
1.2 18.24
0.6 9.12
1.2 18.24
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Fig. 9. Comparison of ECG signals obtained using two acquisition systems. (a) Standard electrical acquisition system with an instrumentation amplifier, after filtering, and (b) raw signal; (c) the proposed EO sensor, after filtering, and (d) raw signal.
compare these results with a conventional bioelectric acquisition, the modulator was replaced by an instrumentation amplifier. An INA129 (Burr Brown, Texas Instruments) with an input capacitance of 2 pF was used with a gain set to 1000 V/V. The electrodes and the filtering stage were the same for both recordings. Fig. 9 shows the ECG signals obtained for both MZI modulator and instrumentation amplifier. ECGs obtained with both procedures have shown good resolution and quality for accurate detection of typical wave components such as P wave (atrial depolarization), QRS complex (ventricular depolarization), T wave (ventricular repolarization) and PR
interval. In particular, the clear and prominent R-peak presented in both raw signals allows to detect the heart rate more accurately. This parameter is often determined by analyzing the QRS complex, i.e., determining the number of QRS events in a specific period of time, and multiply it to achieve the number of beats per minute [23]. Therefore, assessment of ECG patterns provides useful information about the status of the heart, where typical deviations from normal include: abnormalities in rate, rhythm and cardiac myopathies [23]. In addition, resorting to developed algorithms, such as the one proposed in [24], it is possible to extract information about respiration function.
Fig. 10. Comparison of ECG power spectrum obtained using two acquisition systems. (a) Standard electrical acquisition system with an instrumentation amplifier, after filtering, and (b) raw signal; (c) the proposed EO sensor, after filtering, and (d) raw signal.
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Signals obtained have interference components predominant at 50 Hz, which can be seen in spectrum power charts from each acquired signal, shown in Fig. 10. Spectral components are similar for each approach, showing a higher spectrum power for low frequencies (0–40 Hz). Overall filtering stage has shown good performance in terms of amplifying the knowledge-content frequencies while attenuating unwanted interference components. 5. Conclusions A photonic bioelectric sensor for wearable applications based on EO techniques is proposed. The technology used is based on the EO techniques and uses a Lithium Niobate (LiBnO3 ) Mach-Zehnder Interferometer (MZI) modulator as the sensing component. The optic and electric components of the photonic sensor were characterized in a conventional high-speed optical communication setup. The ability to perform bioelectric measurements was validated by acquiring an ECG with the photonic setup designed. It was demonstrated that the clinical performance of the sensor was adequate, showing good resolution and frequency response. It was found that the bandwidth of the overall sensor is larger then the required. Standard systems use a bandwidth of 35 Hz, and the presented system has a bandwidth of 39 Hz. It is important to highlight that this bandwidth is not limited by the EO transducer, but was set by the electronics, which may be changed by design. Finally, the system overall gain was set to 1000 V/V, a value that may be changed by design. Standard gains are in the 1000–10,000 V/V, depending on the acquired biopotential and electrode impedance. The main difference from standard systems is the fact that this gain, usually, is programmable to accommodate different types of electrodes. The results were compared with clinical conventional electronics and have shown comparable performances. The proposed sensor can be developed for acquisition of the remaining bioelectric signals such as EEG, EMG and EOG. This opens the way to explore an all-optic multi-parameter wearable suit, covering other physiological parameters as for example temperature and pressure. Acknowledgments We would like to acknowledge the MIT-Portugal Program and Portuguese Foundation for Science and Technology for supporting this work (PTDC/EEA-TEL/65286/2006). References [1] Wearable Wireless Sensors, ABI Research, 2010, Research Reports. ˜ H.C. Kung, Deaths: preliminary [2] K.D. Kochanek, J. Xu, S.L. Murphy, A.M. Minino, data for 2009, National Vital Statistics Reports 59 (4) (2011). [3] G. Constantine, D.I. Fotiadis, Wearable devices in healthcare, in: B.G. Silverman, A. Jain, A. Ichalkaranje, L.C. Jain (Eds.), Intelligent Paradigms for Healthcare Enterprises, Springer Berlin, Berlin, Germany, 2005, pp. 237–264. [4] J.M. Winters, Y. Wang, Wearable sensors and telerehabilitation, IEEE Engineering in Medicine and Biology Magazine 22 (3) (2003) 56–65. [5] R.F. Yazicioglu, T. Torfs, P. Merken, et al., Ultra-low-power biopotential interfaces and their applications in wearable and implantable systems, Microelectronics Journal 40 (September (9)) (2009) 1313–1321. [6] Y. Chuo, M. Marzencki, B. Hung, et al., Mechanically flexible wireless multisensor platform for human physical activity and vitals monitoring, IEEE Transactions on Biomedical Circuits and Systems 4 (October (5)) (2010) 281–294. [7] Y.D. Lee, W.Y. Chung, Wireless sensor network based wearable smart shirt for ubiquitous health and activity monitoring, Sensors and Actuators B-Chemical 140 (July (2)) (2009) 390–395.
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Biographies Mariana Fernandes is a consultant of Sociedade Portuguesa de Inovac¸ão (SPI) working as a consultant in Research, Development and Innovation (RDI), focused on national and international projects funded by the European Union (R & D and RDI). Mariana Fernandes has a PhD in Bioengineering Systems from the MIT-Portugal Program (2008–2011), and a Biomedical Engineering Integrated Master Degree (2002–2007) from the University of Minho (Braga, Portugal). In 2009, Mariana Fernandes went to the Massachusetts Institute of Technology (MIT) as a PhD Visiting Student to the Research Laboratory of Electronics (RLE), under the supervision of Prof. Rajeev Ram. Her current fields of interest include areas such as photonic sensors, wearable devices, electronics, electrophysiology, medical sensors. Jose Higino Correia graduated in Physical Engineering from University of Coimbra, Portugal in 1990. He obtained in 1999 a PhD degree at the Laboratory for Electronic Instrumentation, Delft University of Technology, The Netherlands, working in the field of microsystems for optical spectral analysis. Presently, he is a Full Professor in Department of Industrial Electronics, University of Minho, Portugal. He was the General-Chairman of Eurosensors 2003 and MME 2007, Guimarães, Portugal. His professional interests are in micromachining and microfabrication technology for mixed-mode systems, solid-state integrated sensors, microactuators and microsystems. P.M. Mendes graduated in 1995, and obtained his MSc degree in Electrical Engineering, in 1999, both from University of Coimbra, Portugal. He obtained his PhD degree in Industrial Electronics, from University of Minho, in 2005. He is an Associate Professor at University of Minho, and a researcher at the Algoritmi Center. His interests are in the field of the project, fabrication, and characterization of RF microdevices for wireless microsystems. P.M. Mendes is Member of the European Microwave Association, of the IEEE Antennas and Propagation Society, and of the IEEE Engineering in Medicine and Biology Society.