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Portable piezoelectric cardiac abnormality detection
Alexandria Engineering Journal (2019) xxx, xxx–xxx
H O S T E D BY
Alexandria University
Alexandria Engineering Journal www.elsevier.com/locate/aej www.sciencedirect.com
ORIGINAL ARTICLE
Portable piezoelectric cardiac abnormality detection Shina Mokhtari, Mahmoud Al Ahmad * Electrical Engineering Department, United Arab Emirates University, Al Ain 15551, United Arab Emirates Received 3 September 2019; accepted 14 September 2019
KEYWORDS Bluetooth; Blood pressure; Heart beat; Piezoelectric materials; Sensors; Vital sign
Abstract Continuous portable monitoring system for cardiac activity is very important to keep a patient under control. This work addresses the design and realization of a Bluetooth based portable system incorporating a piezoelectric sensor sheet. When the sensor is attached to a human chest, the cardiac mechanical activity will induce a local strain in the sensor incorporated piezoelectric materials. This strain is converted to an electrical output voltage which is then collected and transmitted through the Bluetooth-based system. Piezoelectric theory and signal processing methods have been used to extract the heart rate, pulse pressure, and its corresponding period of several cycles. These parameters have been validated using conventional methods. The realized system is 50 g with a length of 5 cm, width of 3 cm and a thickness of 2 cm with biasing voltage of 3 V. Its ease of use, compactness, lightweightness, and low energy consumption will make it ideal to be utilized in a wide range of applications. In addition, this work addresses development of an efficient algorithm to detect abnormalities in cardiac activities using correlation techniques. The proposed algorithm for detecting abnormalities reduces the processing time hugely and the processing power considerably. Ó 2019 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction Portable based health monitoring systems are gaining more importance due to their impact on human health [1]. Prognostication of change in cardiac cycle performance is crucial and has a very important effect on heart diseases [2]. Telecommunications handset terminals can eliminate the gaps between the * Corresponding author. E-mail address: [email protected] (M. Al Ahmad). q This work was supported by the UAE University under Grant 03370. Peer review under responsibility of Faculty of Engineering, Alexandria University.
patient and the doctor [3]. Thus, the cost as well as the time needed for doctor-patient communication can be reduced [4]. A portable cardiac parameters monitoring device could address some of the challenge faced by the medical community and serve as a real-time monitoring and analyzing data system [5]. Using such a device, patients can regularly monitor their health and the medical staff can analyze the collected data remotely [6]. Such wireless based health monitoring system could potentially reduce heart disease and offer a control system functionality by providing early detection capabilities of heart abnormalities [7]. Several piezoelectric-based sensors have previously been developed to measure the heartbeat rate [8-14]. Piezoelectric based bio-sensor exhibits electromechanical interactions which
https://doi.org/10.1016/j.aej.2019.09.008 1110-0168 Ó 2019 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Please cite this article in press as: S. Mokhtari, M. Al Ahmad, Portable piezoelectric cardiac abnormality detection, Alexandria Eng. J. (2019), https://doi.org/ 10.1016/j.aej.2019.09.008
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S. Mokhtari, M. Al Ahmad
results in an electrical charge accumulation when stress is applied [15]. The applied stress forces the piezoelectric material domains to be displaced generating an output voltage corresponding to the applied stress magnitude and its instantaneous variation [16]. The mechanical design of the incorporated piezoelectric sensor’s structure affects the electrical performance [17]. This performance strongly depends on interactions between the mechanical and electrical impedances [18]. The main use of piezoelectric sensors is due to their low power consumption, compact size, very simple signal conditioning requirements, and the fact that they do not require any external electrical biasing [16]. In one study, Buxi et al. suggested using piezoelectric transducers array to overcome the motion artifact by measuring signals at various locations simultaneously assisted by subsequent signal processing [19]. Furthermore, Patel et al. proposed the use of biochip that contains several biosensor arrays along with advancement in signal processing to mitigate motion artifacts utilizing the concept of multiple detections [20]. Klap et al. also demonstrated the use of a piezoelectric based sensor to record a superposition signal that can be decomposed of body motion, chest wall movement, and cardio ballistic recoil of the human body due to heart pulse [21]. Moreover, Setyowati et al. developed a heart rate monitor utilizing the sound of a heartbeat using a piezoelectric sensor [22]. In another study, Al Taradeh et al. have proposed a non-invasive piezoelectric-based measurement of heartbeat rate and blood pressure. This was performed by capturing a temporal and spatial cardiac representative signal over the chest with the help of proper signal processing [23]. It has been shown that when the piezoelectric theory is coupled with advanced signal processing techniques, this ends up with a unique combination that enables a wide range of medical applications. Therefore, a wearable wireless piezoelectric based sensor technology to enhance and promote the progress of portable medical health is of great interest. This work demonstrates the development of a Bluetooth-enabled piezoelectric heart rate portable monitoring system. 2. Background Fig. 1 shows a two parallel elements model of the left ventricular wall functionality [24]. The formulations of the heart mechanical activities based on longitudinal wave provide a simple and fast analytical model. The heart muscle wall has been modeled as an inactive elastic spring of length (L), which is equal to the current length of the wall. The effective stress is denoted by rs, which affect the ventricular from the surrounding areas including the chest. This stress is composed of contracting stress (rA) that represents the depolarization pulses based on resultant stress of the left cardiac ventricular fibers with negative protraction, and the relaxation stress (rp) that is generated from polarization pulse of the left ventricular with positive protraction. When a piezoelectric sensor is located on the chest surface, it detects the strain produced by the contraction and expansion of the heart muscle. The output voltage (V) of the sensor as a function of the strain (S) is expressed as: VðtÞ ¼ cp tc d31 e1 SðtÞ
ð1Þ
Fig. 1 The proposed model for heart mechanical activities adapted from Remme et al. [24].
where e, cp, tc, and d31 are the material dielectric constant, elasticity coefficient, thickness and piezoelectric voltage constant, respectively [25]. S is the available stress at the external chest surface and the effective stress is denoted by rs, is the acting stress generated by the heart muscle on the inner chest surface (see Fig. 2). Fig. 2 provides an overview of the electro-mechanical interactions. The heart mechanical activity through the effective surrounding will induce vibrations in the chest wall, which can be collected using the piezoelectric sensor attached to the external (outer) chest surface [25]. The incorporated piezoelectric sheet in the sensor will deform and thus producing a voltage signal. As per Eq. (1), the corresponding output signal voltage is mapped conformally with the acting strain on the piezoelectric material. In general, the stress is proportional to strain and it is correlated with the young modulus of the material. The effectively available strain (stress) at the outer chest surface is also conformally mapped with the effective available strain (stress) at the inner chest surface. The bulky chest wall could be modeled as a
Fig. 2 An overview of the coupled mechanical-electrical interactions system model.
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Portable piezoelectric cardiac abnormality detection
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bulky attenuator with a certain time delay [26]. Therefore, this shows that the sensor output voltage signal is conformally mapped with the cardiac cyclic features. Hence, the variation in the stress (rS) generates an output voltage signal which has the same variation in the time domain. 3. Prototype fabrication The details of the realized portable system are shown in Fig. 3. The integrated system is composed of two main components: the piezoelectric transducer [27] and the Adafruit Bluetoothbased transmitter [28]. A lithium battery is used to provide the required DC bias of the Adafruit transmitter. The transmitter is assembled on the PCB on one side, as depicted in Fig. 3b. The Adafruit transmitter includes an Atmega328p chip attached to an Adafruit Bluetooth transmitter unit, which can be programmed by Arduino software [28]. The low-energy Bluetooth sensor transmits the real-time instantaneous measured data wirelessly to the laptop equipped with MATLAB. Two Metallization pads are placed on the other side of the PCB (see Fig. 3c) and two piezoelectric transducer electrodes are soldered to metallization pads. The complete prototype is shown in Fig. 3e. The employed piezoelectric material has a d31 of 315 pm/V and the customized sensor dimensions are 46 20 0.26 mm and are composed of lead zirconate titanate (PZT) [27]. Fig. 4 shows the frequency characteristics of the employed piezoelectric materials over a frequency range of 1 Hz to 100 kHz. The heart-induced pressure signal obtained using this sensor has a low bandwidth of approximately 800 MHz to 3 Hz. The piezoelectric structure exhibits capacitive behavior with a phase approximately close to 90 degree and a high impedance magnitude of around 1 M ohms. Therefore, the sensor electro-mechanical interactions will not
Fig. 4 Impedance magnitude and phase plotted as a function of the frequency of the utilized piezoelectric material.
be further attenuated or delayed and hence not affect the output electrical voltage magnitude or phase. 4. Measurements and analysis The developed data acquisition system is illustrated in Fig. 5 along with the measured piezoelectric voltage signals. A schematic of the human chest and the fabricated portable prototype of this study is illustrated in Fig. 5a and b, respectively. To collect the cardiac signal, the bottom surface of the piezoelectric transducer, shown in Fig. 5b, is placed on the chest surface. The laptop used to receive the transmitted signal is shown in Fig. 5c. Fig. 5d depicts the output voltage signal received by
Fig. 3 Functional prototype (fetching and wireless sending heart signal device): (a) lithium battery; (b) Adafruit transmitter; (c) PCB board; (d) sensor, and (e) complete prototype.
Please cite this article in press as: S. Mokhtari, M. Al Ahmad, Portable piezoelectric cardiac abnormality detection, Alexandria Eng. J. (2019), https://doi.org/ 10.1016/j.aej.2019.09.008
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S. Mokhtari, M. Al Ahmad
Fig. 5 Acquisition system and piezoelectric signals: (a) schematic of the human chest, (b) functional prototype, (c) used laptop, (d) measured output voltage, and (e) two cardiac cycle window.
the laptop with acquisition performed using MATLAB code [29]. The signal train was trended and filtered with built-in MATLAB functions to remove the noise contribution due to transmission. The acquired signal through the BT module is automatically filtered and amplified using the build in components with low pass filter of 120 Hz; which works well with this application. The default preprocessing parameters of the employed Adafruit transmitter (includes an Atmega328p) chip are sufficient and reproduce the same data collected directly by the oscilloscope. Hence, the default setting did not affect the results. Data trended filtering was needed to overcome any abrupt changes or events in the underlying dynamics of the acquired signal in time domain and further to enhance the received signal quality. The signal exhibits a conformally mapping shape corresponds to the heart muscle physiological activity, namely the systolic and diastolic blood pressure and heartbeat rate [30]. Fig. 5e shows that two cycles in the desired range are observed. Each cycle, in fact, is a result of the expansion and contraction of the heart. After the required post-processing of the input signal, proper cycles can be selected that are confined between –20 mv and +20 mv. Fig. 6a superimposed several cycles aligned with their maximum positive peaks. The pulse pressure is extracted from the measured cycles using the formulas reported and is shown in Fig. 6b [23]. The extracted pulse pressure ranges from 30 to 40 mmHg. Fig. 6c represents the period
of each cycle along with its average. As observed, almost all cycles have a period of approximately 0.75 s (80 beats per minute). As experimentally reported previously, the heart rate (beats per minute) is considered to be the number of positive peaks counted within a time interval mapped to one minute [23]. Fig. 6d demonstrates the computed cross-correlation between the selected cycles of Fig. 6a. When the crosscorrelation coefficient is close to unity, the cardiac cycles are well aligned and have the maximum match between them indicating a stable behavior of the heart muscle. However, as the cross-correlation decreases away from unity, the cardiac cycles are less matched and consequently, the probability of an unstable heart functionality is recorded. This instability could be due to a change in emotional status of the subject or cardiac abnormalities [31]. In this study, we constructed an average cycle per individual subject from a noise free cycles. This cycle is used as a references and for correlation and matching computations. One of the remarkable features of the current proposed monitor is its capability to extract the cardiac parameters and check abnormality for each real time measured cycle. The manually selected cycles are only used for constructing the average cycle. The process can then be automated using a match filter, which can be utilized for maximizing of the signal to noise ratio (SNR) assuming additive stochastic noise. Nevertheless, the mismatch between the cycles can be connected with an instan-
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Fig. 6 Extracted cardiac parameters per cycle: (a) superimposed cycles, (b) extracted pulse pressure per cycle, (c) heart beat period per cycle (d) cycles cross-correlation. The heart-beat rate is then set to be the number of positive peaks counted in one minute [23].
the subjects hold their breath. Compared to other techniques, the proposed approach provides a mechanism for wireless diagnosis and can be used with further development to anticipate heart failure or other cardiac abnormalities. In addition, the employed sensors are easy to handle, cause minimal discomfort to the patient, and minimally impact the clinics [33]. Finally, the novel contribution of this work is in terms of the system capability to extract the heart rate, pulse pressure, and to detect any instantaneous abnormality in the cardiac activities by observing the cycles cross correlation coefficient. We have compared the differences between the current model and approach and other existing piezoelectric based sensors continuous monitoring systems (see Table 2). The presented approach and device can be further optimized in terms of size and increase its functionality extracting the ECG based on the presented approach [25] along with the measurements of the systolic and diastolic of blood pressures values. Moreover;
taneous abnormality occurrence or emotional status. The results will change significantly if ‘‘improper” ones are additionally selected for average cycle calculations. Therefore, it is important that only ‘‘proper” cycles are included. It is worth mentioning that the cycles are not normalized in Fig. 6. The diagonal values of Fig. 6d are the cross-correlation of the average cycle with itself. The other values represents the cycle’s cross-correlation between one cycle and all the cycles within a specific time frame. Fig. 6d is a 3D representation form of these data which is symmetry across the diagonal. Furthermore, the fabricated prototype was used to collect signals from various human subjects. Table 1 summarizes the data collected from ten individuals along with their extracted heart rate using the proposed method and the conventional meter [32]. The conventional readings and the extracted values using our device and approach are matched within 3% error. It is worth mentioning that all measurements were taken while
Table 1
Extracted heat rates of all individuals.
Subject
#1
#2
#3
#4
#5
#6
#7
#8
#9
#10
This method with error ± 1 Conventional with error ± 3
70 66
81 83
76 75
82 79
69 70
80 79
75 76
77 75
67 69
74 70
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S. Mokhtari, M. Al Ahmad Table 2
Comparison between current work and some existing prototypes.
Reference
Size [mm2]
Cost
Features
position
Parameters
Other
[22]
medium
Local
medium
Heart rate
Non portable utilized the acoustic property of piezoelectric materials Not applicable
[35]
20 50
high
Respiration rate and pulse rate
apnea detection
[36]
280 280
expensive
Local monitoring Local monitoring Wireless
Close to the wrest rest
Heart rate
[34]
Very large 15 30
analysis of sleep stages
This work
20 40
medium
Wireless
Heart Rate, Breathing, Stress Level; Sleep Cycles Heart rate and pulse pressure per cycle. Cycle period
Abdomen and chest under bed chest
the proposed monitoring system can be used for person reidentification based on their biometric data through online algorithms that can be adopted and developed along this device. For example; the system is capable of detecting and identifying individuals with abnormalities in heart functionality among groups of people. Setyowati et al. has proposed an acoustic piezoelectric sensor for heart pulse monitoring that can be placed on the wrist equipped with a mild pressure. Arduino microcontroller has been use along with the sensor to collect the possible sounds of the heart. In addition, the iRhythum - Zio XT patch is a wearable device that can continuously measure the heart rate in a discreet and wireless manner [reference]. This technology records heartbeat during sleeping as well as during day to day activities up to 14 days. After the recording period, the data is sent to the iRhythum clinical app for analysis [37]. Moreover; Medtronic SEEQ Mobile Cardiac Telemetry (MCT) System is another wireless external heart monitor which helps in detecting and diagnosing the cause of irregular heartbeats in patients [38]. In addition; NeuroSky- ECG Biosensor is equipped with monitoring the performance of the heart [39]. In this study, we used a portable system that should be placed on the chest close to the arterial pressure and utilizes the conversion of mechanical pressure to electrical signal using the mechanical electrical energy conversion principle [23]. Fig. 7 shows the photo of the sensor attached to the chest of subject. 5. Abnormality detection This section addresses development of an efficient algorithm to detect abnormalities in cardiac activities using correlation techniques. Fig. 8 illustrates the measured output signals of ECG and piezoelectric sensors. Both signals have been normalized in magnitude. The normalization of the amplitude of both signals to unity has been carried out to align their peaks and directly map the cardiac physiological activities in order to determine the corresponding features in the output voltage of the piezoelectric sensor. For the subject under study, his/ her corresponding piezoelectric and ECG signals are of a periodic nature with a time interval of 0.8 as depicted in Fig. 8. The ECG traces for seven corresponding well-known heart abnormalities are shown in Fig. 9. The typical normal ECG signal cycle is shown in Fig. 9a. As has been explained previously and stated clearly by Fig. 8, any change in the ECG trace will be reflected in the piezoelectric output signal trace. The corresponding piezoelec-
Abnormality detection in cardiac functionality
Fig. 7 The fabricated prototype attached to human subject using double sided medical tap.
tric signal trace can be directly correlated with the ECG trace as was previously reported [25]. Therefore, to detect any abnormality in the cardiac response, the instantaneous measured piezoelectric signal is correlated with the typical normal piezoelectric signal that was determined (Fig. 8) and stored in a database. The stored piezoelectric trace is used as a reference. If the computed correlation coefficient is approximately unity, the instantaneous piezoelectric output signal is very close to the typical stored reference trace. Hence, the two signals are identical and there is no abnormality to be detected. If the correlation coefficient differ from unity, then this is considered an abnormality in cardiac function because there is a mismatch between the instantaneous measured signal and the reference signal. To identify the present abnormality, the instantaneous piezoelectric signal should be correlated with the other piezoelectric corresponding piezoelectric signal of the ECG abnormal traces shown in Fig. 9b–h. The abnormality in the cardiac function corresponds to the largest correlation coefficient. Table 3 represents the computed correlation coefficients between the normal corresponding piezoelectric signals to the ECG disease traces. This table demonstrates that the highest correlation coefficient and corresponds to a case free of abnormality. Meanwhile the other coefficients are away from unity. Therefore, the instantaneous
Please cite this article in press as: S. Mokhtari, M. Al Ahmad, Portable piezoelectric cardiac abnormality detection, Alexandria Eng. J. (2019), https://doi.org/ 10.1016/j.aej.2019.09.008
Portable piezoelectric cardiac abnormality detection
7 Table 3 correlation coefficient* between normal cycle and cycles of different diseases.
Fig. 8 Superposition of normalized measured typical normal ECG and the corresponding measured representative piezoelectric cycles. This figure represent the normalized data to show the systolic and diastolic and correlated with the piezoelectric signal. The cross correlation coefficient listed in Table 3, are the cross correlation coefficient between the signals of Fig. 8(a) with the other from (b)–(h). Please see the comment added to Table 3.
Case
Coefficient
Comments
Normal
1
Ischemia
0.01
Injury
0.25
Infraction
0.56
Hypokalemia
0.54
Hypocalcemia
0.05
Hyperkalemia
0.51
Hypercalcemia
0.15
Cross correlation and (a) Cross correlation and (b) Cross correlation and (c) Cross correlation and (d) Cross correlation and (e) Cross correlation and (f) Cross correlation and (g) Cross correlation and (h)
between signal (a) between signal (a) between signal (a) between signal (a) between signal (a) between signal (a) between signal (a) between signal (a)
*The coefficient of correlation is a finite number whose value comes between +1 and 1 inclusive, where 1 represents the total positive correlation, 0 is no correlation, and 1 represents total negative correlation.
signal should be correlated with the 8 stored cases of Fig. 9, and then consider the highest coefficient value, which indicates the case of the disease. It is worth to add that when an abnormality is detected, the subject should be advised to visit a doctor for further evaluation and verification. The experiment setup depicted in Fig. 10 was used to verify the abnormality detection. The setup incorporates ECG synthesizer that is used to generate the corresponding abnormalities from b to h represented in Fig. 9. An oscilloscope is used to display the generated abnormality signal. The signal is instantaneously transmitted over bluetooth module. The detection code based on correlation were implemented using android. The produced correlation coefficient are very close (with ±1%) with their corresponding values listed in Table 3. The detection accuracy was approximately more than 95%. The numbers listed in Table 3 are average values. The threshold will depend on the number of abnormality cases
Fig. 9 Normal and abnormal ECG traces with their corresponding diseases: (a) normal, (b) ischemia, (c) injury, (d) infraction, (e) hypokalemia, (f) hypocalcemia, (g) hyperkalemia and (h) hypercalcemia. The dataset were collected from medical reports/book [31].
Fig. 10
Experimental validation setup.
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8 involved. For the 7 cases presented in Fig. 9, the threshold then can be settled ±5% of the average value to avoid overlap. 6. Conclusions This work represents a wearable cardiac basic monitoring system which can be used to monitor heart rate, beat period, and pulse pressure. The system consists of piezoelectric sensor utilized to generate a conformally mapped output voltage signal, a Bluetooth enable transmitter, and a laptop MATLAB based application for remote data analysis. A user experience test conducted on 10 participants provided a good match with existing meters. In addition, the system usability and adaptability of the proposed technology have proven to be competitive with other methods. Furthermore, conventional meters were utilized to validate our extracted data acquired here. Our system can be further developed and optimized to be utilized in the detection of heart failure based on spatiotemporal measurements and could potentially be used to construct a future heart activity component in prediction models. Acknowledgement The author wishes to acknowledge the support for the UAE ICT-Fund. This work has been approved by Al Ain Medical District Human Research Ethics Committee – Protocol No. 14/67. Author Contributions MA conceived the concept. MA and SM fabricated the prototype and performed the measurements. MA and SM have performed the corresponding signal processing. Declaration of Competing Interest The authors declare that they have no competing interests. References [1] S. Majumder, T. Mondal, M.J. Deen, Wearable Sensors for Remote Health Monitoring, Sensors 17 (2017). [2] J.B. Strait, E.G. Lakatta, Aging-associated cardiovascular changes and their relationship to heart failure, Heart Fail. Clin. 8 (2012) 143–164. [3] S.J. Lee, A.L. Back, S.D. Block, S.K. Stewart, Enhancing physician-patient communication, Hematol. Am. Soc. Hematol. Educ. Program 464–483 (2002). [4] T.L. Kay, Volume and intensity of Medicare physicians’ services: An overview, Health Care Financ. Rev. 11 (1990) 133–146. [5] H. Banaee, M.U. Ahmed, A. Loutfi, Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges, Sensors 13 (2013) 17472–17500. [6] C.L. Ventola, Mobile devices and apps for health care professionals: uses and benefits, P T Peer-Rev. J. Formul. Manag. 39 (2014) 356–364. [7] M. Suh et al, A remote patient monitoring system for congestive heart failure, J. Med. Syst. 35 (2011) 1165–1179. Gubner, M. Rodstein, H.E. Ungerleider, [8] R.S. Ballistocardiography; an appraisal of technic, physiologic principles, and clinical value, Circulation 7 (1953) 268–286.
S. Mokhtari, M. Al Ahmad [9] O.T. Inan et al, Ballistocardiography and Seismocardiography: A review of recent advances, IEEE J. Biomed. Health Inform. 19 (2015) 1414–1427. [10] J. Ben-Ari, E. Zimlichman, N. Adi, P. Sorkine, Contactless respiratory and heart rate monitoring: validation of an innovative tool, J. Med. Eng. Technol. 34 (2010) 393–398. [11] P. Flandrin, G. Rilling, P. Goncalves, Empirical mode decomposition as a filter bank, IEEE Signal Process. Lett. 11 (2004) 112–114. [12] Thomas Zeugmann, Pascal Poupart, James Kennedy, Xin Jin, Jiawei Han, Lorenza Saitta, Michele Sebag, Jan Peters, J. Andrew Bagnell, Walter Daelemans, Geoffrey I. Webb, Kai Ming Ting, Kai Ming Ting, Geoffrey I. Webb, Jelber Sayyad Shirabad, Johannes Fu¨rnkranz, Eyke Hu¨llermeier, Stan Matwin, Yasubumi Sakakibara, Pierre Flener, Ute Schmid, Cecilia M. Procopiuc, Nicolas Lachiche, Johannes Fu¨rnkranz, Particle Swarm Optimization 10.1007/978-0-387-30164-8_630, in: Claude Sammut, Geoffrey I. Webb (Eds.), Encyclopedia of Machine Learning, Springer US, Boston, MA, 2010, pp. 760– 766. [13] M.M. Breunig, H.P. Kriegel, R.T. Ng, J. Sander, LOF: Identifying Density-based Local Outliers, Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, ACM, 2000, doi:10.1145/342009.335388. [14] M. Migliorini et al, Monitoring nocturnal heart rate with bed sensor, Meth. Inf. Med. 53 (2014) 308–313. [15] H. Li, C. Tian, Z.D. Deng, Energy harvesting from low frequency applications using piezoelectric materials, Appl. Phys. Rev. 1 (2014) 041301. [16] R. Calio` et al, Piezoelectric energy harvesting solutions, Sensors 14 (2014) 4755–4790. [17] J. Sirohi, I. Chopra, Fundamental understanding of piezoelectric strain sensors, J. Intell. Mater. Syst. Struct. 11 (2000) 246–257. [18] M.A. Ahmad, A.M. Elshurafa, K.N. Salama, H.N. Alshareef, Modeling of MEMS piezoelectric energy harvesters using electromagnetic and power system theories, Smart Mater. Struct. 20 (2011) 085001. [19] D. Buxi, J. Penders, C. van Hoof, Early results on wrist-based heart rate monitoring using mechanical transducers, Conf. Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Conf. 2010 (2010) 4407–4410. [20] S. Patel, H. Park, P. Bonato, L. Chan, M. Rodgers, A review of wearable sensors and systems with application in rehabilitation, J. NeuroEng. Rehabil. 9 (2012) 21. [21] CINC 2013 - Computing in Cardiology Conference. Available at:(Accessed: 2nd January 2019). http://i3a.unizar.es/cinc2013/. [22] Setyowati, V., Muninggar, J. & Shanti.N.A, M. R. S. Design of heart rate monitor based on piezoelectric sensor using an Arduino. J. Phys. Conf. Ser. 795, 012016 (2017). [23] N.A. Taradeh, N. Bastaki, I. Saadat, M.A. Ahmad, Noninvasive piezoelectric detection of heartbeat rate and blood pressure, Electron. Lett. 51 (2015) 452–454. [24] E.W. Remme, A. Opdahl, O.A. Smiseth, Mechanics of left ventricular relaxation, early diastolic lengthening, and suction investigated in a mathematical model, Am. J. Physiol. Heart Circ. Physiol. 300 (2011) H1678–H1687. [25] M.A. Ahmad, Piezoelectric extraction of ECG signal, Sci. Rep. 6 (2016) 37093. [26] A. Leung, S. Sehati, J.D. Young, C. McLeod, Sound transmission between 50 and 600 Hz in excised pig lungs filled with air and helium, J. Appl. Physiol. Bethesda Md 1985 (89) (2000) 2472–2482. [27] https://www.piezoproducts.com/de/. (Accessed: 17th February 2019). [28] Bluetooth: Adafruit Industries, Unique & fun DIY electronics and kits. Available at. https://www.adafruit.com/category/255. (Accessed: 17th February 2019).
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Portable piezoelectric cardiac abnormality detection [29] MATLAB MathWorks. Available at. https:// www.mathworks.com/products/matlab.html (Accessed: 2nd January 2019). [30] A.C. Leary, A.D. Struthers, P.T. Donnan, T.M. MacDonald, M.B. Murphy, The morning surge in blood pressure and heart rate is dependent on levels of physical activity after waking, J. Hypertens. 20 (2002) 865–870. [31] S. Ahmed, A. Hilal-Alnaqbi, M. Al, M. Al, ECG abnormality detection algorithm, Int. J. Adv. Comput. Sci. Appl. 9 (2018). [32] Gear, C. M. Leader in Medical Supplies for Healthcare Professionals. Chic Medical Gear Available at. https:// chicmedicalgear.com/ (Accessed: 2nd January 2019). [33] M.A. Ahmad, S. Ahmed, Piezologist: a novel wearable piezoelectric-based cardiorespiratory monitoring system, 2018 Innovations in Intelligent Systems and Applications (INISTA), 2018, doi:10.1109/INISTA.2018.8466275. [34] Y. Shu, C. Li, Z. Wang, W. Mi, Y. Li, T.L. Ren, A pressure sensing system for heart rate monitoring with polymer-based pressure sensors and an anti-interference post processing circuit, Sensors (Basel, Switzerland) 15 (2) (2015) 3224–3235, https:// doi.org/10.3390/s150203224. [35] Yi Xin, Xiaohui Qi, Chenghui Qian, Hongying Tian, Zhenbao Ling, Zijiang Jiang, A Wearable Respiration and Pulse Monitoring System Based on PVDF Piezoelectric Film, Taylor & Francis Integrated Ferroelectrics 158 (1) (2014) 43–51, https:// doi.org/10.1080/10584587.2014.957060. [36] https://www.earlysense.com/digital-health/ (Accessed: 17th June 2019). [37] https://www.irhythmtech.com/products-services/zio-xt. (Accessed: 17th June 2019).
9 [38] https://www.dicardiology.com/content/medtronic-discontinueseeq-mct-wearable-telemetry-system. (Accessed: 17th June 2019). [39] http://neurosky.com/biosensors/ecg-sensor/. (Accessed: 17th June 2019). Shina Mokhtari Obtained her MSc in Electrical Engineering from UAE University in 2018. Her research interest is the development of models for heart failure detection. Mahmoud Al Ahmad is a Senior Member of IEEE. He received his BSc in Electrical Engineering from Birzeit University, Ramallah, West Bank in 1999. Both M.Sc. and the doctoral degrees were in Microwave Engineering from Technische Universitaet Mnuechen, Munich, Germany, received in 2002 and 2006, respectively. Currently he is a faculty member at the Department of Electrical Engineering at the United Arab Emirates University. His research interest is in the design and fabrication of self-powered, low powered nano-based electronic devices and systems along with applied electromagnetic for biomedical applications. Dr. Al Ahmad has over ten years of electronic materials and device fabrication research experience in academia, national laboratories and industry. He has managed several research projects and teams with annual budgets of up to US $1 million. He is also a principle (lead) author of around 55 published articles in journals and international conferences and has over 40 presentations at international conferences (several of which has been invited). Dr. Al Ahmad has conducted research in energy harvesting technologies and frequency agile circuits at the Siemens AG/CNRS and King Abdullah University (KAUST) and has published more than 20 journal and 35 conference papers in this domain with more under review.
Please cite this article in press as: S. Mokhtari, M. Al Ahmad, Portable piezoelectric cardiac abnormality detection, Alexandria Eng. J. (2019), https://doi.org/ 10.1016/j.aej.2019.09.008
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Alexandria University
Alexandria Engineering Journal www.elsevier.com/locate/aej www.sciencedirect.com
ORIGINAL ARTICLE
Portable piezoelectric cardiac abnormality detection Shina Mokhtari, Mahmoud Al Ahmad * Electrical Engineering Department, United Arab Emirates University, Al Ain 15551, United Arab Emirates Received 3 September 2019; accepted 14 September 2019
KEYWORDS Bluetooth; Blood pressure; Heart beat; Piezoelectric materials; Sensors; Vital sign
Abstract Continuous portable monitoring system for cardiac activity is very important to keep a patient under control. This work addresses the design and realization of a Bluetooth based portable system incorporating a piezoelectric sensor sheet. When the sensor is attached to a human chest, the cardiac mechanical activity will induce a local strain in the sensor incorporated piezoelectric materials. This strain is converted to an electrical output voltage which is then collected and transmitted through the Bluetooth-based system. Piezoelectric theory and signal processing methods have been used to extract the heart rate, pulse pressure, and its corresponding period of several cycles. These parameters have been validated using conventional methods. The realized system is 50 g with a length of 5 cm, width of 3 cm and a thickness of 2 cm with biasing voltage of 3 V. Its ease of use, compactness, lightweightness, and low energy consumption will make it ideal to be utilized in a wide range of applications. In addition, this work addresses development of an efficient algorithm to detect abnormalities in cardiac activities using correlation techniques. The proposed algorithm for detecting abnormalities reduces the processing time hugely and the processing power considerably. Ó 2019 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction Portable based health monitoring systems are gaining more importance due to their impact on human health [1]. Prognostication of change in cardiac cycle performance is crucial and has a very important effect on heart diseases [2]. Telecommunications handset terminals can eliminate the gaps between the * Corresponding author. E-mail address: [email protected] (M. Al Ahmad). q This work was supported by the UAE University under Grant 03370. Peer review under responsibility of Faculty of Engineering, Alexandria University.
patient and the doctor [3]. Thus, the cost as well as the time needed for doctor-patient communication can be reduced [4]. A portable cardiac parameters monitoring device could address some of the challenge faced by the medical community and serve as a real-time monitoring and analyzing data system [5]. Using such a device, patients can regularly monitor their health and the medical staff can analyze the collected data remotely [6]. Such wireless based health monitoring system could potentially reduce heart disease and offer a control system functionality by providing early detection capabilities of heart abnormalities [7]. Several piezoelectric-based sensors have previously been developed to measure the heartbeat rate [8-14]. Piezoelectric based bio-sensor exhibits electromechanical interactions which
https://doi.org/10.1016/j.aej.2019.09.008 1110-0168 Ó 2019 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Please cite this article in press as: S. Mokhtari, M. Al Ahmad, Portable piezoelectric cardiac abnormality detection, Alexandria Eng. J. (2019), https://doi.org/ 10.1016/j.aej.2019.09.008
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results in an electrical charge accumulation when stress is applied [15]. The applied stress forces the piezoelectric material domains to be displaced generating an output voltage corresponding to the applied stress magnitude and its instantaneous variation [16]. The mechanical design of the incorporated piezoelectric sensor’s structure affects the electrical performance [17]. This performance strongly depends on interactions between the mechanical and electrical impedances [18]. The main use of piezoelectric sensors is due to their low power consumption, compact size, very simple signal conditioning requirements, and the fact that they do not require any external electrical biasing [16]. In one study, Buxi et al. suggested using piezoelectric transducers array to overcome the motion artifact by measuring signals at various locations simultaneously assisted by subsequent signal processing [19]. Furthermore, Patel et al. proposed the use of biochip that contains several biosensor arrays along with advancement in signal processing to mitigate motion artifacts utilizing the concept of multiple detections [20]. Klap et al. also demonstrated the use of a piezoelectric based sensor to record a superposition signal that can be decomposed of body motion, chest wall movement, and cardio ballistic recoil of the human body due to heart pulse [21]. Moreover, Setyowati et al. developed a heart rate monitor utilizing the sound of a heartbeat using a piezoelectric sensor [22]. In another study, Al Taradeh et al. have proposed a non-invasive piezoelectric-based measurement of heartbeat rate and blood pressure. This was performed by capturing a temporal and spatial cardiac representative signal over the chest with the help of proper signal processing [23]. It has been shown that when the piezoelectric theory is coupled with advanced signal processing techniques, this ends up with a unique combination that enables a wide range of medical applications. Therefore, a wearable wireless piezoelectric based sensor technology to enhance and promote the progress of portable medical health is of great interest. This work demonstrates the development of a Bluetooth-enabled piezoelectric heart rate portable monitoring system. 2. Background Fig. 1 shows a two parallel elements model of the left ventricular wall functionality [24]. The formulations of the heart mechanical activities based on longitudinal wave provide a simple and fast analytical model. The heart muscle wall has been modeled as an inactive elastic spring of length (L), which is equal to the current length of the wall. The effective stress is denoted by rs, which affect the ventricular from the surrounding areas including the chest. This stress is composed of contracting stress (rA) that represents the depolarization pulses based on resultant stress of the left cardiac ventricular fibers with negative protraction, and the relaxation stress (rp) that is generated from polarization pulse of the left ventricular with positive protraction. When a piezoelectric sensor is located on the chest surface, it detects the strain produced by the contraction and expansion of the heart muscle. The output voltage (V) of the sensor as a function of the strain (S) is expressed as: VðtÞ ¼ cp tc d31 e1 SðtÞ
ð1Þ
Fig. 1 The proposed model for heart mechanical activities adapted from Remme et al. [24].
where e, cp, tc, and d31 are the material dielectric constant, elasticity coefficient, thickness and piezoelectric voltage constant, respectively [25]. S is the available stress at the external chest surface and the effective stress is denoted by rs, is the acting stress generated by the heart muscle on the inner chest surface (see Fig. 2). Fig. 2 provides an overview of the electro-mechanical interactions. The heart mechanical activity through the effective surrounding will induce vibrations in the chest wall, which can be collected using the piezoelectric sensor attached to the external (outer) chest surface [25]. The incorporated piezoelectric sheet in the sensor will deform and thus producing a voltage signal. As per Eq. (1), the corresponding output signal voltage is mapped conformally with the acting strain on the piezoelectric material. In general, the stress is proportional to strain and it is correlated with the young modulus of the material. The effectively available strain (stress) at the outer chest surface is also conformally mapped with the effective available strain (stress) at the inner chest surface. The bulky chest wall could be modeled as a
Fig. 2 An overview of the coupled mechanical-electrical interactions system model.
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Portable piezoelectric cardiac abnormality detection
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bulky attenuator with a certain time delay [26]. Therefore, this shows that the sensor output voltage signal is conformally mapped with the cardiac cyclic features. Hence, the variation in the stress (rS) generates an output voltage signal which has the same variation in the time domain. 3. Prototype fabrication The details of the realized portable system are shown in Fig. 3. The integrated system is composed of two main components: the piezoelectric transducer [27] and the Adafruit Bluetoothbased transmitter [28]. A lithium battery is used to provide the required DC bias of the Adafruit transmitter. The transmitter is assembled on the PCB on one side, as depicted in Fig. 3b. The Adafruit transmitter includes an Atmega328p chip attached to an Adafruit Bluetooth transmitter unit, which can be programmed by Arduino software [28]. The low-energy Bluetooth sensor transmits the real-time instantaneous measured data wirelessly to the laptop equipped with MATLAB. Two Metallization pads are placed on the other side of the PCB (see Fig. 3c) and two piezoelectric transducer electrodes are soldered to metallization pads. The complete prototype is shown in Fig. 3e. The employed piezoelectric material has a d31 of 315 pm/V and the customized sensor dimensions are 46 20 0.26 mm and are composed of lead zirconate titanate (PZT) [27]. Fig. 4 shows the frequency characteristics of the employed piezoelectric materials over a frequency range of 1 Hz to 100 kHz. The heart-induced pressure signal obtained using this sensor has a low bandwidth of approximately 800 MHz to 3 Hz. The piezoelectric structure exhibits capacitive behavior with a phase approximately close to 90 degree and a high impedance magnitude of around 1 M ohms. Therefore, the sensor electro-mechanical interactions will not
Fig. 4 Impedance magnitude and phase plotted as a function of the frequency of the utilized piezoelectric material.
be further attenuated or delayed and hence not affect the output electrical voltage magnitude or phase. 4. Measurements and analysis The developed data acquisition system is illustrated in Fig. 5 along with the measured piezoelectric voltage signals. A schematic of the human chest and the fabricated portable prototype of this study is illustrated in Fig. 5a and b, respectively. To collect the cardiac signal, the bottom surface of the piezoelectric transducer, shown in Fig. 5b, is placed on the chest surface. The laptop used to receive the transmitted signal is shown in Fig. 5c. Fig. 5d depicts the output voltage signal received by
Fig. 3 Functional prototype (fetching and wireless sending heart signal device): (a) lithium battery; (b) Adafruit transmitter; (c) PCB board; (d) sensor, and (e) complete prototype.
Please cite this article in press as: S. Mokhtari, M. Al Ahmad, Portable piezoelectric cardiac abnormality detection, Alexandria Eng. J. (2019), https://doi.org/ 10.1016/j.aej.2019.09.008
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S. Mokhtari, M. Al Ahmad
Fig. 5 Acquisition system and piezoelectric signals: (a) schematic of the human chest, (b) functional prototype, (c) used laptop, (d) measured output voltage, and (e) two cardiac cycle window.
the laptop with acquisition performed using MATLAB code [29]. The signal train was trended and filtered with built-in MATLAB functions to remove the noise contribution due to transmission. The acquired signal through the BT module is automatically filtered and amplified using the build in components with low pass filter of 120 Hz; which works well with this application. The default preprocessing parameters of the employed Adafruit transmitter (includes an Atmega328p) chip are sufficient and reproduce the same data collected directly by the oscilloscope. Hence, the default setting did not affect the results. Data trended filtering was needed to overcome any abrupt changes or events in the underlying dynamics of the acquired signal in time domain and further to enhance the received signal quality. The signal exhibits a conformally mapping shape corresponds to the heart muscle physiological activity, namely the systolic and diastolic blood pressure and heartbeat rate [30]. Fig. 5e shows that two cycles in the desired range are observed. Each cycle, in fact, is a result of the expansion and contraction of the heart. After the required post-processing of the input signal, proper cycles can be selected that are confined between –20 mv and +20 mv. Fig. 6a superimposed several cycles aligned with their maximum positive peaks. The pulse pressure is extracted from the measured cycles using the formulas reported and is shown in Fig. 6b [23]. The extracted pulse pressure ranges from 30 to 40 mmHg. Fig. 6c represents the period
of each cycle along with its average. As observed, almost all cycles have a period of approximately 0.75 s (80 beats per minute). As experimentally reported previously, the heart rate (beats per minute) is considered to be the number of positive peaks counted within a time interval mapped to one minute [23]. Fig. 6d demonstrates the computed cross-correlation between the selected cycles of Fig. 6a. When the crosscorrelation coefficient is close to unity, the cardiac cycles are well aligned and have the maximum match between them indicating a stable behavior of the heart muscle. However, as the cross-correlation decreases away from unity, the cardiac cycles are less matched and consequently, the probability of an unstable heart functionality is recorded. This instability could be due to a change in emotional status of the subject or cardiac abnormalities [31]. In this study, we constructed an average cycle per individual subject from a noise free cycles. This cycle is used as a references and for correlation and matching computations. One of the remarkable features of the current proposed monitor is its capability to extract the cardiac parameters and check abnormality for each real time measured cycle. The manually selected cycles are only used for constructing the average cycle. The process can then be automated using a match filter, which can be utilized for maximizing of the signal to noise ratio (SNR) assuming additive stochastic noise. Nevertheless, the mismatch between the cycles can be connected with an instan-
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Fig. 6 Extracted cardiac parameters per cycle: (a) superimposed cycles, (b) extracted pulse pressure per cycle, (c) heart beat period per cycle (d) cycles cross-correlation. The heart-beat rate is then set to be the number of positive peaks counted in one minute [23].
the subjects hold their breath. Compared to other techniques, the proposed approach provides a mechanism for wireless diagnosis and can be used with further development to anticipate heart failure or other cardiac abnormalities. In addition, the employed sensors are easy to handle, cause minimal discomfort to the patient, and minimally impact the clinics [33]. Finally, the novel contribution of this work is in terms of the system capability to extract the heart rate, pulse pressure, and to detect any instantaneous abnormality in the cardiac activities by observing the cycles cross correlation coefficient. We have compared the differences between the current model and approach and other existing piezoelectric based sensors continuous monitoring systems (see Table 2). The presented approach and device can be further optimized in terms of size and increase its functionality extracting the ECG based on the presented approach [25] along with the measurements of the systolic and diastolic of blood pressures values. Moreover;
taneous abnormality occurrence or emotional status. The results will change significantly if ‘‘improper” ones are additionally selected for average cycle calculations. Therefore, it is important that only ‘‘proper” cycles are included. It is worth mentioning that the cycles are not normalized in Fig. 6. The diagonal values of Fig. 6d are the cross-correlation of the average cycle with itself. The other values represents the cycle’s cross-correlation between one cycle and all the cycles within a specific time frame. Fig. 6d is a 3D representation form of these data which is symmetry across the diagonal. Furthermore, the fabricated prototype was used to collect signals from various human subjects. Table 1 summarizes the data collected from ten individuals along with their extracted heart rate using the proposed method and the conventional meter [32]. The conventional readings and the extracted values using our device and approach are matched within 3% error. It is worth mentioning that all measurements were taken while
Table 1
Extracted heat rates of all individuals.
Subject
#1
#2
#3
#4
#5
#6
#7
#8
#9
#10
This method with error ± 1 Conventional with error ± 3
70 66
81 83
76 75
82 79
69 70
80 79
75 76
77 75
67 69
74 70
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S. Mokhtari, M. Al Ahmad Table 2
Comparison between current work and some existing prototypes.
Reference
Size [mm2]
Cost
Features
position
Parameters
Other
[22]
medium
Local
medium
Heart rate
Non portable utilized the acoustic property of piezoelectric materials Not applicable
[35]
20 50
high
Respiration rate and pulse rate
apnea detection
[36]
280 280
expensive
Local monitoring Local monitoring Wireless
Close to the wrest rest
Heart rate
[34]
Very large 15 30
analysis of sleep stages
This work
20 40
medium
Wireless
Heart Rate, Breathing, Stress Level; Sleep Cycles Heart rate and pulse pressure per cycle. Cycle period
Abdomen and chest under bed chest
the proposed monitoring system can be used for person reidentification based on their biometric data through online algorithms that can be adopted and developed along this device. For example; the system is capable of detecting and identifying individuals with abnormalities in heart functionality among groups of people. Setyowati et al. has proposed an acoustic piezoelectric sensor for heart pulse monitoring that can be placed on the wrist equipped with a mild pressure. Arduino microcontroller has been use along with the sensor to collect the possible sounds of the heart. In addition, the iRhythum - Zio XT patch is a wearable device that can continuously measure the heart rate in a discreet and wireless manner [reference]. This technology records heartbeat during sleeping as well as during day to day activities up to 14 days. After the recording period, the data is sent to the iRhythum clinical app for analysis [37]. Moreover; Medtronic SEEQ Mobile Cardiac Telemetry (MCT) System is another wireless external heart monitor which helps in detecting and diagnosing the cause of irregular heartbeats in patients [38]. In addition; NeuroSky- ECG Biosensor is equipped with monitoring the performance of the heart [39]. In this study, we used a portable system that should be placed on the chest close to the arterial pressure and utilizes the conversion of mechanical pressure to electrical signal using the mechanical electrical energy conversion principle [23]. Fig. 7 shows the photo of the sensor attached to the chest of subject. 5. Abnormality detection This section addresses development of an efficient algorithm to detect abnormalities in cardiac activities using correlation techniques. Fig. 8 illustrates the measured output signals of ECG and piezoelectric sensors. Both signals have been normalized in magnitude. The normalization of the amplitude of both signals to unity has been carried out to align their peaks and directly map the cardiac physiological activities in order to determine the corresponding features in the output voltage of the piezoelectric sensor. For the subject under study, his/ her corresponding piezoelectric and ECG signals are of a periodic nature with a time interval of 0.8 as depicted in Fig. 8. The ECG traces for seven corresponding well-known heart abnormalities are shown in Fig. 9. The typical normal ECG signal cycle is shown in Fig. 9a. As has been explained previously and stated clearly by Fig. 8, any change in the ECG trace will be reflected in the piezoelectric output signal trace. The corresponding piezoelec-
Abnormality detection in cardiac functionality
Fig. 7 The fabricated prototype attached to human subject using double sided medical tap.
tric signal trace can be directly correlated with the ECG trace as was previously reported [25]. Therefore, to detect any abnormality in the cardiac response, the instantaneous measured piezoelectric signal is correlated with the typical normal piezoelectric signal that was determined (Fig. 8) and stored in a database. The stored piezoelectric trace is used as a reference. If the computed correlation coefficient is approximately unity, the instantaneous piezoelectric output signal is very close to the typical stored reference trace. Hence, the two signals are identical and there is no abnormality to be detected. If the correlation coefficient differ from unity, then this is considered an abnormality in cardiac function because there is a mismatch between the instantaneous measured signal and the reference signal. To identify the present abnormality, the instantaneous piezoelectric signal should be correlated with the other piezoelectric corresponding piezoelectric signal of the ECG abnormal traces shown in Fig. 9b–h. The abnormality in the cardiac function corresponds to the largest correlation coefficient. Table 3 represents the computed correlation coefficients between the normal corresponding piezoelectric signals to the ECG disease traces. This table demonstrates that the highest correlation coefficient and corresponds to a case free of abnormality. Meanwhile the other coefficients are away from unity. Therefore, the instantaneous
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Portable piezoelectric cardiac abnormality detection
7 Table 3 correlation coefficient* between normal cycle and cycles of different diseases.
Fig. 8 Superposition of normalized measured typical normal ECG and the corresponding measured representative piezoelectric cycles. This figure represent the normalized data to show the systolic and diastolic and correlated with the piezoelectric signal. The cross correlation coefficient listed in Table 3, are the cross correlation coefficient between the signals of Fig. 8(a) with the other from (b)–(h). Please see the comment added to Table 3.
Case
Coefficient
Comments
Normal
1
Ischemia
0.01
Injury
0.25
Infraction
0.56
Hypokalemia
0.54
Hypocalcemia
0.05
Hyperkalemia
0.51
Hypercalcemia
0.15
Cross correlation and (a) Cross correlation and (b) Cross correlation and (c) Cross correlation and (d) Cross correlation and (e) Cross correlation and (f) Cross correlation and (g) Cross correlation and (h)
between signal (a) between signal (a) between signal (a) between signal (a) between signal (a) between signal (a) between signal (a) between signal (a)
*The coefficient of correlation is a finite number whose value comes between +1 and 1 inclusive, where 1 represents the total positive correlation, 0 is no correlation, and 1 represents total negative correlation.
signal should be correlated with the 8 stored cases of Fig. 9, and then consider the highest coefficient value, which indicates the case of the disease. It is worth to add that when an abnormality is detected, the subject should be advised to visit a doctor for further evaluation and verification. The experiment setup depicted in Fig. 10 was used to verify the abnormality detection. The setup incorporates ECG synthesizer that is used to generate the corresponding abnormalities from b to h represented in Fig. 9. An oscilloscope is used to display the generated abnormality signal. The signal is instantaneously transmitted over bluetooth module. The detection code based on correlation were implemented using android. The produced correlation coefficient are very close (with ±1%) with their corresponding values listed in Table 3. The detection accuracy was approximately more than 95%. The numbers listed in Table 3 are average values. The threshold will depend on the number of abnormality cases
Fig. 9 Normal and abnormal ECG traces with their corresponding diseases: (a) normal, (b) ischemia, (c) injury, (d) infraction, (e) hypokalemia, (f) hypocalcemia, (g) hyperkalemia and (h) hypercalcemia. The dataset were collected from medical reports/book [31].
Fig. 10
Experimental validation setup.
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8 involved. For the 7 cases presented in Fig. 9, the threshold then can be settled ±5% of the average value to avoid overlap. 6. Conclusions This work represents a wearable cardiac basic monitoring system which can be used to monitor heart rate, beat period, and pulse pressure. The system consists of piezoelectric sensor utilized to generate a conformally mapped output voltage signal, a Bluetooth enable transmitter, and a laptop MATLAB based application for remote data analysis. A user experience test conducted on 10 participants provided a good match with existing meters. In addition, the system usability and adaptability of the proposed technology have proven to be competitive with other methods. Furthermore, conventional meters were utilized to validate our extracted data acquired here. Our system can be further developed and optimized to be utilized in the detection of heart failure based on spatiotemporal measurements and could potentially be used to construct a future heart activity component in prediction models. Acknowledgement The author wishes to acknowledge the support for the UAE ICT-Fund. This work has been approved by Al Ain Medical District Human Research Ethics Committee – Protocol No. 14/67. Author Contributions MA conceived the concept. MA and SM fabricated the prototype and performed the measurements. MA and SM have performed the corresponding signal processing. Declaration of Competing Interest The authors declare that they have no competing interests. References [1] S. Majumder, T. Mondal, M.J. Deen, Wearable Sensors for Remote Health Monitoring, Sensors 17 (2017). [2] J.B. Strait, E.G. Lakatta, Aging-associated cardiovascular changes and their relationship to heart failure, Heart Fail. Clin. 8 (2012) 143–164. [3] S.J. Lee, A.L. Back, S.D. Block, S.K. Stewart, Enhancing physician-patient communication, Hematol. Am. Soc. Hematol. Educ. Program 464–483 (2002). [4] T.L. Kay, Volume and intensity of Medicare physicians’ services: An overview, Health Care Financ. Rev. 11 (1990) 133–146. [5] H. Banaee, M.U. Ahmed, A. Loutfi, Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges, Sensors 13 (2013) 17472–17500. [6] C.L. Ventola, Mobile devices and apps for health care professionals: uses and benefits, P T Peer-Rev. J. Formul. Manag. 39 (2014) 356–364. [7] M. Suh et al, A remote patient monitoring system for congestive heart failure, J. Med. Syst. 35 (2011) 1165–1179. Gubner, M. Rodstein, H.E. Ungerleider, [8] R.S. Ballistocardiography; an appraisal of technic, physiologic principles, and clinical value, Circulation 7 (1953) 268–286.
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Portable piezoelectric cardiac abnormality detection [29] MATLAB MathWorks. Available at. https:// www.mathworks.com/products/matlab.html (Accessed: 2nd January 2019). [30] A.C. Leary, A.D. Struthers, P.T. Donnan, T.M. MacDonald, M.B. Murphy, The morning surge in blood pressure and heart rate is dependent on levels of physical activity after waking, J. Hypertens. 20 (2002) 865–870. [31] S. Ahmed, A. Hilal-Alnaqbi, M. Al, M. Al, ECG abnormality detection algorithm, Int. J. Adv. Comput. Sci. Appl. 9 (2018). [32] Gear, C. M. Leader in Medical Supplies for Healthcare Professionals. Chic Medical Gear Available at. https:// chicmedicalgear.com/ (Accessed: 2nd January 2019). [33] M.A. Ahmad, S. Ahmed, Piezologist: a novel wearable piezoelectric-based cardiorespiratory monitoring system, 2018 Innovations in Intelligent Systems and Applications (INISTA), 2018, doi:10.1109/INISTA.2018.8466275. [34] Y. Shu, C. Li, Z. Wang, W. Mi, Y. Li, T.L. Ren, A pressure sensing system for heart rate monitoring with polymer-based pressure sensors and an anti-interference post processing circuit, Sensors (Basel, Switzerland) 15 (2) (2015) 3224–3235, https:// doi.org/10.3390/s150203224. [35] Yi Xin, Xiaohui Qi, Chenghui Qian, Hongying Tian, Zhenbao Ling, Zijiang Jiang, A Wearable Respiration and Pulse Monitoring System Based on PVDF Piezoelectric Film, Taylor & Francis Integrated Ferroelectrics 158 (1) (2014) 43–51, https:// doi.org/10.1080/10584587.2014.957060. [36] https://www.earlysense.com/digital-health/ (Accessed: 17th June 2019). [37] https://www.irhythmtech.com/products-services/zio-xt. (Accessed: 17th June 2019).
9 [38] https://www.dicardiology.com/content/medtronic-discontinueseeq-mct-wearable-telemetry-system. (Accessed: 17th June 2019). [39] http://neurosky.com/biosensors/ecg-sensor/. (Accessed: 17th June 2019). Shina Mokhtari Obtained her MSc in Electrical Engineering from UAE University in 2018. Her research interest is the development of models for heart failure detection. Mahmoud Al Ahmad is a Senior Member of IEEE. He received his BSc in Electrical Engineering from Birzeit University, Ramallah, West Bank in 1999. Both M.Sc. and the doctoral degrees were in Microwave Engineering from Technische Universitaet Mnuechen, Munich, Germany, received in 2002 and 2006, respectively. Currently he is a faculty member at the Department of Electrical Engineering at the United Arab Emirates University. His research interest is in the design and fabrication of self-powered, low powered nano-based electronic devices and systems along with applied electromagnetic for biomedical applications. Dr. Al Ahmad has over ten years of electronic materials and device fabrication research experience in academia, national laboratories and industry. He has managed several research projects and teams with annual budgets of up to US $1 million. He is also a principle (lead) author of around 55 published articles in journals and international conferences and has over 40 presentations at international conferences (several of which has been invited). Dr. Al Ahmad has conducted research in energy harvesting technologies and frequency agile circuits at the Siemens AG/CNRS and King Abdullah University (KAUST) and has published more than 20 journal and 35 conference papers in this domain with more under review.
Please cite this article in press as: S. Mokhtari, M. Al Ahmad, Portable piezoelectric cardiac abnormality detection, Alexandria Eng. J. (2019), https://doi.org/ 10.1016/j.aej.2019.09.008