A wearable cardiorespiratory sensor system for analyzing the sleep condition

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Expert Systems with Applications Expert Systems with Applications 35 (2008) 317–329 www.elsevier.com/locate/eswa

A wearable cardiorespiratory sensor system for analyzing the sleep condition Samjin Choi, Zhongwei Jiang

*

Department of Mechanical Engineering, Faculty of Engineering, Yamaguchi University, 2-16-1, Tokiwadai, Ube, Yamaguchi 755-8611, Japan

Abstract This paper describes a long-term cardiorespiratory sensor system, which is supposed to be used for monitoring sleep condition at home environment. This system consists of belt sensor probe, data acquisition and communication devices, and the data processing algorithms. New wearable sensor probe with a couple of conductive fabric sheets material and a PVDF film material is developed. To obtain clear cardiorespiratory responses from the belt sensor probe, adaptive hardware filters with signal conditioners are designed and further software data processing algorithms are proposed for extraction of the relative cardiorespiratory information, such as respiratory cycle (RC) and RR interval (RRI). These simple and powerful data processing algorithms for extraction of the proposed RC and RRI information are described and testified in detail. And two commercial sensor devices such as thermistor-type pneumography sensor and 3-lead electrocardiogram sensor are used simultaneously to validate the performance and efficiency of the proposed sensor system. Furthermore, the belt type sensor demonstrated that PVDF film and conductive fabric sensors can complement each other, especially in RRI extraction. Finally, for a case study, the sleep conditions are estimated experimentally by analyzing the heart rate variability (HRV) from the extracted RRI information.  2007 Elsevier Ltd. All rights reserved. Keywords: Cardiorespiratory information; Respiratory cycle (RC); RR interval (RRI); Data processing algorithm; Sleep condition; Spectral heart rate variability (HRV) analysis

1. Introduction The sleep disorders like the sleep apnea syndrome (SAS) and the sudden infant death syndrome (SIDS) have been increased constantly. In general, these disorders or diseases are difficult to be detected by the physicians without a long-term monitoring of the change in cardiorespiratory functions. Therefore the close and long-term monitoring methods for assisting the physicians to identify whether the patient is in healthy condition are highly demanded. Changes in cardiorespiratory functions during sleep reflect changes in the autonomic nervous system dominance. Sympathetic nerve activity, blood pressure and *

Corresponding author. Tel./fax: +81 836 85 9137. E-mail addresses: [email protected], [email protected] (S. Choi), [email protected] (Z. Jiang). 0957-4174/$ - see front matter  2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2007.06.014

heart rate are lower in non-rapid eye movement (NREM) sleep than in wakefulness. In the rapid eye movement (REM) sleep, sympathetic nerve activities increase and are greater than them in wakefulness. It shows obviously that the sympathetic control of cardiorespiratory functions increases during sleep. Because the periodic characteristics of heart rate, i.e., heart rate variability (HRV), influence autonomic nerve activity, they are useful for assessing autonomic control under various physiologic and clinical conditions. Furthermore spectrum analysis of HRV is a non-invasive method that provides quantitative information on sympathetic nerve and parasympathetic nerve activities (Appenzeller, 1970; Baharav et al., 1995; Guyton & Hall, 2000). Using time and frequency analyses on HRV signal, some researchers have demonstrated parasympathetic nerve activity during NREM sleep and increased sympathetic nerve activity in REM sleep (Baharav et al.,

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1995; Gaultier, 1995). Dougherty and Burr (1992) and Huikuri et al. (1992) discussed HRV is clinically useful to risk stratify cardiac arrest survivors. Myers et al. (1986) showed that the power of high frequency in HRV might be a useful predictor of sudden death. Therefore in order to monitoring the body condition, especially sleep condition, this study focuses on two cardiorespiratory features as (1) Respiratory cycle (RC) information consisting of the expiratory and inspiratory cycles. (2) RR interval (RRI) information as time duration between two consecutive electrocardiogram (ECG) R-waves. On the other hand, it is evident that cardiorespiratory signals such as heartbeats and respiratory cycle are kinds of the health indicators, and using these cardiorespiratory signals to assess a healthy condition needs a long-term monitoring activity. Accordingly, many researches have concentrated on the development of the wearable cardiorespiratory monitoring devices for home health care, for instance, the PVDF film sensor system (Wang, Tanaka, & Chonan, 2003) for unconscious cardiorespiratory monitoring during sleep; the ring sensor (Yang, Rhee, & Asada, 1998) has been developed for blood oxygen saturation monitoring by two different wavelength LEDs; textile sensors (Catrysse et al., 2004) have been embedded in a suit for children. The objective of this research is to develop a wearable cardiorespiratory sensor system for the long-term monitoring of cardiorespiratory signals and for analyzing the sleep conditions. The cardiorespiratory signals like heart rate and respiratory cycle are kinds of the health indicators so that these cardiorespiratory functions are important to estimate person’s health condition. Thus two cardiorespiratory features such as RC and RRI information from signals obtained by the cardiorespiratory sensor device are proposed for analyzing sleep condition at home environment. The proposed sensor system is composed of a belt type sensor probe, two filter modules according to corresponding sensor materials, and communication circuit. As the sensing materials for the belt type sensor probe, a PVDF film and conductive fabrics are selected. Simple and efficient software data processing algorithms that non-filtering experience person as well as beginners on biomedical engineering can easily do the applications of the cardiorespiratory information extraction with the aid of computer are introduced according to corresponding information. The proposed cardiorespiratory sensor system is also validated experimentally with comparison to the commercial thermistor-type pneumography (TPG) sensor and 3-lead ECG sensor. Furthermore, it might turn out that using the combination of PVDF film sensor and conductive fabric sensor enables us to recover with each other for getting more reliable signals, especially RRI information. Finally, by applying the extracted RRI information at some sleep states to HRV spectrum, the sleep condition is evaluated

experimentally via the sympathetic nerve and the parasympathetic nerve activities. The results show evidently that the parasympathetic nerves are strongly activated in sleep whereas the sympathetic nerves are strongly activated in others. Analysis based on RC information will be performed in the future. This paper is organized as follows. In Section 2, the hardware specifications of wearable cardiorespiratory sensor system are described. Section 3 provides the descriptions of software data processing algorithms. Signals and information obtained from the proposed cardiorespiratory sensor device are compared with the commercial TPG and ECG sensors. In Section 4, the sleep condition is estimated by applying the extracted RRI information in different sleep states to HRV spectrum. The conclusions are presented in Section 5. 2. Wearable cardiorespiratory sensor system Fig. 1 depicts the overview of the wearable cardiorespiratory sensor system, which is developed for monitoring the sleep condition, especially for the realization of the unconscious and unstrained sensor system. This system consists of a belt type sensor probe, data acquisition and communication devices, and software data processing algorithms in a personal computer for extracting the proposed cardiorespiratory information. In this section, the hardware specifications of system are described in detail. 2.1. Belt type sensor probe The oval-shape sensor probe as depicted in Fig. 1a was designed to measure the cardiorespiratory responses from the waist, especially to analyze the sleep condition (Fig. 1b). A sensor probe is embedded with a polyvinylidene fluoride (PVDF) film and two sheets of conductive fabrics working as a sensor. PVDF film sensor was selected to measure RC information due to the abdomen rising and falling, sudden body movement information (excluded in this study) due to the rolling over in sleep and RRI (or heart rates) information alternatively. Furthermore, conductive fabrics were used mainly to measure RRI information during sleep. In addition, the standard USB shielded I/O cable assembly was used as the cable linking sensor probe to the following data acquisition and communication devices. 2.2. Data acquisition and communication devices Data acquisition and communication devices consisting of two filter modules, a microcontroller module and USB module are represented in Fig. 1c. The specifications of two filter modules designed and constructed to acquire signals from both PVDF film and conductive fabrics are summarized in Table 1. High common mode rejection ratio (CMRR) and power supply rejection ratio (PSRR) filter modules were designed sufficiently to eliminate common

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Fig. 1. General overview of the unconscious and unstrained cardiorespiratory sensor system. (a) Belt type sensor probe, (b) wearing appearance in sleep, (c) data acquisition and communication devices, and (d) a personal computer.

Table 1 Specifications of the proposed sequential filter modules

Pre-amplifier with low-pass filter (LPF) Fourth-order Butterworth band-pass filter (BPF) High-Q band-rejection filter (BRF) Fifth-order Butterworth BPF Common mode rejection ratio (CMRR) Power supply rejection ratio (PSRR)

PVDF film sensor

Conductive fabric sensor

500 Hz (10 dB) 0.3–30 Hz (25 dB)

500 Hz (10 dB)

converters (ADCs) packed into the microcontroller were used for each sensor. The sampling rate of ADC was set at 1 kHz in the following experiments. 2.3. Experiment setup and measurement

0.01–130 Hz (24 dB) 60 Hz (35 dB)

88 dB

0.01–120 Hz (10 dB) 104 dB

108 dB

109 dB

mode interference and power supply noises. Furthermore, in microcontroller module, PIC16F873A was selected to control the data acquiring circuit and to communicate with a personal computer (Fig. 1d) by software interrupt and scheduling ways via USB. Two 10 bits analog-to-digital

In order to validate the performance of the proposed belt type cardiorespiratory sensor system, commercial TPG sensor and 3-lead ECG sensor were used simultaneously as illustrated in Fig. 2. Namely, for validating PVDF sensor signals, a TPG probe (Fig. 2a) was placed in the outer nasal passage to measure the resistance change due to the temperature difference between inspired and expired air. For validating conductive fabric sensor signals, ECG electrodes (Fig. 2b) were attached at three places. Fig. 2c illustrates our developed belt type sensor probe, which is worn at abdominal circumference. Fig. 3 represents two example signals of PVDF sensor and conductive fabric sensor obtained from a 23 year-old male healthy volunteer using the proposed belt type sensor

Fig. 2. Wearing appearances of each sensor used in this research, such as (a) thermistor-type pneumography (TPG) sensor, (b) 3-lead ECG sensor, and (c) the proposed belt type sensor probe.

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Fig. 3. Long-term measuring data of PVDF sensor (left panels) and conductive fabric sensor (right panels) obtained during sleep.

during sleep. Each panel (a) shows long-term signals during 9000 s. Panels (b)–(d) are the enlarged waveforms corresponding to the selected areas from the original data in panel (a). Some spikes appeared in panels indicate body movements as subject is rolling over during sleep. Furthermore, looking at the enlarged panels (b)–(d), they are clearly composed of the expected cardiorespiratory signals such as heart rates, respiration and body movements although the sensor’s outputs have different performances at different time. Specifically, for extracting the proposed cardiorespiratory information from the original signals, some adaptive data processing algorithms both for PVDF film sensor and for conductive fabric sensor should be considered and developed. Simple and powerful data processing algorithms according to cardiorespiratory information to extract from PVDF sensor output and conductive fabric sensor output are therefore described and testified in following. 3. Cardiorespiratory information extraction In general, the digitalized signal recorded in the computer has certain noises such as motion artifact, baseline wander, needless frequency components, 60 Hz power noises, and so forth. Thus to eliminate these corrupted noises, the data processing algorithms corresponding to

the proposed RC and RRI information will be designed and testified, which is one important part of this study. That is, simple and powerful data processing algorithms that non-filtering experience person as well as beginners on biomedical engineering can easily do the applications of the cardiorespiratory information extraction with the aid of computer are described in detail. 3.1. Data processing algorithms 3.1.1. Respiratory cycle (RC) In this study, only PVDF film sensor was used to measure the respiratory cycle RC during sleep. A schematic block diagram of the proposed RC information extracting data processing algorithm is depicted in Fig. 4. The overall RC extracting algorithm can be divided into four stages as (1) (2) (3) (4)

Wavelet-based pre-processing, Low-pass filtering, Relative time delay compensating, Decision-making.

Each task of the proposed data processing algorithm is described in following. First of all, suppose the discrete signal serial obtained from PVDF film sensor is given by x[n]. In general case,

Fig. 4. A block diagram of data processing procedure for RC information extraction from PVDF film sensor signals.

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the signal x[n] includes motion artifact and baseline wandering noises due to the wearing properties and manners of sensor. Hence to eliminate these noises, an adaptive software filter based on the discrete wavelet transform (DWT) is firstly introduced in data processing. DWT coefficients ci,j of x(n) can be defined as follows: ci;j ¼

N 1 X

xðnÞ  2i=2 wð2i n  jÞ;

ð1Þ

n¼0

where w(n) denotes the wavelet function. DWT was implemented in MATLAB. The Daubechies Db15 type wavelet was selected as a mother wavelet. Since the respiration might be usually larger than 1 s and signal x[n] might be sampled by 1 kHz, the wavelet decomposition at the approximation A7 was used to cut off the unwanted frequency components and the approximation A15 was used for eliminating the baseline wandering (see Table 2). So the resulting signal xw[n] was reconstructed by the components of A7–A14. Since the respiratory cycle RC is generally about 3 s (Barbieri, Triedman, & Saul, 2002; Cox & McGrath, 1999; Keyl et al., 2003), a second-order low-pass filter (LPF) with cutoff frequency of 0.3 Hz is further requested for treating the pre-processing signal xw[n]. The second Butterworth LPF is given by 1

Y ½z ¼

2

b0 þ b1 z þ b2 z X w ½z; a0 þ a1 z1 þ a2 z2

ð2Þ

where the coefficients [a0, a1, a2] and [b0, b1, b2], calculated by the LPF with cutoff frequency of 0.3 Hz, are summarized in Table 2. However, the resultant signal y[n] has a significant time delay (Dt) because of the remarkable low frequency filtering. The relative time delay can be calculated by using the cross correlation function, that is, the relative time delay Dt can be estimated by selecting the peak of the cross correlation function between zero and FS/fc, where FS denotes the sampling frequency and fc denotes the cutoff frequency. So the final resultant signal Y[n] are defined by Y[n] = y[n + Dt].

Table 2 Specifications of data processing algorithm for RC information extraction Wavelet decomposition Second-order 0.3 Hz Butterworth LPF (Eq. (2))

A7 (0–3.9063 Hz), A15 (0–0.0153 Hz) [a0, a1, a2] = [1.0, 1.9973, 0.9973] [b0, b1, b2] = [0.0887, 0.1774, 0.0887] · 105

321

Finally, to perform the decision-making for extracting RC information, a thresholding scheme was applied to the resultant signal Y[n]. The thresholding scheme can be formulated as if Y ½n P THV ) ERC; if Y ½n < THV ) IRC;

ð3Þ

where THV denotes the mean value (usually about zero) for 50 s and [ERC, IRC] denote the expiratory cycle and the inspiratory cycle respectively. They were determined from time intervals between the minimum values of each formulated result of Eq. (3). Herein RC information was calculated by the summation of each cycle, i.e., RC = ERC + IRC. 3.1.2. RR interval (RRI) In the meanwhile, conductive fabric sensor was used mainly to measure RR interval RRI in sleep. Furthermore PVDF film sensor was alternatively used to measure RRI. A schematic block diagram of the proposed RRI information extracting data processing algorithm is depicted in Fig. 5. In case of conductive fabric sensor, the overall RRI extracting algorithm can be divided into five stages as (1) (2) (3) (4) (5)

Wavelet-based pre-processing, Band-rejection filtering, Band-pass filtering, Low-pass filtering, Decision-making.

In case PVDF film, the same data processing algorithm as used for conductive fabric sensor was applied to PVDF output signals, except for a 60 Hz band-rejection filtering. Each task of the proposed RRI extracting algorithm is also described in next. For the RRI information extraction from conductive fabric signals, it used different data processing algorithm as compared to method used for extraction of RC information from PVDF signals, at which it was composed of the wavelet decomposition and unitary Butterworth-type LPF. Namely, since the heart cycle RRI might be usually a lot faster than the respiratory cycle RC, the wavelet decomposition for cutting the unwanted high frequency noises was selected at A2 with the Daubechies Db15 type mother wavelet (see Table 3). Furthermore, the approximation at A14 was selected for eliminating the baseline wandering. Thus the signal xw[n] reconstructed by the

Fig. 5. A block diagram of data processing procedure for RRI information extraction from conductive fabric sensor and PVDF film sensor signals.

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Table 3 Specifications of data processing algorithm for RRI information extraction Wavelet decomposition For conductive fabric signal For PVDF film signal Second-order 60 Hz Butterworth BRF (Eq. (5))

A2 (0–125 Hz), A14 (0–0.0305 Hz) A2 (0–125 Hz), A6 (0–7.8125 Hz) [a0, a1, a2, a3, a4] = [1.0, 3.7027, 5.4097, 3.6699, 0.9824] [b0, b1, b2, b3, b4] = [0.9912, 3.6863, 5.4098, 3.6863, 0.9912] [a0, a1, a2, a3, a4] = [1.0, 3.8685, 5.6373, 3.6675, 0.8989] [b0, b1, b2, b3, b4] = [0.0013, 0, 0.0027, 0, 0.0013] [a0, a1, a2] = [1.0, 1.9556, 0.9565] [b0, b1, b2] = [0.2414, 0.4827, 0.2414] · 103

Second-order 13–25 Hz Butterworth BPF (Eq. (5)) Second-order 5 Hz Butterworth LPF (Eq. (2))

components of A2–A14 from the original conductive fabric signal x[n] was better to be used as the pre-filtered data in extracting the RRI information process. At next, the resultant signal xw[n] is normalized by xw ½nnorm ¼

jxw ½nj : maxi ðjxw ½ijÞ

ð4Þ

However, since the resultant signal xw[n]norm contains the power noise at frequency 60 Hz, a 60 Hz band-rejection filter (BRF) as follows: Y ½z ¼

b0 þ b1 z1 þ b2 z2 þ b3 z3 þ b4 z4 X w ½znorm ; a0 þ a1 z1 þ a2 z2 þ a3 z4 þ a4 z4

ð5Þ

where coefficients [a0, a1, a2, a3, a4] and [b0, b1, b2, b3, b4] are summarized in Table 3, is designed and applied to the normalized signal xw[n]norm. At next, in order to extract RRI information, a second-order band-pass filter (BPF) with cutoff frequency of 13–25 Hz from Eq. (5), where coefficients [a0, a1, a2, a3, a4] and [b0, b1, b2, b3, b4] are summarized in Table 3, is designed and applied to the resulting signal y[n] filtered by 60 Hz BRF. And then, the second-order LPF with the cutoff frequency of 5 Hz is further introduced, and their coefficients in Eq. (2) are calculated as in Table 3. Finally, to perform the decision-making for extracting RRI information, a thresholding scheme was also applied to the resultant signal Y[n]. As for the decision-making to calculate the RRI information, the thresholding scheme for searching ECG R-wave can be formulated as if Y ½n P THV ) ECG R-wave; if Y ½n < THV ) None;

ð6Þ

where THV denotes the adaptive value. Herein, the new THVnew is updated locally by THVnew ¼ mfg þ ð1  fÞTHVold ;

ð7Þ

where m denotes the maximum value calculated locally from signal Y[n], f denotes the forgetting parameter which is restricted to the positive integer numbers, like e.g., 0 6 f 6 1, and g denotes the weighting parameter, which means the degree of the contributions for calculating THV. In this study, the weighting parameter was set at g = 0.2, namely, it used a 20% value of the peak value of ECG R-wave when a new ECG R-wave was detected (Chen & Chen, 2003; Chen, Chen, & Chan, 2006). Then

RRI information is determined by counting the time intervals between ECG R-waves marked by s (see Fig. 7). As for RRI information extraction from PVDF film signal, on the other hand, suppose the original signal of PVDF film sensor is defined by x[n]. The unwanted high frequency noises and the baseline wander were firstly eliminated by using the wavelet decomposition of A2–A6 as the pre-filter (see Table 3). And then, a second-order Butterworth BPF with frequency ranges of 13–25 Hz and second Butterworth LPF with cutoff frequency of 5 Hz were constructed and applied to the signal sequentially. Finally, for the decision-making to calculate the RRI information, by counting the interval of the peaks corresponding to ECG R-wave based on the thresholding scheme as Eqs. (6) and (7), RRIs are then easily obtained. 3.2. Results and discussions As mentioned previously, the data processing algorithms used for RC and RRI information extractions from PVDF sensor signals and conductive fabric sensor signals were presented in detail. In this section, with these data processing algorithms, the cardiorespiratory waveforms and [RC, RRI] information extracted from the actual PVDF film and conductive fabric signals of belt type sensor system are compared with them of commercial sensor devices, i.e., TPG sensor and ECG sensor. Furthermore, from the fact that the developed belt sensor system does not look working well always due to the wearing properties and manners of sensor in sleep, in order to get more reliable signals, particularly RRI information, the interdependent characteristics that they can recover with each other by means of the combination of PVDF sensor and conductive fabric sensor are presented. 3.2.1. Respiratory cycle (RC) First of all, in order to testify the effect of PVDF film sensor and the data processing algorithm for extracting the RC information, a signal (left panels of Fig. 6) obtained from PVDF film sensor and TPG sensor was selected during 50 s as an example. In left panels of Fig. 6 (25 year-old male subject), it is a selection where the waveform includes the intensive ECG signal on the respiratory signal. Fig. 6a shows the outputs x[n] of PVDF film sensor which are selected from the raw long-term signals. The resultant

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Fig. 6. Analysis of RC (left panels) and RRI (right panels) information on PVDF film sensor signals, where the top panels display PVDF film sensor output signals, middle panels are the extracted waveforms and information, and bottom panels are TPG sensor waveform (c) and ECG sensor waveform (f) for comparison.

waveform and RCs obtained by applying the data processing algorithm to PVDF signals are plotted in Fig. 6b. Marks [d, s] indicate RC information consisting of IRC (s to d) and ERC (d to s). Fig. 6c shows TPG sensor output. Looking at the quantitative results, the average RCs for PVDF film sensor and TPG sensor are about 3.1734 s (with standard deviation of 0.1922 s) and 3.1781 s (with standard deviation of 0.1972 s). The results say that there is a close correlation between two waveforms. At a result, it shows obviously that the extracted waveform and RCs of PVDF sensor (Fig. 6b) are in good agreement with them of TPG sensor (Fig. 6c). 3.2.2. RR interval (RRI) The extraction of RRI information from PVDF film signals is demonstrated in Fig. 6d and e, to validate the efficiency of the proposed data processing algorithm. A waveform pattern during 12 s at the left panels of Fig. 6 is selected and plotted in Fig. 6d and e. Although there is a little time delay at the extracted waveforms (Fig. 6e) compared to the ECG sensor waveform (Fig. 6f), RRIs marked by s are corresponding quit well to ECG R-waves in ECG sensor waveform. Furthermore, the mean RRIs extracted and calculated from PVDF film sensor and ECG sensor during 12 s are 0.8388 s (71.53 beats/min) and 0.8451 s (71.00 beats/min), see Fig. 8. From the results, it shows obviously that the RRI information extracted from PVDF film sensor signals shows a good agreement with it of 3lead ECG sensor signals. On the other hand, for RRI information extraction from conductive fabric signals, three examples are demonstrated.

At first, left panels of Fig. 7 show the selected conductive fabric sensor output signals, which are measured by the proposed belt type sensor, at different situations or environments. In left panels of Fig. 7, Fig. 7a is a typical ECG pattern, Fig. 7b is a case with the influence of the respiratory-related rhythms and the significant unwanted artifact noises, and Fig. 7c is an example with an unstable reference by inflowing the common mode interference. Herein, by applying the data processing algorithm to these signals, the resultant signals and RRI information are obtained and plotted in right panels of Fig. 7. From the results, it is obvious that RRI information can be extracted quite well. Additionally, quantitative results like the mean RRIs (s) and heartbeats (beats/min) are summarized in Fig. 8. In case of Fig. 7a, the mean RRIs for conductive fabric sensor and ECG sensor are 0.8259 s (72.65 beats/ min) and 0.8247 s (72.75 beats/min), with standard deviations of 0.0247 s and 0.0031 s, respectively. In case of Fig. 7b, the mean RRIs for conductive fabric and ECG sensors are [0.7545, 0.7542 s] with standard deviation of [0.0209, 0.0117 s]. In case Fig. 7c, 0.9417 s (63.71 beats/ min) and 0.9422 s (63.68 beats/min) with standard deviations of 0.0280 s and 0.0128 s, respectively. From Figs. 7 and 8, the RRI information of conductive fabric sensor is equivalent to the RRI information of ECG sensor. As a result, it is obvious that conductive fabric sensor is in good agreement with 3-lead ECG sensor so that the proposed data processing algorithm (Fig. 5) and cardiorespiratory sensor system are validated for extracting RRI information. Hereinafter, these results are being used for the HRV spectrum analysis (Heart rate variability, 1996).

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Fig. 7. Analysis of RRI information on conductive fabric sensor signals, where left panels are some conductive fabric sensor output signals, and right panels are the extracted waveforms and RRI information.

Fig. 8. Summary of RRI information calculated from belt sensor signals (by light shaded bars) and ECG sensor signals (by dark shaded bars) as shown in right panels of Fig. 6 and Fig. 7.

3.2.3. Complement of two sensor materials In this study we proposed two sensor materials like the conductive fabric and PVDF film installed to belt sensor probe. As demonstrated above, the conductive fabric sensor with the proposed data processing algorithms worked well for extracting RRI information and it could be substituted for ECG sensor. Also PVDF film sensor with the proposed data processing algorithms worked well for extracting RC and RRI information and indicated that it could take over the TPG sensor and ECG sensor. However, PVDF film sensor does not look working well always, because they might be lost their functions for a period of time in sleep due to the shifting or the moving of the belt sensor probe from the abdomen by body movements. Moreover using conductive fabric sensor (Avenel-Audran,

Goossens, Zimerson, & Bruze, 2003; Corazza, Maranini, Malfa, & Virgili, 1998; Corbman, 1975), it should be always contacted with the skin and the skin should be keeping humid (Liedtke, 1998). Actually the sensor will output significant noises due to rise skin impedance when the skin becomes dry (Catrysse et al., 2004; Wijesiriwardana, Mitcham, & Dias, 2004). However, our proposed belt sensor can recover these problems. Figs. 9 and 10 demonstrate the case. Fig. 9a demonstrates PVDF film sensor signals during 12 s. The amplitude is so very small that it is difficult to extract RRI (even RC) information as shown in Fig. 9b. Fortunately, conductive fabric sensor seems to do work normally at this moment as shown in Fig. 9c. This indicates that the combination of PVDF film sensor and conductive fabric sensor will recover with each other for getting more reliable signals. Fig. 10 demonstrates an example, where conductive fabric sensor loses the effect but the PVDF sensor is working well. For conductive fabric sensor material, the example is displayed in Fig. 10a and the resultant signal by the proposed data processing algorithm is described in Fig. 10b. It shows evidently that RRI information (artifacts d) cannot be extracted correctly in this case. In contrast, Fig. 10c shows the PVDF film sensor signal which is recorded from conductive fabric sensor at the same time. The resultant signal applied by the data processing algorithm is described in Fig. 10d. It is obvious that RRI information (s) can be extracted well from PVDF film sensor signal whereas conductive fabric sensor does not. These indicate that the combination of PVDF film sensor

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heart rate RRI generally display strong variability, and further, typically are irregular and non-stationary (Scha¨fer, Rosenblum, Hans-Henning, & Kurths, 1999). For evaluating and analyzing the variations of RC and RRI, the heart rate variability HRV is one of the most efficient methods (Heart rate variability, 1996). There has been a growing interest in the spectral analysis of HRV as a tool for non-invasive assessment of the autonomic nervous system function. In following, we introduce this method, the spectral HRV analysis, to evaluate the sleep condition. 4.1. Spectral heart rate variability (HRV) analysis

Fig. 9. Interdependence case of (a) PVDF film sensor and (c) conductive fabric sensor signals. That is (b) when it might be difficult to extract RRI or RC information from PVDF film sensor sometimes, it can recover their information from conductive fabric sensor signals.

(Fig. 10d) and conductive fabric sensor (Fig. 9c) will recover each other for getting more reliable RRI information. 4. Sleep condition analysis As mentioned previously, the belt-type sensor with the proposed data processing algorithms worked well for extracting [RC, RRI] information and it also shows the potential to take over the commercial TPG and ECG sensors. In essence the signals of both respiration RC and

The spectral analysis of HRV signal has proven to be independent predictors of sudden cardiac death after acute myocardial infarction, chronic heart failure or dilated cardiomyopathy (Heart rate variability, 1996; Kleiger, Miller, Bigger, & Moss, 1987; Malik, Padmanabhan, & Olson, 1999;Szabo et al., 1997; Tsuji et al., 1996; Wessel et al., 2000). Furthermore, prior studies have demonstrated the existence of three major periodic components in HRV each of which is believed to reflect specific physiological process. That is, they are as follows: (1) High frequency (HF) component, i.e., 0.15–0.4 Hz, which is centered on the breathing frequency commonly referred to as the respiratory sinus arrhythmia. (2) Low frequency (LF) component, i.e., 0.04–0.15 Hz, which is believed to be related to baroreflex dynamics. (3) Very low frequency (VLF) component, i.e., 0.01– 0.04 Hz, which is related to thermoregulation as well as LF periodicities in respiration. It is widely accepted that the HF component is mediated primarily through vagal cardiac control. Consequently the power of the HF band, which is expressed either in absolute values (ms2) or normalized units (n.u.) against total

Fig. 10. Interdependence case of (a, b) conductive fabric sensor and (c, d) PVDF film sensor signals. RRI information of conductive fabrics is useless whereas RRI information of PVDF film is useful.

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power, has been used to quantify the parasympathetic nerve activity. Furthermore, changes in the LF component can be due to both sympathetic nerve and parasympathetic nerve activities. For these reasons, the LF/HF ratio is generally considered to provide a good index of sympathetic nervous modulation (Appenzeller, 1970; Dougherty & Burr, 1992; Guyton & Hall, 2000; Heart rate variability, 1996; Huikuri et al., 1992; Myers et al., 1986). To analyze the sleep condition using the RRI information extracted from the proposed cardiorespiratory sensor system, three considerations are introduced as shown in following: (1) The mean value of HRV (t), which means the heart beat rhythm condition. (2) Total power (PT), LF power (PL), HF power (PH), LF norm (normPL), and HF norm (normPH), which could be used to quantify the sympathetic nerve and the parasympathetic nerve activities. (3) LF/HF ratio (c) to provide a good index of sympathetic nervous modulation.

Table 4 Three sleep states proposed and used experimentally in this study Sleep state

Definition

STATE-1 STATE-2 STATE-3

5 min late after lying down on bed In deep sleep Just after awaking

In addition because it might be difficult to extract a VLF component from short-term analysis (Heart rate variability, 1996), a VLF band was ignored in this study. 4.2. Autoregressive power spectral density (AR-PSD) estimation method In this study, the autoregressive power spectral density (AR-PSD) estimation is introduced for analyzing the sleep conditions by quantification of the spectral characteristics of HRV which is induced from the proposed belt-type sensor system. The HRV is obtained in the way as shown in following. A RRI data series is first detected from the sensor output, such as the signals in Fig. 7, by the proposed data processing algorithm. Suppose the RRI data series x(n), n = 1, . . . , N  1, where N denotes the number of the detected R-wave of QRS. Since x(n) series are an irregularly time-sampled series, it should be interpolated prior to HRV spectrum analysis. The interpolation of x(n) is treated by four points curve fitting and the resampled data is then defined as ^xðnÞ, n = 1, 2, . . . , 4 Æ (N  1). Because the estimation of AR parameters can be done easily by solving linear equation, AR method is the most frequent and useful parametric methods. Accordingly, for AR model, the current value ^xðnÞ can be described by a linear combination of its previous values ^xðn  kÞ and a white noise input e, which represents the error term. The AR model of order p is defined as follows:

Fig. 11. Some HRV (RRI) signals and their spectra according to three sleep states (STATE-1 to STATE-3). In HRV signal, t denotes the mean value. In spectrum of HRV signal, the LF and HF components are indicated by light shaded and dark shaded areas respectively. PT denotes the total power of the spectrum.

S. Choi, Z. Jiang / Expert Systems with Applications 35 (2008) 317–329

^xðnÞ ¼ 

p X

ak ^xðn  kÞ þ e;

ð8Þ

k¼1

where ak denotes the AR coefficients. Then using 256 consecutive ^xðnÞ series with Hanning window, the PSD estimation can be obtained, namely, the AR-PSD is given by P AR ðf Þ ¼

j1 þ

Pp

T r2w

k¼1 ak e

j2pfkT j2

;

ð9Þ

327

where r2w denotes the variance of the driving noise input and T denotes the sampling period. Furthermore Eq. (9) can be approximated by P AR ðf Þ ¼ T

C 1 X

rxx ej2pmkT ;

ð10Þ

m¼1

where C (=128) is selected as a power of 2 and rxx denotes an AR-based extrapolation of the biased estimate of the

Fig. 12. Summary of spectral HRV analysis with respect to three sleep states as shown in Fig. 11 and Table 4. (a)–(g) indicate the mean value of HRV t, total power PT, LF power PL, HF power PH, LF norm normPL, HF norm normPH, and LH/HF ratio c, respectively.

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S. Choi, Z. Jiang / Expert Systems with Applications 35 (2008) 317–329

autocorrelation series derived from the data series. The linear prediction extrapolation is given below: rxx ðnÞ ¼ 

p X

ak rxx ðn  kÞ:

ð11Þ

k¼1

The results of AR-PSD estimation can, therefore, be calculated from the coefficients ak and r2w . In this paper, the Burg method for estimating the AR parameters was used with the order p = 13, in which the coefficients could be obtained by directly from the data series without additional estimation (Gu¨ler, Hardalac, & Mu¨ldu¨r, 2001; Heart rate variability, 1996; Kay & Marple, 1981; Press, Teukolsky, Vetterling, & Flannery, 1992). 4.3. Experimental results and discussions To investigate the efficiency of the HRV spectrum analysis for evaluation of the sleep conditions, three selected sleep situations were tested as summarized in Table 4, i.e., STATE-1 means the state 5 min late after lying down on bed, STATE-2 is in deep sleep, and STATE-3 is the state immediately after awaking (Le Bon et al., 2000). These data are selected from a long-term measured data. The top panels of Fig. 11 show the HRV (or RRI) signals in length of 256 points corresponding to the three sleep states. Their mean values are t = 924, 1173 and 775 ms, respectively. Looking at the value t, it might say that the high value indicates the state in a deep sleep and it will have low value when the sympathetic nerve is in active. Next, the spectrum analysis is applied on the HRV signal and their results are plotted in the bottom panels of Fig. 11. In the HRV spectrum graphs, the light shaded areas indicate the LF component and the dark shaded areas present the HF component. Looking at the quantitative results according to three sleep states, in case of STATE-1, the total power PT of the spectrum is 11037 ms2, and its LF power PL and HF power PH in absolute values are [2991.80, 3302.12 ms2], respectively. Expressed in normalized units, LF norm and HF norm are normPL = 50.44 n.u. and normPH = 55.69 n.u., respectively. The LF/HF ratio c is 0.9057. In case STATE-2, PT is 1291 ms2, and its LF power and HF power in absolute values and in normalized units are [476.65, 698.84 ms2] and [41.23, 60.45 n.u.], respectively. The LF/ HF ratio is c = 0.6821. In case STATE-3, the power PT is 6609 ms2. LF and HF powers in absolute values and in normalized units are [PL = 2629.75, PH = 1592.02 ms2] and [normPL = 94.39, normPH = 57.14 n.u.] respectively. The LF/HF ratio is c = 1.6519. Fig. 12 represents the summary of above obtained results, namely, experimental considered spectral HRV features, like e.g., the mean value t of HRV, the total power PT, LF PL, HF PH, LF norm normPL, HF norm normPH, and LF/HF ratio c, calculated from the extracted RRI information. As for mean value t of HRV, the results in STATE-2 are bigger than other states. However, the powers such as [PT, PL, PH] are

remarkably decreased compared to other sleep states. As another viewpoint to analyze sleep condition, that is, from the relationship between LF components and HF components, HF components [PH, normPH] are bigger than LF components [PL, normPL] for case STATE-2. At a result, the LF/HF ratio c is decreased, like e.g., 0.6821. It shows obviously that the parasympathetic nerves are strongly activated in sleep. Furthermore, in STATE-3, [PL, normPL] becomes dominant compared to [PH, normPH] and the resulting LF/HF ratio is increased. like e.g., c = 1.6519. It shows also obviously that the sympathetic nerves are strongly activated. Consequently, it is obviously that the proposed cardiorespiratory sensor system might be validated for analyzing sleep condition. However, applying the analysis via cardiorespiratory information extracted from PVDF film sensor and conductive fabric sensor signals into the practical use still needs an amount of clinical tests. Therefore, the detailed analysis and evaluation via heart rate variability HRV or the respiratory variability according to sleep states will be continued in our future works. 5. Conclusions In this paper, the proposed long-term cardiorespiratory sensor system with the corresponding data processing algorithms for monitoring sleep condition was presented in detail. A wearable belt type sensor probe installed with conductive fabric sensor and PVDF film sensor materials was developed to measure cardiorespiratory responses during sleep. Also, the data acquisition device consisting of two filter modules corresponding to two sensors and communication device for data transfer were designed. The cardiorespiratory responses like the respiration, the heartbeats, and the body movement could easily be obtained using this system. Furthermore, the commercial sensor devices like TPG sensor and 3-lead ECG sensor were used simultaneously to validate the performance and efficiency of the proposed cardiorespiratory sensor system. The cardiorespiratory information such as respiratory cycle RC and RR interval RRI was proposed and extracted successfully by the simple and powerful data processing algorithms. Furthermore the proposed algorithms were described in detail so even general users could use the applications using them. Compared with the results obtained by the commercial sensor devices, the proposed belt type sensor system showed it potential to take over TPG sensor and ECG sensor. Using both PVDF film sensor and conductive fabric sensor in a belt type sensor realized the complement of each other, especially in extraction of RRI information. Finally, by applying the extracted cardiorespiratory information to spectral HRV estimation, a case study according to sleep states was demonstrated to validate the usefulness and efficiency of the cardiorespiratory sensor system.

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