Validating Force Sensitive Resistor Strip Sensors for Cardiorespiratory Measurement during Sleep: A Preliminary Study

Abstract

Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring system that can reliably and accurately measure cardiorespiratory parameters while causing minimal discomfort to the user's sleep. We developed a low-cost Out of Center Sleep Testing (OCST) system with low complexity to measure cardiorespiratory parameters. We tested and validated two force-sensitive resistor strip sensors under the bed mattress covering the thoracic and abdominal regions. Twenty subjects were recruited, including 12 males and 8 females. The ballistocardiogram signal was processed using the 4th smooth level of the discrete wavelet transform and the 2nd order of the Butterworth bandpass filter to measure the heart rate and respiration rate, respectively. We reached a total error (concerning the reference sensors) of 3.24 beats per minute and 2.32 rates for heart rate and respiration rate, respectively. For males and females, heart rate errors were 3.47 and 2.68, and respiration rate errors were 2.32 and 2.33, respectively. We developed and verified the reliability and applicability of the system. It showed a minor dependency on sleeping positions, one of the major cumbersome sleep measurements. We identified the sensor under the thoracic region as the optimal configuration for cardiorespiratory measurement. Although testing the system with healthy subjects and regular patterns of cardiorespiratory parameters showed promising results, further investigation is required with the bandwidth frequency and validation of the system with larger groups of subjects, including patients.

Keywords: ballistocardiography; heart rate; noninvasive sleep measurement; respiration rate; sleep monitoring; wavelet signal processing.

Figure 1

The approach offers a noninvasive, nonintrusive, and unobtrusive measurement of cardiorespiratory parameters. The sensors and system setup are located under the mattress and attached to the bed net without inconveniencing the user.

Figure 2

We have performed the experiment in all four regular sleeping positions. The subject switched position after every 80 s.

Figure 3

The signal processing was performed in two pipelines using discrete wavelet transform and Butterworth bandpass filter.

Figure 4

The label description of the figure from top to bottom are as follows: the acquired signal from the strip sensor under the thoracic region (Raw signal), the reference ECG signal as the gold standard (ECG signal), BCG signal and the processed signal acquired from the strip sensor for HR estimation (BCG signal and 4th smooth level), the reference respiration signal from the thoracic region as the gold standard (RIP THO signal), and the respiration signal from the strip sensor under the thoracic region (Respiration signal): 20 s of the 9th subject’s signals in $P4$ position. The sensitivity of the FSR strip enabled us to detect the smallest movements caused by cardiac and breathing activity.

Figure 5

The LoA between the respiratory estimation of $Se1$ (left) and $Se2$ (right) under the thoracic and abdominal regions. From top to bottom: LoA of all subjects, males, and females. In total $Se2$ showed a better agreement with the reference data.

Figure 6

The LoA between the heart rate estimation of $Se1$ (left) and $Se2$ (right) under the thoracic and abdominal regions. From top to bottom: LoA of all subjects, males, and females. For females, $Se1$ showed significantly better agreement with the reference data.