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Sleep Monitoring Based on a Tri-Axial Accelerometer and a Pressure Sensor

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Sleep Monitoring Based on a Tri-Axial Accelerometer and a Pressure Sensor

Yunyoung Nam et al. Sensors (Basel).

Abstract

Sleep disorders are a common affliction for many people even though sleep is one of the most important factors in maintaining good physiological and emotional health. Numerous researchers have proposed various approaches to monitor sleep, such as polysomnography and actigraphy. However, such approaches are costly and often require overnight treatment in clinics. With this in mind, the research presented here has emerged from the question: "Can data be easily collected and analyzed without causing discomfort to patients?" Therefore, the aim of this study is to provide a novel monitoring system for quantifying sleep quality. The data acquisition system is equipped with multimodal sensors, including a three-axis accelerometer and a pressure sensor. To identify sleep quality based on measured data, a novel algorithm, which uses numerous physiological parameters, was proposed. Such parameters include non-REM sleep time, the number of apneic episodes, and sleep durations for dominant poses. To assess the effectiveness of the proposed system, three participants were enrolled in this experimental study for a duration of 20 days. From the experimental results, it can be seen that the proposed monitoring system is effective for quantifying sleep quality.

Keywords: breathing; health care; heart rate variability; polysomnography; pressure sensor; sleep apnea; sleep monitoring; sleeping pose.

Figures

Figure 1
Figure 1
Process flow for sleep quality monitoring.
Figure 2
Figure 2
Sleeping postures.
Figure 3
Figure 3
Our test-bed environment, (a) test-bed; (b) Polysomnography.
Figure 4
Figure 4
Screenshots of the user interfaces and examples of signals obtained from the sleep quality monitoring system. Screenshots of user interfaces in the sleep quality monitoring system. (a) screenshots of user interfaces in the sleep quality monitoring system; (b) example signals obtained from the sleep quality monitoring system.
Figure 5
Figure 5
Example of signals obtained from the PSG.
Figure 6
Figure 6
Analog-to-digital converter (ADC) board. (a) ADC board; (b) architecture of ADC board.
Figure 7
Figure 7
Heart rate variability and respiratory rate. (a) Raw data obtained from ADC (100 samples/s, 12-bit resolution); (b) Filtering data using an infinite impulse (IIR) filter for estimating the heart rate; (c) Filtering data using an infinite impulse (IIR) filter for estimating the respiratory rate; (d) Heart peak obtained from filtering data after noise reduction; (e) Selected heart peak.
Figure 7
Figure 7
Heart rate variability and respiratory rate. (a) Raw data obtained from ADC (100 samples/s, 12-bit resolution); (b) Filtering data using an infinite impulse (IIR) filter for estimating the heart rate; (c) Filtering data using an infinite impulse (IIR) filter for estimating the respiratory rate; (d) Heart peak obtained from filtering data after noise reduction; (e) Selected heart peak.
Figure 8
Figure 8
Bland–Altman plot with a mean difference of 0.076 that shows the limit of agreement of 95% (dashed lines are mean differences ± the limit of agreement) between the continuous heart rate (HR) of pressure signal and its corresponding electro-cardiogram (ECG) signal.
Figure 9
Figure 9
Results of the sleeping pose rate and sleeping pose detection. (a) Sleeping pose rate; (b) Sleeping pose detection.
Figure 10
Figure 10
Sleep apnea detection.
Figure 11
Figure 11
Results of the sleep stage classification.
Figure 12
Figure 12
Example of signals obtained from sensors in the presence of motion artifact. (a) Example of signals obtained from the PSG in the presence of motion artifact; (b) Example of signals obtained from an accelerometer sensor and a pressure sensor in the presence of motion artifacts.

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