Abstract
The current approaches for diagnosing sleep disorders are burdensome, intrusive, and can affect the patient’s sleep quality. As a result, there is a crucial need for less cumbersome systems to diagnose sleep-related problems. We propose to use a novel nonintrusive sleep monitoring system based on a microbend fiber-optic mat placed under the bed mattress. The sleep quality is assessed based on different parameters, including heart rate, breathing rate, body movements, wake up time, sleep time, night movement, and bedtime. The proposed system has been validated in a health and wellness environment in addition to a clinical environment as follows. In the former case, the heart rate is measured from noisy ballistocardiogram signals acquired from 50 human volunteers in a sitting position using a massage chair. The signals are unobtrusively collected from a microbend fiber optic sensor embedded within the headrest of the chair and then transmitted to a computer through a Bluetooth connection. The heart rate is computed using the multiresolution analysis of the maximal overlap discrete wavelet transform. The error between the proposed method and the reference ECG is estimated in beats per minute using the mean absolute error where the system achieved relatively good results (10.12 ± 4.69) despite the remarkable amount of motion artifact produced owing to the frequent body movements and/or vibrations of the massage chair during stress relief massage. Unlike the complete ensemble empirical mode decomposition algorithm, previously employed for heart rate estimation, the suggested system is much faster. Hence, it can be used in real-time applications. In the latter case, we evaluated the capacity of the microbend fiber optic sensor to monitor heart rate and respiration unobtrusively. In addition, we tested the capacity of the sensor in discriminating between shallow breathing and no breathing. The proposed sensor was compared to a three-channel portable monitoring device (ApneaLink) in a clinical setting during a drug-induced sleep endoscopy. Across all ten patients recruited for our study, the system achieved satisfactory results in the mean heart rate and the mean respiratory rate with an error of 0.55±0.59 beats/minute and 0.38 ± 0.32 breaths/minute, respectively. Besides, the Pearson correlation coefficient between the proposed sensor and the reference device was 0.96 and 0.78 for heart rate and respiration, respectively. On the contrary, the proposed sensor provided a very low sensitivity (24.24 ± 12.81%) and a relatively high specificity (85.88 ± 6.01%) for sleep apnea detection. It is expected that this preliminary research will pave the way toward unobtrusive detection of obstructive sleep apnea in real-time. Following successful validation of the proposed system, we have successfully deployed our sleep monitoring system in thirteen apartments with mainly senior residents over six months. Nevertheless, in this research, we concentrate on a one-month deployment with three senior female residents. The proposed system shows an agreement with a user’s survey collected before the study. Furthermore, the system is integrated within an existing ambient assisted living platform with a user-friendly interface to make it more convenient for the caregivers to follow-up the sleep parameters of the residents.