A New Approach for Detecting Sleep Apnea Using a Contactless Bed Sensor: Comparison Study

Abstract

Background At present, there is an increased demand for accurate and personalized patient monitoring because of the various challenges facing health care systems. For instance, rising costs and lack of physicians are two serious problems affecting the patient’s care. Nonintrusive monitoring of vital signs is a potential solution to close current gaps in patient monitoring. As an example, bed-embedded ballistocardiogram (BCG) sensors can help physicians identify cardiac arrhythmia and obstructive sleep apnea (OSA) nonintrusively without interfering with the patient’s everyday activities. Detecting OSA using BCG sensors is gaining popularity among researchers because of its simple installation and accessibility, that is, their nonwearable nature. In the field of nonintrusive vital sign monitoring, a microbend fiber optic sensor (MFOS), among other sensors, has proven to be suitable. Nevertheless, few studies have examined apnea detection. Objective This study aims to assess the capabilities of an MFOS for nonintrusive vital signs and sleep apnea detection during an in-lab sleep study. Data were collected from patients with sleep apnea in the sleep laboratory at Khoo Teck Puat Hospital. Methods In total, 10 participants underwent full polysomnography (PSG), and the MFOS was placed under the patient’s mattress for BCG data collection. The apneic event detection algorithm was evaluated against the manually scored events obtained from the PSG study on a minute-by-minute basis. Furthermore, normalized mean absolute error (NMAE), normalized root mean square error (NRMSE), and mean absolute percentage error (MAPE) were employed to evaluate the sensor capabilities for vital sign detection, comprising heart rate (HR) and respiratory rate (RR). Vital signs were evaluated based on a 30-second time window, with an overlap of 15 seconds. In this study, electrocardiogram and thoracic effort signals were used as references to estimate the performance of the proposed vital sign detection algorithms. Results For the 10 patients recruited for the study, the proposed system achieved reasonable results compared with PSG for sleep apnea detection, such as an accuracy of 49.96% (SD 6.39), a sensitivity of 57.07% (SD 12.63), and a specificity of 45.26% (SD 9.51). In addition, the system achieved close results for HR and RR estimation, such as an NMAE of 5.42% (SD 0.57), an NRMSE of 6.54% (SD 0.56), and an MAPE of 5.41% (SD 0.58) for HR, whereas an NMAE of 11.42% (SD 2.62), an NRMSE of 13.85% (SD 2.78), and an MAPE of 11.60% (SD 2.84) for RR. Conclusions Overall, the recommended system produced reasonably good results for apneic event detection, considering the fact that we are using a single-channel BCG sensor. Conversely, satisfactory results were obtained for vital sign detection when compared with the PSG outcomes. These results provide preliminary support for the potential use of the MFOS for sleep apnea detection.

Estimation of Apnea-Hypopnea Index Using Deep Learning On 3-D Craniofacial Scans

“…Potential of depth and thermal cameras has also been explored in breathing monitoring at an early stage [52], [53]. Even contactless bed sensors have been proposed, although only with moderate success so far [54]. The benefit of using the mentioned approaches is that the person is more comfortable sleeping in their own home wearing few or no wires.…”

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“…Abnormal respiratory rates such as bradypnea and tarchypnea may be clinically caused by pain, anxiety, stress, shock, medications, and medical conditions (e.g., cerebral lesions, pulmonary heart disease, neuromuscular disorders, and so on). Sadek et al [17] used a microbend fiber optic sensor to collect ballistocardiogram data to monitor sleep apnea and vital signs. To quantify the performance of their study, normalized mean absolute error (NMAE), normalized root mean square error (NRMSE), and mean absolute percentage error (MAPE) proportions were adopted.…”

A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems

“…Ballistocardiography (BCG) is a noninvasive method based on the measurement of body motion (body movements such as displacement, velocity, and acceleration), generated by the ejection of blood by the heart, at each cardiac cycle. This is measured using devices that can measure the body recoil force produced as a result of ejection of blood [ 10 ].…”