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.

BACKGROUND There is nowadays an increased demand for accurate and personalized healthcare monitoring due to the different challenges facing healthcare care systems, namely rising costs, and a shortage of clinicians. 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 provide information about abnormal cardiac patients (e.g., patients with arrhythmias) and abnormal breathing (viz., obstructive sleep apnea) non-intrusively without disturbing the patient’s daily activities. Detecting obstructive sleep apnea via BCG sensors is gaining increased attention from many researchers due to their simple installation and accessibility, i.e., their non-wearable nature. In the field of nonintrusive vital sign monitoring; microbend fiber optic sensor (MFOS), among other sensors, has proven suitable. Yet, few studies looked into apnea detection. OBJECTIVE This study aimed at assessing the capabilities of an MFOS for nonintrusive vital signs and sleep apnea detection during an in-lab sleep study. Data were collected from patients having sleep apnea in the sleep lab of Khoo Teck Puat Hospital. METHODS 10 participants underwent full polysomnography (PSG) and the MFOS was placed under the patient’s bed-mattress for our BCG data collection. The predictive capabilities of the suggested sensor for apnea detection against the gold-standard PSG were assessed based on the accuracy (Acc), sensitivity (Sens), and specificity (Spec). The sleep apnea events were detected through a histogram based-thresholding method and the detection was performed on a minute by minute basis. The apneic events detection algorithm was evaluated against the manually scored events obtained from the PSG study. Further, normalized mean absolute error (NMAE) and normalized root-mean-square error (NRMSE) were employed to assess the sensor capabilities for vital signs measurement, comprising the heart and respiratory rates. Heart rates were detected form derived BCG signals via the multi-resolution analysis of the maximal overlap discrete wavelet transform. Respiratory rates were detected from derived respiratory effort signals using a sliding window peak detection approach. 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 assess the proposed vital signs detection algorithms. RESULTS Across the 10-patients recruited for the study, the system achieved impartial results to PSG for sleep apnea detection such as an accuracy of 50.36%±6.61%, a sensitivity of 56.14%±13.40%, and a specificity of 46.47%±10.59%. Besides, the system achieved close results for heart and respiratory rates such as an NMAE of 4.92%±2.58% and an NRMSE of 8.15%±2.72% for heart rate, while an NMAE of 11.16%±3.15% and an NRMSE of 15.10%±3.4% for respiratory rate. CONCLUSIONS Overall, the recommended system produced average results for apneic events detection considering the complexity of sensors required to diagnose this syndrome in a clinical setting, whereas satisfying results were obtained for vital signs detection compared to the PSG outcomes. These results provide preliminary support for the potential use of the MFOS for sleep apnea detection. The proposed sensor is not a replacement for the PSG. However, it can be thought of as a complementary monitoring method for clinicians, in the case where they don’t have access to the patient’s health status (e.g., out-of-hospital/patients’ home).

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