Assessing the severity of sleep apnea syndrome based on ballistocardiogram

Abstract

BackgroundSleep Apnea Syndrome (SAS) is a common sleep-related breathing disorder, which affects about 4-7% males and 2-4% females all around the world. Different approaches have been adopted to diagnose SAS and measure its severity, including the gold standard Polysomnography (PSG) in sleep study field as well as several alternative techniques such as single-channel ECG, pulse oximeter and so on. However, many shortcomings still limit their generalization in home environment. In this study, we aim to propose an efficient approach to automatically assess the severity of sleep apnea syndrome based on the ballistocardiogram (BCG) signal, which is non-intrusive and suitable for in home environment.MethodsWe develop an unobtrusive sleep monitoring system to capture the BCG signals, based on which we put forward a three-stage sleep apnea syndrome severity assessment framework, i.e., data preprocessing, sleep-related breathing events (SBEs) detection, and sleep apnea syndrome severity evaluation. First, in the data preprocessing stage, to overcome the limits of BCG signals (e.g., low precision and reliability), we utilize wavelet decomposition to obtain the outline information of heartbeats, and apply a RR correction algorithm to handle missing or spurious RR intervals. Afterwards, in the event detection stage, we propose an automatic sleep-related breathing event detection algorithm named Physio_ICSS based on the iterative cumulative sums of squares (i.e., the ICSS algorithm), which is originally used to detect structural breakpoints in a time series. In particular, to efficiently detect sleep-related breathing events in the obtained time series of RR intervals, the proposed algorithm not only explores the practical factors of sleep-related breathing events (e.g., the limit of lasting duration and possible occurrence sleep stages) but also overcomes the event segmentation issue (e.g., equal-length segmentation method might divide one sleep-related breathing event into different fragments and lead to incorrect results) of existing approaches. Finally, by fusing features extracted from multiple domains, we can identify sleep-related breathing events and assess the severity level of sleep apnea syndrome effectively.ConclusionsExperimental results on 136 individuals of different sleep apnea syndrome severities validate the effectiveness of the proposed framework, with the accuracy of 94.12% (128/136).

Unobtrusive Mattress-Based Identification of Hypertension by Integrating Classification and Association Rule Mining

“…Afterwards, BCG signal is separated from the original composite pressure signal by performing drift compensation and digital filtering based on the built-in software, and eventually displayed on the PC. In [23], a real-time comparison between BCG signal collected by RS-611 and ECG signal collected by a commercial ECG collector (Prince 180D, ) was made, whose experimental results indicated that each wave trough of the collected BCG signal corresponded to a local minimum point of the collected ECG signal, which demonstrated the suitability of the collected BCG signal for analyzing and diagnosing cardiovascular diseases. In comparison with other wearable signal acquisition devices like ECG Holter [24], smart watch [25], smart chair [26], PPG based non-invasive blood pressure estimator [27] and cuff-based mercury sphygmomanometer, the MSM-based RS-611 is completely unobtrusive and the users do not need to attach any electrodes or devices on their body.…”

Correlation between metabolic syndrome and sleep apnea

“…These repetitive partial or, in certain advanced cases complete obstructive episodes of the upper airway during periods of sleep, are characterized by cessation of breathing for at least 10 s per minute resulting in hypopneic scenarios leading to apnea[ 7 , 9 , 10 ]. Grading of OSA is based on the apnea-hypopnea index (AHI), which is defined as number of apnea and hypopnea episode per hour of sleep[ 17 ]. Mild OSA has an AHI of 5-14/h, moderate OSA 15-30/h AHI, and severe OSA has an AHI of > 30/h[ 18 , 19 ] (Table 1 ).…”

Joint Apnea and Body Position Analysis for Home Sleep Studies Using a Wireless Audio and Motion Sensor

“…This, in turn, leads to a high false positive result and a failure in the exclusion of low-risk patients [26]. Among newly proposed methods of OSA screening are photoplethysmography [27], ballistocardiography [28], piezoelectric sensors [29], accelerometers [30], audio signals [31], [32], and radar-based systems with non-contact sensing for sleep apnea and sleep body position, respectively: [33], [34]. Most methods which could be used for home detection of OSA were recently reviewed by Mendonca et al [11].…”