Assessing the severity of sleep apnea syndrome based on ballistocardiogram

<div><p>Background</p><p>Sleep 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.</p><p>Methods</p><p>We 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.</p><p>Conclusions</p><p>Experimental 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).</p></div

Time consumption of the original ICSS and Physio_ICSS.

<p>Time consumption of the original ICSS and Physio_ICSS.</p

Framework of the proposed approach.

<p>Framework of the proposed approach.</p

<i>p-values</i> of the apnea hypopnoea index among different groups.

<p><i>p-values</i> of the apnea hypopnoea index among different groups.</p

An example of leak and fault checked RR intervals.

<p>A: Leak check. B: Fault check.</p

<i>p-values</i> of the age among different groups.

<p><i>p-values</i> of the age among different groups.</p

Performance of the Physio_ICSS algorithm.

<p>Performance of the Physio_ICSS algorithm.</p

Performance of the micro-movement sensitive mattress.

<p>Performance of the micro-movement sensitive mattress.</p

The sleep monitoring system.

<p>The sleep monitoring system.</p

Sudden change detection performance of original ICSS and Physio_ICSS.

<p>A: ICSS on SAS data. B: Physio_ICSS on SAS data. C: ICSS on NSAS data. D: Physio_ICSS on NSAS data.</p