Abstract
The quality of sleep can be affected by the occurrence of a sleep related disorder and, among these disorders, obstructive sleep apnea is commonly undiagnosed. Polysomnography is considered to be the gold standard for sleep analysis. However, it is an expensive and labor-intensive exam that is unavailable to a large group of the world population. To address these issues, the main goal of this work was to develop an automatic scoring algorithm to analyze the single-lead electrocardiogram signal, performing a minute-by-minute and an overall estimation of both quality of sleep and obstructive sleep apnea. The method employs a cross-spectral coherence technique which produces a spectrographic image that fed three one-dimensional convolutional neural networks for the classification ensemble. The predicted quality of sleep was based on the electroencephalogram cyclic alternating pattern rate, a sleep stability metric. Two methods were developed to indirectly evaluate this metric, creating two sleep quality predictions that were combined with the sleep apnea diagnosis to achieve the final global sleep quality estimation. It was verified that the quality of sleep of the nineteen tested subjects was correctly identified by the proposed model, advocating the significance of clinical analysis. The model was implemented in a non-invasive and simple to self-assemble device, producing a tool that can estimate the quality of sleep and diagnose the obstructive sleep apnea at the patient's home without requiring the attendance of a specialized technician. Therefore, increasing the accessibility of the population to sleep analysis.
Highlights
The quality of sleep is one of the most important aspects that can affect physical and mental health since sleep related complaints are the second most usual causes for pursuing medical care, only superseded by the feel of pain [1]
This information is in agreement with the findings reported in the state of the art where it was verified that the power in the high frequency is associated with physiologic respiratory sinus arrhythmia, deep sleep, and absence of Cyclic Alternating Pattern (CAP) periods while the power in the very low and low frequency bands was associated with wake or Rapid Eye Movement (REM) periods, the presence of CAP and the occurrence of Obstructive Sleep Apnea (OSA) or sleep fragmentation [20], [64]
The model based on the average Cross-Spectral Coherence (CSC) metric achieved the lowest performance for the sleep quality estimation, with a moderate agreement according to the k value, while the combined approach for the global sleep quality estimation attained the highest possible performance, improving the results that were reached by applying a threshold to the minutes classified as CAP (m-CAP)-tib estimation
Summary
The quality of sleep is one of the most important aspects that can affect physical and mental health since sleep related complaints are the second most usual causes for pursuing medical care, only superseded by the feel of pain [1]. Another relevant factor is the prevalence of poor sleep quality in older adults, The associate editor coordinating the review of this manuscript and approving it for publication was Zhanpeng Jin. where it was projected that it affects approximately half of the population [2].
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