An Effective Method for Distinguishing Sleep Apnea and Hypopnea Based on ECG Signals

Yao Wang,Wang Jinhai,Xiaohong Wang,Xiaoyun Zhao,Tianshun Yang,Huiquan Wang,Siyu Ji

doi:10.1109/access.2021.3077030

Yao Wang, Wang Jinhai + Show 5 more

Open Access

https://doi.org/10.1109/access.2021.3077030

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Abstract

Good sleep quality is essential to our life and work. The two major challenges in evaluating sleep quality are the scoring of sleep stages and sleep apnea detection. In addition to sleep apnea, hypopnea is also a respiratory event that occurs at an equal or even higher frequency compared to that of apnea. However, the two phenomena are not only different in terms of physical manifestation, there are also significant differences in the methods and means of clinical treatment. Therefore, in this study we propose an effective algorithm to distinguish between hypopnea and apnea events. We consider two aspects of feature extraction. On the one hand, from the original time-domain waveform of electrocardiogram signals, we extract the time difference sequence between the appearance of the R-R wave in two adjacent QRS waves (RR intervals) and explore the way they change when apnea and hypopnea event occur. On the other hand, considering that the degree of sequence disorder caused by hypopnea and apnea events is different, multi-scale entropy and complexity are chosen as the features that describe this change. The results obtained using the proposed algorithm show that the classification between hypopnea events and any type of apnea events all reach an accuracy rate greater than 90%. For obstructive apnea events, which occur most frequently among the three different types of apneas, the classification accuracy, sensitivity, specificity and F1-score were 91.78%, 92.21%, 91.30%, 92.20%, respectively. The classification results between central apnea events and hypopnea events reached an accuracy of 93.18%, sensitivity of 95.00%, specificity of 91.67% and F1-score of 92.68%. At the same time, the algorithm achieved an accuracy of 92.59%, sensitivity of 90.38%, specificity of 94.64% and F1-score of 92.16% when classifying mixed apnea and hypopnea events. In the classification of normal breathing, hypopnea events and apnea events, the total accuracy rate achieved was 91.92%, with that for normal breathing reaching 93.62%, for hypopnea events reaching 90.39%, and 91.91% for apnea events, with a Kappa coefficient of 0.99. The algorithm proposed in this study constitutes an efficient and convenient approach for apnea and hypopnea event detection and classification.

Highlights

In the short term, lack of sleep can cause mental fatigue and drowsiness during daytime work, while long-term occurrence of the condition can have a significantly negative impact on physical health, such as memory loss, emotional instability, etc
The purpose of this study is to propose an efficient, reliable, and easy-toimplement detection and classification algorithm that can be applied to home or clinical wearable applications to assist diagnosis of hypopnea and apnea events to solve the problem that due to external factors, PSG data cannot be recorded in time for apnea and hypopnea event assessment
The proposed algorithm in our study achieves an accuracy of 97.96%, sensitivity of 98.10%, specificity of 97.83% and F1-score of 98.10% in detecting Sleep apnea-hypopnea syndrome (SAHS) events, and achieves classification accuracies of 90% and above for distinctions between hypopnea events and the three different types of apnea events

Summary

Introduction

Lack of sleep can cause mental fatigue and drowsiness during daytime work, while long-term occurrence of the condition can have a significantly negative impact on physical health, such as memory loss, emotional instability, etc. Sleep apnea-hypopnea syndrome (SAHS) events are one of the most common sleep disorders that deteriorate sleep quality, and affect about 5-20% of adults [1,2]. SAHS will result in recurrent respiratory-related events and both shortterm symptomatic consequences and long-term physiologic consequences, which include gasping for air while sleeping, headaches and other adverse outcomes. The increased incidence of motor vehicle collisions accidents due to driving while drowsy, as well as increased incidence of cardiopulmonary complications, cerebrovascular events and neurocognitive effects are all related to SAHS events.

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