A Signal Segmentation‐Free Model for Electrocardiogram‐Based Obstructive Sleep Apnea Severity Classification

Abstract

Obstructive sleep apnea (OSA) has been a common sleep disorder for years, and polysomnography (PSG) remains the gold standard for diagnosing OSA. Nevertheless, PSG is a time and money consuming test, and patients have to wait long for arranging a PSG test in a hospital. In light of this, portable and wearable tools for OSA classification have been developed recently as a low‐cost and easy‐to‐use screening method before undergoing PSG. Using unsegmented electrocardiogram (ECG) signals, a deep neural network (DNN)‐based model is developed here to categorize OSA severity with the following features. First, the model takes unsegmented ECG signals recorded overnight as input, and then generates a four‐level scale as output. Since all the input ECG signals are unsegmented, the tremendous amount of effort spent on signal annotation can be fully saved. Second, the largest amount of data is used to test the model and consequently provide a high generalization ability, as compared with others in the literature. The overall outperformance of this work is highlighted at the end of this article, and this work is validated as an easy‐to‐use and effective screening tool for OSA accordingly.

Predicting Apnea‐Hypopnea Index in Patients with Obstructive Sleep Apnea Using Unsegmented ECG‐Signal‐Based Algorithms

“…In order to overcome the limitation of Ref. [5], a regression method was adopted in the Head module to directly output the AHI value, which is the first time to be presented in the literature. The input of the model is a one-dimensional ECG signal of 6-h duration at 100 Hz sampling rate, getting the input shape = 2 160 000 × 1, as the same as that in Ref.…”

“…The input of the model is a one-dimensional ECG signal of 6-h duration at 100 Hz sampling rate, getting the input shape = 2 160 000 × 1, as the same as that in Ref. [5]. Accordingly, only one-dimensional convolutional layers, abbreviated as Conv1Ds, were used in Fig.…”

“…The proposed method is innovative and overcomes the limitation of Ref. [5]. In this way, this work can provide more details of OSA assessment to users and doctors.…”

“…In view of this, portable and wearable screening tools for OSA detection have been developed recently as a low-cost and easy-to-use method before undergoing PSG. In addition, considerable efforts have been made to detect OSA severity using a single signal, especially in electrocardiography (ECG) signal alone [2][3][4][5].…”

“…Recently, we have developed a signal segmentation-free model for ECG-based OSA severity classification [5]. The model takes an unsegmented ECG signal recorded overnight as input, and predicts a four-level scale as output.…”