Automatic detection of sleep apnea from single-lead ECG signal using enhanced-deep belief network model. (February 2023)

• Record Type: Journal Article
• Authors: Kumar Tyagi, Praveen; Agrawal, Dheeraj
• Is Part Of: Biomedical signal processing and control. Volume 80:Part 2(2023)
• Publication Date: 2023-02
• Subjects: Deep Belief Network -- Deep Learning -- ECG Signal -- Restricted Boltzmann Machine -- Sleep Apnea; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104401
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Sleep apnea (SLA) is a commonly reported sleep disorder that is characterized by frequent breathing interruptions during sleep. In recent years, various approaches have been made to developing a less complex and cost-effective process for diagnosing SLA patients, as opposed to using the inconvenient, complex and costly polysomnography test. This study proposed a novel approach of cascading two different types of Restricted Boltzmann Machine (RBM) in Deep Belief Networks (DBN) method for the SLA classification using single-lead electrocardiogram (ECG) signals. The proposed framework uses two types of RBM, namely Gaussian-Bernoulli, and Bernoulli-Bernoulli, which are modified forms of the Boltzmann Machine, to develop an enhanced- DBN (E-DBN) structure for significant feature learning and detection of SLA and normal events. At each ECG data signal, Heart Rate Variability (HRV) and ECG-Derived Respiration (EDR) signals are obtained from the 1-minute segmented ECG signal. The E-DBN model can also enhance the parameter detection performance as a key feature extractor and classification method. The Apnea-ECG datasets from physionet were used to train the presented fine-tuned E-DBN model and validate its performance for detecting SLA incidents. The proposed method shows significant improvements in performance when compared to other SLA detection methods for per-segment detection and achieved the highest accuracy of 89.11%, with specificity, sensitivity, and F1-score of 92.28%, 83.89%, and 0.913, respectively. For per-recording detection, accuracy of 97.17% and correction coefficient values of 0.938 are obtained. The proposed approach develops a method that analyses single-lead electrocardiography data from patients and diagnoses the SLA condition of patients.