Bidirectional Long Short-Term Memory (Bi-LSTM) with Convolutional Neural Networks (cNN) Based Obstructive Sleep Apnea Detection Using ECG Signals

Abstract

Recent advances in artificial intelligence (AI) have significantly impacted various fields, including finance, manufacturing, and bio-signal analysis. Obstructive sleep apnea (OSA) is a common disorder characterized by recurrent episodes of partial or complete airway obstruction during sleep. Traditionally, it is diagnosed using polysomnography (PSG), which involves overnight monitoring of various physiological signals, including ECG. This process can be both time-consuming and uncomfortable for patients. Therefore, efficient and accurate OSA detection through bio-signal analysis is essential.ECG signals represent time-series data that exhibit high temporal dependency and non-stationary characteristics, meaning their features change dynamically over time. To address this complexity, we propose a hybrid model that integrates Bidirectional Long Short-Term Memory (Bi-LSTM) with Convolutional Neural Networks (cNN) to detect OSA events from ECG signals. This model processes key features extracted from cNN layers, capturing both past and future contexts simultaneously in the Bi-LSTM sub-module. This approach enhances the detection of subtle differences in temporal dependencies.For our study, we sampled 72 ECG signals, considering gender and severity levels from the publicly available PSG-Audio dataset, and segmented them into 30-second intervals. Following a filtering process, we applied dimensionality reduction using the EMD algorithm based on prior results. Our experiments demonstrated that the proposed model outperformed the reference model from a previous study, achieving an accuracy of 88.68%, sensitivity of 86.94%, specificity of 90.38%, and an F1 score of 0.895.These results highlight the effectiveness of the proposed model in detecting OSA, which could enhance diagnostic accuracy through advanced bio-signal analysis.

Keywords: Deep learning, Healthcare, Electrocardiogram, Obstructive sleep apnea, Medical diagnoses

DOI: 10.54941/ahfe1005940