Sleep Apnea Detection From Raw Ecg Signal Using Deep Learning And Machine Learning
Date
2023
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Sleep apnea, a prevalent yet underdiagnosed sleep disorder, necessitates robust and accurate diagnostic tools. In this project, we undertook an in-depth exploration of machine learning (ML) and deep learning (DL) models for sleep apnea detection, specifically utilizing raw electrocardiogram (ECG) signals. Our comparative analysis encompassed a range of ML models, including Random Forest, Logistic Regression, Decision Tree, AdaBoost, and XGBoost, and a specialized 1D-CNN model within the DL domain. Results underscore the exceptional performance of the 1D-CNN model, achieving a remarkable accuracy of 99.56%, sensitivity of 96.05%, and specificity of 99.66%. This outperforms traditional ML models, signifying the prowess of DL in extracting intricate patterns from raw ECG signals for accurate sleep apnea detection. The 1D-CNN model's ability to discern subtle features proves crucial for accurately identifying apnea events. Our study not only emphasizes the effectiveness of the 1D-CNN model for sleep apnea detection and highlights the transformative potential of deep learning in healthcare diagnostics. This research contributes valuable insights into the optimal choice of models for sleep apnea detection, paving the way for enhanced diagnostic accuracy and improved patient care.
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Electrical and Computer Engineering
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North South University