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MPCNN: A Novel Matrix Profile Approach for CNN-based Single Lead Sleep Apnea in Classification Problem
Summary
This study introduces a novel ECG feature extraction method for sleep apnea (SA) detection using deep learning. The new approach significantly improves diagnostic accuracy, showing promise for home sleep apnea tests and IoT devices.
Area of Science:
- Cardiology
- Biomedical Engineering
- Artificial Intelligence
Background:
- Sleep apnea (SA) is a critical global health issue.
- Deep Learning (DL) shows promise for electrocardiogram (ECG)-based SA diagnosis.
- Conventional ECG features (R-peaks, RR intervals) may miss vital signal information.
Purpose of the Study:
- To develop an innovative ECG feature extraction method for improved SA detection.
- To address the limitations of traditional feature extraction in DL models for SA diagnosis.
- To enhance the performance of lightweight DL models for SA detection.
Main Methods:
- Inspired by Matrix Profile algorithms, derived MinDP, MaxDP, and MeanDP from ECG signal subsequences.
- Utilized modified LeNet-5, BAFNet, and SE-MSCNN deep learning models.
- Validated the approach on PhysioNet Apnea-ECG and UCDDB datasets.
Main Results:
- Achieved up to 92.11% and 81.25% per-segment accuracy on the datasets.
- Reached 100% per-recording accuracy on the PhysioNet data.
- Obtained a high correlation of 0.989 with state-of-the-art methods.
Conclusions:
- The novel distance-based feature extraction method enhances DL model performance for SA detection.
- The approach shows potential for application in home sleep apnea tests (HSAT) and IoT devices.
- Publicly available source code facilitates further research and development.
