Efficient sleep apnea detection using single-lead ECG: A CNN-Transformer-LSTM approach.

Background: Sleep apnea (SA), a prevalent sleep-related breathing disorder, disrupts normal respiratory patterns during sleep. This disruption can have a cascading effect on the body, potentially leading to complications in various organs, including the heart, brain, and lungs. Due to the potential for these complications, early and accurate detection of SA is critical. Electrocardiograms (ECG), due to their ability to continuously monitor heart rhythms and detect subtle changes in cardiac activity, such as heart rate variability and arrhythmias, which are often linked to sleep disruptions, have become crucial in identifying individuals at risk for SA.

Method: In this study, we propose a hybrid neural network model named CNN-Transformer-LSTM that uses a single-lead ECG signal to detect SA automatically. This method captures spatial and temporal features in the ECG data to improve classification performance. Our model utilizes RR intervals (RRI) and R-peak signals derived from ECG data as input and then classifies SA and normal states on a per-segment and per-recording basis. We evaluated the model using the Physionet Apnea-ECG dataset, consisting of 70 single-lead ECG recordings annotated by medical professionals, and the UCD St. Vincent's University Hospital's sleep apnea database (UCDDB) containing polysomnogram records from 25 patients.

Results: Our model achieved an accuracy of 91.6% for per-segment classification on the Physionet Apnea-ECG dataset using hold-out validation and the highest accuracy of 94.1% using five-fold cross-validation. As for per-recording classification, our model achieved an accuracy of 100% and the highest correlation coefficient value of 0.9996 using five-fold cross-validation. On the UCDDB dataset, our model achieved an accuracy of 99.37% on the reduced dataset excluding 4 patients and 98.34% on the full dataset. Compared to previous works, our model improved the per-segment classification accuracy by nearly 3% over the existing best result, thereby demonstrating that our model outperforms existing state-of-the-art methods in accurately detecting SA from a single-lead ECG signal.

Conclusion: These results highlight the effectiveness of the CNN-Transformer-LSTM model for SA detection and its potential to be used in SA detection devices for home health care and clinical settings.