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Predict-OSA: Integrative Multimodal-based Early Prediction of Sleep Apnea using Single-lead ECG Signal
  • Amit Bhongade,
  • Tapan Kumar Gandhi
Amit Bhongade
Indian Institute of Technology Delhi
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Tapan Kumar Gandhi
Indian Institute of Technology Delhi

Corresponding Author:tgandhi@ee.iitd.ac.in

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Abstract

Obstructive sleep apnea (OSA) is a sleep condition characterized by the partial or full interruption of breathing while a person is asleep. Several techniques have been suggested for the automated recognition of OSA events. However, there has been little research on the prediction of these events using prior data, which is valuable for the advancement of medical devices that observe respiration while sleeping. In this research, we have proposed four techniques for forecasting OSA events by using a fusion of time-frequency representation (TFR) of electrocardiogram (ECG) signals and deep neural networks (Gabor-CNN, Wavelet-CNN, Wigner-CNN, and STFT-CNN). Overall, these techniques are called Predict-OSA. These techniques use raw single-lead ECG data collected at a sampling rate of 100 Hz without the inclusion of any manually extracted features. By analyzing the preceding 120 seconds of data, we can predict the occurrences of OSA (apnea) and regular breathing episodes in a lead time of 60 seconds. The findings obtained from analyzing a dataset consisting of more than 34270 60-second segments from 70 ECG recordings indicate that all four models had a high level of accuracy, surpassing 83%. The Wigner-CNN model demonstrated superior performance, achieving accuracy, specificity, and sensitivity of 89.75%, 91.89%, and 86.25%, respectively. The findings indicate that OSA occurrences may be reliably anticipated by analyzing single-lead ECG signals, which creates possibilities for designing devices that can prevent the incidence of OSA events from occurring during sleep. Our algorithms may be implemented in devices with limited storage space due to their ability to handle low sampling rates. This makes them well-suited for use in at-home environments.