Towards deep learning-based detection scheme with raw ECG signal for wearable telehealth systems

The electrocardiogram (ECG) signal, as one of the most important vital signs, can provide indications of many heart-related diseases. Nonetheless, in the case of telehealth context, the automated analysis and accurate detection of ECG signals remain unsolved issues, because the poor data quality collected by the wearable devices and unprofessional users further increases the complexity of hand-crafted feature extraction, ultimately affecting the efficiency of feature extraction and the detection accuracy. To address this issue and improve the detection accuracy, in this paper we present a novel detection scheme with the raw ECG signal in wearable telehealth system. Our systembenefits from the concept of big data, sensing and pervasive computing and the emerging deep learning technology. In particular, a Deep Heartbeat Classification (DHC) scheme is proposed to analyze the ECG signal for arrhythmia detection. Distinct from existing solutions, the detection model in DHC can be trained directly on the raw ECG signal without hand-crafted feature extraction. A cloud-based prototypical system is also designed and implemented with the functions of data acquisition, wireless transmission, back-end data management, and ECG detection. The experimental results demonstrate that our prototypical system is feasible and effective in real-world practice, and extensive experimentation based on the MIT-BIH database demonstrates that the proposed DHC scheme outperforms baseline schemes.