Scalable, Multiplatform, and Autonomous ECG Processor Supported by AI for Telemedicine Center

Abstract

Background: Wearable devices play an essential role in the early diagnosis of heart diseases. However, effective management of long-term ECG measurements (1-3 weeks) by a telemedicine center (TMC) requires specifically designed software. Method: We used the multiplatform framework.NET to build the application. Deep-learning models for QRS detection, classification, and rhythm analysis were trained in the PyTorch framework; models were trained using data from Medical Data Transfer, s. r. o. Czechia (N=73,450 and 12,111). The ONNX runtime libraries were used for model inference, including acceleration by graphic cards Results: The pre-production benchmark (recordings of 82 patients) showed a mean accuracy of 0.97 ± 0.04 for QRS detection and classification into three classes; it also showed a mean accuracy of 0.97 ± 0.03 for rhythm classification into seven classes. Conclusion: The presented software is a fully automated, multiplatform, and scalable back-end application to process incoming ECG data in the TMC Although it is not freely accessible, we are open to considering processing ECG data for research and strictly non-commercial purposes.