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 cardsResults: 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.
This paper introduces our solution (ISIBrno-AIMT team) to the Physionet Challenge 2022. The main goal of the challenge was a classification of heart murmurs from phonocardiographic recordings into three mutually exclusive classes (i.e., present, unknown, and not present) and to determine whether the patient's overall status is Normal or Abnormal. We propose a deep learning method that classifies whether there is a heart murmur in the phonocardiographic recording and also provides heart sound segmentation. Furthermore, the expert feature classifier assesses whether the patient's status is normal or abnormal. Our approach achieved a hidden test challenge score of 0.755 in the murmur classification task and a score of 12313 in the patient outcome classification task. Our team was ranked as 9th and 12th out of 40 teams in the official ranking for murmur and outcome classification, respectively.
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