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https://atpasesignaling.com/a-case-of-advanced-breast-cancer-in-which-recovered-through-soften/ To be able to improve ECG beat category, device learning and deep learning methods have now been studied. However, current research reports have restrictions in model rigidity, design complexity, and inference speed. OBJECTIVE To classify ECG beats efficiently and efficiently, we propose a baseline design with recurrent neural networks (RNNs). Furthermore, we additionally suggest a lightweight design with fused RNN for accelerating the prediction time on central handling devices (CPUs). PRACTICES We used 48 ECGs from the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) Arrhythmia Database, and 76 ECGs had been gathered with S-Patch devices developed by Samsung SDS. We developed both baseline and lightweight models on the MXNet framework. We trained both designs on graphics processing devices and sized both models' inference times on CPUs. OUTCOMES Our models realized total beat classification accuracies of 99.72% for the baseline design with RNN and 99.80% for the lightweight model with fused RNN. More over, our lightweight model paid down the inference time on CPUs with no loss in reliability. The inference time when it comes to lightweight model for 24-hour ECGs was 3 minutes, that is 5 times faster compared to the baseline design. CONCLUSIONS Both our baseline and lightweight models achieved cardiologist-level accuracies. Moreover, our lightweight model is competitive on CPU-based wearable hardware. ©Eunjoo Jeon, Kyusam Oh, Soonhwan Kwon, HyeongGwan Son, Yongkeun Yun, Eun-Soo Jung, Min Soo Kim. Initially posted in JMIR Medical Informatics (http//medinform.jmir.org), 12.03.2020.BACKGROUND A virtual patient (VP) are a good device to foster the development of medical history-taking skills without having the inherent limitations for the bedside setting. Although VPs hold the guarantee of causing the development of students' abilities, documenting and as

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