Conception and realization of an iot enabled deep cnn decision support system for automated arrhythmia classi cation

HIGHLIGHTS

• who: MIT-BIH and collaborators from the In summary, an automated IoT-enabled arrhythmia detection system is proposed with a deep learning model to classify the heartbeat with an overall testing accuracy of, %, which is higher than reported in the literature. A training accuracy of, .09% with precision, recall, and, scores ofis achieved. An , Wi-Fi-enabled module is used for real-time ECG signal transfer from the source to the server where the analysis is performed. Model complexity is minimized due to fewer convolution layers employed, making it suitable for hardware implementation. The heartbeats are classified . . .
  

   

  

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