Iot-Based Patient ECG Monitoring for Arrhythmia Classification via Optimized Deep Convolutional Neural Network

Cardiac arrhythmia is one among a type of cardiovascular diseases (CVDs), which reports 12% of total deaths all-over the world. Even though there is a lot of growth in IoT-health monitoring, the manual method suffers from a lot of drawbacks. Hence, there is a need for an automatic method in health-care specifically, for classification of arrhythmia, for which an optimized deep convolutional neural network will be proposed. The IoT network will be simulated for collecting the ECG signals from the patients, and the signals will be processed for classification of arrhythmia in patients, which assures continuous health monitoring of patients. The proposed model named optimized deep convolutional neural network will be implemented and compared with the existing methods in order to reveal the effectiveness based on the performance metrics, such as accuracy, sensitivity, and specificity.