Key result
An optimized deep convolutional neural network is proposed for IoT-based ECG monitoring to automatically classify cardiac arrhythmia.
Why the study?
Manual methods for arrhythmia monitoring have notable drawbacks despite growth in IoT healthcare, creating a need for automated arrhythmia classification methods.
Proposes an optimized deep convolutional neural network for automated arrhythmia classification using IoT-based ECG monitoring.
May enable IoT arrhythmia detection; leaves open need for prospective clinical validation.
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.
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Aniket Sonawane (2022) studied Cardiac arrhythmia. Optimized deep convolutional neural network for IoT-based ECG monitoring vs. Existing methods was evaluated on Classification of arrhythmia (accuracy, sensitivity, and specificity). An optimized deep convolutional neural network is proposed for IoT-based ECG monitoring to automatically classify cardiac arrhythmia.
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