ADNET-AI: AN OPTIMIZED DEEP LEARNING FRAMEWORK FOR MULTI-CLASS ECG ARRHYTHMIA DETECTION IN CLINICAL DECISION SUPPORT SYSTEMS

An irregular pulse is reflected by a cardiac condition called an arrhythmia. ECG is the widely used data for the identification of arrhythmia. Healthcare professionals can understand the abnormalities in heartbeats using ECG data. However, with the emergence of AI, there is a need for a CDSS that can help doctors diagnose arrhythmia. We suggested a DL An optimized framework for automatically identifying arrhythmias in ECG data. Our deep learning framework has mechanisms required for data acquisition, data transformation training, a deep learning classifier, and automatic detection of arrhythmia. We proposed a novel DL architecture called ADNet based on the CNN model. The suggested structure has been set up to take advantage of the enhanced CNN model for efficient arrhythmia detection. We presented the Learning-based Arrhythmia Detection method and Classification (LbADC), exploiting Utilizing arrhythmia, the suggested deep learning models detection performance. Our experimental investigation using the MIT-BIH benchmark dataset shows that the suggested method, LbADC, surpasses several cutting-edge models with the best accuracy of 98.68%. Therefore, the proposed AI-enabled system can be integrated with existing healthcare applications to automatically screen arrhythmias in ECG data.