Convolutional Neural Network Enable Optoelectronic System for Predicting Cardiac Response by Analyzing Auction-Based Optimization Algorithms

Abstract

One of the body’s most important organs is the heart. An electrocardiogram (ECG) is a common diagnostic tool because it provides continuous tracings of the heart’s electrophysiological activity. The study’s overarching objective is the development and implementation of an artificial intelligence (AI)-based abnormal heart beat detection system with potential applications in the early diagnosis and timely treatment of cardiovascular diseases. Through the transmission of signals to the healthcare monitoring system, these wearable devices enable doctors to keep constant, reliable tabs on their patients’ health statuses. In addition to alerting the doctors and nurses, this serves as a warning to the patient so that they, too, can take preventative measures. Several scientific teams utilizing AI contributed to the victory. Predicting cardiovascular disease using information gathered from smart devices is challenging due to low accuracy and time complexity. We propose a new optimization strategy based on deep learning to tackle these problems. In particular, it relies on the Condition-Convolutional Neural Network (Condition-CNN) based Auction-based Optimization algorithm, which analyzes optimization algorithms (ABO) while also considering Opto electronics property (sensor and detector characteristics, MOSFET) mechanism details, and the active element triumvirate.