سامانه همایش های دانشگاه آزاد اسلامی واحد نجف آباد

صفحه اصلی / اولین همایش بین المللی تحول خدمات سلامت: تلفیق مراقبت، درمان، بازتوانی و امیدبخشی

An Intelligent Deep Learning and Telemedicine-Based System for Cardiac Event Prediction and Remote Monitoring

نویسندگان :

Naeimeh Kouhi Fayegh¹ Saeid Rafei²

1- دانشگاه آزاد اسلامی واحد تهران جنوب 2- دانشگاه آزاد اسلامی واحد شهرکرد

کلمات کلیدی :

Cardiovascular Diseases،Myocardial Infarction،Deep Learning،Telemedicine،Remote Monitoring

چکیده :

Cardiovascular diseases (CVDs) are the foremost cause of global mortality, responsible for over 18 million deaths annually. Early identification of patients at risk of acute cardiac events remains a major clinical challenge. Recent advances in wearable sensing, telemedicine, and deep learning have enabled the development of intelligent systems for continuous cardiovascular surveillance and risk stratification. This study presents an integrated telecardiology framework for real-time monitoring and prediction of heart attack risk. The proposed architecture comprises four layers: (1) acquisition of multimodal physiological signals through wearable devices, (2) preprocessing and secure transmission of patient data, (3) predictive analysis using a hybrid deep learning model combining Convolutional Neural Networks (CNN) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal dependency modeling, and (4) an interactive clinical interface to support remote assessment by healthcare providers and patient self-management. The proposed framework demonstrated robust predictive performance, with reported cardiovascular risk discrimination of AUC = 0.83 and sensitivity of 0.85 for myocardial infarction detection. Integration with telemedicine infrastructure enabled continuous remote supervision and automated early-warning alerts. In addition, evidence from clinical telemonitoring interventions suggests a potential reduction of up to 39% in cardiovascular-related hospital admissions. The convergence of deep learning and telemedicine offers a clinically relevant approach for proactive cardiovascular care. The proposed framework may improve early diagnosis, optimize clinical workflows, and support personalized preventive interventions, thereby enhancing patient outcomes and reducing healthcare utilization.