Intelligent ECG and EEG Signal Analysis for Real Time Cardiac and Neurological Monitoring

Abstract

Continuous monitoring of cardiac and neurological activity is essential for early detection of life threatening conditions and long term disease management. Electrocardiogram and electroencephalogram signals provide rich physiological information, yet their interpretation in real time remains challenging due to noise, inter patient variability, and complex temporal patterns. Intelligent signal analysis techniques based on machine learning have demonstrated strong potential to enhance accuracy, responsiveness, and scalability of monitoring systems. This study presents a comprehensive investigation of intelligent ECG and EEG signal analysis methods for real time cardiac and neuro- logical monitoring. The proposed approach integrates advanced signal preprocessing, feature learning, and adaptive classification to support timely clinical decision making while maintaining computational efficiency suitable for continuous deployment.