This research addresses the integration of Artificial Intelligence (AI) into electrocardiogram (ECG) signal processing to improve detection and classification of cardiac anomalies. We present an AI-driven ECG analysis framework that employs reinforcement learning (RL) together with a hybrid CNN–LSTM architecture to enhance PQRST complex detection and arrhythmia classification. AI agents autonomously detect and label ECG features using RL for adaptive peak detection, while the CNN–LSTM model performs arrhythmia classification. Using the MIT-BIH Arrhythmia Database, the system achieved 99.58% PQRST detection accuracy, 99.85% classification accuracy, and 99.85% anomaly detection precision. A CNN extracts key ECG features, an LSTM models temporal dependencies, and a Softmax prediction module (SMP) produces the final classification. The proposed AI model advances real-time cardiovascular monitoring and IoT-based diagnostics, offering a highly accurate, automated solution for early cardiac disease detection.

Artificial Intelligence (AI), Reinforcement Learning (RL), Convolutional Neural Network (CNN), LSTM, ECG signal processing, PQRST detection, arrhythmia classifica- tion, Internet of Things (IoT), Healthcare