eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2026-05-12 19 2 71797 article Kamal Upreti 1 Jossy George 1 Bosco Paul Alapatt 1 Rituraj Jain 2 Ganeshavishwaa Veluswwamy Radhakrishnan 3 Department of Computer Science, Chrıst University, Delhi NCR, Ghaziabad, India Department of Information Technology, Marwadi University, Rajkot, Gujarat, India Department of Economics and Finance, Kalinga Institute of Industrial Technology, Bhubaneswar, India Early identification of abnormal cardiac activity through electrocardiogram (ECG) screening is essential for improving clinical outcomes and enabling timely intervention. Manual ECG interpretation is labor-intensive, subject to inter-observer variability, and difficult to scale for continuous monitoring, highlighting the need for automated screening support. This study presents a deep learning–assisted ECG screening framework based on a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture for automated detection of abnormal cardiac activity. The proposed framework explicitly models short-term temporal dependencies by processing multi-step sequential ECG segments, allowing LSTM layers to learn the evolution of abnormal patterns across consecutive samples. CNN layers extract clinically relevant morphological features related to P-waves, QRS complexes, and T-waves, while LSTM layers capture their temporal progression across three- and six-step windows. Evaluation on a benchmark ECG dataset demonstrates strong screening performance, achieving an accuracy of 99.10%, precision of 99.38%, recall of 99.38%, and an F1-score of 99.38%. Although real-time clinical deployment was not assessed, the lightweight architecture and low inference latency indicate suitability for future wearable and IoT-enabled cardiac monitoring systems. https://biomedpharmajournal.org/vol19no2/deep-learning-assisted-ecg-screening-for-detection-of-abnormal-cardiac-activity/ Abnormal Cardiac Activity Cardiac Signal Analysis Clinical Decision Support Deep Learning Electrocardiogram Screening Wearable Health Monitoring