INTELLIGENT ECG SIGNAL ANALYSIS USING DEEP RECURRENT NETWORKS FOR HEART RHYTHM DISORDERS

Abstract

Electrocardiogram (ECG) analysis is one of the most essential tools for identifying cardiac rhythm abnormalities and assessing cardiovascular health [1], [2]. Accurate interpretation of ECG signals is crucial for detecting disorders such as arrhythmias, atrial fibrillation, and other irregular heart activities that can lead to severe medical complications [3], [4]. However, traditional diagnostic methods heavily rely on manual inspection by clinicians, which is not only labor-intensive but also susceptible to subjective bias and diagnostic inconsistencies [5]. To address these limitations, this research presents an intelligent and automated ECG signal analysis framework utilizing Deep Recurrent Neural Networks (DRNNs) for reliable classification of heart rhythm disorders [6], [7]. The proposed approach leverages the temporal learning capabilities of recurrent architectures such as Long ShortTerm Memory (LSTM) and Gated Recurrent Unit (GRU) networks to model the complex time-dependent characteristics inherent in ECG sequences [8], [9]. By doing so, the system effectively captures both short-term and long-term dependencies in heartbeat patterns, enabling more precise identification of abnormal cardiac activity [10]. Prior to model training, several signal preprocessing techniques—including denoising, baseline drift removal, segmentation, and feature scaling—are applied to enhance signal quality and reduce artifacts caused by noise or motion interference [11], [12]. Subsequently, a feature extraction phase converts raw ECG data into time– frequency representations to enrich the model’s understanding of rhythm morphology and improve classification accuracy [13], [14]. The proposed model is trained and validated using publicly available benchmark datasets such as MIT-BIH Arrhythmia and PTB Diagnostic ECG Database [15], [16], ensuring generalizability across diverse cardiac conditions and patient profiles. Comprehensive performance evaluation reveals that the DRNN-based system significantly outperforms traditional machine learning algorithms—including Support Vector Machines (SVMs), Random Forests, and Decision Trees—achieving superior results in accuracy, sensitivity, and specificity [17], [18]. Moreover, the system demonstrates robust generalization on unseen ECG data, confirming its potential for real-world deployment in both clinical environments and wearable health monitoring devices [19], [20]. Overall, this study presents a data-driven, intelligent ECG analysis solution capable of real-time arrhythmia detection and continuous cardiac monitoring [21], [22]. The proposed framework not only enhances diagnostic precision but also supports physicians in clinical decision-making, ultimately contributing to faster, more reliable, and accessible cardiac healthcare systems. With further refinement, integration with IoT-based platforms and edge computing technologies could transform this framework into a cornerstone of next-generation AI-assisted cardiovascular care [23]–[25].