J Vis Exp. 2026 May 22;(231). doi: 10.3791/69541.
ABSTRACT
Cardiovascular diseases, especially arrhythmias, are a leading cause of death worldwide. This highlights the need for automated systems that can detect and diagnose these conditions early. This research introduces a deep learning model that identifies arrhythmias using electrocardiogram (ECG) signals. The model focuses on five main types of heartbeats: Normal (N), Left Bundle Branch Block (L), Right Bundle Branch Block (R), Atrial Premature Beat (A), and Premature Ventricular Contraction (V). The system uses Lead I signals from several databases, including MIT-BIH Arrhythmia, Supraventricular, INCART 12-lead, and Sudden Cardiac Death Holter. This provides more than 3.9 million training segments and 112,575 testing segments. The data is preprocessed by dividing it into fixed windows of 180 samples, scaling it using Min-Max normalization, and balancing the classes with the Synthetic Minority Over-sampling Technique. The model combines 1D Convolutional Neural Networks to extract spatial features and transformer layers to capture time-based patterns. It uses the Adam optimizer and includes dropout and batch normalization to enhance performance. The system achieves 99.99% accuracy, precision, and F1-score across all classes, which is better than the TN4 model and other top-performing models. The use of Convolutional Neural Networks and deep hybrid architectures improves the robustness of features. This model shows great potential for scalable and real-time arrhythmia detection and contributes to the advancement of AI-driven, personalized digital healthcare.
PMID:42258419 | DOI:10.3791/69541