Convolutional Neural Network-Transformer Model to Predict and Classify Early Arrhythmia Using Electrocardiogram Signal

As a major cause of death worldwide, cardiovascular diseases-especially arrhythmias-require the creation of precise and automated technologies for early diagnosis and detection. To identify arrhythmias from electrocardiogram (ECG) signals, this paper introduces a deep learning-based classification model that focuses on five main heartbeat types: Normal (N), Left Bundle Branch Block (L), Right Bundle Branch Block (R), Atrial Premature Beat (A), and Premature Ventricular Contraction (V). We leverage Lead I signals from several sources, such as the INCART 12-lead, Sudden Cardiac Death Holter, Supraventricular, and MIT-BIH Arrhythmia databases, yielding more than 3.9 million training and 112,575 testing segments. Examples of data preparation include 180 sample, fixed-window segmentation, Min-Max normalization, and class balancing with the Synthetic Minority Over-sampling Technique (SMOTE). The hybrid architecture uses Transformer layers to model temporal dependencies and 1D Convolutional Neural Networks (CNNs) to extract spatial features. The Adam optimizer with dropout and batch normalization for regularization trains the model. The proposed system outperforms the TN4 model and other cutting-edge benchmarks, achieving 99.99% accuracy, precision, and F1-score across all classes. Feature robustness is further improved by applying deep hybrid architectures and convolutional neural networks, which were motivated by earlier studies. The suggested paradigm advances artificial intelligence-driven, individualized digital healthcare and has great promise for scalable, real-time arrhythmia identification.