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Arrhythmia detection in electrocardiogram (ECG) signals is a vital aspect of cardiovascular health monitoring. Current automated methods for arrhythmia classification often struggle to attain satisfactory performance in the detection of various heart conditions, particularly when dealing with imbalanced datasets. This study introduces a novel deep learning approach for the detection and classification of ECG arrhythmia plot images. Our methodology features a Lightweight Custom Convolutional Neural Network model(LC-CNN), comprising just three convolutional layers and a transfer learning model with MobileNet-V2 architecture that leverages pre-trained features to enhance arrhythmia classification. Data preprocessing of the ECG signals involving noise reduction with a Butterworth filter and precise beat segmentation via R-peak detection, ensure high-quality input for our model. Furthermore, a notable contribution for ECG data augmentation, adopting the implementation of an Auxiliary Classifier Generative Adversarial Network (ACGAN), specifically addressing class imbalance in the benchmark MIT-BIH dataset to classify four types of ECG heartbeats. This approach enriches the dataset, enhancing the models&#x2019; ability to detect underrepresented arrhythmia classes. The proposed system demonstrates an impressive average classification accuracy achieving 99.22% using the LC-CNN model. Closely followed by the fine-tuned MobileNet-V2 model with 98.69% accuracy, outperforming other methods and underscoring its effectiveness when faced with diverse irregular heartbeats and arrhythmia.

A Novel Approach Based Lightweight Custom Convolutional Neural Network and Fine-tuned MobileNet-V2 for ECG Arrhythmia Signals Classification