CadCNN 1.0, a hybrid deep learning framework combining 1D-CNN and BiLSTM, achieved 99.92% accuracy and 99.81% sensitivity in classifying ECG signals into five arrhythmia categories.
The CadCNN 1.0 hybrid deep learning model demonstrates near-perfect accuracy in automated ECG-based arrhythmia detection.
Cardiac Arrhythmia, which encompasses irregular heart rhythms such as tachycardia, bradycardia, ectopic pulses, represents a significant cause of cardiovascular morbidity and mortality worldwide, caused by disruptions in the heart’s electrical conduction system. Accurate and automated interpretation of the Electrocardiogram (ECG) is consequently crucial for the timely diagnosis and selection of treatment. This study introduces CadCNN 1.0, a novel hybrid Deep Learning (DL) framework that combines one-dimensional Convolutional Neural Networks (1D-CNN) for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) for temporal sequence modeling. This methodology enables the accurate characterization of ECG waveforms and the model was trained and validated using the MIT-BIH Arrhythmia Database, classifying ECG signals into five categories: Normal (N), Fusion (F), Supraventricular Ectopic Beat (SVEB), Ventricular Ectopic Beat (VEB), and Unknown (Q) Beat. The Gray Wolf Optimization (GWO) algorithm was employed to optimize feature selection and model convergence, resulting in exceptional diagnostic performance with an Accuracy of 99.92%, Sensitivity (Recall) of 99.81%, Specificity of 99.93%, Precision of 99.92%, Formula: see text1-score of 99.92% along with MCC of 99.84% with Number of Wolves (Nw) of 10 represents the number of wolves in the population, corresponding to the number of candidate feature subsets simultaneously explored in each iteration, that enhances population diversity and prevents premature convergence and Max Iteration of 10 which denotes the maximum number of optimization cycles during which the wolves update their positions toward the optimal feature subset, ensuring iterative refinement, stability of global optimum. The confusion matrix demonstrates CadCNN 1.0’s discriminative ability to differentiate morphologically equivalent Arrhythmic classes with near-perfect precision, as evidenced by its strong diagonal dominance and minimal off-diagonal misclassifications. Furthermore, the Breast Cancer Dataset performed cross-domain validation, resulting in an Accuracy of 97.50%, Sensitivity (Recall) of 96.49%, Specificity of 97.18%, Precision of 96.49%, Formula: see text1-score of 96.49%, MCC of 92.53%. These results underscore the model’s adaptability to heterogeneous biomedical data. CadCNN 1.0 provides a clinically reliable, generalizable, computationally efficient computer-aided diagnostic system that advances automated Arrhythmia detection and enables intelligent, patient-centered healthcare diagnostics by integrating spatial-temporal feature learning, meta-heuristic optimization, and interpretable confusion-matrix validation.
Patnaik et al. (Fri,) conducted a other in Cardiac Arrhythmia. CadCNN 1.0 (1D-CNN and BiLSTM with Gray Wolf Optimization) was evaluated on Diagnostic accuracy for classifying ECG signals into five categories. CadCNN 1.0, a hybrid deep learning framework combining 1D-CNN and BiLSTM, achieved 99.92% accuracy and 99.81% sensitivity in classifying ECG signals into five arrhythmia categories.