ATTENTION-GUIDED HYBRID LSTM–CNN FRAMEWORK FOR EARLY CARDIAC ANOMALY DETECTION

Wearable physiological sensors have enabled the continuous acquisition of cardiac signals that has supported early health monitoring outside clinical environments. However, the variability, noise, and temporal complexity of wearable signals have limited the reliability of conventional analytical models. Existing approaches have struggled with capturing both long-term temporal dependencies and localized morphological patterns within the same framework, which has reduced their clinical applicability for early cardiac anomaly detection. The accurate identification of early-stage cardiac anomalies from wearable signals has remained challenging due to signal artifacts, inter-subject variability, and the imbalance between normal and abnormal patterns. Traditional machine learning models have relied on handcrafted features that have failed to generalize across diverse populations. Deep models without interpretability have also raised concerns regarding trust and deployment in real-world monitoring systems. This study has proposed a hybrid attention-guided LSTM–CNN architecture that has integrated temporal sequence learning with spatial feature extraction. A convolutional neural network has extracted localized signal characteristics, while a long short-term memory network has modeled sequential dependencies that have evolved over time. An attention mechanism that has selectively emphasized clinically relevant segments has improved feature weighting and interpretability. The model has trained on preprocessed wearable cardiac signals that have undergone normalization, denoising, and segmentation. Experimental evaluation has demonstrated that the proposed model has achieved superior detection accuracy, sensitivity, and specificity compared with baseline CNN and LSTM models. The attention module has contributed to improved robustness under noisy conditions and has enhanced early anomaly recognition. Statistical analysis has confirmed consistent performance gains across multiple evaluation folds, indicating reliable generalization.