Artificial Intelligence in Early Cardiac Event Detection: A Predictive Diagnostic Approach

Abstract

Cardiovascular diseases have been a major health burden in the world, and due to this, effective diagnostic frameworks must be in place that will enable diagnosis of abnormalities to be done accurately and early. The use of artificial intelligence-based diagnostic plans has become more popular because it is capable of consolidating and decoding complex physiological indicators. This study proposes a multimodal cardiac analysis system which is a combination of electrocardiogram (ECG), photoplethysmography (PPG) and echocardiographic characteristics to improve the accuracy of abnormality detection. The analysis uses complex preprocessing methods, such as noise removal, baseline removal and region-of-interest extraction, which guarantees quality signals to be analyzed. Convolutional and recurrent neural network architectures of machine learning and deep learning models were used to obtain temporal, morphological, and hemodynamic biomarkers of the processedelectrocardiogram (ECG) signals. The effectiveness of the suggested framework was confirmed with the help of several quantitative measures. The essential ECG parameters under statistical analysis proved the existence of stable physiological parameters, mean values of RR were 0.78 seconds, QS was 96.3 ms, and QT was 382 ms. The multimodal model attained the accuracy of 92.4, precision of 89.1, recall of 90.7, F1-score of 89.9 and the AUC of 0.94, which is a high discriminative capability. The confusion table also indicated that the classification was balanced with the false negatives (n = 5) and false positives (n = 6). Analysis of feature importance identified electrophysiological parameters, including QRS and HR variability, as the most significant predictors, which are accompanied by hemodynamic characteristics, pulse transit time and perfusion index. The results indicate that the application of multimodal biosignals can effectively detect abnormalities of the heart as compared to conventional mono-signals. The suggested system has high levels of robustness, interpretability and generalization capabilities, and therefore makes it appropriate in real-time, remote and clinical diagnostic applications. The present study highlights the importance of AI-powered multimodal frameworks to enhance precision cardiology and succeed in the field of early intervention.