Deep Learning-Based ECG Analysis for Anomaly Detection and Classification Using DCNN

This investigation shows how effective deep learning-based Electrocardiogram (ECG) analysis for anomaly detection and classification can be realized in an IoT-enabled health monitoring system, employing Deep Convolutional Neural Networks (DCNN). In this research ECG data from IoT sensors, transmitting to the cloud storage and are preprocessed for the study. Dataset contains 3200 records, and of this data 70% is used for training, whereas the rest (30%) are test instances. After that preprocessing the raw ECG signals are denoised and artefact is corrected, normalized then segmented. Different models like DCNN, LSTM networks, SVM and RF were trained & tested. The highest accuracy with the DCNN model was 96.88%, along with showing 97.20, 96.50, and 96.85 for precision, recall as well as F1 score respectively in giving us a very high understanding that it can accurately predict steriod induced cardiac anomalies. The LSTM model also had a well performance with 94.5% accuracy, and SVM about 91.2%, RF was the less predictable one achieving on average only 88.98%. Confusion matrix also attested the generalizability of DCNN with 460 true positives and 515 true negatives. This study illustrates the promise of deep learning models to model even more nuanced features from ECG signals and produce trustworthy, truthful forecasts. This research could be integrated into IoT health monitoring solutions to provide continuous real-time cardiac health surveillance, early anomaly detection and timely medical intervention resulting in better patient care and outcomes for clinical as well as home healthcare.