Intelligent deep model based on convolutional neural network's and multi-layer perceptron to classify cardiac...

The ECG is a crucial tool in the medical field for recording the heartbeat signal over time, aiding in the identification of various cardiac diseases. Commonly, the interpretation of ECGs necessitates specialized knowledge. However, this paper explores the application of machine learning algorithms and deep learning algorithm to autonomously identify cardiac diseases in diabetic patients in the absence of expert intervention. Two models are introduced in this study: The MLP model effectively distinguishes between individuals with heart diseases and those without, achieving a high level of accuracy. Subsequently, the deep CNN model further refines the identification of specific cardiac conditions. The PTB-Diagnostic ECG dataset commonly used in the field of biomedical signal processing and machine learning, particularly for tasks related to electrocardiogram (ECG) analysis. a widely recognized dataset in the field, is employed for training, testing, and validation of both the MLP and CNN models. This dataset comprises a diverse range of ECG recordings, providing a comprehensive representation of cardiac conditions. The proposed models feature two hidden layers with weights and biases in the MLP, and a three-layer CNN, facilitating the mapping of ECG data to different disease classes. The experimental results demonstrate that the MLP and deep CNN based models attain accuracy levels of up to 90.0% and 98.35%, and sensitivity 97.8%, 95.77%, specificity 88.9%, 96.3% F1-Score 93.13%, 95.84% respectively. These outcomes underscore the efficacy of deep learning approaches in automating the diagnosis of cardiac diseases through ECG analysis, showcasing the potential for accurate and efficient healthcare solutions.