Analysis of Critical Diseases from ECG Signal Using Hybrid CNN and LSTM
Keywords:
EEG, Machine Learning, CNN, LSTMAbstract
The application of machine learning algorithms for the analysis and diagnosis of severe diseases using electrocardiogram (ECG) measurements is a key area of research in the field of healthcare. Investigating, evaluating, and comparing the performance of several machine learning algorithms for the detection and diagnosis of severe diseases using ECG data is the aim of this study. Among the methods considered are convolutional neural networks (CNN), decision trees, random forests, extra trees classifiers, dense models, and hybrid CNN-LSTM models. A detailed analysis of the body of work on machine learning, ECG signal processing, and healthcare applications is done at the outset of the project. In order to ensure a diverse representation of the target population, the study makes use of a painstakingly selected and annotated dataset that comprises ECG signals from both healthy persons and those with major disorders. When it comes to binary classification, the CNN and CNN-LSTM models consistently outperform other algorithms thanks to their high accuracy, F1-scores, and AUC-ROC values. These algorithms demonstrate their ability to accurately classify ECG signals into significant disease and non-disease categories. The results of the multiclass classification provide as additional proof of the CNN and CNN-LSTM models' superior accuracy and F1-scores when used to classify a wide range of illnesses. In conclusion, this research contributes to the field of healthcare analytics by providing a complete assessment and comparison of machine learning algorithms for the diagnosis and analysis of severe diseases using ECG data. The results demonstrate the effectiveness of the CNN and CNN-LSTM models in terms of achieving high accuracy and F1-scores, paving the way for their potential application in clinical praxis. The article offers recommendations for additional research and progress in the field of ECG signal processing as well as emphasises the challenges and considerations that must be made when putting these algorithms into operation.
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