Detection system of congestive heart failure in ECG signals through machine learnin
DOI:
https://doi.org/10.46842/ipn.cien.v28n2a06Keywords:
Classification, Electrocardiogram, Signal analysis, Machine learning, Web platformAbstract
Cardiovascular diseases, such as Congestive Heart Failure (CHF), are leading causes of global mortality and deteriorate quality of life. The Electrocardiogram (ECG), efficient and non-invasive, is essential for detecting CHF by analyzing the heart's electrical activity. This work integrates supervised machine learning into ECG analysis, using classification models to distinguish between normal signals and those affected by CHF. A web platform is developed to support research and the development of Telemedicine systems, facilitating the automated classification of ECG signals and contributing to remote medical diagnosis, data recording, and medical consultations. The present study demonstrates that the models used achieve up to 99% accuracy, with only 5 errors in 720 test samples. However, the system’s generalization may be limited by signal variability and acquisition conditions. The main contribution of this work is the investigation of specific ECG features to train classification models, providing a foundation for future research in clinical environments.
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