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ECG signal processing and feature extraction to validate feature significance for arrythmia detection
John M. De Moura, Ana L. Espinoza, Maria A. Flores, Juan A. Zavaleta
Arrhythmias, such as tachycardia and bradycardia, are prevalent in postoperative patients, especially within the first week after surgery. These conditions can lead to significant health risks, particularly in settings with inadequate monitoring resources, such as rural areas in Peru. This paper proposes an advanced approach to arrhythmia detection using ECG signal processing and feature extraction to feed artificial intelligence (AI) models. By optimizing training data and comparing ECG signal features, this method aims to enhance the accuracy and efficiency of arrhythmia identification, thereby improving patient monitoring and reducing associated health risks.
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