Automatic ECG Beat Arrhythmia Detection
M. Bazarghan, Y. Jaberi, R. Amandi, M. Abedi

TL;DR
This paper presents an automated ECG arrhythmia detection method using Probabilistic Neural Networks optimized with Genetic Algorithms, achieving high accuracy by preprocessing and reducing ECG signals.
Contribution
The study introduces a novel combination of wavelet transform, median filtering, and genetic algorithm optimization for ECG arrhythmia classification using PNNs.
Findings
Second approach achieves 99.42% accuracy
Preprocessing improves classification performance
Optimized feature extraction enhances detection accuracy
Abstract
Background: In recent years automated data analysis techniques have drawn great attention and are used in almost every field of research including biomedical. Artificial Neural Networks (ANNs) are one of the Computer- Aided- Diagnosis tools which are used extensively by advances in computer hardware technology. The application of these techniques for disease diagnosis has made great progress and is widely used by physicians. An Electrocardiogram carries vital information about heart activity and physicians use this signal for cardiac disease diagnosis which was the great motivation towards our study. Methods: In this study we are using Probabilistic Neural Networks (PNN) as an automatic technique for ECG signal analysis along with a Genetic Algorithm (GA). As every real signal recorded by the equipment can have different artifacts, we need to do some preprocessing steps before feeding…
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Taxonomy
TopicsECG Monitoring and Analysis · Neural Networks and Applications · Time Series Analysis and Forecasting
