Key result
A proposed method combining Fast Fourier Transform for feature extraction and an Artificial Neural Network for classification detected cardiac arrhythmias with an accuracy of 98.48%.
An algorithm combining Fast Fourier Transform and Artificial Neural Networks can classify cardiac arrhythmias from ECG signals with 98.48% accuracy.
Hypothesis-generating for automated ECG analysis; prospective clinical validation required before practice adoption.
Cardiac Arrhythmias shows a condition of abnor-mal electrical activity in the heart which is a threat to humans. This paper presents a method to analyze electrocardiogram (ECG) signal, extract the fea-tures, for the classification of heart beats according to different arrhythmias. Data were obtained from 40 records of the MIT-BIH arrhythmia database (only one lead). Cardiac arrhythmias which are found are Tachycardia, Bradycardia, Supraventricular Tachycardia, Incomplete Bundle Branch Block, Bundle Branch Block, Ventricular Tachycardia. A learning dataset for the neural network was obtained from a twenty records set which were manually classified using MIT-BIH Arrhythmia Database Directory and docu- mentation, taking advantage of the professional experience of a cardiologist. Fast Fourier transforms are used to identify the peaks in the ECG signal and then Neural Networks are applied to identify the diseases. Levenberg Marquardt Back-Propagation algorithm is used to train the network. The results obtained have better efficiency then the previously proposed methods.
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Gothwal et al. (2011) studied Cardiac Arrhythmias (n=40). Fast Fourier Transform and Artificial Neural Network vs. Previous classification methods was evaluated on Classification accuracy. A proposed method combining Fast Fourier Transform for feature extraction and an Artificial Neural Network for classification detected cardiac arrhythmias with an accuracy of 98.48%.
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