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Alexakis, C., Nyongesal, H.O., Saatchi, R., Harris, N.D., Davies, C., Emery, C., Ireland, R.H. and Helle, S.R. (2003) Feature extraction and classification of electrocardiogram (ECG) signals related to hypoglycaemia. Computers in Cardiology, 537-540.

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• TITLE: Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network

  AUTHORS: Himanshu Gothwal, Silky Kedawat, Rajesh Kumar

  KEYWORDS: Cardiac Arrhythmias; Neural Network; Electrocardiogram (ECG); Fast Fourier Transform (FFT)

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.4 No.4, April 22, 2011

  ABSTRACT: 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.