ECG Arrhythmia Classification using Fast Fourier Transform and Principal Component Analysis
Namrata Vilas Raut, Rugved V. Deolekar
ECGarrhythmia; DWT; Fast Fourier Transformation; Neural Network; Principal Component analysis
Most of cardiovascular disorders and diseases can be prevented, but death count happens of it rises due to inadequate treatment and misdiagnose. One such kind of disease is popularly known as Arrhythmia. An arrhythmia is a disorder of the heart rate (pulse) or heart rhythm, when it beats too fast it is called as tachycardiaand when too slow it is called as bradycardia, therefore the timely detection of arrhythmia proves lifesaving for the cardiac patients. The detection is performed analyzing the electrocardiogram (ECG) signals and extracting some features from them. Arrhythmia comes under the cardiovascular disease. Sometimes it becomes difficult to analyze electrocardiogram (ECG) recording for Arrhythmia detection. So it became need of the hour to develop an error proof method to be applied in the computer to train the system for the detection of Arrhythmia. Here one can seek help of Artificial Neural Network. It starts to be widely used for Speech Recognition, Bioinformatics, Computer Vision, and many others. The Present research puts forth FFT and PCA to classify Arrhythmia. The researchers compared the result to other existing algorithms to show that FFT and PCA methods achieve better accuracy of classification of Arrhythmia.
Article Details
Unique Paper ID: 152730

Publication Volume & Issue: Volume 8, Issue 4

Page(s): 786 - 791
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