Improved ECG signal analysis using wavelet and feature extraction
A Matsuyama, M Jonkman… - Methods of Information in …, 2007 - thieme-connect.com
Objectives: Automatic detection of arrhythmias is important for diagnosis of heart problems.
However, in ECG signals, there is significant variation of waveforms in both normal and
abnormal beats. It is this phenomenon, which makes it difficult to analyse ECG signals. The
aim of developing methodology is to distinguish between normal beats and abnormal beats
in an ECG signal. Methods: ECG signals were first decomposed using wavelet transform.
The feature vectors were then extracted from these decomposed signals as normalised …
However, in ECG signals, there is significant variation of waveforms in both normal and
abnormal beats. It is this phenomenon, which makes it difficult to analyse ECG signals. The
aim of developing methodology is to distinguish between normal beats and abnormal beats
in an ECG signal. Methods: ECG signals were first decomposed using wavelet transform.
The feature vectors were then extracted from these decomposed signals as normalised …
Objectives : Automatic detection of arrhythmias is important for diagnosis of heart problems. However, in ECG signals, there is significant variation of waveforms in both normal and abnormal beats. It is this phenomenon, which makes it difficult to analyse ECG signals. The aim of developing methodology is to distinguish between normal beats and abnormal beats in an ECG signal.
Methods : ECG signals were first decomposed using wavelet transform. The feature vectors were then extracted from these decomposed signals as normalised energy and entropy. To improve the classification of the feature vectors of normal and abnormal beats, the normal beats which occur before and after the abnormal beats were eliminated from the group of normal beats.
Results : With our proposed methods, the normal beats and abnormal beats formed different clusters of vector points. By eliminating normal beats which occur before and after the abnormal beats, the clusters of different types of beats showed more apparent separation.
Conclusions : The combination of wavelet decomposition and the classification using feature vectors of the beats in ECG signals separate abnormal beats from normal beats. The elimination of the normal beats which occur before and after the abnormal beats succeeded in minimising the size of normal beats cluster.
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