Improved ECG Signal Analysis Using Wavelet and Feature Extraction

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.

Improved ECG signal analysis using wavelet and feature extraction

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.

The application of wavelet and feature vectors to ECG signals

The Electrocardiogram (ECG) is one of the most commonly known biological signals. Traditionally ECG recordings are analysed in the time-domain by skilled physicians. However, pathological conditions may not always be obvious in the original time-domain signal. Fourier analysis provides frequency information but has the disadvantage that time characteristics will be lost. Wavelet analysis, which provides both time and frequency information, can overcome this limitation. Here a new method, the combination of wavelet analysis and feature vectors, is applied with the intent to investigate its suitability as a diagnostic tool. ECG signals with normal and abnormal beats were examined. There were two stages in analysing ECG signals: feature extraction and feature classification. To extract features from ECG signals, wavelet decomposition was first applied and feature vectors of normalised energy and entropy were constructed. These feature vectors were used to classify signals. The results showed that normal beats and abnormal beats composed different clusters in most cases. In conclusion, the combination of wavelet transform and feature vectors has shown potential in detecting abnormalities in an ECG recording. It was also found that normalised energy and entropy are features, which are suitable for classification of ECG signals.