Feature Extraction and Classification

Feature Extraction and Classification

Copyright: © 2014 |Pages: 11
DOI: 10.4018/978-1-4666-6146-2.ch011
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Abstract

In the previous chapter, the first stage for detecting the ECG noise removal was investigated. In this chapter, the second and the third stages are explained. The Second stage is to extract the effective features of the ECG signals. The final stage is to use MLP and PSO algorithms for classification of ECG signals to detect the 4 common heart disorders including the normal signals. Common disorders are Normal, Supraventricular, Brunch bundle block, Anterior myocardial infarction (Anterior MI), and Interior myocardial infarction (Interior MI).
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2 Features Of Disorders

Consultation with heart specialists determined that 4 types of heart disorders, namely, bundle branch block, supraventricular tachycardia, anterior myocardial infarction (anterior MI) and inferior myocardial infarction (inferior MI) are common and would be detected in this work. Sample ECG signals for the 4 classes and normal ECG signals are given in Figure 1. Specialists detect these disorders by observing the PQRST waveform. For example, bundle branch block causes a widened and possibly jagged QRS waveform, while supraventricular tachycardia typically exhibits a narrow QRS complex on the ECG. The ST segment, which is normally iso-electric (flat and in line with the PQ segment) may be elevated or depressed due to myocardial ischemia or myocardial infarction. Table 1 shows the ECG classes and representation of outputs for each class.

Figure 1.

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Table 1.
Representation of outputs for each class
DisorderClassNeural Network Output
Normal sinus00 0 0
Bundle branch block10 0 1
Supraventricular tachycardia20 1 0
Anterior myocardial infarction30 1 1
Inferior myocardial infarction41 0 0

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