Abstract
An automatic configuration that can detect the position of R-waves, classify the normal sinus rhythm (NSR) and other four arrhythmic types from the continuous ECG signals obtained from the MIT-BIH arrhythmia database is proposed. In this configuration, a support vector machine (SVM) was used to detect and mark the ECG heartbeats with raw signals and differential signals of a lead ECG. An algorithm based on the extracted markers segments waveforms of Lead II and V1 of the ECG as the pattern classification features. A self-constructing neural fuzzy inference network (SoNFIN) was used to classify NSR and four arrhythmia types, including premature ventricular contraction (PVC), premature atrium contraction (PAC), left bundle branch block (LBBB), and right bundle branch block (RBBB). In a real scenario, the classification results show the accuracy achieved is 96.4%. This performance is suitable for a portable ECG monitor system for home care purposes.
Highlights
We propose an automatic configuration integrating digital signal processing and an artificial intelligence method to detect the position of heartbeats and recognize these heartbeats as belonging to the normal sinus rhythm (NSR) or four arrhythmic types
Since we only focus on the Lead II and V1 signals for pre-processing, 33 of the 48 files were selected to test the performance of support vector machine (SVM) and self-constructing neural fuzzy inference network (SoNFIN)
For each heartbeat type (NSR, premature ventricular contraction (PVC), left bundle branch block (LBBB), and right bundle branch block (RBBB)) 26 patterns were extracted from 33 files that did not belong to the five-minute test data
Summary
Fan used approximate entropy (ApEn) and Lempel-Ziv complexity as a nonlinear quantification to measure the depth of anaesthesia [16] In these studies, the normal sinus ECG signal added different noise types and energy was used to evaluate the performance of these algorithms. Several researchers have extracted the features of ECG waveforms to detect the QRS complexes based on the arrhythmia database. These include artificial neural networks [11], fuzzy neural networks [18], Hermite functions combined with self-organizing maps [19], and wavelet analysis combined with radial basis function neural networks [20] In these methods, the ECG waveform of each beat was picked up manually and different features were extracted to classify the arrhythmic types. The heartbeat detection accuracy has been increased by the SoNFIN classification results
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