Abstract
Introduction This paper presents a complete approach for the automatic classification of heartbeats to assist experts in the diagnosis of typical arrhythmias, such as right bundle branch block, left bundle branch block, premature ventricular beats, premature atrial beats and paced beats. Methods A pre-processing step was performed on the electrocardiograms (ECG) for baseline removal. Next, a QRS complex detection algorithm was implemented to detect the heartbeats, which contain the primary information that is employed in the classification approach. Next, ECG segmentation was performed, by which a set of features based on the RR interval and the beat waveform morphology were extracted from the ECG signal. The size of the feature vector was reduced by principal component analysis. Finally, the reduced feature vector was employed as the input to an artificial neural network. Results Our approach was tested on the Massachusetts Institute of Technology arrhythmia database. The classification performance on a test set of 18 ECG records of 30 min each achieved an accuracy of 96.97%, a sensitivity of 95.05%, a specificity of 90.88%, a positive predictive value of 95.11%, and a negative predictive value of 92.7%. Conclusion The proposed approach achieved high accuracy for classifying ECG heartbeats and could be used to assist cardiologists in telecardiology services. The main contribution of our classification strategy is in the feature selection step, which reduced classification complexity without major changes in the performance.
Highlights
This paper presents a complete approach for the automatic classification of heartbeats to assist experts in the diagnosis of typical arrhythmias, such as right bundle branch block, left bundle branch block, premature ventricular beats, premature atrial beats and paced beats
The main telecardiology service deals with the remote diagnostics of abnormalities presents in electrocardiograms (ECGs), which are sent through the internet to a telehealth center (Marcolino et al, 2012)
We employed the widely known Massachusetts Institute of Technology (MIT) arrhythmia database, which is available on the website of the PhysioNet group (Goldberger et al, 2000)
Summary
This paper presents a complete approach for the automatic classification of heartbeats to assist experts in the diagnosis of typical arrhythmias, such as right bundle branch block, left bundle branch block, premature ventricular beats, premature atrial beats and paced beats. Conclusion: The proposed approach achieved high accuracy for classifying ECG heartbeats and could be used to assist cardiologists in telecardiology services. Technological developments and cost reductions associated with internet access have contributed to the growth of telehealth services These services are suitable in situations in which there is lack of health professionals or the nearest medical service center is located a great distance from those who require care. The main telecardiology service deals with the remote diagnostics of abnormalities presents in electrocardiograms (ECGs), which are sent through the internet to a telehealth center (Marcolino et al, 2012). The response time of the telecardiology service is critical depending on the severity of the diagnosis, as serious diseases require priority, early diagnosis and immediate treatment To prioritize these cases, telecardiology services, in general, provide the health professional the option to manually classify that the request as urgent.
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