Key result
The proposed real-time system using discrete wavelet transformation achieved 100% accuracy in differentiating between normal and abnormal cardiac signals.
A proposed real-time system using discrete wavelet transformation achieved 100% accuracy in classifying normal and abnormal ECG signals in an experimental setting.
Cardiac arrhythmias is a very uncommon life threating arrhythmia which can even cause sudden death. Healthcare professionals are always looking to find out the ways in order to reduce the death rate. The new method of feature extraction and classification of arrhythmias has been developed by the authors of this paper in their previous works. In this paper, authors have proposed the methodology for the development of a real-time system for efficient processing of arrhythmic signals in order to differentiate between normal and abnormal patients. The purpose of this work is to develop a real-time system for processing the real-time signals or signals obtained from MIT-BIH arrhythmia database. For carrying out this work, we have taken the signals from MIT-BIH Supraventricular arrhythmia database and MIT-BIH Fantasia database. Authors have achieved 100% accuracy by using this method.
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Saraswat et al. (2017) studied Cardiac arrhythmias. Real-time system using discrete wavelet transformation was evaluated on Accuracy of signal classification (normal vs abnormal). The proposed real-time system using discrete wavelet transformation achieved 100% accuracy in differentiating between normal and abnormal cardiac signals.
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