Key result
A Neural Network classifier using discrete and dual tree complex wavelet transforms achieved 98-99% accuracy in the automatic detection and classification of ventricular and supraventricular arrhythmias.
A neural network classifier using combined DWT and DTCWT features can accurately detect and classify cardiac arrhythmias from ECG signals with 98-99% accuracy.
Hypothesis-generating for automated ECG arrhythmia detection; leaves open clinical adoption pending prospective validation.
This paper proposes a Neural Network classifier model for the automatic identification of the ventricular and supraventricular arrhythmias cardiac arrhythmias. The wavelet transform (DWT) and dual tree complex wavelet transform (DTCWT) is applied for QRS complex detec-tion. After segmentation both feature of DWT and DTCWT is combined for feature extraction, statistical feature has been calculated to re-duce the overhead of classifier. An adaptive filtering with the soft computed wavelet thersholding to the signals before the extraction is done in pre-processing. Different ECG database is considered to evaluate the propose work with MIT-BIH database Normal Sinus Rhythm Da-tabase (NSRD) , and MIT-BIH Supraventricular Arrhythmia Database (svdb) .The evaluated outcomes of ECG classification claims 98 -99 % of accuracy under different training and testing situation.
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Raju et al. (2018) studied Cardiac Arrhythmia. Neural Network classifier with DWT and DTCWT vs. Ground truth labels was evaluated on Classification accuracy. A Neural Network classifier using discrete and dual tree complex wavelet transforms achieved 98-99% accuracy in the automatic detection and classification of ventricular and supraventricular arrhythmias.
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