A Time-Frequency Grounded ECG Feature Abstraction Systems based Advanced Design for Improvement of ANN for CA classification
Keywords:
ANN, ECG, PSD, ARAbstract
Finding the aberrant waves of the ECG pattern allows for the majority of heart illnesses to be identified. Automated ECG feature extraction, in addition to the traditional human approach, is extremely important since it improves categorization. The extracted and selected features must be redundant and irrelevant in order to differentiate the classes to a significant level. The many different cardiac anomalies can be effectively classified with the help of a suitable feature extraction methodology. Hence, both the role of feature extraction and the categorization of patterns is crucial. The focus of this chapter is on extracting characteristics from several domains, including time, frequency, and time-frequency. The retrieved features are provided as inputs for an ANN that will classify the ECG arrhythmias. The de-noised ECG data undergo additional processing in order to extract features. Several domains, including Time, Frequency, and Time-Frequency (Wavelet) domains, are used to extract the characteristics. AR (Auto-regressive) constants are abstracted in the time domain. PSD (Power Spectral Density) values are convalesced in the frequency domain, and comparative wavelet energy at various disintegration levels is removed in the wavelet domain. In order to accurately classify arrhythmias, these features are used to create a fully connected Artificial Neural Network (ANN), with a performance evaluation of (96.85%) accuracy. The simulation findings reveal that compared to the other two ANN models, the one based on wavelet energy delivers better performance in terms of network complexity and accuracy in classifying cardiac arrhythmias. So, this model aids doctors in making the ultimate call in life-or-death circumstances.
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