Cardiovascular Diseases Identification Using Wavelet Optimization and Modify Cuckoo Search Algorithm

Abstract

The problem the author aims to solve is the extraction of discriminatory features in ECG (Electrocardiogram) signals for classification purposes. The scope of the work is to propose a new method for building wavelets that best reflect the discriminatory capacity of ECG signals. The approach involves optimizing the wavelets specifically for the classification function under consideration. To address the problem, the author proposes a novel method for creating wavelets that optimize discriminatory feature extraction in ECG signals. The approach utilizes the poly-phase demonstration of the filter bank and incorporates the Modified Cuckoo Search (MCS) algorithm to project the problem context. The experiments are conducted using the MIT/BIH arrhythmia database to evaluate the performance of the proposed method against existing state-of-the-art techniques. The Support Vector Machine (SVM) classifier is used to demonstrate the effectiveness, precision, and robustness of the projected strategy on standard wavelets like Daubechies and Symlet. The extent of the author's work involves developing a new method for wavelet construction to enhance discriminatory feature extraction in ECG signals. Important variables controlled in the study include the choice of wavelet parameters, the application of the MCS algorithm, and the evaluation of results against standard wavelet-based classification methods. The experiment results demonstrate the superiority of the proposed wavelet construction method over traditional wavelets like Daubechies and Symlet for ECG signal classification. The new wavelet shows improved discriminatory capacity, leading to higher classification precision and accuracy. The findings imply that optimizing wavelets for the classification of ECG signals using the Modified Cuckoo Search algorithm can significantly enhance discriminatory feature extraction and improve classification precision. The results are potentially generalizable and can be applied to various ECG signal classification tasks, contributing to advancements in medical diagnostics and monitoring. Background: The extraction of discriminatory features from ECG signals is a critical task in the field of medical signal processing. Accurate classification of these features plays a crucial role in diagnosing various cardiac arrhythmias and abnormalities. Wavelet-based techniques have shown promise in this domain, but further optimizations are necessary to achieve higher classification precision and robustness. Current Status in the Field: The field of ECG signal classification continues to evolve, with researchers exploring various approaches to improve discriminatory feature extraction. Wavelet-based methods have gained popularity due to their ability to capture both frequency and time-domain information, but fine-tuning the wavelets for specific classification tasks remains an on-going area of research. Study and Analysis: The experiments were conducted using the MIT/BIH arrhythmia database, which contains a diverse set of ECG signals. The proposed wavelet construction method was evaluated against existing standard wavelets using the Support Vector Machine (SVM) classifier. The Modified Cuckoo Search algorithm was used to optimize the wavelets for better discriminatory capacity. The results were then statistically analysed to demonstrate the effectiveness of the proposed approach.