Abstract
This paper introduced a Particle Swarm Optimization-Radial Basis Function Neural Networks (PSO-RBFNN)-based system for heart disease detection that used the PSO algorithm to optimize RBFNN parameters. The newly developed signal digital algorithm presents the results of a new image contrast enhancement approach using Double Density Discrete Wavelet transform DDDWT for extraction of features, using adaptive DDDWT for the elimination of noise, and the use of PSO and ANN methods to classify the output from the Electrocardiogram (EGGS). It also provides identification of all techniques and MATLAB codes used to improve the processes. This approach merged the global search power of the PSO algorithm with the high efficiency of RBFNN's local optimums, overcome the inconsistency of the PSO algorithm and the RBFNN downside, quickly leading to a local minimum. The results show that, as compared to other approaches, the PSO-RBFNN model of heart disease diagnosis is highly accurate in detecting and predicting.
Highlights
Electro Cardiogram (ECG) is a key tool for cardiac anomaly diagnosis and detection
This study aims to determine the optimum, easier, and cheaper diagnostic system for normal heart disease diagnostics, the new approach to integrating double density discrete wavelet transform (DDDWT) hybrid soft computing with an active filter for BW noise reduction decreases distortion of the S-T segment of the ECG signal and the higher frequencies of sampling
The experiment was done with and without using PSO to pre-train RBFNN connection weights, and the results showed that using PSO to pretrain connection weights improved correct classification
Summary
ECG is a key tool for cardiac anomaly diagnosis and detection. The ECG is a bio-electric signal that tracks the heart muscle electrical activity and transmits to the body surface in the form of signals as electrical events. It is important to propose and develop more and better-automated methods for heart disease detection. Electro Cardiogram (ECG) analysis has been growing for human health diagnosis became a very helpful tool to assure the patient's help. The ECG is Publishing rights belongs to University of Technology’s Press, Baghdad, Iraq. Important pointers to provide helpful information from the reflected waveform shape of the nature, of heart diseases [1]. The physician diagnosis has a difficult problem and misdiagnosis can be occurring sometime with the ECG recorder. Artificial intelligence with the ECG recorder can be considered as one of the best techniques for the classification of ECGs into different diagnostic groups for early diagnosis of a heart attack
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
