Human machine interfacing technique for diagnosis of ventricular arrhythmia using supervisory machine learning algorithms

Abstract

Summary The state of art to integrate bio‐signals with computer based diagnosis is taking dominance. The man‐machine interface is useful for early and immediate clinical interpretation. The electrocardiogram (ECG) signal plays a vital role in revealing the possible data towards categorizing normal and abnormal cardiac functioning. The fatal conditions exhibited by ventricular arrhythmias (VA) pose a remarkable change in the feature set of the ECG signals. In this work, a novel approach to segregate the superior feature toward the ventricular arrhythmias are extracted using feature ranking score algorithm (FRSA). The FRSA collects feature vectors in three different domains and ranks it to find out the more prevalent feature for diagnosis of VA. The Support Vector Machine (SVM) classifier is administered by supervisory machine learning optimization algorithm Mean Grey Wolf Optimization (MGWO). The performance estimates of SVM‐MGWO is compared for classification of VA signals with other optimization also like SVM‐Particle Swarm Optimization (SVM‐PSO) and SVM‐Grey Wolf Optimization (SVM‐GWO). The non‐parametric and parametric analysis evidently shows the improved performance of feature parameter estimates for classification. The accuracy of classification for SVM‐MGWO attains 100% for finding test data with VA at a minimal convergence iteration while comparing it with the other mentioned supervisory algorithms. The standard deviation during parametric analysis is negligible, which reveals the fact that reductant feature extracted and utilized for testing of ECG data is minimal. The performance estimates attained by the proposed algorithm shows the selection of optimal feature for the findings of VA through ECG. The man‐machine interface aides in the early diagnosis of ventricular arrhythmias using non‐invasive diagnosing tool, the ECG.

Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study

“…Artificial intelligence (AI), such as machine learning (ML), shows great promise for improving clinical decision-making in cardiac diseases by outperforming statistical-based models [ 12 , 13 ], and recent examples include promising models for the prediction of heart disease and heart failure [ 14 - 18 ], as well as cardiac arrhythmias, such as ventricular arrhythmia [ 19 ], atrial fibrillation [ 20 ], and electrical storm [ 21 ]. There are positive attitudes and high expectations among physicians that AI will improve future patient care in fields where data are collected continuously, such as cardiology [ 22 , 23 ].…”

Robust multi‐user detection based on hybrid Grey wolf optimization

“…Due to the singularity of the location of target position, the search range of the algorithm is limited, which leads to the extremely high BER of the estimated data information. Therefore, aiming at the problem of multi-user detection, and combining search direction information of the algorithm, replaces the algorithm position update Equation (17) with Equation (25) to Equation (27).…”

“…Grey wolf optimization algorithm is a novel intelligent optimization algorithm proposed by Mirjalili in 2014. 7 The algorithm has simple structure and fast convergence speed, and it has been applied to many engineering applications, such as power system dispatching, [8][9][10][11] mechanical design, 12,13 maximum flow problem, 14,15 machine learning, 16,17 and so on. However, the local search capability of the Grey wolf optimization algorithm is weak, and a local convergence is easy to occur, which makes the obtained optimization result is not globally optimal solution.…”

ECG Classification Using Orthogonal Matching Pursuit and Machine Learning

“…Classification is usually performed using two or five classes of arrhythmias. For classical methods, SVM classifiers [1][2][3] combined with genetic algorithms [4], Wavelet Transform (WT) [5], or Discrete Wavelet Transform [6,7] have been used for most of them. The evaluation metric was most often Accuracy (ACC), for which the results took values of 91-93% up to five classes of arrhythmias and above five classes, with values between 95.92 and 99.66%.…”