Evaluation of Pattern Recognition Techniques in Response to Cardiac Resynchronization Therapy (CRT)

Mohammad Nejadeh,Peyman Bayat,Hassan Moladoust,Jalal Kheirkhah

doi:10.52547/jist.8.31.197

Abstract

Cardiac resynchronization therapy (CRT) improves cardiac function in patients with heart failure (HF), and the result of this treatment is decrease in death rate and improving quality of life for patients. This research is aimed at predicting CRT response for the prognosis of patients with heart failure under CRT. According to international instructions, in the case of approval of QRS prolongation and decrease in ejection fraction (EF), the patient is recognized as a candidate of implanting recognition device. However, regarding many intervening and effective factors, decision making can be done based on more variables. Computer-based decision-making systems especially machine learning (ML) are considered as a promising method regarding their significant background in medical prediction. Collective intelligence approaches such as particles swarm optimization (PSO) algorithm are used for determining the priorities of medical decision-making variables. This investigation was done on 209 patients and the data was collected over 12 months. In HESHMAT CRT center, 17.7% of patients did not respond to treatment. Recognizing the dominant parameters through combining machine recognition and physician’s viewpoint, and introducing back-propagation of error neural network algorithm in order to decrease classification error are the most important achievements of this research. In this research, an analytical set of individual, clinical, and laboratory variables, echocardiography, and electrocardiography (ECG) are proposed with patients’ response to CRT. Prediction of the response after CRT becomes possible by the support of a set of tools, algorithms, and variables.

Highlights

The unsuccessful function of the heart can result from different causes such as vascular occlusion and high blood pressure, and this problem is referred to as heart failure (HF)
The main purpose of this study is to introduce a set of cardiac synchronization therapy data, identify patients suitable for treatment with machine learning algorithms, determine the role of important features and their priorities
High specificity indicates the quality of prediction in recognition of positive Cardiac resynchronization therapy (CRT) response after total accuracy F-measure should be carefully investigated for comparing efficiency rates

Summary

Introduction

The unsuccessful function of the heart can result from different causes such as vascular occlusion and high blood pressure, and this problem is referred to as heart failure (HF). In this condition, cardiac muscle is not able to pump enough blood in the body [1]. Arrhythmia is a disorder of heartbeat rhythm. This disorder makes the heart unable to effectively pump blood all over the body and patients with arrhythmia usually experience the symptoms of rapid and slow heartbeat [2]. Patients with HF and arrhythmia may be appropriate candidates for CRT [3]. In addition to improvement of cardiac output in a short time, it helps to Nejadeh, Bayat, Kheyrkhah and Moladoust, Evaluation of Pattern Recognition Techniques in Response to Cardiac

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