A New Ensemble Approach for Congestive Heart Failure and Arrhythmia Classification Using Shifted One-Dimensional Local Binary Patterns with Long Short-Term Memory

正常窦性心律 心力衰竭 直方图 超参数 模式识别(心理学) 计算机科学 二进制数 窦性心律 人工智能 心脏病学 医学 数学 心房颤动 算术 图像(数学)
作者
Abidin Çalışkan
出处
期刊:The Computer Journal [Oxford University Press]
卷期号:65 (9): 2535-2546 被引量:13
标识
DOI:10.1093/comjnl/bxac087
摘要

Abstract The electrocardiogram (ECG) is a vital diagnostic tool for identifying a variety of cardiac disorders, including cardiac arrhythmia (ARR), sinus rhythms and heart failure. However, rapid interpretation of ECG recordings is quite important in the diagnosis of heart-related diseases. Many patients can be saved using the systems developed for the rapid and accurate analysis of ECG signals. A novel ensemble method based on shifted one-dimensional local binary patterns (S-1D-LBP) and long short-term memory (LSTM) is presented for the prognosis of ARR, normal sinus rhythm (NSR) and congestive heart failure (CHF) in this study. The ECG signals were first subjected to the S-1D-LBP method. Depending on the R and L parameters of this method, nine different signals are generated. Each of the histograms of these signals is given to LSTM models with the same hyperparameters. ECG signals are classified according to the common decisions of LSTM models with nine different input signals. The suggested method was tested using ECG signals (ARR, NSR and CHF) from the MIT-BIH and BIDMC datasets. Considering the results obtained in the applications carried out with various scenarios, it was observed that a high (99.6%) success rate was attained by the proposed approach. The suggested S-1D-LBP + ELSTM (Ensemble LSTM) model is expected to be safe to employ in the classification of various signals.
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