Heartbeat classification using local transform pattern feature and hybrid neural fuzzy-logic system based on self-organizing map
Heartbeat classification using local transform pattern feature and hybrid neural fuzzy-logic system based on self-organizing map
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DOI：
10.1016/j.bspc.2019.101690
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发表时间：
2020-03-01
影响因子：
5.1
通讯作者：
Lee, Joo-Ho
中科院分区：
文献类型：
--
作者：
Lee, Miran;Song, Tae-Geon;Lee, Joo-Ho
基于心电图的心跳自动分类方法对于辅助医生和专家诊断心脏疾病具有重要意义。在这项研究中，我们介绍了一种新的算法的基础上的局部变换模式（LTP）的混合神经模糊逻辑系统与自组织映射（NF）。我们提取的直方图特征与多维使用三种特征提取方法的基础上LTP作为一个1D局部二进制模式，1D局部梯度模式（1DLGP），和本地邻居描述符模式。然后将自组织映射应用于模糊逻辑系统，以提高分类精度，减少时间消耗。根据医疗仪器的进步的建议，我们验证了建议的心跳分类方法，使用五个心跳类正常或束支分支阻滞，室上性异位，心室异位，心室和正常的融合，和未知的心跳。实验结果表明，所提出的方法使用1DLGP+NF与196（14 × 14）的特征尺寸作为鲁棒性能在87%（灵敏度），73.8%（阳性预测），1.1%（假阳性率），和98.84%（准确率）。我们发现，我们的研究对心跳识别方法产生了重大影响，这是医疗保健和医疗系统中的关键功能。(C)2019爱思唯尔有限公司版权所有。
An automatic heartbeat classification method using electrocardiogram is important in assisting doctors and experts with the diagnosis of cardiac diseases. In this study, we introduce a novel algorithm based on a local transform pattern (LTP) with a hybrid neural fuzzy-logic system with a self-organizing map (NF). We extracted a histogram feature with multi-dimension using three feature extraction methods based on an LTP as a 1D local binary pattern, 1D local gradient pattern (1DLGP), and local neighbor descriptor pattern. The self-organizing map was then applied to a fuzzy-logic system for increasing the classification accuracy and reducing the time consumed. According to the recommendations for the advancement of medical instrumentation, we validated the proposed heartbeat classification method using five heartbeat classes normal or bundle branch block, supraventricular ectopic, ventricular ectopic, fusion of ventricular and normal, and unknown beat. Experimental results show the performances of the proposed method using 1DLGP+NF with 196 (14 by 14) feature dimensions as robust performance at 87% (sensitivity), 73.8% (positive predictivity), 1.1% (false positive rate), and 98.84% (accuracy). We found that our study has a significant impact on heartbeat recognition methods, which are crucial functions in healthcare and medicine systems. (C) 2019 Elsevier Ltd. All rights reserved.