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A Novel Adaptive Feature Extraction for Detection of Cardiac Arrhythmias Using Hybrid Technique MRDWT & MPNN Classifier from ECG Big Data
Big Data Research ( IF 3.5 ) Pub Date : 2018-03-02 , DOI: 10.1016/j.bdr.2018.02.003
Hari Mohan Rai , Kalyan Chatterjee

The efficient automatic detection of cardiac arrhythmia using a hybrid technique from ECG big data has been proposed with novel feature extraction technique using Multiresolution Discrete Wavelet Transform (MRDWT) and Multilayer Probabilistic Neural Network (MPNN) classifier. Big Data of ECG signals have been selected from MIT–BIH arrhythmia database for detection of two types of arrhythmias LBBB (Left Bundle Branch Block) and RBBB (Right Bundle Branch Block). The proposed technique can accurately detect and classify LBBB and RBBB along with normal heartbeat. A novel and hybrid method of detection of cardiac arrhythmia have four main stages: denoising of raw ECG, baseline wander removal, proposed feature extraction, and detection of abnormal heartbeats using MPNN neural classifier. 8600 ECG beats were selected, including 4200 normal and 4400 abnormal beats (2200 LBBB and 2200 RBBB) were utilized for testing the proposed technique. The detection outcome using MPNN was compared with other two neural classifiers: Feed Forward Neural Network (FFNN) and Back Propagation Neural Network (BPNN) classifiers. The accuracy and efficiency of classifiers performance were attained in terms of CER (Classification Error Rate), SP (Specificity), Se (Sensitivity), Pr (Precision), PPr (Positive Predictivity) and F-Score. The system performance is achieved with 96.22%, 97.15% and 99.07% overall accuracy using FFNN, BPNN and MPNN. The average percentage of classification error rate (CER) using MPNN classifier is lowest 0.62% whereas FFNN and BPNN show 2.2% and 1. 90% average CER.



中文翻译:

混合技术MRDWT和MPNN分类器从心电大数据中检测心律失常的新型自适应特征提取

提出了一种使用ECG大数据混合技术的自动检测心律失常的有效方法,并采用了基于多分辨率离散小波变换(MRDWT)和多层概率神经网络(MPNN)分类器的新颖特征提取技术。已从MIT–BIH心律失常数据库中选择了ECG信号的大数据,以检测两种类型的心律不齐LBBB(左束支传导阻滞)和RBBB(右束支传导阻滞)。所提出的技术可以准确地检测和分类LBBB和RBBB以及正常的心跳。一种新颖的混合型心律失常检测方法包括四个主要阶段:原始ECG去噪,基线漂移消除,建议的特征提取以及使用MPNN神经分类器检测异常心跳。选择了8600个ECG搏动,包括4200正常拍和4400异常拍(2200 LBBB和2200 RBBB)被用来测试所提出的技术。使用MPNN的检测结果与其他两个神经分类器进行了比较:前馈神经网络(FFNN)和反向传播神经网络(BPNN)分类器。根据CER(分类错误率),SP(特异性),Se(灵敏度),Pr(精确度),PPr(正预测性)和F评分来获得分类器性能的准确性和效率。使用FFNN,BPNN和MPNN可以达到96.22%,97.15%和99.07%的整体精度。使用MPNN分类器的分类错误率(CER)的平均百分比最低,为0.62%,而FFNN和BPNN显示的平均CER为2.2%和1. 90%。使用MPNN的检测结果与其他两个神经分类器:前馈神经网络(FFNN)和反向传播神经网络(BPNN)分类器进行了比较。根据CER(分类错误率),SP(特异性),Se(灵敏度),Pr(精确度),PPr(正预测性)和F评分来获得分类器性能的准确性和效率。使用FFNN,BPNN和MPNN可以达到96.22%,97.15%和99.07%的整体精度。使用MPNN分类器的分类错误率(CER)的平均百分比最低,为0.62%,而FFNN和BPNN显示的平均CER为2.2%和1. 90%。使用MPNN的检测结果与其他两个神经分类器进行了比较:前馈神经网络(FFNN)和反向传播神经网络(BPNN)分类器。根据CER(分类错误率),SP(特异性),Se(灵敏度),Pr(精确度),PPr(正预测性)和F评分来获得分类器性能的准确性和效率。使用FFNN,BPNN和MPNN可以达到96.22%,97.15%和99.07%的整体精度。使用MPNN分类器的分类错误率(CER)的平均百分比最低,为0.62%,而FFNN和BPNN的平均CER为2.2%和1. 90%。根据CER(分类错误率),SP(特异性),Se(灵敏度),Pr(精确度),PPr(正预测性)和F评分来获得分类器性能的准确性和效率。使用FFNN,BPNN和MPNN可以达到96.22%,97.15%和99.07%的整体精度。使用MPNN分类器的分类错误率(CER)的平均百分比最低,为0.62%,而FFNN和BPNN显示的平均CER为2.2%和1. 90%。根据CER(分类错误率),SP(特异性),Se(灵敏度),Pr(精确度),PPr(正预测性)和F评分来获得分类器性能的准确性和效率。使用FFNN,BPNN和MPNN可以达到96.22%,97.15%和99.07%的整体精度。使用MPNN分类器的分类错误率(CER)的平均百分比最低,为0.62%,而FFNN和BPNN显示的平均CER为2.2%和1. 90%。

更新日期:2018-03-02
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