去趋势波动分析
支持向量机
赫斯特指数
关联维数
模式识别(心理学)
近似熵
人工智能
室性心动过速
心室颤动
非线性系统
熵(时间箭头)
分形维数
数学
离散小波变换
心源性猝死
计算机科学
小波
算法
分形
小波变换
统计
心脏病学
医学
物理
数学分析
缩放比例
量子力学
几何学
作者
U. Rajendra Acharya,Hamido Fujita,Vidya K. Sudarshan,V V V Bhagya Sree,Lim Wei Jie Eugene,Dhanjoo N. Ghista,Ru San Tan
标识
DOI:10.1016/j.knosys.2015.03.015
摘要
Early prediction of person at risk of Sudden Cardiac Death (SCD) with or without the onset of Ventricular Tachycardia (VT) or Ventricular Fibrillation (VF) still remains a continuing challenge to clinicians. In this work, we have presented a novel integrated index for prediction of SCD with a high level of accuracy by using electrocardiogram (ECG) signals. To achieve this, nonlinear features (Fractal Dimension (FD), Hurst’s exponent (H), Detrended Fluctuation Analysis (DFA), Approximate Entropy (ApproxEnt), Sample Entropy (SampEnt), and Correlation Dimension (CD)) are first extracted from the second level Discrete Wavelet Transform (DWT) decomposed ECG signal. The extracted nonlinear features are ranked using t-value and then, a combination of highly ranked features are used in the formulation and employment of an integrated Sudden Cardiac Death Index (SCDI). This calculated novel SCDI can be used to accurately predict SCD (four minutes before the occurrence) by using just one numerical value four minutes before the SCD episode. Also, the nonlinear features are fed to the following classifiers: Decision Tree (DT), k-Nearest Neighbour (KNN), and Support Vector Machine (SVM). The combination of DWT and nonlinear analysis of ECG signals is able to predict SCD with an accuracy of 92.11% (KNN), 98.68% (SVM), 93.42% (KNN) and 92.11% (SVM) for first, second, third and fourth minutes before the occurrence of SCD, respectively. The proposed SCDI will constitute a valuable tool for the medical professionals to enable them in SCD prediction.
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