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基于关节力矩和熵测量对寨卡病毒先天性综合征和west 综合征的继发高度失律进行鉴别。

Discrimination of secondary hypsarrhythmias to Zika virus congenital syndrome and west syndrome based on joint moments and entropy measurements.

机构信息

Department of Electrical Engineering, Laboratory for Biological Information Processing (PIB), Federal University of Maranhão (UFMA), São Luís, MA, CEP 65080-805, Brazil.

Department of ElectroElectronics, Federal Institute of Maranhão (IFMA), São Luís, MA, 65030-005, Brazil.

出版信息

Sci Rep. 2022 May 5;12(1):7389. doi: 10.1038/s41598-022-11395-2.

DOI:10.1038/s41598-022-11395-2
PMID:35513477
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9072419/
Abstract

Hypsarrhythmia is a specific chaotic morphology, present in the interictal period of the electroencephalogram (EEG) signal in patients with West Syndrome (WS), a severe form of childhood epilepsy and that, recently, was also identified in the examinations of patients with Zika Virus Congenital Syndrome (ZVCS). This innovative work proposes the development of a computational methodology for analysis and differentiation, based on the time-frequency domain, between the chaotic pattern of WS and ZVCS hypsarrhythmia. The EEG signal time-frequency analysis is carried out from the Continuous Wavelet Transform (CWT). Four joint moments-joint mean-[Formula: see text], joint variance-[Formula: see text], joint skewness-[Formula: see text], and joint kurtosis-[Formula: see text]-and four entropy measurements-Shannon, Log Energy, Norm, and Sure-are obtained from the CWT to compose the representative feature vector of the EEG hypsarrhythmic signals under analysis. The performance of eight classical types of machine learning algorithms are verified in classification using the k-fold cross validation and leave-one-patient-out cross validation methods. Discrimination results provided 78.08% accuracy, 85.55% sensitivity, 73.21% specificity, and AUC = 0.89 for the ANN classifier.

摘要

高度失律是一种特定的混沌形态，存在于 West 综合征（WS）患者的脑电图（EEG）信号的发作间期，WS 是一种严重的儿童癫痫形式，最近也在 Zika 病毒先天性综合征（ZVCS）患者的检查中被发现。这项创新性工作提出了一种基于时频域的计算方法，用于分析和区分 WS 和 ZVCS 高度失律的混沌模式。EEG 信号的时频分析是从连续小波变换（CWT）进行的。从 CWT 中获得了四个联合矩——联合均值-[Formula: see text]、联合方差-[Formula: see text]、联合偏度-[Formula: see text]和联合峰度-[Formula: see text]——以及四个熵测量——Shannon、Log Energy、Norm 和 Sure——以构成分析的 EEG 高度失律信号的代表性特征向量。使用 k 折交叉验证和留一患者交叉验证方法，在分类中验证了八种经典类型的机器学习算法的性能。ANN 分类器的判别结果为 78.08%的准确率、85.55%的灵敏度、73.21%的特异性和 AUC=0.89。

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