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

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

Priscila Lima Rocha  1 , Washington Luis Santos Silva  2 , Patrícia da Silva Sousa  3 , Antônio Augusto Moura da Silva  4 , Allan Kardec Barros  5
Affiliations

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

Priscila Lima Rocha et al. Sci Rep. .

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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Flow chart of the proposed methodology.
Figure 2
Figure 2
Hypsarrhythmic segments Zika virus congenital syndrome—Hips-ZVCS.
Figure 3
Figure 3
Hypsarrhythmic segments west syndrome—hips-WS.
Figure 4
Figure 4
Distribution of the the joint time-frequency moment indices Fμ(t,f)[T].
Figure 5
Figure 5
Distribution of the the joint time-frequency moment indices Fσ(t,f)2[T].
Figure 6
Figure 6
Distribution of the the joint time-frequency moment indices Fλ(t,f)[T].
Figure 7
Figure 7
Distribution of the the joint time-frequency moment indices Fκ(t,f)[T].
Figure 8
Figure 8
Distribution of the FEShannon[T] entropy measurement indices.
Figure 9
Figure 9
Distribution of the FELogEnergy[T] entropy measurement indices.
Figure 10
Figure 10
Distribution of the FENorm[T] entropy measurement indices.
Figure 11
Figure 11
Distribution of the FESure[T] entropy measurement indices.
Figure 12
Figure 12
Visualization of Hips-ZVCS and Hips-WS classes by the t-SNE algorithm.
See this image and copyright information in PMC

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