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

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.

Figure 1

Flow chart of the proposed methodology.

Figure 2

Hypsarrhythmic segments Zika virus congenital syndrome—Hips-ZVCS.

Figure 3

Hypsarrhythmic segments west syndrome—hips-WS.

Figure 4

Distribution of the the joint time-frequency moment indices $F_{{\mu }_{(t,f)}}[T]$.

Figure 5

Distribution of the the joint time-frequency moment indices $F_{{\sigma }_{(t,f)}^2}[T]$.

Figure 6

Distribution of the the joint time-frequency moment indices $F_{{\lambda }_{(t,f)}}[T]$.

Figure 7

Distribution of the the joint time-frequency moment indices $F_{{\kappa }_{(t,f)}}[T]$.

Figure 8

Distribution of the $F_{E_{\mathit{Shannon}}}[T]$ entropy measurement indices.

Figure 9

Distribution of the $F_{E_{\mathit{LogEnergy}}}[T]$ entropy measurement indices.

Figure 10

Distribution of the $F_{E_{\mathit{Norm}}}[T]$ entropy measurement indices.

Figure 11

Distribution of the $F_{E_{\mathit{Sure}}}[T]$ entropy measurement indices.

Figure 12

Visualization of Hips-ZVCS and Hips-WS classes by the t-SNE algorithm.