Wavelet Ridges in EEG Diagnostic Features Extraction: Epilepsy Long-Time Monitoring and Rehabilitation after Traumatic Brain Injury

Abstract

Interchannel EEG synchronization, as well as its violation, is an important diagnostic sign of a number of diseases. In particular, during an epileptic seizure, such synchronization occurs starting from some pairs of channels up to many pairs in a generalized seizure. Additionally, for example, after traumatic brain injury, the destruction of interneuronal connections occurs, which leads to a violation of interchannel synchronization when performing motor or cognitive tests. Within the framework of a unified approach to the analysis of interchannel EEG synchronization using the ridges of wavelet spectra, two problems were solved. First, the segmentation of the initial data of long-term monitoring of scalp EEG with various artifacts into fragments suspicious of epileptic seizures in order to reduce the total duration of the fragments analyzed by the doctor. Second, assessments of recovery after rehabilitation of cognitive functions in patients with moderate traumatic brain injury. In the first task, the initial EEG was segmented into fragments in which at least two channels were synchronized, and by the adaptive threshold method into fragments with a high value of the EEG power spectral density. Overlapping in time synchronized fragments with fragments of high spectral power density was determined. As a result, the total duration of the fragments for analysis by the doctor was reduced by more than 60 times. In the second task, the network of phase-related EEG channels was determined during the cognitive test before and after rehabilitation. Calculation-logical and spatial-pattern cognitive tests were used. The positive dynamics of rehabilitation was determined during the initialization of interhemispheric connections and connections in the frontal cortex of the brain.

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

“…In the Petrosian method, rapid FD estimation is performed, and the results show that this method has satisfactory results. The mathematical theory of the Petrosian method is shown in (18) [88]:…”

“…The most significant difference between CADS based on ML and DL is in the feature extraction step [9]. In CDAS based on ML, the most important feature extraction techniques include the time domain, frequency, and nonlinear features [18]. Choosing different feature extraction algorithms together to reach a high diagnosis accuracy demands a fair amount of knowledge in the field of ML [19,20].…”

Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic Time Series Classification, Python Package for NNetEn Calculation

“…The EEG signal's entropy may be a sign of poor separation ability since it has a wide spectral range (0-250 Hz), and the main information about brain activity lies in a relatively narrow set of frequencies [50]. The wavelet transform can be used to decompose the EEG signal into separate frequency components and increase the information content.…”

Potential of Overcomplete Wavelet Frame Expansion for Facilitating Electroencephalogram Information Mining

“…Li employed wavelet packet transform (WPT) to decompose the non-stationary measurement signal in time and frequency domain, and selects the frequency band information related to the imagination task to reconstruct the EEG signal features (Li et al, 2021 ). Obukhov utilized ridges of wavelet spectra for automatic diagnose of epileptic seizure (Obukhov et al, 2021 ). It can be seen that the classical wavelet transform is mainly used to decompose EEG signals in the literature, and there is no in-depth study on the impact of wavelet transform on the decomposition results.…”