Entropy Measures of Electroencephalograms towards the Diagnosis of Psychogenic Non-Epileptic Seizures

Abstract

Psychogenic non-epileptic seizures (PNES) may resemble epileptic seizures but are not caused by epileptic activity. However, the analysis of electroencephalogram (EEG) signals with entropy algorithms could help identify patterns that differentiate PNES and epilepsy. Furthermore, the use of machine learning could reduce the current diagnosis costs by automating classification. The current study extracted the approximate sample, spectral, singular value decomposition, and Renyi entropies from interictal EEGs and electrocardiograms (ECG)s of 48 PNES and 29 epilepsy subjects in the broad, delta, theta, alpha, beta, and gamma frequency bands. Each feature-band pair was classified by a support vector machine (SVM), k-nearest neighbour (kNN), random forest (RF), and gradient boosting machine (GBM). In most cases, the broad band returned higher accuracy, gamma returned the lowest, and combining the six bands together improved classifier performance. The Renyi entropy was the best feature and returned high accuracy in every band. The highest balanced accuracy, 95.03%, was obtained by the kNN with Renyi entropy and combining all bands except broad. This analysis showed that entropy measures can differentiate between interictal PNES and epilepsy with high accuracy, and improved performances indicate that combining bands is an effective improvement for diagnosing PNES from EEGs and ECGs.

A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer’s Disease with Electroencephalography in Resting-State Adults

“…In this direction, often, Fast Fourier Transforms [34], and Discrete Wavelet Trans-forms [35] have proven helpful in extracting features from EEG data before training models with machine learning algorithms. Similarly, Multiway Array Decomposition [36], Principal Dynamic Mode (PDM) analysis [37], Singular Value Decomposition [9], and Principal Component Analysis [38] have all demonstrated utility in such endeavour.…”

“…Moreover, there is an abundance of EEG denoising pipelines present in the literature, as previous studies have applied various techniques for extracting high-level features from EEG data, such as wavelet transforms [4], fractal dimensions [5], entropy-based features [6], and the Hurst exponent [7]. Similar techniques have been used for extracting features for detecting and diagnosing Parkinson's [8] epilepsy [9,10], schizophrenia [11], and other neurological disorders. However, there is limited research on comparing and assessing the utility of such feature extraction techniques for discriminating between Alzheimer's and frontotemporal patients, as well as from healthy controls (HCs).…”

Leveraging SVD Entropy and Explainable Machine Learning for Alzheimer's and Frontotemporal Dementia Detection using EEG

“…[40]. SVD Entropy has also been used for the diagnosis of Psychogenic Non-Epileptic Seizures (PNES) by extracting features from EEG signals [41].…”

“…A higher entropy value suggests a more complex and unpredictable signal, while a lower entropy suggests the opposite. Furthermore, SVD Entropy has been regularly employed as an effective EEG feature extraction measure for diagnosing or analysing multiple neurological disorders such as epilepsy [4,5] and schizophrenia [6].…”

Classification of Epileptic Seizures Using LSTM Based Zebra Optimization Algorithm with Hyperparameter Tunin

“…The EEG signals from standard datasets consist of various frequency bands. Hence to maintain uniform sensitivity, the signals are decomposed using decomposition methods [10][11][12]. The decomposed signals are then extracted for generating optimal features to select discriminative features.…”