Textural feature based intelligent approach for neurological abnormality detection from brain signal data

Abstract

The diagnosis of neurological diseases is one of the biggest challenges in modern medicine, which is a major issue at the moment. Electroencephalography (EEG) recordings is usually used to identify various neurological diseases. EEG produces a large volume of multi-channel time-series data that neurologists visually analyze to identify and understand abnormalities within the brain and how they propagate. This is a time-consuming, error-prone, subjective, and exhausting process. Moreover, recent advances in EEG classification have mostly focused on classifying patients of a specific disease from healthy subjects using EEG data, which is not cost effective as it requires multiple systems for checking a subject’s EEG data for different neurological disorders. This forces researchers to advance their work and create a single, unified classification framework for identifying various neurological diseases from EEG signal data. Hence, this study aims to meet this requirement by developing a machine learning (ML) based data mining technique for categorizing multiple abnormalities from EEG data. Textural feature extractors and ML-based classifiers are used on time-frequency spectrogram images to develop the classification system. Initially, noises and artifacts are removed from the signal using filtering techniques and then normalized to reduce computational complexity. Afterwards, normalized signals are segmented into small time segments and spectrogram images are generated from those segments using short-time Fourier transform. Then two histogram based textural feature extractors are used to calculate features separately and principal component analysis is used to select significant features from the extracted features. Finally, four different ML based classifiers are used to categorize those selected features into different disease classes. The developed method is tested on four real-time EEG datasets. The obtained result has shown potential in classifying various abnormality types, indicating that it can be utilized to identify various neurological abnormalities from brain signal data.

Exploring Frequency Band-Based Biomarkers of EEG Signals for Mild Cognitive Impairment Detection

“…Researchers typically utilize the segmentation strategy to tackle this issue. This technique divides the original EEG data into short, informative fragments and gives them the same level as the original signal, increasing the data sample size while preserving an equal ratio [9]- [14], [25]. In this study, we have segmented the filtered signals into three-second (3s) time segments similar to the studies [12]- [14], as those studies have obtained better performance using 3s time segments that are computationally less expensive and also contain enough information to perform the automatic classification task [15].…”

“…Its simplicity of implementation and ability to provide a timefrequency representation of the data were key reasons for its integration into our framework. The calculation of the STFT involves dividing the signal into overlapping windowed blocks, and a hamming window is used to maintain continuity and avoid spectrum leakage [14]. The Fourier transform is then applied to each segment to derive its local frequency spectrum.…”

“…( 3) that an AUC value of 1 indicates that the classifier has flawless discrimination abilities, whereas a value of less than or equal to 0.5 shows that the classifier has no discernment abilities at all [11]. These criteria allow us to grasp an idea about the classifier's behavior on the test data [1], [10], [12]- [14], [29], [30].…”

“…In this study, we have used EEG data for MCI detection as it is non-invasive, cost-effective, widely available, and portable [12], [13]. Moreover, it captures brain electrical activity over time, making it valuable for assessing cognitive concerns [3], [14], [15].…”

“…EEG signal has several rhythm bands like delta (0-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13)(14)(15)(16), beta (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) and gamma (>32 Hz) [21]. Among those rhythm bands, <0.5 Hz and >32 Hz bands are considered noise [1], [18] and can be removed from the classification process.…”