A Hybrid Dimension Reduction Based Linear Discriminant Analysis for Classification of High-Dimensional Data

Zorarpacı, Ezgi

doi:10.1109/cec45853.2021.9504951

Cited by 5 publications

(1 citation statement)

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“…• Linear Discriminant Analysis (LDA): Fourth and final classifier we have used is LDA that performed well in many classification tasks like emotional speech recognition, multimedia information retrieval, face recognition, image identification, etc. [53]. For each class c with a mean μ c and covariance S, LDA is calculated as follows:…”

Section: Plos Onementioning

confidence: 99%

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

et al. 2022

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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.

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Section: Plos Onementioning

confidence: 99%