Segment Based Abnormality Detection in EEG Recordings

Kohad, Neha; Ramesh, Raksha; Roy, Raghav; Irrinki, Samhita; Natarajan, Savithiry

doi:10.1109/conit55038.2022.9848243

Cited by 2 publications

(2 citation statements)

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“…A diverse array of machine learning methods has been utilized to address the challenges inherent in interpreting EEG data for various disorders [12]. These include deep learning approaches such as Convolutional Neural Networks (CNNs) [13] and Recurrent Neural Networks (RNNs) [11], alongside traditional techniques such as Support Vector Machines (SVMs) [14,15], K-nearest Neighbor (KNN) [10], Random Forests (RF) [16,17], Linear Discriminant Analysis (LDA) [18][19][20], Gradient Boosting Decision Tree (GBDT) [16,21], and Logistic Regression (LG) [22].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

“…Building on these advancements in machine learning, researchers have explored various methods of feature extraction to process raw EEG signals. Techniques such as Wavelet Packet Decomposition (WPD) [21,23], Discrete Wavelet Transform (DWT) [16,24,25], Dual-Tree Complex Wavelet Transform (DTCWT) [26], Empirical Wavelet Transform (EWT) [15], Empirical Mode Decomposition (EMD) [15], Fast Fourier Transform (FFT) [27], Shorttime Fourier Transform (STFT) [14,17,28], and entropy-based measures [25,29] have been employed. However, these methods necessitate specialized knowledge and extensive feature extraction and signal processing computation [12].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

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Automatic Detection of Abnormal EEG Signals Using WaveNet and LSTM

Albaqami

Hassan

Datta

2023

Sensors

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Neurological disorders have an extreme impact on global health, affecting an estimated one billion individuals worldwide. According to the World Health Organization (WHO), these neurological disorders contribute to approximately six million deaths annually, representing a significant burden. Early and accurate identification of brain pathological features in electroencephalogram (EEG) recordings is crucial for the diagnosis and management of these disorders. However, manual evaluation of EEG recordings is not only time-consuming but also requires specialized skills. This problem is exacerbated by the scarcity of trained neurologists in the healthcare sector, especially in low- and middle-income countries. These factors emphasize the necessity for automated diagnostic processes. With the advancement of machine learning algorithms, there is a great interest in automating the process of early diagnoses using EEGs. Therefore, this paper presents a novel deep learning model consisting of two distinct paths, WaveNet–Long Short-Term Memory (LSTM) and LSTM, for the automatic detection of abnormal raw EEG data. Through multiple ablation experiments, we demonstrated the effectiveness and importance of all parts of our proposed model. The performance of our proposed model was evaluated using TUH abnormal EEG Corpus V.2.0.0. (TUAB) and achieved a high classification accuracy of 88.76%, which is higher than in the existing state-of-the-art research studies. Moreover, we demonstrated the generalization of our proposed model by evaluating it on another independent dataset, TUEP, without any hyperparameter tuning or adjustment. The obtained accuracy was 97.45% for the classification between normal and abnormal EEG recordings, confirming the robustness of our proposed model.

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