Automatic Detection of Abnormal EEG Signals Using WaveNet and LSTM

Abstract

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.

Analysis and Comparison of Artifact Removal Techniques for Epilepsy EEG Signal

“…EMD distinguishes itself as a data-responsive multi-resolution method by dissecting a signal into physically meaningful components. It is useful for breaking down non-stationary and non-linear signals into discrete parts at different resolutions (18) (19) . By using the notion of scale separation, EMD, like a dynamic filter bank in the time domain, efficiently finds inherent modes of oscillations in any given dataset.…”

“…By using the notion of scale separation, EMD, like a dynamic filter bank in the time domain, efficiently finds inherent modes of oscillations in any given dataset. The intrinsic mode function, which is the phrase used to describe each discrete oscillatory mode, is precisely described as follows (19) .…”

Deep Optimized Electrodes and Frequency Bands in the Phase Space for Identification of Seizures

“…Seizure detection using EEG signals has witnessed remarkable advancements with the application of deep learning techniques [8,9,10,11]. In recent years, numerous studies have explored the utilization of sophisticated neural network architectures such as LSTM networks [12,13,6] networks and Transformers [5,14] for enhancing the accuracy and reliability of seizure detection and prediction.…”

“…LSTM networks [15,16,17] have shown substantial promise in capturing temporal dependencies within EEG signals, making them well-suited for seizure detection and prediction tasks. Hezam et al [8] proposed a novel hybrid LSTM model that leverages both short-term and long-term EEG patterns to achieve enhanced detection accuracy. Similarly, [18] introduced a stacked LSTM architecture to capture complex rela-tionships in EEG signals and reported improved seizure prediction performance.…”

Pediatric and Adolescent Seizure Detection: A Machine Learning Approach Exploring the Influence of Age and Sex in Electroencephalogram Analysis

“…Ziyabari et al [10] combined CNN and Multilayer Perceptron techniques, resulting in a sensitivity of 31.58%. Additionally, Albaqami et al [11] proposed a WaveNet-Long Short-Term Memory (LSTM) approach for the automatic detection of abnormal raw EEG data, achieving a classification accuracy of 88.76%. Although there has been substantial research in the development of seizure detection methods for adult EEGs [10,12,13], the field of pediatric and adolescent-based seizure detection methods remains relatively underdeveloped.…”