Deep Convolutional Bidirectional LSTM Recurrent Neural Network for Epileptic Seizure Detection

Abdelhameed, Ahmed M.; Daoud, Hisham; Bayoumi, Magdy

doi:10.1109/newcas.2018.8585542

Cited by 47 publications

(20 citation statements)

References 8 publications

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“…For example (Acharya et al, 2018), proposed a deep CNN consisting of 13 layers for automatic seizure detection. For the same purpose (Abdelhameed et al, 2018a), designed a system that combined a one-dimensional CNN with a bidirectional long short-term memory (Bi-LSTM) recurrent neural network. Ke et al (2018); Zhou et al (2018), andHossain et al (2019) also used CNN for feature extraction and classification.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Automatic Seizure Detection in Children With Epilepsy

Abdelhameed

Bayoumi

2021

Front. Comput. Neurosci.

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101

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Over the last few decades, electroencephalogram (EEG) has become one of the most vital tools used by physicians to diagnose several neurological disorders of the human brain and, in particular, to detect seizures. Because of its peculiar nature, the consequent impact of epileptic seizures on the quality of life of patients made the precise diagnosis of epilepsy extremely essential. Therefore, this article proposes a novel deep-learning approach for detecting seizures in pediatric patients based on the classification of raw multichannel EEG signal recordings that are minimally pre-processed. The new approach takes advantage of the automatic feature learning capabilities of a two-dimensional deep convolution autoencoder (2D-DCAE) linked to a neural network-based classifier to form a unified system that is trained in a supervised way to achieve the best classification accuracy between the ictal and interictal brain state signals. For testing and evaluating our approach, two models were designed and assessed using three different EEG data segment lengths and a 10-fold cross-validation scheme. Based on five evaluation metrics, the best performing model was a supervised deep convolutional autoencoder (SDCAE) model that uses a bidirectional long short-term memory (Bi-LSTM) – based classifier, and EEG segment length of 4 s. Using the public dataset collected from the Children’s Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), this model has obtained 98.79 ± 0.53% accuracy, 98.72 ± 0.77% sensitivity, 98.86 ± 0.53% specificity, 98.86 ± 0.53% precision, and an F1-score of 98.79 ± 0.53%, respectively. Based on these results, our new approach was able to present one of the most effective seizure detection methods compared to other existing state-of-the-art methods applied to the same dataset.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Automatic Seizure Detection in Children With Epilepsy

Abdelhameed

Bayoumi

2021

Front. Comput. Neurosci.

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101

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“…Dey, et al (2017) proposed a reliable ECG monitoring system, they developed a low cost and improved Zigbee wireless unit [20]. Daoud, et al (2018) introduced a seizure detection system using EMD with Deep Neural Network in [21]. Park, et a.…”

Section: Literature Reviewmentioning

confidence: 99%

Epileptic Seizure Detection in Ioht: A Visual Image-Based Processing Approach

Khalil

Khalaf

Banby

et al. 2022

Journal of Advanced Engineering Trends

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This paper presents a new technique for electroencephalography (EEG) seizure detection from multi-channel EEG signals based on image processing concepts in Internet-of-Health-Things (IoHT) systems. The multi-channel EEG segments are treated as two-dimensional matrices as if they were images. Scale-space analysis with Scale Invariant Feature Transform (SIFT) is used to extract the feature points in the up-mentioned two-dimensional matrices. The number of points is used as a discriminating factor between seizure segments and normal segments. An exhaustive study of the 24 patients of the Children's Hospital Boston (CHB-MIT) database is presented in this paper. The EEG signals are transmitted via WiFi/Bluetooth, then all their signals are segmented into one-second segments, the numbers of features points are extracted from these segments, the Probability Density Function (PDF) of the number of feature points for normal and seizure segments are estimated. The Equal Error Rate (EER) is estimated between PDFs of the numbers of feature points in seizure and normal segments. Simulation results on all patients reveal the ability of the proposed technique to set a patient-specific discrimination threshold of 70% of Max spectral power for seizure detection with an accuracy of 95.6%.

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“…Bidirectional LSTM (Bi-LSTM) is another popular version of LSTM. Abdelhameed et al [16] proposed a Bi-LSTM network for EEG detection. CNN was employed for preprocessing and feature extraction of the EEG data.…”

Section: Introductionmentioning

confidence: 99%

Selection of optimal wavelet features for epileptic EEG signal classification with LSTM

Aliyu

Lim

2021

Neural Comput & Applic

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Epilepsy remains one of the most common chronic neurological disorders; hence, there is a need to further investigate various models for automatic detection of seizure activity. An effective detection model can be achieved by minimizing the complexity of the model in terms of trainable parameters while still maintaining high accuracy. One way to achieve this is to select the minimum possible number of features. In this paper, we propose a long short-term memory (LSTM) network for the classification of epileptic EEG signals. Discrete wavelet transform (DWT) is employed to remove noise and extract 20 eigenvalue features. The optimal features were then identified using correlation and P value analysis. The proposed method significantly reduces the number of trainable LSTM parameters required to attain high accuracy. Finally, our model outperforms other proposed frameworks, including popular classifiers such as logistic regression (LR), support vector machine (SVM), K-nearest neighbor (K-NN) and decision tree (DT).

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Deep Convolutional Bidirectional LSTM Recurrent Neural Network for Epileptic Seizure Detection

Cited by 47 publications

References 8 publications

A Deep Learning Approach for Automatic Seizure Detection in Children With Epilepsy

A Deep Learning Approach for Automatic Seizure Detection in Children With Epilepsy

Epileptic Seizure Detection in Ioht: A Visual Image-Based Processing Approach

Selection of optimal wavelet features for epileptic EEG signal classification with LSTM

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