Epileptic Seizure Detection using Deep Convolutional Autoencoder

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

doi:10.1109/sips.2018.8598447

Cited by 26 publications

(9 citation statements)

References 9 publications

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“…After passing through the entire input sequence, the average of the two outputs of both blocks concatenated together is computed and used for the classification task. Bi-LSTMs are useful in that they take into account the temporal dependence between the current input at a certain time and its previous and subsequent counterparts, which offers a strong advantage for enhancing the classification results (Abdelhameed et al, 2018b). Figure 8 shows a single-layer Bi-LSTM network unrolled over n time steps.…”

Section: Two-dimensional Deep Convolution Autoencoder + Bi-lstmmentioning

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: Two-dimensional Deep Convolution Autoencoder + Bi-lstmmentioning

confidence: 99%

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

Abdelhameed

Bayoumi

2021

Front. Comput. Neurosci.

Self Cite

101

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“…Using the Relu activation function has some advantages which have been discussed in previous studies [25,35] for example, it reduces the probability of vanishing gradient which often occurred when the model is deep. Another example is that it adds nonlinearity and guarantees the robustness of the system against noise in the input signals [2]. The output of the last layer in the decoding part is the reconstructed data of the original input where linear activation is used.…”

Section: Architecturementioning

confidence: 99%

TimeCluster: dimension reduction applied to temporal data for visual analytics

et al. 2019

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There is a need for solutions which assist users to understand long time-series data by observing its changes over time, finding repeated patterns, detecting outliers, and effectively labeling data instances. Although these tasks are quite distinct and are usually tackled separately, we present an interactive visual analytics system and approach that can address these issues in a single system. It enables users to visualize, understand and explore univariate or multivariate long time-series data in one image using a connected scatter plot. It supports interactive analysis and exploration for pattern discovery and outlier detection. Different dimensionality reduction techniques are used and compared in our system. Because of its power of extracting features, deep learning is used for multivariate time-series along with 2D reduction techniques for rapid and easy interpretation and interaction with large amount of time-series data. We deploy our system with different time-series datasets and report two real-world case studies that are used to evaluate our system.

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“…They can be divided into seven types of architecture: (1) convolutional neural networks (CNNs), (2) recurrent neural networks (RNNs), (3) deep belief networks (DBNs), (4) autoencoders (AEs), (5) a new architecture formed by combining CNN with the DBNs or AEs, (6) transformer-based networks; among which 2D-CNNs are the most popular neural network architecture for automated seizure detection [14]. CNN was applied to EEG monitoring and seizure detection diagnosis by transforming EEG signals into one-dimensional or two-dimensional forms and feeding the transformed signals to the CNN model [15, 16, 17, 18, 19, 20, 21]. RNN and its extended models, long short-term memory (LSTM) and gated recurrent units (GRU), were used when the signal has a variation in lengths [22, 23, 24, 25, 26].…”

Section: Introductionmentioning

confidence: 99%

TSD: Transformers for Seizure Detection

Liu

Sabrina

et al. 2023

Preprint

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Epilepsy is a common neurological disorder that substantially deteriorates patients' safety and quality of life. Electroencephalogram (EEG) has been the golden-standard technique for diagnosing this brain disorder and has played an essential role in epilepsy monitoring and disease management. It is extremely laborious and challenging, if not practical, for physicians and expert humans to annotate all recorded signals, particularly in long-term monitoring. The annotation process often involves identifying signal segments with suspected epileptic seizure features or other abnormalities and/or known healthy features. Therefore, automated epilepsy detection becomes a key clinical need because it can greatly improve clinical practice's efficiency and free up human expert time to attend to other important tasks. Current automated seizure detection algorithms generally face two challenges: (1) models trained for specific patients, but such models are patient-specific, hence fail to generalize to other patients and real-world situations; (2) seizure detection models trained on large EEG datasets have low sensitivity and/or high false positive rates, often with an area under the receiver operating characteristic (AUROC) that is not high enough for potential clinical applicability. This paper proposes Transformers for Seizure Detection, which we refer to as TSD in this manuscript. A Transformer is a deep learning architecture based on an encoder-decoder structure and on attention mechanisms, which we apply to recorded brain signals. The AUROC of our proposed model has achieved 92.1%, tested with Temple University's publically available electroencephalogram (EEG) seizure corpus dataset (TUH). Additionally, we highlight the impact of input domains on the model's performance. Specifically, TSD performs best in identifying epileptic seizures when the input domain is a time-frequency. Finally, our proposed model for seizure detection in inference-only mode with EEG recordings shows outstanding performance in classifying seizure types and superior model initialization.

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Epileptic Seizure Detection using Deep Convolutional Autoencoder

Cited by 26 publications

References 9 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

TimeCluster: dimension reduction applied to temporal data for visual analytics

TSD: Transformers for Seizure Detection

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