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

Abstract

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.

Artificial Intelligence in Translational Medicine

“…Considering five evaluation metrics, the best performing ML-based tool was a supervised DCAE. In particular, this model showed 98.79% accuracy, 98.72% sensitivity, 98.86% specificity, 98.86% precision, and an F1-score of 98.79%, respectively [257]. According to this study and other similar research works in the field, the use of ML-based models can be useful in detecting seizure in epilepsy.…”

“…2.2.4. AI/ML in central nervous system (CNS)-related disorders Another interesting area, in which AI/ML and DL have also been widely employed for brain image assessment to develop imaging-based diagnostic and classification systems, is the neurology and central nervous system (CNS)-related disorders such as psychiatric disorders, demyelinating diseases, neurodegenerative disorders, epilepsy, and strokes [121,[253][254][255][256][257][258]. Together with extensive usage in image recognition, language processing, and data mining, ML approaches have obtained growing interest also in neurological-related applications, ranging from automated imaging assessment to disorder prediction.…”

“…According to this study and other similar research works in the field, the use of ML-based models can be useful in detecting seizure in epilepsy. Furthermore, the improvement in processing capabilities, the availability of efficient and more sophisticated ML methods, and the collection of larger datasets, scientists will benefit from these computational approaches along with considerable progress acquired in their use in epilepsy [257,258].…”

Deep Convolutional Neural Network Regularization for Alcoholism Detection Using EEG Signals

“…CNNs have been used in problems such as speech recognition, image classification, recommender systems, and text classification. More recently, CNNs have been shown to classify EEG brain signals for autism [ 46 ], epilepsy [ 46 , 47 , 48 , 49 ], seizure detection in children [ 50 ], schizophrenia [ 51 ], brain–computer interface (BCI) [ 52 ], alcoholism predisposition [ 21 , 37 ], drowsiness detection [ 36 , 53 ], and neurodegeneration and physiological aging [ 54 ] into normal and pathological groups of young and old people.…”

Artificial Intelligence in Translational Medicine

“…Another interesting area in which AI/ML and DL have also been widely employed for brain image assessment to develop imaging-based diagnostic and classification systems is that of neurology and central nervous system (CNS)-related disorders, such as psychiatric disorders, demyelinating diseases, neurodegenerative disorders, epilepsy, and strokes [131,[264][265][266][267][268][269]. Together with extensive usage in image recognition, language processing, and data mining, ML approaches have also obtained growing interest in neurological-related applications, ranging from automated imaging assessment to disorder prediction.…”