Epileptic Signal Classification With Deep EEG Features by Stacked CNNs

Cao, Jiuwen; Zhu, Jiahua; Hu, Wenbin; Kummert, Anton

doi:10.1109/tcds.2019.2936441

Cited by 65 publications

(18 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For each truncated signal, it can be approximately regarded as a stationary signal, and thus, Fourier transform can then be used. The discrete STFT [ 21 ] can be expressed by

where w ( n − m ) is the window function. The result of STFT is a 2-D spectrogram.…”

Section: Methodsmentioning

confidence: 99%

“…The frequency spectrogram obtained by fast Fourier transform (FFT) was treated as the input of CNN for the purpose of epileptic detection in [ 20 ]. The subband mean amplitude of spectrum map (MAS) obtained from different EEG rhythms was adopted for EEG representation in [ 21 ], and stacked CNNs were used for feature extraction and seizure detection. It proved that the MAS has the ability to characterize the different rhythms of EEG signals.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Epileptic Seizure Detection with Hybrid Time-Frequency EEG Input: A Deep Learning Approach

Pan

Zhou

Dong

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

The precise detection of epileptic seizure helps to prevent the serious consequences of seizures. As the electroencephalogram (EEG) reflects the brain activity of patients effectively, it has been widely used in epileptic seizure detection in the past decades. Recently, deep learning-based detection methods which automatically learn features from the EEG signals have attracted much attention. However, with deep learning-based detection methods, different input formats of EEG signals will lead to different detection performances. In this paper, we propose a deep learning-based epileptic seizure detection method with hybrid input formats of EEG signals, i.e., original EEG, Fourier transform of EEG, short-time Fourier transform of EEG, and wavelet transform of EEG. Convolutional neural networks (CNNs) are designed for extracting latent features from these inputs. A feature fusion mechanism is applied to integrate the learned features to generate a more stable syncretic feature for seizure detection. The experimental results show that our proposed hybrid method is effective to improve the seizure detection performance in few-shot scenarios.

show abstract

“…For each truncated signal, it can be approximately regarded as a stationary signal, and thus, Fourier transform can then be used. The discrete STFT [ 21 ] can be expressed by

where w ( n − m ) is the window function. The result of STFT is a 2-D spectrogram.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Epileptic Seizure Detection with Hybrid Time-Frequency EEG Input: A Deep Learning Approach

Pan

Zhou

Dong

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…Three-class, interictal/preictal/ictal; 3. Five-class, interictal/three preictal states/ictal) [66]. To enhance the performance of epilepsy classification, original binary labels, namely interictal epileptiform discharge (IED) and non-IED, were converted into multiple labels used for model training [67].…”

Section: B Disease Detectionmentioning

confidence: 99%

Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

Gong

Xing

Cichocki

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

View full text Add to dashboard Cite

Deep learning has achieved excellent performance in a wide range of domains, especially in speech recognition and computer vision. Relatively less work has been done for EEG, but there is still significant progress attained in the last decade. Due to the lack of a comprehensive and topic widely covered survey for deep learning in EEG, we attempt to summarize recent progress to provide an overview, as well as perspectives for future developments. We first briefly mention the artifacts removal for EEG signal and then introduce deep learning models that have been utilized in EEG processing and classification. Subsequently, the applications of deep learning in EEG are reviewed by categorizing them into groups such as brain-computer interface, disease detection, and emotion recognition. They are followed by the discussion, in which the pros and cons of deep learning are presented and future directions and challenges for deep learning in EEG are proposed. We hope that this paper could serve as a summary of past work for deep learning in EEG and the beginning of further developments and achievements of EEG studies based on deep learning.

show abstract

“…Recently, machine learning based neural networks have been widely used for EEG analysis, such as epilepsy and seizure detection [26][27][28][29], spike detection [30][31][32], brain-computer interfaces [33][34][35], etc. Webber et al [30] implemented spike detection algorithms through spike candidate selection and artificial neural networks (ANN) based classification.Özdamar et al [31] directly adopted ANN to learn on the raw EEGs, and explored the influence of the feature input dimension and network structure parameters.…”

Section: Related Workmentioning

confidence: 99%

BECT Spike Detection Based on Novel EEG Sequence Features and LSTM Algorithms

Wang

Cao³

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

Self Cite

View full text Add to dashboard Cite

The benign epilepsy with spinous waves in the 1 central temporal region (BECT) is the one of the most common 2 epileptic syndromes in children, that seriously threaten the ner-3 vous system development of children. The most obvious feature of BECT is the existence of a large number of electroencephalogram 5 (EEG) spikes in the Rolandic area during the interictal period, 6 that is an important basis to assist neurologists in BECT diag-7 nosis. With this regard, the paper proposes a novel BECT spike 8 detection algorithm based on time domain EEG sequence features 9 and the long short-term memory (LSTM) neural network. Three time domain sequence features, that can obviously characterize the spikes of BECT, are extracted for EEG representation. The 12 synthetic minority oversampling technique (SMOTE) is applied to 13 address the spike imbalance issue in EEGs, and the bi-directional 14 LSTM (BiLSTM) is trained for spike detection. The algorithm is 15 evaluated using the EEG data of 15 BECT patients recorded from 16 the Children's Hospital, Zhejiang University School of Medicine 17 (CHZU). The experiment shows that the proposed algorithm can obtained an average of 88.54% F1 score, 92.04% sensitivity, and 19 85.75% precision, that generally outperforms several state-of-the-20 art spike detection methods.

show abstract

Epileptic Signal Classification With Deep EEG Features by Stacked CNNs

Cited by 65 publications

References 50 publications

Epileptic Seizure Detection with Hybrid Time-Frequency EEG Input: A Deep Learning Approach

Epileptic Seizure Detection with Hybrid Time-Frequency EEG Input: A Deep Learning Approach

Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

BECT Spike Detection Based on Novel EEG Sequence Features and LSTM Algorithms

Contact Info

Product

Resources

About