Automatic epileptic EEG detection using convolutional neural network with improvements in time-domain

Wei, Zuochen; Zou, Junzhong; Zhang, Jian; Xu, Jianqiang

doi:10.1016/j.bspc.2019.04.028

Cited by 140 publications

(73 citation statements)

References 22 publications

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“…After comparing the different metrics, optimizing the GAN for good IS and FID produced the best EEG data approximations [65]. This method did not use any classification algorithms to validate the accuracy, therefore it is not included in Table 3 [73]. Testing the performance on one patient, they used generated data from the other 22 patients involved in the training.…”

Section: Noise Additionmentioning

confidence: 99%

Data augmentation for deep-learning-based electroencephalography

Lashgari

Liang

Maoz

2020

Journal of Neuroscience Methods

277

140

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Highlights • Data augmentation (DA) is increasingly used with deep learning (DL) on EEG • It enhances decoding accuracy left unexplained by 29% on average on the datasets we review • We analyze which specific DA techniques appear to work best for which EEG tasks • We tested various DA techniques on an open motor-imagery task and compared the accuracy gains to demonstrate the usefulness of DA for DL-based EEG analysis • We propose guidelines for reporting parameters for different DA techniques Abstract-Background Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)-increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc.-New method We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected?-Results DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively.-Comparing with existing methods Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8% for recombination of segmentation and 36% for noise addition and from 14% for motor imagery to 56% for mental workload-29% on average.-Conclusions DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications-if adhering to our reporting guidelines-will facilitate more detailed analysis.

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Section: Noise Additionmentioning

confidence: 99%

Data augmentation for deep-learning-based electroencephalography

Lashgari

Liang

Maoz

2020

Journal of Neuroscience Methods

277

140

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“… Iesmantas and Alzbutas (2020) extracted different features from clinical epilepsy EEG signals and applied CNN for training data. Wei et al (2019) used the increasing and decreasing sequences (MIDS) merger to highlight the characteristic of waveforms and a data augmentation method for increasing the sample diversity and EEG information. Furthermore, they applied CNN classifier for epilepsy detection to get 90.57% accuracy.…”

Section: Classificationmentioning

confidence: 99%

Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals

Liu

Xiao

et al. 2021

Front. Syst. Neurosci.

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Epilepsy is one of the most common neurological disorders typically characterized by recurrent and uncontrollable seizures, which seriously affects the quality of life of epilepsy patients. The effective tool utilized in the clinical diagnosis of epilepsy is the Electroencephalogram (EEG). The emergence of machine learning promotes the development of automated epilepsy detection techniques. New algorithms are continuously introduced to shorten the detection time and improve classification accuracy. This minireview summarized the latest research of epilepsy detection techniques that focused on acquiring, preprocessing, feature extraction, and classification of epileptic EEG signals. The application of seizure prediction and localization based on EEG signals in the diagnosis of epilepsy was also introduced. And then, the future development trend of epilepsy detection technology has prospected at the end of the article.

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“…However, recent studies have investigated the use of CNNs for automatic analysis of brain data, including prediction of epileptic seizures, [18] epilepsy classification, [19] EEG artifact identification, [20] and detection of high-frequency EEG oscillations. [21] However, there has been limited work to date using CNNs for the identification and classification of seizure-like patterns in EEG, [22][23][24][25] grading the severity of HIE, [26] and in particular neonatal EEG seizure detection through multichannel EEG recordings. [14,15] The literature suggests that 1D time-series can also be directly fed into various formats of CNN architectures for seizure identification [23] and epilepsy detection.…”

Section: Cnns For Evaluation Of the Post-hi Eegmentioning

confidence: 99%

Advanced Deep Learning Spectroscopy of Scalogram Infused CNN Classifiers for Robust Identification of Post‐Hypoxic Epileptiform EEG Spikes

Abbasi

Gunn

Unsworth

2020

Advanced Intelligent Systems

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Hypoxic-ischemic (HI) insults before and during birth, secondary to events such as placental abruption or umbilical cord occlusion, are a significant contributor to neonatal brain injury (hypoxic-ischemic encephalopathy; HIE). [1,2] The preterm newborn is at greater risk of HIE. [1] In contrast to an overall incidence of 1-3/1000 live births at term in high-income countries, preterm babies born before 37 weeks have an HIE incidence of around 37.3/1000 babies born before 37 weeks of gestation, rising to an overall rate of HIE of 120/ 1000 in infants born before 28 weeks of gestation. [1,3] Survivors face lifelong neurodevelopmental problems, including learning and cognitive impairments, behavioral problems, cerebral palsy, and epilepsy. [4] The burden for individuals and their families and health and education economic costs are substantial. [4,5] To develop effective treatments or to appropriately utilize existing treatments requires that we fully understand how brain injury evolves and to identify biological markers (biomarkers) that allow us to determine phases of injury. Key to effective therapy is the knowledge that injury evolves in different phases over time-a latent phase of recovery of oxidative metabolism, which is followed by a secondary loss of cerebral energy metabolism during which time most brain cell injury occurs, followed by a tertiary phase of both repair and ongoing injury. [1,3,6] Therapeutic hypothermia (TH) is currently the only established treatment for HIE in babies born >36 weeks of gestation with moderate-severe HIE, and this treatment is now being cautiously explored for use in preterm babies. [1,2] The current clinical protocol for TH was based on our preclinical studies in term and preterm fetal sheep, which established that this therapy is only effective if started within 6 h after the end of an HI insult and continued for %3 days. [1-3] However, current clinical data show that many babies do not benefit from TH. [2] This in part reflects late recruitment of babies (typically 4-5 h) into treatment. However, it also reflects the fact that birth cannot always be taken as time zero. Many babies may also have experienced HI insults before birth, so that injury may have already evolved beyond the 6 h window of opportunity for TH efficacy.

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Automatic epileptic EEG detection using convolutional neural network with improvements in time-domain

Cited by 140 publications

References 22 publications

Data augmentation for deep-learning-based electroencephalography

Data augmentation for deep-learning-based electroencephalography

Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals

Advanced Deep Learning Spectroscopy of Scalogram Infused CNN Classifiers for Robust Identification of Post‐Hypoxic Epileptiform EEG Spikes

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