A Review of EEG and MEG Epileptic Spike Detection Algorithms

El‐Samie, Fathi E. Abd; Alotaiby, Turky N.; Khalid, Muhammad Imran; Alshebeili, Saleh A.; Aldosari, Saeed A.

doi:10.1109/access.2018.2875487

Cited by 66 publications

(40 citation statements)

References 77 publications

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“…There has been decades of research into automated detection of seizures and epileptic activity from EEG, with hundreds of papers published each year [5][6][7]. The advent of machine learning and artificial intelligence has sparked a new wave of interest in developing generalizable algorithms that can be trained to recognise epileptic activity in EEG data.…”

Section: Introductionmentioning

confidence: 99%

Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy

Clarke¹,

Karoly

Nurse

et al. 2021

Epilepsy & Behavior

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Epilepsy diagnosis can be costly, time-consuming and not uncommonly inaccurate. The reference standard diagnostic monitoring is continuous video-EEG monitoring, ideally capturing all events or concordant interictal discharges. Automating EEG data review would save time and resources, thus enabling more people to receive reference standard monitoring and also potentially herald a more quantitative approach to therapeutic outcomes. There is substantial research into automated detection of seizures and epileptic activity from EEG. However, automated detection software is not widely used in the clinic; and, despite numerous published algorithms, few methods have regulatory approval for detecting epileptic activity from EEG.This study reports on a deep learning algorithm for computer-assisted EEG review. Deep, convolutional neural networks were trained to detect epileptic discharges using a pre-existing dataset of over 6000 labelled events in a cohort of 103 patients with idiopathic generalized epilepsy (IGE). Patients underwent 24-hour ambulatory outpatient EEG, and all data was curated and confirmed independently by two epilepsy specialists . The resulting automated detection algorithm was then used to review diagnostic scalp-EEG for seven patients (four with IGE and three with events mimicking seizures) to validate performance in a clinical setting.The automated detection algorithm showed state-of-the-art performance for detecting epileptic activity from clinical EEG, with mean sensitivity of >95% and corresponding mean false positive rate of 1 detection per minute. Importantly, diagnostic case studies showed that the automated detection algorithm reduced human review time by 80%-99%, without .

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Section: Introductionmentioning

confidence: 99%

Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy

Clarke¹,

Karoly

Nurse

et al. 2021

Epilepsy & Behavior

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“…Furthermore, during the main processing stage, an essential algorithmic step is spike detection. A popular approach for spike detection is using wavelet transform (WT) but also advances in ML bring promising results using CNNs [ 236 , 237 ]. After spike detection, feature extraction is done mainly using PCA [ 238 ] or local Fisher discriminant analysis (LFDA) [ 239 ].…”

Section: Technological Synergies Driving Neural Rehabilitationmentioning

confidence: 99%

Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges

Nizamis

Athanasiou

Almpani

et al. 2021

Sensors

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Recent advances in the field of neural rehabilitation, facilitated through technological innovation and improved neurophysiological knowledge of impaired motor control, have opened up new research directions. Such advances increase the relevance of existing interventions, as well as allow novel methodologies and technological synergies. New approaches attempt to partially overcome long-term disability caused by spinal cord injury, using either invasive bridging technologies or noninvasive human–machine interfaces. Muscular dystrophies benefit from electromyography and novel sensors that shed light on underlying neuromotor mechanisms in people with Duchenne. Novel wearable robotics devices are being tailored to specific patient populations, such as traumatic brain injury, stroke, and amputated individuals. In addition, developments in robot-assisted rehabilitation may enhance motor learning and generate movement repetitions by decoding the brain activity of patients during therapy. This is further facilitated by artificial intelligence algorithms coupled with faster electronics. The practical impact of integrating such technologies with neural rehabilitation treatment can be substantial. They can potentially empower nontechnically trained individuals—namely, family members and professional carers—to alter the programming of neural rehabilitation robotic setups, to actively get involved and intervene promptly at the point of care. This narrative review considers existing and emerging neural rehabilitation technologies through the perspective of replacing or restoring functions, enhancing, or improving natural neural output, as well as promoting or recruiting dormant neuroplasticity. Upon conclusion, we discuss the future directions for neural rehabilitation research, diagnosis, and treatment based on the discussed technologies and their major roadblocks. This future may eventually become possible through technological evolution and convergence of mutually beneficial technologies to create hybrid solutions.

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“…A recent review of an extensive number of machine learning methods for automated IED detection (SVM, KNN, etc.) reported sensitivity from 30% to 99% [2]. Among all the existing methods, Persyst [3], the industry-standard IED detection software developed by Persyst Corporation, is the only software with FDA approval and has been shown to have similar performance to skilled neurologists [4].…”

Section: Introductionmentioning

confidence: 99%

Automated Inter-Ictal Epileptiform Discharge Detection from Routine EEG

Nhu

Janmohamed

Shakhatreh

et al. 2021

Healthier Lives, Digitally Enabled

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Epilepsy is the most common neurological disorder. The diagnosis commonly requires manual visual electroencephalogram (EEG) analysis which is time-consuming. Deep learning has shown promising performance in detecting interictal epileptiform discharges (IED) and may improve the quality of epilepsy monitoring. However, most of the datasets in the literature are small (n≤100) and collected from single clinical centre, limiting the generalization across different devices and settings. To better automate IED detection, we cross-evaluated a Resnet architecture on 2 sets of routine EEG recordings from patients with idiopathic generalized epilepsy collected at the Alfred Health Hospital and Royal Melbourne Hospital (RMH). We split these EEG recordings into 2s windows with or without IED and evaluated different model variants in terms of how well they classified these windows. The results from our experiment showed that the architecture generalized well across different datasets with an AUC score of 0.894 (95% CI, 0.881–0.907) when trained on Alfred’s dataset and tested on RMH’s dataset, and 0.857 (95% CI, 0.847–0.867) vice versa. In addition, we compared our best model variant with Persyst and observed that the model was comparable.

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A Review of EEG and MEG Epileptic Spike Detection Algorithms

Cited by 66 publications

References 77 publications

Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy

Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy

Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges

Automated Inter-Ictal Epileptiform Discharge Detection from Routine EEG

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