An artificial intelligence-based EEG algorithm for detection of epileptiform EEG discharges: Validation against the diagnostic gold standard

Fürbass, Franz; Kural, Mustafa Aykut; Gritsch, G.; Hartmann, M.; Kluge, Tilmann; Beniczky, Sándor

doi:10.1016/j.clinph.2020.02.032

Cited by 68 publications

(76 citation statements)

References 24 publications

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“…Experienced human EEG readers were found to have pairwise sensitivities of 40-52% for each other's spike markings [26]. Most recently, an artificial intelligence-based algorithm was reported to detect single epileptic spikes with a sensitivity of 82% [9]. In our approach, assessing the sensitivity of spike detection on a single discharge level was not possible.…”

Section: Spike Detectionmentioning

confidence: 92%

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Automated interictal source localisation based on high-density EEG

Vorderwülbecke

Baroumand

Spinelli

et al. 2021

Seizure

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To study the accuracy of automated interictal EEG source localisation based on high-density EEG, and to compare it to low-density EEG. Methods: Thirty patients operated for pharmacoresistant focal epilepsy were retrospectively examined. Twelve months after resective brain surgery, 18 were seizure-free or had 'auras' only, while 12 had persistence of disabling seizures. Presurgical 257-channel EEG lasting 3-20 h was down-sampled to 25, 40, and 204 channels for separate analyses. For each electrode setup, interictal spikes were detected, clustered, and averaged automatically before validation by an expert reviewer. An individual 6-layer finite difference head model and the standardised low-resolution electromagnetic tomography were used to localise the maximum source activity of the most prevalent spike. Sublobar concordance with the resected brain area was visually assessed and related to favourable vs. unfavourable postsurgical outcome. Results: Depending on the EEG setup, epileptic spikes were detected in 21-24 patients (70-80%). The median number of single spikes per average was 470 (range 17-15,066). Diagnostic sensitivity of EEG source localisation was 58-75%, specificity was 50-67%, and overall accuracy was 55-71%. There were no significant differences between low-and high-density EEG setups with 25 to 257 electrodes. Conclusion: Automated high-density EEG source localisation provides meaningful information in the majority of cases. With hundreds of single spikes averaged, diagnostic accuracy is similar in high-and low-density EEG. Therefore, low-density EEG may be sufficient for interictal EEG source localisation if high numbers of spikes are available.

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Section: Spike Detectionmentioning

confidence: 92%

“…5A, together with their 95%-CIs. Across all setups, the average sensitivity was 68%, the average specificity was 61%, and the average diagnostic 15 [6][7][8][9][10][11][12][13][14][15][16][17] 15 [12][13][14][15][16] accuracy was 65%. There were no statistically significant differences between the electrode setups (0.69 < p < 0.86).…”

Section: Source Localisationmentioning

confidence: 99%

Automated interictal source localisation based on high-density EEG

Vorderwülbecke

Baroumand

Spinelli

et al. 2021

Seizure

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“…Issues regarding mental disorders are still of interest to scholars (e.g., [270][271][272][273][274][275][276][277]), in particular, epilepsy, Alzheimer's disease, mild cognitive impairments, synchronization, and attention-deficit/hyperactivity disorders. For epilepsy, recent research is concerned with issues such as proposing a deep learning-driven EEG approach to detect epileptic seizures from EEG discharges [278], epilepsy lateralization through intra-hemispheric brain networks based on resting-state magnetoencephalography data [279], and EEG-based multiclass seizure type classification using CNNs and transfer learning [280]. For Alzheimer's disease, recent studies have been concerned about issues such as classification of Alzheimer's disease [272,274,281], EEG microstate complexity for early diagnosis of Alzheimer's disease [277], and functional integration and segregation in multiplex brain networks for Alzheimer's disease [282].…”

Section: Latest Research Concerning Ai-enhanced Eeg Analysismentioning

confidence: 99%

Global research on artificial intelligence-enhanced human electroencephalogram analysis

Chen

Tao

Wang

et al. 2021

Neural Comput & Applic

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The application of artificial intelligence (AI) technologies in assisting human electroencephalogram (EEG) analysis has become an active scientific field. This study aims to present a comprehensive review of the research field of AI-enhanced human EEG analysis. Using bibliometrics and topic modeling, research articles concerning AI-enhanced human EEG analysis collected from the Web of Science database during the period 2009-2018 were analyzed. After examining 2053 research articles published around the world, it was found that the annual number of articles had significantly grown from 78 to 468, with the USA and China being the most influential and prolific. The results of the keyword analysis showed that ''electroencephalogram,'' ''brain-computer interface,'' ''classification,'' ''support vector machine,'' ''electroencephalography,'' and ''signal'' were the most frequently used. The results of topic modeling and evolution analyses highlighted several important issues, including epileptic seizure detection, brain-machine interface, EEG classification, mental disorders, emotion, and alcoholism and anesthesia. The findings suggest that such visualization and analysis of the research articles could provide a comprehensive overview of the field for communities of practice and inquiry worldwide.

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“…Recently, the study of emotion recognition is mainly used in psychology, emotional calculation, artificial intelligence, computer vision, and medical treatment, etc. (Ramirez et al, 2010;Xin et al, 2019;Fürbass et al, 2020). For example, emotion recognition is helpful to the diagnosis of depression, schizophrenia, and other mental diseases.…”

Section: Introductionmentioning

confidence: 99%

Review on Emotion Recognition Based on Electroencephalography

Liu

Zhang

et al. 2021

Front. Comput. Neurosci.

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Emotions are closely related to human behavior, family, and society. Changes in emotions can cause differences in electroencephalography (EEG) signals, which show different emotional states and are not easy to disguise. EEG-based emotion recognition has been widely used in human-computer interaction, medical diagnosis, military, and other fields. In this paper, we describe the common steps of an emotion recognition algorithm based on EEG from data acquisition, preprocessing, feature extraction, feature selection to classifier. Then, we review the existing EEG-based emotional recognition methods, as well as assess their classification effect. This paper will help researchers quickly understand the basic theory of emotion recognition and provide references for the future development of EEG. Moreover, emotion is an important representation of safety psychology.

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An artificial intelligence-based EEG algorithm for detection of epileptiform EEG discharges: Validation against the diagnostic gold standard

Cited by 68 publications

References 24 publications

Automated interictal source localisation based on high-density EEG

Automated interictal source localisation based on high-density EEG

Global research on artificial intelligence-enhanced human electroencephalogram analysis

Review on Emotion Recognition Based on Electroencephalography

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