A fast machine learning approach to facilitate the detection of interictal epileptiform discharges in the scalp electroencephalogram

Bagheri, Elham; Jin, Jing; Dauwels, Justin; Cash, Sydney S.; Westover, M. Brandon

doi:10.1016/j.jneumeth.2019.108362

Cited by 29 publications

(20 citation statements)

References 39 publications

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“…We aim to find the factors A, B, and C which minimize the weighted objective function (6). We derive the gradient of (6) by calculating the partial derivatives of f w with respect to each factor matrix.…”

Section: Ied Detection Based On Sca-iedpmentioning

confidence: 99%

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Incorporating Uncertainty In Data Labeling Into Detection of Brain Interictal Epileptiform Discharges From EEG Using Weighted optimization

Abdi-Sargezeh

Valentin

Alarcón

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Interictal epileptiform discharges (IEDs) can have various morphologies as well as spatial distributions and sometimes are associated with other brain activities, resulting in uncertainty in their labeling. Such an uncertainty corresponds to the probability of a waveform being an IED. Here, we incorporate this probability in an IED detection system which combines spatial component analysis (SCA) with the IED probabilities referred to as SCA-IEDP-based method. For comparison, we also propose SCA-based method in which the probability of being IED is ignored. The outcome shows that the SCA-IEDP outperforms SCA.

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Section: Ied Detection Based On Sca-iedpmentioning

confidence: 99%

“…For example, two clinical neurophysiologists annotated the IEDs in Massachusetts General Hospital dataset. One of the experts annotated 26997 IEDs, whereas the other annotated 18199 IEDs [6]. This uncertainty can be mathematically expressed by the probability of the waveform being an IED.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Uncertainty In Data Labeling Into Detection of Brain Interictal Epileptiform Discharges From EEG Using Weighted optimization

Abdi-Sargezeh

Valentin

Alarcón

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…The analysis of this huge amount of data is facilitated by reliable, automated spike detection. ML algorithms were able to identify EEG epochs without spikes, thus excluding them from visual analysis 64 . In a clinical environment, deep learning was found to be robust for automated review and quantification of epileptic discharges in patients with generalized epilepsy 44,65 .…”

Section: Machine Learningmentioning

confidence: 99%

“…ML algorithms were able to identify EEG epochs without spikes, thus excluding them from visual analysis. 64 In a clinical environment, deep learning was found to be robust for automated review and quantification of epileptic discharges in patients with generalized epilepsy. 44,65 Another recently published large-scale study, also used a deep learning-based detection algorithm for epileptiform EEG discharges that was validated against scorings of experts, with remarkable results.…”

Section: For Seizure Detection From Eegmentioning

confidence: 99%

Machine learning and wearable devices of the future

et al. 2020

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Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applications of ML in epilepsy is seizure detection and prediction, using wearable devices (WDs). However, not all currently available algorithms implemented in WDs are using ML. In this review, we summarize the state of the art of using WDs and ML in epilepsy, and we outline future development in these domains. There is published evidence for reliable detection of epileptic seizures using implanted electroencephalography (EEG) electrodes and wearable, non‐EEG devices. Application of ML using the data recorded with WDs from a large number of patients could change radically the way we diagnose and manage patients with epilepsy.

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“…Rodrigues et al [9] used neural networks for recognizing diseases such as alcoholism, Bi et al [10] for recognizing early Alzheimer's disease, Shim et al [11] for detect schizophrenia disease, Bagheri et al [12] for recognizing the inter-period epileptiform disease. Čukić et al [13] used machine learning to detect depression.…”

Section: Neural Network For Diagnosing Of Diseases By Eegmentioning

confidence: 99%

Review of Neural Networks in the EEG Signal Recognition

Rakhmatulin

2020

SSRN Journal

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In the last decade, unprecedented progress in the development of neural networks influenced dozens of different industries, among which are signal processing for the electroencephalography process (EEG). Electroencephalography, even though it appeared in the first half of the 20th century, to this day didn't change the physical principles of operation. But the signal processing technique due to the use of neural networks progressed significantly in this area. Evidence for this can serve that for the past 5 years more than 1000 publications on the topic of using machine learning have been published in popular libraries. Many different models of neural networks complicate the process of understanding the real situation in this area. In this manuscript, we provided the most comprehensive overview of research where were used neural networks for EEG signal processing.

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A fast machine learning approach to facilitate the detection of interictal epileptiform discharges in the scalp electroencephalogram

Cited by 29 publications

References 39 publications

Incorporating Uncertainty In Data Labeling Into Detection of Brain Interictal Epileptiform Discharges From EEG Using Weighted optimization

Incorporating Uncertainty In Data Labeling Into Detection of Brain Interictal Epileptiform Discharges From EEG Using Weighted optimization

Machine learning and wearable devices of the future

Review of Neural Networks in the EEG Signal Recognition

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