Deep Learning for Interictal Epileptiform Discharge Detection from Scalp EEG Recordings

Electroencephalogram (EEG) is widely used for the diagnosis of neurological conditions like epilepsy, neurodegenerative illnesses and sleep related disorders. Proper interpretation of EEG recordings requires the expertise of trained neurologists, a resource which is scarce in the developing world. Neurologists spend a significant portion of their time sifting through EEG recordings looking for abnormalities. Most recordings turn out to be completely normal, owing to the low yield of EEG tests. To minimize such wastage of time and effort, automatic algorithms could be used to provide pre-diagnostic screening to separate normal from abnormal EEG. Data driven machine learning offers a way forward however, design and verification of modern machine learning algorithms require properly curated labeled datasets. To avoid bias, deep learning based methods must be trained on large datasets from diverse sources. This work presents a new open-source dataset, named the NMT Scalp EEG Dataset, consisting of 2,417 recordings from unique participants spanning almost 625 h. Each recording is labeled as normal or abnormal by a team of qualified neurologists. Demographic information such as gender and age of the patient are also included. Our dataset focuses on the South Asian population. Several existing state-of-the-art deep learning architectures developed for pre-diagnostic screening of EEG are implemented and evaluated on the NMT, and referenced against baseline performance on the well-known Temple University Hospital EEG Abnormal Corpus. Generalization of deep learning based architectures across the NMT and the reference datasets is also investigated. The NMT dataset is being released to increase the diversity of EEG datasets and to overcome the scarcity of accurately annotated publicly available datasets for EEG research.

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Interictal Epileptiform Discharge Detection in EEG in Different Practice Settings

Journal of Clinical Neurophysiology ◽

10.1097/wnp.0000000000000492 ◽

2018 ◽

Vol 35 (5) ◽

pp. 375-380 ◽

Cited By ~ 9

Author(s):

Jonathan J. Halford ◽

M. Brandon Westover ◽

Suzette M. LaRoche ◽

Micheal P. Macken ◽

Ekrem Kutluay ◽

...

Keyword(s):

Epileptiform Discharge ◽

Interictal Epileptiform Discharge ◽

Discharge Detection

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F85. Deep learning for detection of epileptiform discharges from scalp EEG recordings

Clinical Neurophysiology ◽

10.1016/j.clinph.2018.04.248 ◽

2018 ◽

Vol 129 ◽

pp. e98-e99 ◽

Cited By ~ 2

Author(s):

Michel J. van Putten ◽

Rafael de Carvalho ◽

Marleen C. Tjepkema-Cloostermans

Keyword(s):

Deep Learning ◽

Scalp Eeg ◽

Epileptiform Discharges ◽

Eeg Recordings

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Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning

SLEEP ◽

10.1093/sleep/zsaa112 ◽

2020 ◽

Vol 43 (11) ◽

Author(s):

Maurice Abou Jaoude ◽

Haoqi Sun ◽

Kyle R Pellerin ◽

Milena Pavlova ◽

Rani A Sarkis ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Learning Algorithm ◽

Sleep Stages ◽

Sleep Staging ◽

Cohen’S Kappa ◽

Scalp Eeg ◽

Cohen's Kappa ◽

Eeg Recordings

Abstract Study Objectives Develop a high-performing, automated sleep scoring algorithm that can be applied to long-term scalp electroencephalography (EEG) recordings. Methods Using a clinical dataset of polysomnograms from 6,431 patients (MGH–PSG dataset), we trained a deep neural network to classify sleep stages based on scalp EEG data. The algorithm consists of a convolutional neural network for feature extraction, followed by a recurrent neural network that extracts temporal dependencies of sleep stages. The algorithm’s inputs are four scalp EEG bipolar channels (F3-C3, C3-O1, F4-C4, and C4-O2), which can be derived from any standard PSG or scalp EEG recording. We initially trained the algorithm on the MGH–PSG dataset and used transfer learning to fine-tune it on a dataset of long-term (24–72 h) scalp EEG recordings from 112 patients (scalpEEG dataset). Results The algorithm achieved a Cohen’s kappa of 0.74 on the MGH–PSG holdout testing set and cross-validated Cohen’s kappa of 0.78 after optimization on the scalpEEG dataset. The algorithm also performed well on two publicly available PSG datasets, demonstrating high generalizability. Performance on all datasets was comparable to the inter-rater agreement of human sleep staging experts (Cohen’s kappa ~ 0.75 ± 0.11). The algorithm’s performance on long-term scalp EEGs was robust over a wide age range and across common EEG background abnormalities. Conclusion We developed a deep learning algorithm that achieves human expert level sleep staging performance on long-term scalp EEG recordings. This algorithm, which we have made publicly available, greatly facilitates the use of large long-term EEG clinical datasets for sleep-related research.

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Deep learning for detection of focal epileptiform discharges from scalp EEG recordings

Clinical Neurophysiology ◽

10.1016/j.clinph.2018.06.024 ◽

2018 ◽

Vol 129 (10) ◽

pp. 2191-2196 ◽

Cited By ~ 26

Author(s):

Marleen C. Tjepkema-Cloostermans ◽

Rafael C.V. de Carvalho ◽

Michel J.A.M. van Putten

Keyword(s):

Deep Learning ◽

Scalp Eeg ◽

Epileptiform Discharges ◽

Eeg Recordings

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Neural silences can be localized rapidly using noninvasive scalp EEG

Communications Biology ◽

10.1038/s42003-021-01768-0 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Alireza Chamanzar ◽

Marlene Behrmann ◽

Pulkit Grover

Keyword(s):

Center Of Mass ◽

Cost Effective ◽

Human Head ◽

Head Model ◽

Cortical Function ◽

Scalp Eeg ◽

Localization Algorithms ◽

Important Benefit ◽

Eeg Recordings ◽

Detection And Localization

AbstractA rapid and cost-effective noninvasive tool to detect and characterize neural silences can be of important benefit in diagnosing and treating many disorders. We propose an algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural silences, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric cortical resection patients, using fewer than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53 mm), as well for 100 different simulated regions of silence based on a real human head model (12 ± 0.7 mm vs. 54 ± 2.2 mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.

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Zero-crossing patterns reveal subtle epileptiform discharges in the scalp EEG

Scientific Reports ◽

10.1038/s41598-021-83337-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jan Pyrzowski ◽

Jean- Eudes Le Douget ◽

Amal Fouad ◽

Mariusz Siemiński ◽

Joanna Jędrzejczak ◽

...

Keyword(s):

Normal Subjects ◽

Intracranial Eeg ◽

Zero Crossing ◽

Temporal Precision ◽

Scalp Eeg ◽

Epileptiform Discharges ◽

Interictal Epileptiform Discharges ◽

Eeg Recordings ◽

Patients With Epilepsy ◽

Low Amplitude

AbstractClinical diagnosis of epilepsy depends heavily on the detection of interictal epileptiform discharges (IEDs) from scalp electroencephalographic (EEG) signals, which by purely visual means is far from straightforward. Here, we introduce a simple signal analysis procedure based on scalp EEG zero-crossing patterns which can extract the spatiotemporal structure of scalp voltage fluctuations. We analyzed simultaneous scalp and intracranial EEG recordings from patients with pharmacoresistant temporal lobe epilepsy. Our data show that a large proportion of intracranial IEDs manifest only as subtle, low-amplitude waveforms below scalp EEG background and could, therefore, not be detected visually. We found that scalp zero-crossing patterns allow detection of these intracranial IEDs on a single-trial level with millisecond temporal precision and including some mesial temporal discharges that do not propagate to the neocortex. Applied to an independent dataset, our method discriminated accurately between patients with epilepsy and normal subjects, confirming its practical applicability.

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EARLY PREDICTION OF MEDICATION REFRACTORINESS IN CHILDREN WITH IDIOPATHIC EPILEPSY BASED ON SCALP EEG ANALYSIS

International Journal of Neural Systems ◽

10.1142/s0129065714500233 ◽

2014 ◽

Vol 24 (07) ◽

pp. 1450023 ◽

Cited By ~ 14

Author(s):

LUNG-CHANG LIN ◽

CHEN-SEN OUYANG ◽

CHING-TAI CHIANG ◽

REI-CHENG YANG ◽

RONG-CHING WU ◽

...

Keyword(s):

Refractory Epilepsy ◽

Recall Rate ◽

Idiopathic Epilepsy ◽

Spectral Edge Frequency ◽

Scalp Eeg ◽

Feature Descriptors ◽

Antiepileptic Drug Treatment ◽

Eeg Recordings ◽

Precision Rate

Refractory epilepsy often has deleterious effects on an individual's health and quality of life. Early identification of patients whose seizures are refractory to antiepileptic drugs is important in considering the use of alternative treatments. Although idiopathic epilepsy is regarded as having a significantly lower risk factor of developing refractory epilepsy, still a subset of patients with idiopathic epilepsy might be refractory to medical treatment. In this study, we developed an effective method to predict the refractoriness of idiopathic epilepsy. Sixteen EEG segments from 12 well-controlled patients and 14 EEG segments from 11 refractory patients were analyzed at the time of first EEG recordings before antiepileptic drug treatment. Ten crucial EEG feature descriptors were selected for classification. Three of 10 were related to decorrelation time, and four of 10 were related to relative power of delta/gamma. There were significantly higher values in these seven feature descriptors in the well-controlled group as compared to the refractory group. On the contrary, the remaining three feature descriptors related to spectral edge frequency, kurtosis, and energy of wavelet coefficients demonstrated significantly lower values in the well-controlled group as compared to the refractory group. The analyses yielded a weighted precision rate of 94.2%, and a 93.3% recall rate. Therefore, the developed method is a useful tool in identifying the possibility of developing refractory epilepsy in patients with idiopathic epilepsy.

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