Nejvíce citovaný článek - PubMed ID 24531133
Manual visual review, annotation and categorization of electroencephalography (EEG) is a time-consuming task that is often associated with human bias and requires trained electrophysiology experts with specific domain knowledge. This challenge is now compounded by development of measurement technologies and devices allowing large-scale heterogeneous, multi-channel recordings spanning multiple brain regions over days, weeks. Currently, supervised deep-learning techniques were shown to be an effective tool for analyzing big data sets, including EEG. However, the most significant caveat in training the supervised deep-learning models in a clinical research setting is the lack of adequate gold-standard annotations created by electrophysiology experts. Here, we propose a semi-supervised machine learning technique that utilizes deep-learning methods with a minimal amount of gold-standard labels. The method utilizes a temporal autoencoder for dimensionality reduction and a small number of the expert-provided gold-standard labels used for kernel density estimating (KDE) maps. We used data from electrophysiological intracranial EEG (iEEG) recordings acquired in two hospitals with different recording systems across 39 patients to validate the method. The method achieved iEEG classification (Pathologic vs. Normal vs. Artifacts) results with an area under the receiver operating characteristic (AUROC) scores of 0.862 ± 0.037, 0.879 ± 0.042, and area under the precision-recall curve (AUPRC) scores of 0.740 ± 0.740, 0.714 ± 0.042. This demonstrates that semi-supervised methods can provide acceptable results while requiring only 100 gold-standard data samples in each classification category. Subsequently, we deployed the technique to 12 novel patients in a pseudo-prospective framework for detecting Interictal epileptiform discharges (IEDs). We show that the proposed temporal autoencoder was able to generalize to novel patients while achieving AUROC of 0.877 ± 0.067 and AUPRC of 0.705 ± 0.154.
OBJECTIVE: Epilepsy develops in 70 to 90% of children with tuberous sclerosis complex (TSC) and is often resistant to medication. Recently, the concept of preventive antiepileptic treatment to modify the natural history of epilepsy has been proposed. EPISTOP was a clinical trial designed to compare preventive versus conventional antiepileptic treatment in TSC infants. METHODS: In this multicenter study, 94 infants with TSC without seizure history were followed with monthly video electroencephalography (EEG), and received vigabatrin either as conventional antiepileptic treatment, started after the first electrographic or clinical seizure, or preventively when epileptiform EEG activity before seizures was detected. At 6 sites, subjects were randomly allocated to treatment in a 1:1 ratio in a randomized controlled trial (RCT). At 4 sites, treatment allocation was fixed; this was denoted an open-label trial (OLT). Subjects were followed until 2 years of age. The primary endpoint was the time to first clinical seizure. RESULTS: In 54 subjects, epileptiform EEG abnormalities were identified before seizures. Twenty-seven were included in the RCT and 27 in the OLT. The time to the first clinical seizure was significantly longer with preventive than conventional treatment [RCT: 364 days (95% confidence interval [CI] = 223-535) vs 124 days (95% CI = 33-149); OLT: 426 days (95% CI = 258-628) vs 106 days (95% CI = 11-149)]. At 24 months, our pooled analysis showed preventive treatment reduced the risk of clinical seizures (odds ratio [OR] = 0.21, p = 0.032), drug-resistant epilepsy (OR = 0.23, p = 0.022), and infantile spasms (OR = 0, p < 0.001). No adverse events related to preventive treatment were noted. INTERPRETATION: Preventive treatment with vigabatrin was safe and modified the natural history of seizures in TSC, reducing the risk and severity of epilepsy. ANN NEUROL 2021;89:304-314.
- MeSH
- antikonvulziva terapeutické užití MeSH
- elektroencefalografie MeSH
- epilepsie farmakoterapie etiologie patofyziologie prevence a kontrola MeSH
- kojenec MeSH
- křeče u dětí prevence a kontrola MeSH
- lidé MeSH
- novorozenec MeSH
- plošný screening MeSH
- refrakterní epilepsie prevence a kontrola MeSH
- tuberózní skleróza komplikace patofyziologie MeSH
- vigabatrin terapeutické užití MeSH
- záchvaty diagnóza farmakoterapie etiologie prevence a kontrola MeSH
- Check Tag
- kojenec MeSH
- lidé MeSH
- mužské pohlaví MeSH
- novorozenec MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- randomizované kontrolované studie MeSH
- Názvy látek
- antikonvulziva MeSH
- vigabatrin MeSH
The electroencephalogram (EEG) is a cornerstone of neurophysiological research and clinical neurology. Historically, the classification of EEG as showing normal physiological or abnormal pathological activity has been performed by expert visual review. The potential value of unbiased, automated EEG classification has long been recognized, and in recent years the application of machine learning methods has received significant attention. A variety of solutions using convolutional neural networks (CNN) for EEG classification have emerged with impressive results. However, interpretation of CNN results and their connection with underlying basic electrophysiology has been unclear. This paper proposes a CNN architecture, which enables interpretation of intracranial EEG (iEEG) transients driving classification of brain activity as normal, pathological or artifactual. The goal is accomplished using CNN with long short-term memory (LSTM). We show that the method allows the visualization of iEEG graphoelements with the highest contribution to the final classification result using a classification heatmap and thus enables review of the raw iEEG data and interpret the decision of the model by electrophysiology means.
- MeSH
- artefakty MeSH
- datové soubory jako téma MeSH
- deep learning * MeSH
- elektroencefalografie klasifikace přístrojové vybavení metody MeSH
- lidé MeSH
- ROC křivka MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- pozorovací studie MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
- validační studie MeSH
