Comparison of different input modalities and network structures for deep learning-based seizure detection

Abstract

The manual review of an electroencephalogram (EEG) for seizure detection is a laborious and errorprone process. Thus, automated seizure detection based on machine learning has been studied for decades. Recently, deep learning has been adopted in order to avoid manual feature extraction and selection. In the present study, we systematically compared the performance of different combinations of input modalities and network structures on a fixed window size and dataset to ascertain an optimal combination of input modalities and network structures. The raw time-series EEG, periodogram of the EEG, 2D images of short-time Fourier transform results, and 2D images of raw EEG waveforms were obtained from 5-s segments of intracranial EEGs recorded from a mouse model of epilepsy. A fully connected neural network (FCNN), recurrent neural network (RNN), and convolutional neural network (CNN) were implemented to classify the various inputs. The classification results for the test dataset showed that CNN performed better than FCNN and RNN, with the area under the curve (AUC) for the receiver operating characteristics curves ranging from 0.983 to 0.984, from 0.985 to 0.989, and from 0.989 to 0.993 for FCNN, RNN, and CNN, respectively. As for input modalities, 2D images of raw EEG waveforms yielded the best result with an AUC of 0.993. Thus, CNN can be the most suitable network structure for automated seizure detection when applied to the images of raw EEG waveforms, since CNN can effectively learn a general spatially-invariant representation of seizure patterns in 2D representations of raw EEG.

How Deep Learning Solved My Seizure Detection Problems

“…We trained a model based on the 1D CNN EEG model from Cho and Jang 8 and tested its performance on seizure detection. The model was trained and tested on local field potential (LFP)/EEG data obtained from chronically epileptic mice generated using the intrahippocampal kainate model in our laboratory 9 (LFP/EEG recordings obtained by Dr Trina Basu and human-annotated validation performed by Dr Trina Basu and Dr Pantelis Antonoudiou; Figure 1 ).…”

Accurate detection of spontaneous seizures using a generalized linear model with external validation

“…• Seizures can be automatically detected on electroencephalography (EEG) using a generalized linear model trained on a descriptive feature space • Two independently constructed EEG data sets yielded models with high sensitivity and specificity, demonstrating the general validity of the method • Latency to detection using this method is under 5 seconds for 80% of seizures • This approach can be utilized to create large, accurately labeled EEG data sets for a seizure-prediction pipeline trained on a two-dimensional (2D) image of raw EEG waveforms in a mouse model, yielding an AUROC of 0.993. 20 Another used a random forest classifier trained on envelopes of wavelet coefficients to produce the highest performing AUROC of 0.995. 21 Table S1 has a summary of performance in other recent seizure-detection studies.…”

FV18 Towards epileptogenesis staging with deep neural networks

“…Example applications include the detection of Alzheimer's disease [14], autism [15], or Parkinson's disease [16]. In the context of epilepsy, DL has been applied for abnormal brain activity detection [17,18] as well as seizure detection and prediction [19,20,21,22,23]. Roy et al proposed a hybrid CNN and gated recurrent units (GRU) in classifying normal and abnormal brain activity, which takes time series EEG data as input and outputs the probability of being normal and abnormal, which is one of the first steps to understand the state of the brain activity in order to improve the accuracy of the diagnosis and the quality of patient care [17].…”