Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture

Abstract

A deep learning classifier for detecting seizures in neonates is proposed. This architecture is designed to detect seizure events from raw electroencephalogram (EEG) signals as opposed to the state-of-the-art hand engineered feature-based representation employed in traditional machine learning based solutions. The seizure detection system utilises only convolutional layers in order to process the multichannel time domain signal and is designed to exploit the large amount of weakly labelled data in the training stage. The system performance is assessed on a large database of continuous EEG recordings of 834h in duration; this is further validated on a held-out publicly available dataset and compared with two baseline SVM based systems.The developed system achieves a 56% relative improvement with respect to a feature-based state-of-the art baseline, reaching an AUC of 98.5%; this also compares favourably both in terms of performance and run-time.The effect of varying architectural parameters is thoroughly studied. The performance improvement is achieved through novel architecture design which allows more efficient usage of available training data and end-to-end optimisation from the front-end feature extraction to the back-end classification. The proposed architecture opens new avenues for the application of deep learning to neonatal EEG, where the performance becomes a function of the amount of training data with less dependency on the availability of precise clinical labels.

Topics and trends in artificial intelligence assisted human brain research

“…Latest trends in the applications of deep learning techniques in the AI-enhanced human brain research should be highlighted, which is covered within the topic Network. For example, O'Shea et al [105] proposed an innovative deep-learning classifier for seizures detection by detecting seizure events from raw EEG signals. With the basis of deep neural networks and hidden Markov random field models, Fan et al [106] proposed an unsupervised cerebrovascular segmentation method of time-of-flight magnetic resonance angiography images.…”

Recent Advances in the Diagnosis and Treatment of Neonatal Seizures

“…O'Shea et al showed that the fully convolutional neural network could classify raw EEG features as seizures versus non-seizures comparable to a previously developed algorithm that had used heavily engineered features. 22 Although complete replacement of the expert human reviewer is not possible at this time, user-friendly alarm to notify bedside providers about the probability of seizures can be particularly helpful in the early diagnosis and treatment of neonatal seizures.…”

Advanced Deep Learning Spectroscopy of Scalogram Infused CNN Classifiers for Robust Identification of Post‐Hypoxic Epileptiform EEG Spikes

“…[12] Current studies on the application of automated convolutional neural network (CNN)-based strategies in clinical neonatal EEG studies have primarily focused on developing consistently interpretable determinations of seizures and other EEG features, but not microseizures or post-HI EEG biomarkers. [13][14][15][16][17] Our team has previously shown various successful fusion strategies for automatic detection of post-HI microscale EEG transients. [8,10] Further, we have undertaken preliminary examination of the wavelet-scalogram (WS) CNN structure to robustly identify post-HI sharp waves from an EEG background and artifact during the latent phase after the HI insult with 95.34% accuracy.…”

“…[21] However, there has been limited work to date using CNNs for the identification and classification of seizure-like patterns in EEG, [22][23][24][25] grading the severity of HIE, [26] and in particular neonatal EEG seizure detection through multichannel EEG recordings. [14,15] The literature suggests that 1D time-series can also be directly fed into various formats of CNN architectures for seizure identification [23] and epilepsy detection. [27] Feature extraction through wavelet transform of data (time-frequency images) has been shown to enhance the performance of the conventional CNN approaches.…”