Neonatal Seizure Detection Using Deep Convolutional Neural Networks

Ansari, Amir H.; Cherian, Perumpillichira J.; Caicedo, Alexander; Naulaers, Gunnar; Vos, Maarten De; Huffel, Sabine Van

doi:10.1142/s0129065718500119

Cited by 188 publications

(90 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent research has also shown promising results for CNN-based EEG classification. In seizure detection (Acharya et al, 2018a;Ansari et al, 2018a), depression detection (Liu et al, 2017) and sleep stage classification (Acharya et al, 2018b;Ansari et al, 2018b), CNN have shown promising classification capabilities for EEG data. A CNN for EEG-based speech stimulus reconstruction was presented recently (de Taillez et al, 2017), showing that deep learning is a feasible alternative to linear decoding methods.…”

Section: Introductionmentioning

confidence: 99%

EEG-based detection of the locus of auditory attention with convolutional neural networks

Vandecappelle

Deckers

Das

et al. 2018

Preprint

View full text Add to dashboard Cite

When multiple people talk simultaneously, the healthy human auditory system is able to attend to one particular speaker of interest. Recently, it has been demonstrated that it is possible to infer to which speaker someone is attending by relating the neural activity, recorded by electroencephalography (EEG), with the speech signals. This is relevant for an effective noise suppression in hearing devices, in order to detect the target speaker in a multi-speaker scenario. Most auditory attention detection algorithms use a linear EEG decoder to reconstruct the attended stimulus envelope, which is then compared to the original stimuli envelopes to determine the attended speaker. Classifying attention within a short time interval remains the main challenge. We present two different convolutional neural network (CNN)-based approaches to solve this problem. One aims to select the attended speaker from a given set of individual speaker envelopes, and the other extracts the locus of auditory attention (left or right), without knowledge of the speech envelopes. Our results show that it is possible to decode attention within 1-2 seconds, with a median accuracy around 80%, without access to the speech envelopes. This is promising for neuro-steered noise suppression in hearing aids, which requires fast and accurate attention detection. Furthermore, the possibility of detecting the locus of auditory attention without access to the speech envelopes is promising for the scenarios in which per-speaker envelopes are unavailable. It will also enable establishing a fast and objective attention measure in future studies. Index TermsConvolutional neural networks (CNN), auditory attention detection (AAD), electroencephalography (EEG), neurosteered auditory prosthesis, brain-computer interface (BCI)

show abstract

Section: Introductionmentioning

confidence: 99%

EEG-based detection of the locus of auditory attention with convolutional neural networks

Vandecappelle

Deckers

Das

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…When we used a CNN, the best result was achieved with just two convolutional-pooling layers. In contrast, many researchers implemented more than 6 layers for the CNN 26,[37][38][39] . However, these studies did not include multiple fully connected hidden layers between the convolutional and output layers.…”

Section: Discussionmentioning

confidence: 99%

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

Cho

Jang

2020

Sci Rep

View full text Add to dashboard Cite

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.

show abstract

“…Very recent studies also show that heuristic algorithms can be developed to identify spike trains when the maximums of nonlinear energy components of the signal are compared to the background EEG activity, resulting in an overall good detection rate (GDR) of 95%, tested over 353 h recordings from 81 infants [35]. More recent work has demonstrated that a combinational scheme based on the convolutional neural networks (CNN) and random forest can help to automatically identify neonatal seizures in human babies with 77% overall accuracy [36].…”

Section: Related Workmentioning

confidence: 99%

Latent Phase Identification of High-Frequency Micro-Scale Gamma Spike Transients in the Hypoxic Ischemic EEG of Preterm Fetal Sheep Using Spectral Analysis and Fuzzy Classifiers

Abbasi

Gunn

Unsworth

2020

Sensors

View full text Add to dashboard Cite

Premature babies are at high risk of serious neurodevelopmental disabilities, which in many cases are related to perinatal hypoxic–ischemic encephalopathy (HIE). Studies of neuroprotection in animal models consistently suggest that treatment must be started as early as possible in the first 6 h after hypoxia–ischemia (HI), the so-called latent phase before secondary deterioration, to improve outcomes. We have shown in preterm sheep that EEG biomarkers of injury, in the form of high-frequency micro-scale spike transients, develop and evolve in this critical latent phase after severe asphyxia. Real-time automatic identification of such events is important for the early and accurate detection of HI injury, so that the right treatment can be implemented at the right time. We have previously reported successful strategies for accurate identification of EEG patterns after HI. In this study, we report an alternative high-performance approach based on the fusion of spectral Fourier analysis and Type-I fuzzy classifiers (FFT-Type-I-FLC). We assessed its performance in over 2520 min of latent phase EEG recordings from seven asphyxiated in utero preterm fetal sheep exposed to a range of different occlusion periods. The FFT-Type-I-FLC classifier demonstrated 98.9 ± 1.0% accuracy for identification of high-frequency spike transients in the gamma frequency band (namely 80–120 Hz) post-HI. The spectral-based approach (FFT-Type-I-FLC classifier) has similar accuracy to our previous reverse biorthogonal wavelets rbio2.8 basis function and type-1 fuzzy classifier (rbio-WT-Type-1-FLC), providing competitive performance (within the margin of error: 0.89%), but it is computationally simpler and would be readily adapted to identify other potentially relevant EEG waveforms.

show abstract

Neonatal Seizure Detection Using Deep Convolutional Neural Networks

Cited by 188 publications

References 60 publications

EEG-based detection of the locus of auditory attention with convolutional neural networks

EEG-based detection of the locus of auditory attention with convolutional neural networks

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

Latent Phase Identification of High-Frequency Micro-Scale Gamma Spike Transients in the Hypoxic Ischemic EEG of Preterm Fetal Sheep Using Spectral Analysis and Fuzzy Classifiers

Contact Info

Product

Resources

About