A Stacked Sparse Autoencoder-Based Detector for Automatic Identification of Neuromagnetic High Frequency Oscillations in Epilepsy

Guo, Jiayang; Yang, Kun; Liu, Hongyi; Yin, Chunli; Xiang, Jing; Li, Hailong; Ji, Rongrong; Gao, Yue

doi:10.1109/tmi.2018.2836965

Cited by 34 publications

(44 citation statements)

References 45 publications

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“…(2018) employed the stacked sparse auto encoder (SSAE) to facilitate the clinical detection of HFOs in the MEG records from 20 patients with localization related epilepsy. The proposed SSAE detector also outperformed the threshold-based models by achieving 89.9% in accuracy, 88.2% in sensitivity and 91.6% in specificity 27 . The LSTM architecture is better suited for the analysis of time series data compared to other neural network architectures and the LSTM networks have been started to attract interest in the field of EEG analysis.…”

Section: Discussionmentioning

confidence: 90%

A Long Short-Term Memory neural network for the detection of epileptiform spikes and high frequency oscillations

Medvedev

Agoureeva

Murro

2019

Sci Rep

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Over the last two decades, the evidence has been growing that in addition to epileptic spikes high frequency oscillations (HFOs) are important biomarkers of epileptogenic tissue. New methods of artificial intelligence such as deep learning neural networks can provide additional tools for automated analysis of EEG. Here we present a Long Short-Term Memory neural network for detection of spikes, ripples and ripples-on-spikes (RonS). We used intracranial EEG (iEEG) from two independent datasets. First dataset (7 patients) was used for network training and testing. The second dataset (5 patients) was used for cross-institutional validation. 1000 events of each class (spike, RonS, ripple and baseline) were selected from the candidates initially found using a novel threshold method. Network training was performed using random selections of 50–500 events (per class) from all patients from the 1st dataset. This ‘global’ network was then tested on other events for each patient from both datasets. The network was able to detect events with a good generalisability namely, with total accuracy and specificity for each class exceeding 90% in all cases, and sensitivity less than 86% in only two cases (82.5% for spikes in one patient and 81.9% for ripples in another patient). The deep learning networks can significantly accelerate the analysis of iEEG data and increase their diagnostic value which may improve surgical outcome in patients with localization-related intractable epilepsy.

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Section: Discussionmentioning

confidence: 90%

A Long Short-Term Memory neural network for the detection of epileptiform spikes and high frequency oscillations

Medvedev

Agoureeva

Murro

2019

Sci Rep

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“…Previous reports ( Xiang et al , 2014 ; Fedele et al , 2016 ; Migliorelli et al , 2017 ; van Klink et al , 2017 ; Guo et al , 2018 ) have shown the possibility to automatically detect HFOs, which include ripples and fast ripples, by using a set of criteria, such as amplitude and number of oscillations. However, even ripples and fast ripples are detected, it is still unclear if these detected ripples or fast ripples are pathological or physiological.…”

Section: Discussionmentioning

confidence: 99%

Kurtosis and skewness of high-frequency brain signals are altered in paediatric epilepsy

Xiang

Maue

Fan

et al. 2020

Brain Communications

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Intracranial studies provide solid evidence that high-frequency brain signals are a new biomarker for epilepsy. Unfortunately, epileptic (pathological) high-frequency signals can be intermingled with physiological high-frequency signals making these signals difficult to differentiate. Recent success in non-invasive detection of high-frequency brain signals opens a new avenue for distinguishing pathological from physiological high-frequency signals. The objective of the present study is to characterize pathological and physiological high-frequency signals at source levels by using kurtosis and skewness analyses. Twenty-three children with medically intractable epilepsy and age-/gender-matched healthy controls were studied using magnetoencephalography. Magnetoencephalographic data in three frequency bands, which included 2–80 Hz (the conventional low-frequency signals), 80–250 Hz (ripples) and 250–600 Hz (fast ripples), were analysed. The kurtosis and skewness of virtual electrode signals in eight brain regions, which included left/right frontal, temporal, parietal and occipital cortices, were calculated and analysed. Differences between epilepsy and controls were quantitatively compared for each cerebral lobe in each frequency band in terms of kurtosis and skewness measurements. Virtual electrode signals from clinical epileptogenic zones and brain areas outside of the epileptogenic zones were also compared with kurtosis and skewness analyses. Compared to controls, patients with epilepsy showed significant elevation in kurtosis and skewness of virtual electrode signals. The spatial and frequency patterns of the kurtosis and skewness of virtual electrode signals among the eight cerebral lobes in three frequency bands were also significantly different from that of the controls (2–80 Hz, P < 0.001; 80–250 Hz, P < 0.00001; 250–600 Hz, P < 0.0001). Compared to signals from non-epileptogenic zones, virtual electrode signals from epileptogenic zones showed significantly altered kurtosis and skewness (P < 0.001). Compared to normative data from the control group, aberrant virtual electrode signals were, for each patient, more pronounced in the epileptogenic lobes than in other lobes(kurtosis analysis of virtual electrode signals in 250–600 Hz; odds ratio = 27.9; P < 0.0001). The kurtosis values of virtual electrode signals in 80–250 and 250–600 Hz showed the highest sensitivity (88.23%) and specificity (89.09%) for revealing epileptogenic lobe, respectively. The combination of virtual electrode and kurtosis/skewness measurements provides a new quantitative approach to distinguishing pathological from physiological high-frequency signals for paediatric epilepsy. Non-invasive identification of pathological high-frequency signals may provide novel important information to guide clinical invasive recordings and direct surgical treatment of epilepsy.

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“…Over the past few years, an increasing body of the literature confirmed the success of feature construction using deep learning methods. Deep learning has been demonstrated to outperform traditional machine learning algorithms on numerous recognition and classification tasks [24][25][26][27][28][29], which inspires the researchers in the ASD community to apply deep learning approaches on ASD classification. Earlier, deep neural networks (DNNs) have been applied to identify ASD patients using rs-fMRI [26].…”

Section: Introductionmentioning

confidence: 99%

Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

et al. 2020

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Autism spectrum disorder (ASD) is a developmental disorder that impacts more than 1.6% of children aged 8 across the United States. It is characterized by impairments in social interaction and communication, as well as by a restricted repertoire of activity and interests. The current standardized clinical diagnosis of ASD remains to be a subjective diagnosis, mainly relying on behavior-based tests. However, the diagnostic process for ASD is not only time consuming, but also costly, causing a tremendous financial burden for patients’ families. Therefore, automated diagnosis approaches have been an attractive solution for earlier identification of ASD. In this work, we set to develop a deep learning model for automated diagnosis of ASD. Specifically, a multichannel deep attention neural network (DANN) was proposed by integrating multiple layers of neural networks, attention mechanism, and feature fusion to capture the interrelationships in multimodality data. We evaluated the proposed multichannel DANN model on the Autism Brain Imaging Data Exchange (ABIDE) repository with 809 subjects (408 ASD patients and 401 typical development controls). Our model achieved a state-of-the-art accuracy of 0.732 on ASD classification by integrating three scales of brain functional connectomes and personal characteristic data, outperforming multiple peer machine learning models in a k-fold cross validation experiment. Additional k-fold and leave-one-site-out cross validation were conducted to test the generalizability and robustness of the proposed multichannel DANN model. The results show promise for deep learning models to aid the future automated clinical diagnosis of ASD.

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A Stacked Sparse Autoencoder-Based Detector for Automatic Identification of Neuromagnetic High Frequency Oscillations in Epilepsy

Cited by 34 publications

References 45 publications

A Long Short-Term Memory neural network for the detection of epileptiform spikes and high frequency oscillations

A Long Short-Term Memory neural network for the detection of epileptiform spikes and high frequency oscillations

Kurtosis and skewness of high-frequency brain signals are altered in paediatric epilepsy

Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

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