Automatic Detection and Classification of High-Frequency Oscillations in Depth-EEG Signals

Jrad, Nisrine; Kachenoura, Amar; Merlet, Isabelle; Bartolomei, Fabricē; Nica, Anca; Biraben, Arnaud; Wendling, Fabrice

doi:10.1109/tbme.2016.2633391

Cited by 61 publications

(56 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, they confirmed that HFO classification is a multiclass problem. Subsequently, different classification methods have been tested: LDA [19], support vector machine (SVM) [13,20,21], and decision tree [22], combined with different feature sets.…”

Section: Introductionmentioning

confidence: 99%

Double-Step Machine Learning Based Procedure for HFOs Detection and Classification

Sciaraffa

Klados²,

Borghini

et al. 2020

Brain Sciences

View full text Add to dashboard Cite

The need for automatic detection and classification of high-frequency oscillations (HFOs) as biomarkers of the epileptogenic tissue is strongly felt in the clinical field. In this context, the employment of artificial intelligence methods could be the missing piece to achieve this goal. This work proposed a double-step procedure based on machine learning algorithms and tested it on an intracranial electroencephalogram (iEEG) dataset available online. The first step aimed to define the optimal length for signal segmentation, allowing for an optimal discrimination of segments with HFO relative to those without. In this case, binary classifiers have been tested on a set of energy features. The second step aimed to classify these segments into ripples, fast ripples and fast ripples occurring during ripples. Results suggest that LDA applied to 10 ms segmentation could provide the highest sensitivity (0.874) and 0.776 specificity for the discrimination of HFOs from no-HFO segments. Regarding the three-class classification, non-linear methods provided the highest values (around 90%) in terms of specificity and sensitivity, significantly different to the other three employed algorithms. Therefore, this machine-learning-based procedure could help clinicians to automatically reduce the quantity of irrelevant data.

show abstract

Section: Introductionmentioning

confidence: 99%

Double-Step Machine Learning Based Procedure for HFOs Detection and Classification

Sciaraffa

Klados²,

Borghini

et al. 2020

Brain Sciences

View full text Add to dashboard Cite

show abstract

“…However, FDRs computed on real iEEGs were higher than FDRs computed on simulated data. This clearly reflected that iEEG signals were highly contaminated with artefacts and revealed the need of classifying the detected events like we did in [17].…”

Section: B Results and Discussionmentioning

confidence: 51%

“…The Gabor atoms were chosen optimally regarding the time-frequency uncertainly principle (see [9] and our previous works [17,18] for more details). Gabor function is defined as a Gaussian envelope modulated by complex sinusoids:…”

Section: B Hfos Detection Methodsmentioning

confidence: 99%

A Page-Hinkley based method for HFOs detection in epileptic depth-EEG

Jrad¹,

Kachenoura²,

Nica³

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, HFOs-related studies to identify the possible seizure onset channels need the long-time iEEG data to calculate the baseline. Jrad et al proposed automatic HFO detection with multi-class SVM in depth-EEG signals 59 . In their study, the performance evaluation matrices for evaluating the system were used in terms of sensitivity and false discovery rate (FDR).…”

Section: Discussionmentioning

confidence: 99%

“…The combination of short-time energy (STE) and CNN also used in recent study for identifying HFOs 62 . In their study, the performance of the system in terms of sensitivity and FDR are used to evaluate their system and compared with three related existing studies 32,36,59 . They achieved higher average results with five adult patients for ripple (Sen: 81.1% and FDR: 30.2%) and fast ripple (Sen: 74.6% and FDR: 6.3%).…”

Section: Discussionmentioning

confidence: 99%

Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG

Akter

Islam

Iimura

et al. 2020

Preprint

View full text Add to dashboard Cite

Presurgical investigations for categorizing focal patterns are crucial, leading to localization and surgical removal of the epileptic focus. This paper presents a machine learning approach using information theoretic features extracted from high-frequency subbands to detect the epileptic focus from interictal intracranial electroencephalogram (iEEG). It is known that high-frequency subbands (>80 Hz) include important biomarkers such as high-frequency oscillations (HFOs) for identifying epileptic focus commonly referred to as the seizure on-set zone (SOZ). In this analysis, the multi-channel interictal iEEG signals were splitted into segments and each segment was decomposed into multiple high-frequency subbands. The different types of entropy were calculated for each of the subbands and the sparse linear discriminant analysis (sLDA) was applied to select the prominent entropy features. Due to the imbalance of SOZ and non-SOZ channels in iEEG data, the use of machine learning techniques is always tricky. To deal with the imbalanced learning problem, an adaptive synthetic oversampling approach (ADASYN) with radial basis function kernel-based SVM was used to detect the focal segments. Finally, the epileptic focus was identified based on detection of focal segments on SOZ and non-SOZ channels. Eight patients were examined to observe the efficiency of the automatic detector. The experimental results and statistical tests indicate that the proposed automatic detector can identify the epileptic focus accurately and efficiently.

show abstract

Automatic Detection and Classification of High-Frequency Oscillations in Depth-EEG Signals

Abstract: Experimental results show the feasibility of a robust and universal detector.

Cited by 61 publications

References 29 publications

Double-Step Machine Learning Based Procedure for HFOs Detection and Classification

Double-Step Machine Learning Based Procedure for HFOs Detection and Classification

A Page-Hinkley based method for HFOs detection in epileptic depth-EEG

Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG

Contact Info

Product

Resources

About