Automated Detection of High-Frequency Oscillations in Epilepsy Based on a Convolutional Neural Network

Zuo, Rui; Wei, Jing; Li, Xiaonan; Li, Chunlin; Zhao, Cui; Ren, Zhaohui; Liang, Ying; Geng, Xin; Jiang, Chenxi; Yang, Xiaofeng; Zhang, Xu

doi:10.3389/fncom.2019.00006

Cited by 61 publications

(59 citation statements)

References 43 publications

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“…Approximate entropy has been used as a feature to identify HFOs with respect to baseline segments: during an HFO, the complexity of the signal is higher, therefore its approximate entropy is higher. Finally, a convolutional neural network (CNN) has been used for automatic detection of ripples and fast ripples [30]. The CNN's performance has been compared with the four reference-detectors collected in the RIPPLELAB application [31] and showed much higher sensitivity (77.04% for ripples and 83.23% for fast ripples) and specificity (72.27% for ripples 79.36% for fast ripples) except than the specificity of Staba detector [7] for ripples (83.14%) and the sensitivity of the MNI detector [6] for fast ripples (74.77%).…”

Section: Introductionmentioning

confidence: 99%

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

Sciaraffa

Klados²,

Borghini

et al. 2020

Brain Sciences

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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.

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Section: Introductionmentioning

confidence: 99%

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

Sciaraffa

Klados²,

Borghini

et al. 2020

Brain Sciences

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show abstract

“…The result showed in the above section that the selection of prominent features and the oversampling method can significantly improve the performance of an automatic system. Hence, we are the first one to use high-frequency components (ripple and fast ripple) from interictal iEEG, the performances of localizing individual segments were observed in terms of sensitivity, specificity, precision, fallout, and F-score for the comparison study similar to HFOs-and low frequency-based related studies 10,[19][20][21][31][32][33][34][35] computed from the confusion matrix (shown in Table 6). It is observed from the Table 2 that the proposed method achieved the highest performance for localizing individual segments with the adult patients Pt5 (SEN: 79.25%; fall-out: 2.50%), Pt6 (SEN: 54.82%; fall-out: 3.58%), and Pt8 (SEN: 88.52%; fall-out: 1.46%).…”

Section: Resultsmentioning

confidence: 99%

“…31 . Zuo et al 33 proposed the CNN-based method for identifying the two kind of HFOs in ripple and fast-ripple separately and achieved average results with sensitivity (77.04% and 83.23% for ripples) and specificity (72.27% and 79.36% for fast ripples) compared to four traditional automated methods proposed in the RIPPLELAB toolbox 32 . The combination of short-time energy (STE) and CNN also used in recent study for identifying HFOs 62 .…”

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

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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.

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“…RippleNet's implementation differs from Zuo et al 2019, one of very few deep CNN based algorithms specifically designed for detection of high-frequency oscillations (HFO), that is, epileptogenic zone seizures in intracranial electroencephalogram (iEEG) recordings, in that (1) explicit conversion of 1D input sequences with multiple rows into gray-scale images are avoided;…”

Section: Introductionmentioning

confidence: 99%

RippleNet: A Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection

Hagen

Chambers

Einevoll

et al. 2020

Preprint

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Hippocampal sharp wave ripples (SPW-R) have been identified as key bio-markers of important brain functions such as memory consolidation and decision making. SPW-R detection typically relies on hand-crafted feature extraction, and laborious manual curation is often required. In this multidisciplinary study, we propose a novel, self-improving artificial intelligence (AI) method in the form of deep Recurrent Neural Networks (RNN) with Long Short-Term memory (LSTM) layers that can learn features of SPW-R events from raw, labeled input data. The algorithm is trained using supervised learning on hand-curated data sets with SPW-R events. The input to the algorithm is the local field potential (LFP), the lowfrequency part of extracellularly recorded electric potentials from the CA1 region of the hippocampus. The output prediction can be interpreted as the time-varying probability of SPW-R events for the duration of the input. A simple thresholding applied to the output probabilities is found to identify times of events with high precision. The reference implementation of the algorithm, named 'RippleNet', is open source, freely available, and implemented using a common open-source framework for neural networks (tensorflow.keras) and can be easily incorporated into existing data analysis workflows for processing experimental data.Keywords: Machine learning, deep learning, recurrent neural networks, neuroscience, sharp wave ripples (SPW-R), Hippocampus CA1, local field potential (LFP). Different existing and novel real time algorithms for SPW-R detection were reviewed and tested on synthesized datas by Sethi and Kemere 2014. Such methods are applied with band-pass filtered LFP data, commonly in the 150 − 250 Hz range, and may incorporate adaptive thresholding (Fritsch, Ibanez, and Parrilla 1999;Jadhav et al. 2012). Deep learning:Recent years have seen a surge in different supervised and unsupervised learning algorithms, propelled by hardware acceleration, better training datasets and the advent of deep convolutional neural networks (CNN) in image classification and segmentation tasks (see e.g., Le-Cun, Bengio, and Hinton 2015;Rawat and Wang 2017). Deep CNNs are, however, not yet as commonplace for time series classification tasks . Unlike traditional neural networks (NNs) and CNNs which typically employ a feed-forward hierarchical propagation of activation across layers, recurrent neural networks (RNN) have feedback connections, and is suitable for sequential data such as speech and written text. One architecture of RNNs is Long Short-Term Memory (LSTM) RNNs (Hochreiter and Schmidhuber 1997), capable of classifying, processing and predicting events in time-series data, even in the presence of lags of unknown duration.

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Automated Detection of High-Frequency Oscillations in Epilepsy Based on a Convolutional Neural Network

Cited by 61 publications

References 43 publications

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

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

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

RippleNet: A Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection

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