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Qu Z, Luo J, Chen X, Zhang Y, Yu S, Shu H. Association between Removal of High-Frequency Oscillations and the Effect of Epilepsy Surgery: A Meta-Analysis. J Neurol Surg A Cent Eur Neurosurg 2024; 85:294-301. [PMID: 37918885 PMCID: PMC10984718 DOI: 10.1055/a-2202-9344] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/11/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND High-frequency oscillations (HFOs) are spontaneous electroencephalographic (EEG) events that occur within the frequency range of 80 to 500 Hz and consist of at least four distinct oscillations that stand out from the background activity. They can be further classified into "ripples" (80-250 Hz) and "fast ripples" (FR; 250-500 Hz) based on different frequency bands. Studies have indicated that HFOs may serve as important markers for identifying epileptogenic regions and networks in patients with refractory epilepsy. Furthermore, a higher extent of removal of brain regions generating HFOs could potentially lead to improved prognosis. However, the clinical application criteria for HFOs remain controversial, and the results from different research groups exhibit inconsistencies. Given this controversy, the aim of this study was to conduct a meta-analysis to explore the utility of HFOs in predicting postoperative seizure outcomes by examining the prognosis of refractory epilepsy patients with varying ratios of HFO removal. METHODS Prospective and retrospective studies that analyzed HFOs and postoperative seizure outcomes in epilepsy patients who underwent resective surgery were included in the meta-analysis. The patients in these studies were grouped based on the ratio of HFOs removed, resulting in four groups: completely removed FR (C-FR), completely removed ripples (C-Ripples), mostly removed FR (P-FR), and partial ripples removal (P-Ripples). The prognosis of patients within each group was compared to investigate the correlation between the ratio of HFO removal and patient prognosis. RESULTS A total of nine studies were included in the meta-analysis. The prognosis of patients in the C-FR group was significantly better than that of patients with incomplete FR removal (odds ratio [OR] = 6.62; 95% confidence interval [CI]: 3.10-14.15; p < 0.00001). Similarly, patients in the C-Ripples group had a more favorable prognosis compared with those with incomplete ripples removal (OR = 4.45; 95% CI: 1.33-14.89; p = 0.02). Patients in the P-FR group had better prognosis than those with a majority of FR remaining untouched (OR = 6.23; 95% CI: 2.04-19.06; p = 0.001). In the P-Ripples group, the prognosis of patients with a majority of ripples removed was superior to that of patients with a majority of ripples remaining untouched (OR = 8.14; 95% CI: 2.62-25.33; p = 0.0003). CONCLUSIONS There is a positive correlation between the greater removal of brain regions generating HFOs and more favorable postoperative seizure outcomes. However, further investigations, particularly through clinical trials, are necessary to justify the clinical application of HFOs in guiding epilepsy surgery.
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Affiliation(s)
- Zhichuang Qu
- Department of Neurosurgery, Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
| | - Juan Luo
- Department of Neurosurgery, Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
| | - Xin Chen
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
| | - Yuanyuan Zhang
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
- Southwest Jiaotong University, Chengdu, China
| | - Sixun Yu
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
| | - Haifeng Shu
- Department of Neurosurgery, Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
- Southwest Jiaotong University, Chengdu, China
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Karpychev V, Balatskaya A, Utyashev N, Pedyash N, Zuev A, Dragoy O, Fedele T. Epileptogenic high-frequency oscillations present larger amplitude both in mesial temporal and neocortical regions. Front Hum Neurosci 2022; 16:984306. [PMID: 36248681 PMCID: PMC9557004 DOI: 10.3389/fnhum.2022.984306] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/12/2022] [Indexed: 11/17/2022] Open
Abstract
High-frequency oscillations (HFO) are a promising biomarker for the identification of epileptogenic tissue. While HFO rates have been shown to predict seizure outcome, it is not yet clear whether their morphological features might improve this prediction. We validated HFO rates against seizure outcome and delineated the distribution of HFO morphological features. We collected stereo-EEG recordings from 20 patients (231 electrodes; 1,943 contacts). We computed HFO rates (the co-occurrence of ripples and fast ripples) through a validated automated detector during non-rapid eye movement sleep. Applying machine learning, we delineated HFO morphological features within and outside epileptogenic tissue across mesial temporal lobe (MTL) and Neocortex. HFO rates predicted seizure outcome with 85% accuracy, 79% specificity, 100% sensitivity, 100% negative predictive value, and 67% positive predictive value. The analysis of HFO features showed larger amplitude in the epileptogenic tissue, similar morphology for epileptogenic HFO in MTL and Neocortex, and larger amplitude for physiological HFO in MTL. We confirmed HFO rates as a reliable biomarker for epilepsy surgery and characterized the potential clinical relevance of HFO morphological features. Our results support the prospective use of HFO in epilepsy surgery and contribute to the anatomical mapping of HFO morphology.
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Affiliation(s)
- Victor Karpychev
- Center for Language and Brain, HSE University, Moscow, Russia
- *Correspondence: Victor Karpychev,
| | | | - Nikita Utyashev
- National Medical and Surgical Center named after N.I. Pirogov, Moscow, Russia
| | - Nikita Pedyash
- National Medical and Surgical Center named after N.I. Pirogov, Moscow, Russia
| | - Andrey Zuev
- National Medical and Surgical Center named after N.I. Pirogov, Moscow, Russia
| | - Olga Dragoy
- Center for Language and Brain, HSE University, Moscow, Russia
- Institute of Linguistics, Russian Academy of Sciences, Moscow, Russia
| | - Tommaso Fedele
- Institute for Cognitive Neuroscience, HSE University, Moscow, Russia
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3
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Accurate differentiation between physiological and pathological ripples recorded by scalp-EEG. Clin Neurophysiol 2022; 143:172-181. [DOI: 10.1016/j.clinph.2022.08.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 07/21/2022] [Accepted: 08/16/2022] [Indexed: 11/18/2022]
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4
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Ren G, Sun Y, Wang D, Ren J, Dai J, Mei S, Li Y, Wang X, Yang X, Yan J, Wang Q. Identification of Epileptogenic and Non-epileptogenic High-Frequency Oscillations Using a Multi-Feature Convolutional Neural Network Model. Front Neurol 2021; 12:640526. [PMID: 34721249 PMCID: PMC8553964 DOI: 10.3389/fneur.2021.640526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 09/06/2021] [Indexed: 11/26/2022] Open
Abstract
Accurately identifying epileptogenic zone (EZ) using high-frequency oscillations (HFOs) is a challenge that must be mastered to transfer HFOs into clinical use. We analyzed the ability of a convolutional neural network (CNN) model to distinguish EZ and non-EZ HFOs. Nineteen medically intractable epilepsy patients with good surgical outcomes 2 years after surgery were studied. Five-minute interictal intracranial electroencephalogram epochs of slow-wave sleep were selected randomly. Then 5 s segments of ripples (80–200 Hz) and fast ripples (FRs, 200–500 Hz) were detected automatically. The EZs and non-EZs were identified using the surgery resection range. We innovatively converted all epochs into four types of images using two scales: original waveforms, filtered waveforms, wavelet spectrum images, and smoothed pseudo Wigner–Ville distribution (SPWVD) spectrum images. Two scales were fixed and fitted scales. We then used a CNN model to classify the HFOs into EZ and non-EZ categories. As a result, 7,000 epochs of ripples and 2,000 epochs of FRs were randomly selected from the EZ and non-EZ data for analysis. Our CNN model can distinguish EZ and non-EZ HFOs successfully. Except for original ripple waveforms, the results from CNN models that are trained using fixed-scale images are significantly better than those from models trained using fitted-scale images (p < 0.05). Of the four fixed-scale transformations, the CNN based on the adjusted SPWVD (ASPWVD) produced the best accuracies (80.89 ± 1.43% and 77.85 ± 1.61% for ripples and FRs, respectively, p < 0.05). The CNN using ASPWVD transformation images is an effective deep learning method that can be used to classify EZ and non-EZ HFOs.
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Affiliation(s)
- Guoping Ren
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yueqian Sun
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China
| | - Dan Wang
- Department of Neurology, Xingtai People's Hospital, Hebei, China
| | - Jiechuan Ren
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jindong Dai
- Department of Functional Neurosurgery, Beijing Haidian Hospital, Beijing, China
| | - Shanshan Mei
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yunlin Li
- Department of Neurosurgery, Capital Institute of Pediatrics, Children's Hospital, Beijing, China
| | - Xiaofei Wang
- Department of Neurology, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | | | - Jiaqing Yan
- College of Electrical and Control Engineering, North China University of Technology, Beijing, China
| | - Qun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China
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5
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Fan Y, Dong L, Liu X, Wang H, Liu Y. Recent advances in the noninvasive detection of high-frequency oscillations in the human brain. Rev Neurosci 2020; 32:305-321. [PMID: 33661582 DOI: 10.1515/revneuro-2020-0073] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 10/23/2020] [Indexed: 01/10/2023]
Abstract
In recent decades, a significant body of evidence based on invasive clinical research has showed that high-frequency oscillations (HFOs) are a promising biomarker for localization of the seizure onset zone (SOZ), and therefore, have the potential to improve postsurgical outcomes in patients with epilepsy. Emerging clinical literature has demonstrated that HFOs can be recorded noninvasively using methods such as scalp electroencephalography (EEG) and magnetoencephalography (MEG). Not only are HFOs considered to be a useful biomarker of the SOZ, they also have the potential to gauge disease severity, monitor treatment, and evaluate prognostic outcomes. In this article, we review recent clinical research on noninvasively detected HFOs in the human brain, with a focus on epilepsy. Noninvasively detected scalp HFOs have been investigated in various types of epilepsy. HFOs have also been studied noninvasively in other pathologic brain disorders, such as migraine and autism. Herein, we discuss the challenges reported in noninvasive HFO studies, including the scarcity of MEG and high-density EEG equipment in clinical settings, low signal-to-noise ratio, lack of clinically approved automated detection methods, and the difficulty in differentiating between physiologic and pathologic HFOs. Additional studies on noninvasive recording methods for HFOs are needed, especially prospective multicenter studies. Further research is fundamental, and extensive work is needed before HFOs can routinely be assessed in clinical settings; however, the future appears promising.
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Affiliation(s)
- Yuying Fan
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, China
| | - Liping Dong
- Library of China Medical University, Shenyang, China
| | - Xueyan Liu
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, China
| | - Hua Wang
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yunhui Liu
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, China
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6
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Wong SM, Arski ON, Workewych AM, Donner E, Ochi A, Otsubo H, Snead OC, Ibrahim GM. Detection of high-frequency oscillations in electroencephalography: A scoping review and an adaptable open-source framework. Seizure 2020; 84:23-33. [PMID: 33271473 DOI: 10.1016/j.seizure.2020.11.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 11/11/2020] [Accepted: 11/12/2020] [Indexed: 11/19/2022] Open
Abstract
PURPOSE High frequency oscillations (HFOs) are putative biomarkers of epileptogenicity. These electrophysiological phenomena can be effectively detected in electroencephalography using automated methods. Nonetheless, the implementation of these methods into clinical practice remains challenging as significant variability exists between algorithms and their characterizations of HFOs. Here, we perform a scoping review of the literature pertaining to automated HFO detection methods. In addition, we propose a framework for defining and detecting HFOs based on a simplified single-stage time-frequency based detection algorithm with clinically-familiar parameters. METHODS Several databases (OVID Medline, Web of Science, PubMed) were searched for articles presenting novel, automated HFO detection methods. Details related to the algorithm and various stages of data acquisition, pre-processing, and analysis were abstracted from included studies. RESULTS From the 261 records screened, 57 articles presented novel, automated HFO detection methods and were included in the scoping review. These algorithms were categorized into 3 groups based on their most salient features: energy thresholding, time-frequency analysis, and data mining/machine learning. Algorithms were optimized for specific datasets and suffered from low specificity. A framework for user-constrained inputs is proposed to circumvent some of the weaknesses of highly performant detectors. CONCLUSIONS Further efforts are required to optimize and validate existing automated HFO detection methods for clinical utility. The proposed framework may be applied to understand and standardize the variations in HFO definitions across institutions.
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Affiliation(s)
- Simeon M Wong
- Program in Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Olivia N Arski
- Program in Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, Canada; Institute of Medical Science, University of Toronto, Toronto, Canada
| | - Adriana M Workewych
- Program in Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, Canada; Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Elizabeth Donner
- Division of Neurology, Hospital for Sick Children, Toronto, Canada
| | - Ayako Ochi
- Division of Neurology, Hospital for Sick Children, Toronto, Canada
| | - Hiroshi Otsubo
- Division of Neurology, Hospital for Sick Children, Toronto, Canada
| | - O Carter Snead
- Division of Neurology, Hospital for Sick Children, Toronto, Canada
| | - George M Ibrahim
- Program in Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, Canada; Institute of Medical Science, University of Toronto, Toronto, Canada; Division of Neurosurgery, Hospital for Sick Children, Department of Surgery, University of Toronto, Toronto, Canada.
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7
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Cimbalnik J, Pail M, Klimes P, Travnicek V, Roman R, Vajcner A, Brazdil M. Cognitive Processing Impacts High Frequency Intracranial EEG Activity of Human Hippocampus in Patients With Pharmacoresistant Focal Epilepsy. Front Neurol 2020; 11:578571. [PMID: 33193030 PMCID: PMC7655124 DOI: 10.3389/fneur.2020.578571] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 09/18/2020] [Indexed: 12/26/2022] Open
Abstract
The electrophysiological EEG features such as high frequency oscillations, spikes and functional connectivity are often used for delineation of epileptogenic tissue and study of the normal function of the brain. The epileptogenic activity is also known to be suppressed by cognitive processing. However, differences between epileptic and healthy brain behavior during rest and task were not studied in detail. In this study we investigate the impact of cognitive processing on epileptogenic and non-epileptogenic hippocampus and the intracranial EEG features representing the underlying electrophysiological processes. We investigated intracranial EEG in 24 epileptic and 24 non-epileptic hippocampi in patients with intractable focal epilepsy during a resting state period and during performance of various cognitive tasks. We evaluated the behavior of features derived from high frequency oscillations, interictal epileptiform discharges and functional connectivity and their changes in relation to cognitive processing. Subsequently, we performed an analysis whether cognitive processing can contribute to classification of epileptic and non-epileptic hippocampus using a machine learning approach. The results show that cognitive processing suppresses epileptogenic activity in epileptic hippocampus while it causes a shift toward higher frequencies in non-epileptic hippocampus. Statistical analysis reveals significantly different electrophysiological reactions of epileptic and non-epileptic hippocampus during cognitive processing, which can be measured by high frequency oscillations, interictal epileptiform discharges and functional connectivity. The calculated features showed high classification potential for epileptic hippocampus (AUC = 0.93). In conclusion, the differences between epileptic and non-epileptic hippocampus during cognitive processing bring new insight in delineation between pathological and physiological processes. Analysis of computed iEEG features in rest and task condition can improve the functional mapping during pre-surgical evaluation and provide additional guidance for distinguishing between epileptic and non-epileptic structure which is absolutely crucial for achieving the best possible outcome with as little side effects as possible.
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Affiliation(s)
- Jan Cimbalnik
- International Clinical Research Center, St. Anne's University Hospital, Brno, Czechia
| | - Martin Pail
- Department of Neurology, Faculty of Medicine, Brno Epilepsy Center, St. Anne's University Hospital, Masaryk University, Brno, Czechia
| | - Petr Klimes
- International Clinical Research Center, St. Anne's University Hospital, Brno, Czechia.,Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czechia
| | - Vojtech Travnicek
- International Clinical Research Center, St. Anne's University Hospital, Brno, Czechia.,Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czechia
| | - Robert Roman
- Department of Neurology, Faculty of Medicine, Brno Epilepsy Center, St. Anne's University Hospital, Masaryk University, Brno, Czechia.,Behavioral and Social Neuroscience Research Group, CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czechia
| | - Adam Vajcner
- Department of Neurology, Faculty of Medicine, Brno Epilepsy Center, St. Anne's University Hospital, Masaryk University, Brno, Czechia.,Department of Sports Medicine and Rehabilitation, Faculty of Medicine, St. Anne's University Hospital, Masaryk University, Brno, Czechia
| | - Milan Brazdil
- Department of Neurology, Faculty of Medicine, Brno Epilepsy Center, St. Anne's University Hospital, Masaryk University, Brno, Czechia.,Behavioral and Social Neuroscience Research Group, CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czechia
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8
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Pail M, Cimbálník J, Roman R, Daniel P, Shaw DJ, Chrastina J, Brázdil M. High frequency oscillations in epileptic and non-epileptic human hippocampus during a cognitive task. Sci Rep 2020; 10:18147. [PMID: 33097749 PMCID: PMC7585420 DOI: 10.1038/s41598-020-74306-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 09/23/2020] [Indexed: 12/04/2022] Open
Abstract
Hippocampal high-frequency electrographic activity (HFOs) represents one of the major discoveries not only in epilepsy research but also in cognitive science over the past few decades. A fundamental challenge, however, has been the fact that physiological HFOs associated with normal brain function overlap in frequency with pathological HFOs. We investigated the impact of a cognitive task on HFOs with the aim of improving differentiation between epileptic and non-epileptic hippocampi in humans. Hippocampal activity was recorded with depth electrodes in 15 patients with focal epilepsy during a resting period and subsequently during a cognitive task. HFOs in ripple and fast ripple frequency ranges were evaluated in both conditions, and their rate, spectral entropy, relative amplitude and duration were compared in epileptic and non-epileptic hippocampi. The similarity of HFOs properties recorded at rest in epileptic and non-epileptic hippocampi suggests that they cannot be used alone to distinguish between hippocampi. However, both ripples and fast ripples were observed with higher rates, higher relative amplitudes and longer durations at rest as well as during a cognitive task in epileptic compared with non-epileptic hippocampi. Moreover, during a cognitive task, significant reductions of HFOs rates were found in epileptic hippocampi. These reductions were not observed in non-epileptic hippocampi. Our results indicate that although both hippocampi generate HFOs with similar features that probably reflect non-pathological phenomena, it is possible to differentiate between epileptic and non-epileptic hippocampi using a simple odd-ball task.
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Affiliation(s)
- Martin Pail
- First Department of Neurology, Brno Epilepsy Center (Full member of the ERN EpiCARE), St. Anne's University Hospital and Medical Faculty of Masaryk University, Pekařská 53, Brno, 65691, Czech Republic.
| | - Jan Cimbálník
- International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Robert Roman
- First Department of Neurology, Brno Epilepsy Center (Full member of the ERN EpiCARE), St. Anne's University Hospital and Medical Faculty of Masaryk University, Pekařská 53, Brno, 65691, Czech Republic.,CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Pavel Daniel
- First Department of Neurology, Brno Epilepsy Center (Full member of the ERN EpiCARE), St. Anne's University Hospital and Medical Faculty of Masaryk University, Pekařská 53, Brno, 65691, Czech Republic.,CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Daniel J Shaw
- CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic.,School of Life and Health Sciences, Aston University, Birmingham, UK
| | - Jan Chrastina
- Department of Neurosurgery, Brno Epilepsy Center, St. Anne's University Hospital and Medical Faculty of Masaryk University, Brno, Czech Republic
| | - Milan Brázdil
- First Department of Neurology, Brno Epilepsy Center (Full member of the ERN EpiCARE), St. Anne's University Hospital and Medical Faculty of Masaryk University, Pekařská 53, Brno, 65691, Czech Republic.,CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic
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9
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Minthe A, Janzarik WG, Lachner-Piza D, Reinacher P, Schulze-Bonhage A, Dümpelmann M, Jacobs J. Stable high frequency background EEG activity distinguishes epileptic from healthy brain regions. Brain Commun 2020; 2:fcaa107. [PMID: 32954347 PMCID: PMC7475693 DOI: 10.1093/braincomms/fcaa107] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 05/06/2020] [Accepted: 05/15/2020] [Indexed: 11/24/2022] Open
Abstract
High-frequency oscillations are markers of epileptic tissue. Recently, different patterns of EEG background activity were described from which high-frequency oscillations occur: high-frequency oscillations with continuously oscillating background were found to be primarily physiological, those from quiet background were linked to epileptic tissue. It is unclear, whether these interactions remain stable over several days and during different sleep-wake stages. High-frequency oscillation patterns (oscillatory vs. quiet background) were analysed in 23 patients implanted with depth and subdural grid electrodes. Pattern scoring was performed on every channel in 10 s intervals in three separate day- and night-time EEG segments. An entropy value, measuring variability of patterns per channel, was calculated. A low entropy value indicated a stable occurrence of the same pattern in one channel, whereas a high value indicated pattern instability. Differences in pattern distribution and entropy were analysed for 143 280 10 s intervals with allocated patterns from inside and outside the seizure onset zone, different electrode types and brain regions. We found a strong association between high-frequency oscillations out of quiet background activity, and channels of the seizure onset zone (35.2% inside versus 9.7% outside the seizure onset zone, P < 0.001), no association was found for high-frequency oscillations from continuous oscillatory background (P = 0.563). The type of background activity remained stable over the same brain region over several days and was independent of sleep stage and recording technique. Stability of background activity was significantly higher in channels of the seizure onset zone (entropy mean value 0.56 ± 0.39 versus 0.64 ± 0.41; P < 0.001). This was especially true for the presumed epileptic high-frequency oscillations out of quiet background (0.57 ± 0.39 inside versus 0.72 ± 0.37 outside the seizure onset zone; P < 0.001). In contrast, presumed physiological high-frequency oscillations from continuous oscillatory backgrounds were significantly more stable outside the seizure onset zone (0.72 ± 0.45 versus 0.48 ± 0.53; P < 0.001). The overall low entropy values suggest that interactions between high-frequency oscillations and background activity are a stable phenomenon specific to the function of brain regions. High-frequency oscillations occurring from a quiet background are strongly linked to the seizure onset zone whereas high-frequency oscillations from an oscillatory background are not. Pattern stability suggests distinct underlying mechanisms. Analysing short time segments of high-frequency oscillations and background activity could help distinguishing epileptic from physiologically active brain regions.
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Affiliation(s)
- Annika Minthe
- Department of Neuropediatrics and Muscular Diseases, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Wibke G Janzarik
- Department of Neuropediatrics and Muscular Diseases, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Daniel Lachner-Piza
- Department of Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Peter Reinacher
- Division of Stereotactic and Functional Neurosurgery, Department of Neurosurgery, Clinic for Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Department of Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Matthias Dümpelmann
- Department of Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Julia Jacobs
- Department of Neuropediatrics and Muscular Diseases, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany.,Department of Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
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10
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He JW, Rabiller G, Nishijima Y, Akamatsu Y, Khateeb K, Yazdan-Shahmorad A, Liu J. Experimental cortical stroke induces aberrant increase of sharp-wave-associated ripples in the hippocampus and disrupts cortico-hippocampal communication. J Cereb Blood Flow Metab 2020; 40:1778-1796. [PMID: 31558106 PMCID: PMC7446570 DOI: 10.1177/0271678x19877889] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 07/22/2019] [Accepted: 07/25/2019] [Indexed: 11/16/2022]
Abstract
The functional consequences of ischemic stroke in the remote brain regions are not well characterized. The current study sought to determine changes in hippocampal oscillatory activity that may underlie the cognitive impairment observed following distal middle cerebral artery occlusion (dMCAO) without causing hippocampal structural damage. Local field potentials were recorded from the dorsal hippocampus and cortex in urethane-anesthetized rats with multichannel silicon probes during dMCAO and reperfusion, or mild ischemia induced by bilateral common carotid artery occlusion (CCAO). Bilateral change of brain state was evidenced by reduced theta/delta amplitude ratio and shortened high theta duration following acute dMCAO but not CCAO. An aberrant increase in the occurrence of sharp-wave-associated ripples (150-250 Hz), crucial for memory consolidation, was only detected after dMCAO reperfusion, coinciding with an increased occurrence of high-frequency discharges (250-450 Hz). dMCAO also significantly affected the modulation of gamma amplitude in the cortex coupled to hippocampal theta phase, although both hippocampal theta and gamma power were temporarily decreased during dMCAO. Our results suggest that MCAO may disrupt the balance between excitatory and inhibitory circuits in the hippocampus and alter the function of cortico-hippocampal network, providing a novel insight in how cortical stroke affects function in remote brain regions.
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Affiliation(s)
- Ji-Wei He
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
- Department of Neurological Surgery, SFVAMC, San Francisco, CA, USA
| | - Gratianne Rabiller
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
- Department of Neurological Surgery, SFVAMC, San Francisco, CA, USA
| | - Yasuo Nishijima
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
- Department of Neurological Surgery, SFVAMC, San Francisco, CA, USA
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Yosuke Akamatsu
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
- Department of Neurological Surgery, SFVAMC, San Francisco, CA, USA
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Karam Khateeb
- Departments of Bioengineering and Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Azadeh Yazdan-Shahmorad
- Departments of Bioengineering and Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
- Center for Integrative Neuroscience and Department of Physiology, University of California, San Francisco, CA, USA
| | - Jialing Liu
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
- Department of Neurological Surgery, SFVAMC, San Francisco, CA, USA
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11
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Ren G, Yan J, Sun Y, Ren J, Dai J, Mei S, Li Y, Wang X, Yang X, Wang Q. Association Between Interictal High-Frequency Oscillations and Slow Wave in Refractory Focal Epilepsy With Good Surgical Outcome. Front Hum Neurosci 2020; 14:335. [PMID: 33005137 PMCID: PMC7479180 DOI: 10.3389/fnhum.2020.00335] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 07/29/2020] [Indexed: 11/13/2022] Open
Abstract
High-frequency oscillations (HFOs) have been proposed as a promising biomarker of the epileptogenic zone (EZ). But accurate delineation of EZ based on HFOs is still challenging. Our study compared HFOs from EZ and non-EZ on the basis of their associations with interictal slow waves, aiming at exploring a new way to localize EZ. Nineteen medically intractable epilepsy patients with good surgical outcome were included. Five minute interictal intracranial electroencephalography (EEG) epochs of slow-wave sleep were randomly selected; then ripples (80–200 Hz), fast ripples (FRs; 200–500 Hz), and slow waves (0.1–4 Hz) were automatically analyzed. The EZ and non-EZ were identified by resection range during the surgeries. We found that both ripples and FRs superimposed more frequently on slow waves in EZ than in non-EZ (P < 0.01). Although ripples preferred to occur on the down state of slow waves in both two groups, ripples in EZ tended to be closer to the down-state peak of slow wave than in non-EZ (-174 vs. -231 ms, P = 0.008). As for FR, no statistical difference was found between the two groups (P = 0.430). Additionally, slow wave-containing ripples in EZ had a steeper slope (1.7 vs. 1.5 μV/ms, P < 0.001) and wider distribution ratio (32.3 vs. 30.1%, P < 0.001) than those in the non-EZ. But for slow wave-containing FR, only a steeper slope (1.7 vs. 1.4 μV/ms, P < 0.001) was observed. Our study innovatively compared the different features of association between HFOs and slow wave in EZ and non-EZ from refractory focal epilepsy with good surgical outcome, proposing a new method to localize EZ and facilitating the surgical plan.
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Affiliation(s)
- Guoping Ren
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jiaqing Yan
- College of Electrical and Control Engineering, North China University of Technology, Beijing, China
| | - Yueqian Sun
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Laboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Ministry of Science and Technology, Beijing, China
| | - Jiechuan Ren
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jindong Dai
- Department of Functional Neurosurgery, Beijing Haidian Hospital, Beijing, China
| | - Shanshan Mei
- Department of Functional Neurosurgery, Beijing Haidian Hospital, Beijing, China
| | - Yunlin Li
- Department of Functional Neurosurgery, Beijing Haidian Hospital, Beijing, China
| | - Xiaofei Wang
- Department of Functional Neurosurgery, Beijing Haidian Hospital, Beijing, China
| | - Xiaofeng Yang
- Laboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Ministry of Science and Technology, Beijing, China.,Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing, China.,Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Qun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Laboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Ministry of Science and Technology, Beijing, China
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12
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Charupanit K, Sen-Gupta I, Lin JJ, Lopour BA. Amplitude of high frequency oscillations as a biomarker of the seizure onset zone. Clin Neurophysiol 2020; 131:2542-2550. [PMID: 32927209 DOI: 10.1016/j.clinph.2020.07.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 07/13/2020] [Accepted: 07/19/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVE Studies of high frequency oscillations (HFOs) in epilepsy have primarily tested the HFO rate as a biomarker of the seizure onset zone (SOZ), but the rate varies over time and is not robust for all individual subjects. As an alternative, we tested the performance of HFO amplitude as a potential SOZ biomarker using two automated detection algorithms. METHOD HFOs were detected in intracranial electroencephalogram (iEEG) from 11 patients using a machine learning algorithm and a standard amplitude-based algorithm. For each detector, SOZ and non-SOZ channels were classified using the rate and amplitude of high frequency events, and performance was compared using receiver operating characteristic curves. RESULTS The amplitude of detected events was significantly higher in SOZ. Across subjects, amplitude more accurately classified SOZ/non-SOZ than rate (higher values of area under the ROC curve and sensitivity, and lower false positive rates). Moreover, amplitude was more consistent across segments of data, indicated by lower coefficient of variation. CONCLUSION As an SOZ biomarker, HFO amplitude offers advantages over HFO rate: it exhibits higher classification accuracy, more consistency over time, and robustness to parameter changes. SIGNIFICANCE This biomarker has the potential to increase the generalizability of HFOs and facilitate clinical implementation as a tool for SOZ localization.
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Affiliation(s)
- Krit Charupanit
- University of California, Irvine, Biomedical Engineering, 3120 Natural Sciences II, University of California, Irvine, CA 92697, USA
| | - Indranil Sen-Gupta
- University of California Irvine Medical Center, Neurology, 101 The City Drive South, Pavilion 1, Orange, CA 92868, USA
| | - Jack J Lin
- University of California, Irvine, Neurology, 101 The City Drive South, Building 22C, 2nd Floor, RT13, Orange, CA 92602, USA
| | - Beth A Lopour
- University of California, Irvine, Biomedical Engineering, 3120 Natural Sciences II, University of California, Irvine, CA 92697, USA.
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13
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Remakanthakurup Sindhu K, Staba R, Lopour BA. Trends in the use of automated algorithms for the detection of high-frequency oscillations associated with human epilepsy. Epilepsia 2020; 61:1553-1569. [PMID: 32729943 DOI: 10.1111/epi.16622] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 06/17/2020] [Accepted: 06/29/2020] [Indexed: 12/11/2022]
Abstract
High-frequency oscillations (HFOs) in intracranial electroencephalography (EEG) are a promising biomarker of the epileptogenic zone and tool for surgical planning. Many studies have shown that a high rate of HFOs (number per minute) is correlated with the seizure-onset zone, and complete removal of HFO-generating brain regions has been associated with seizure-free outcome after surgery. In order to use HFOs as a biomarker, these transient events must first be detected in electrophysiological data. Because visual detection of HFOs is time-consuming and subject to low interrater reliability, many automated algorithms have been developed, and they are being used increasingly for such studies. However, there is little guidance on how to select an algorithm, implement it in a clinical setting, and validate the performance. Therefore, we aim to review automated HFO detection algorithms, focusing on conceptual similarities and differences between them. We summarize the standard steps for data pre-processing, as well as post-processing strategies for rejection of false-positive detections. We also detail four methods for algorithm testing and validation, and we describe the specific goal achieved by each one. We briefly review direct comparisons of automated algorithms applied to the same data set, emphasizing the importance of optimizing detection parameters. Then, to assess trends in the use of automated algorithms and their potential for use in clinical studies, we review evidence for the relationship between automatically detected HFOs and surgical outcome. We conclude with practical recommendations and propose standards for the selection, implementation, and validation of automated HFO-detection algorithms.
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Affiliation(s)
| | | | - Beth A Lopour
- Biomedical Engineering, UC Irvine, Irvine, California, USA
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14
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Wan X, Fang Z, Wu M, Du Y. Automatic detection of HFOs based on singular value decomposition and improved fuzzy c-means clustering for localization of seizure onset zones. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.03.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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15
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Charupanit K, Sen-Gupta I, Lin JJ, Lopour BA. Detection of anomalous high-frequency events in human intracranial EEG. Epilepsia Open 2020; 5:263-273. [PMID: 32524052 PMCID: PMC7278560 DOI: 10.1002/epi4.12397] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 04/09/2020] [Accepted: 04/09/2020] [Indexed: 11/23/2022] Open
Abstract
Objective High‐frequency oscillations (HFOs) are a promising biomarker for the epileptogenic zone. However, no physiological definition of an HFO has been established, so detection relies on the empirical definition of an HFO derived from visual observation. This can bias estimates of HFO features such as amplitude and duration, thereby hindering their utility as biomarkers. Therefore, we set out to develop an algorithm that detects high‐frequency events in the intracranial EEG that are morphologically distinct from background without requiring assumptions about event amplitude or shape. Method We propose the anomaly detection algorithm (ADA), which uses unsupervised machine learning to identify segments of data that are distinct from the background. We apply ADA and a standard HFO detector using a root mean square amplitude threshold to intracranial EEG from 11 patients undergoing evaluation for epilepsy surgery. The rate, amplitude, and duration of the detected events and the percent overlap between the two detectors are compared. Result In the seizure onset zone (SOZ), ADA detected a subset of conventional HFOs. In non‐SOZ channels, ADA detected at least twice as many events as the standard approach, including some conventional HFOs; however, ADA also identified many low and intermediate amplitude events missed by the standard amplitude‐based method. The rate of ADA events was similar across all channels; however, the amplitude of ADA events was significantly higher in SOZ channels (P < .0045), and the amplitude measurement was more stable over time than the HFO rate, as indicated by a lower coefficient of variation (P < .0125). Significance ADA does not require human supervision, parameter optimization, or prior assumptions about event shape, amplitude, or duration. Our results suggest that the algorithm's estimate of event amplitude may differentiate SOZ and non‐SOZ channels. Further studies will examine the utility of HFO amplitude as a biomarker for epilepsy surgical outcome.
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Affiliation(s)
- Krit Charupanit
- Biomedical Engineering University of California, Irvine Irvine CA USA
| | - Indranil Sen-Gupta
- Comprehensive Epilepsy Program Department of Neurology University of California, Irvine Irvine CA USA
| | - Jack J Lin
- Biomedical Engineering University of California, Irvine Irvine CA USA.,Comprehensive Epilepsy Program Department of Neurology University of California, Irvine Irvine CA USA
| | - Beth A Lopour
- Biomedical Engineering University of California, Irvine Irvine CA USA
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16
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Weiss SA, Waldman Z, Raimondo F, Slezak D, Donmez M, Worrell G, Bragin A, Engel J, Staba R, Sperling M. Localizing epileptogenic regions using high-frequency oscillations and machine learning. Biomark Med 2019; 13:409-418. [PMID: 31044598 DOI: 10.2217/bmm-2018-0335] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Pathological high frequency oscillations (HFOs) are putative neurophysiological biomarkers of epileptogenic brain tissue. Utilizing HFOs for epilepsy surgery planning offers the promise of improved seizure outcomes for patients with medically refractory epilepsy. This review discusses possible machine learning strategies that can be applied to HFO biomarkers to better identify epileptogenic regions. We discuss the role of HFO rate, and utilizing features such as explicit HFO properties (spectral content, duration, and power) and phase-amplitude coupling for distinguishing pathological HFO (pHFO) events from physiological HFO events. In addition, the review highlights the importance of neuroanatomical localization in machine learning strategies.
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Affiliation(s)
- Shennan A Weiss
- Departments of Neurology & Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Zachary Waldman
- Departments of Neurology & Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Federico Raimondo
- Department of Computer Science, Faculty of Exact & Natural Sciences, University of Buenos Aires, Buenos Aires, Argentina.,Institute of Research in Computer Science, National Scientific & Technical Research Council, University of Buenos Aires, Buenos Aires, Argentina
| | - Diego Slezak
- Department of Computer Science, Faculty of Exact & Natural Sciences, University of Buenos Aires, Buenos Aires, Argentina.,Institute of Research in Computer Science, National Scientific & Technical Research Council, University of Buenos Aires, Buenos Aires, Argentina
| | - Mustafa Donmez
- Departments of Neurology & Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Gregory Worrell
- Department of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), Mayo Clinic, Rochester, MN 55905, USA
| | - Anatol Bragin
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Jerome Engel
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Richard Staba
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Michael Sperling
- Departments of Neurology & Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA
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17
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Höller P, Trinka E, Höller Y. MEEGIPS-A Modular EEG Investigation and Processing System for Visual and Automated Detection of High Frequency Oscillations. Front Neuroinform 2019; 13:20. [PMID: 31024284 PMCID: PMC6460903 DOI: 10.3389/fninf.2019.00020] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2018] [Accepted: 03/11/2019] [Indexed: 11/21/2022] Open
Abstract
High frequency oscillations (HFOs) are electroencephalographic correlates of brain activity detectable in a frequency range above 80 Hz. They co-occur with physiological processes such as saccades, movement execution, and memory formation, but are also related to pathological processes in patients with epilepsy. Localization of the seizure onset zone, and, more specifically, of the to-be resected area in patients with refractory epilepsy seems to be supported by the detection of HFOs. The visual identification of HFOs is very time consuming with approximately 8 h for 10 min and 20 channels. Therefore, automated detection of HFOs is highly warranted. So far, no software for visual marking or automated detection of HFOs meets the needs of everyday clinical practice and research. In the context of the currently available tools and for the purpose of related local HFO study activities we aimed at converging the advantages of clinical and experimental systems by designing and developing a comprehensive and extensible software framework for HFO analysis that, on the one hand, focuses on the requirements of clinical application and, on the other hand, facilitates the integration of experimental code and algorithms. The development project included the definition of use cases, specification of requirements, software design, implementation, and integration. The work comprised the engineering of component-specific requirements, component design, as well as component- and integration-tests. A functional and tested software package is the deliverable of this activity. The project MEEGIPS, a Modular EEG Investigation and Processing System for visual and automated detection of HFOs, introduces a highly user friendly software that includes five of the most prominent automated detection algorithms. Future evaluation of these, as well as implementation of further algorithms is facilitated by the modular software architecture.
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Affiliation(s)
- Peter Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University, Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University, Salzburg, Austria
| | - Yvonne Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria,Department of Psychology, University of Akureyri, Akureyri, Iceland,*Correspondence: Yvonne Höller
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18
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Thomschewski A, Hincapié AS, Frauscher B. Localization of the Epileptogenic Zone Using High Frequency Oscillations. Front Neurol 2019; 10:94. [PMID: 30804887 PMCID: PMC6378911 DOI: 10.3389/fneur.2019.00094] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 01/23/2019] [Indexed: 01/22/2023] Open
Abstract
For patients with drug-resistant focal epilepsy, surgery is the therapy of choice in order to achieve seizure freedom. Epilepsy surgery foremost requires the identification of the epileptogenic zone (EZ), defined as the brain area indispensable for seizure generation. The current gold standard for identification of the EZ is the seizure-onset zone (SOZ). The fact, however that surgical outcomes are unfavorable in 40-50% of well-selected patients, suggests that the SOZ is a suboptimal biomarker of the EZ, and that new biomarkers resulting in better postsurgical outcomes are needed. Research of recent years suggested that high-frequency oscillations (HFOs) are a promising biomarker of the EZ, with a potential to improve surgical success in patients with drug-resistant epilepsy without the need to record seizures. Nonetheless, in order to establish HFOs as a clinical biomarker, the following issues need to be addressed. First, evidence on HFOs as a clinically relevant biomarker stems predominantly from retrospective assessments with visual marking, leading to problems of reproducibility and reliability. Prospective assessments of the use of HFOs for surgery planning using automatic detection of HFOs are needed in order to determine their clinical value. Second, disentangling physiologic from pathologic HFOs is still an unsolved issue. Considering the appearance and the topographic location of presumed physiologic HFOs could be immanent for the interpretation of HFO findings in a clinical context. Third, recording HFOs non-invasively via scalp electroencephalography (EEG) and magnetoencephalography (MEG) is highly desirable, as it would provide us with the possibility to translate the use of HFOs to the scalp in a large number of patients. This article reviews the literature regarding these three issues. The first part of the article focuses on the clinical value of invasively recorded HFOs in localizing the EZ, the detection of HFOs, as well as their separation from physiologic HFOs. The second part of the article focuses on the current state of the literature regarding non-invasively recorded HFOs with emphasis on findings and technical considerations regarding their localization.
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Affiliation(s)
- Aljoscha Thomschewski
- Department of Neurology, Christian Doppler Medical Center, Paracelsus Medical University, Salzburg, Austria
- Department of Psychology, Paris-Lodron University of Salzburg, Salzburg, Austria
| | - Ana-Sofía Hincapié
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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19
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Lesion localization algorithm of high-frequency epileptic signal based on Teager energy operator. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.08.033] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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20
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Peng SJ, Chou CC, Yu HY, Chen C, Yen DJ, Kwan SY, Hsu SPC, Lin CF, Chen HH, Lee CC. Ictal networks of temporal lobe epilepsy: views from high-frequency oscillations in stereoelectroencephalography. J Neurosurg 2018; 131:1086-1194. [PMID: 30544358 DOI: 10.3171/2018.6.jns172844] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Accepted: 06/27/2018] [Indexed: 11/06/2022]
Abstract
OBJECTIVE In this study, the authors investigated high-frequency oscillation (HFO) networks during seizures in order to determine how HFOs spread from the focal cerebral cortex and become synchronized across various areas of the brain. METHODS All data were obtained from stereoelectroencephalography (SEEG) signals in patients with drug-resistant temporal lobe epilepsy (TLE). The authors calculated intercontact cross-coefficients between all pairs of contacts to construct HFO networks in 20 seizures that occurred in 5 patients. They then calculated HFO network topology metrics (i.e., network density and component size) after normalizing seizure duration data by dividing each seizure into 10 intervals of equal length (labeled I1-I10). RESULTS From the perspective of the dynamic topologies of cortical and subcortical HFO networks, the authors observed a significant increase in network density during intervals I5-I10. A significant increase was also observed in overall energy during intervals I3-I8. The results of subnetwork analysis revealed that the number of components continuously decreased following the onset of seizures, and those results were statistically significant during intervals I3-I10. Furthermore, the majority of nodes were connected to a single dominant component during the propagation of seizures, and the percentage of nodes within the largest component grew significantly until seizure termination. CONCLUSIONS The consistent topological changes that the authors observed suggest that TLE is affected by common epileptogenic patterns. Indeed, the findings help to elucidate the epileptogenic network that characterizes TLE, which may be of interest to researchers and physicians working to improve treatment modalities for epilepsy, including resection, cortical stimulation, and neuromodulation treatments that are responsive to network topologies.
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Affiliation(s)
- Syu-Jyun Peng
- 1Biomedical Electronics Translational Research Center and
- 2Institute of Electronics, National Chiao-Tung University, Hsinchu
| | - Chien-Chen Chou
- Departments of3Neurology and
- 5School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Hsiang-Yu Yu
- Departments of3Neurology and
- 5School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Chien Chen
- Departments of3Neurology and
- 5School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Der-Jen Yen
- Departments of3Neurology and
- 5School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Shang-Yeong Kwan
- Departments of3Neurology and
- 5School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Sanford P C Hsu
- 4Neurosurgery, Neurological Institute, Taipei Veterans General Hospital; and
- 5School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Chun-Fu Lin
- 4Neurosurgery, Neurological Institute, Taipei Veterans General Hospital; and
- 5School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Hsin-Hung Chen
- 4Neurosurgery, Neurological Institute, Taipei Veterans General Hospital; and
- 5School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Cheng-Chia Lee
- 4Neurosurgery, Neurological Institute, Taipei Veterans General Hospital; and
- 5School of Medicine, National Yang-Ming University, Taipei, Taiwan
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21
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Mooij AH, Frauscher B, Goemans SAM, Huiskamp GJM, Braun KPJ, Zijlmans M. Ripples in scalp EEGs of children: co-occurrence with sleep-specific transients and occurrence across sleep stages. Sleep 2018; 41:5076452. [DOI: 10.1093/sleep/zsy169] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Indexed: 12/18/2022] Open
Affiliation(s)
- Anne H Mooij
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Department of Medicine and Center for Neuroscience Studies, Queen’s University, Kingston, Ontario, Canada
| | - Sophie A M Goemans
- Faculty of Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Geertjan J M Huiskamp
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Kees P J Braun
- Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Maeike Zijlmans
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
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22
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Varatharajah Y, Berry B, Cimbalnik J, Kremen V, Van Gompel J, Stead M, Brinkmann B, Iyer R, Worrell G. Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy. J Neural Eng 2018; 15:046035. [PMID: 29855436 PMCID: PMC6108188 DOI: 10.1088/1741-2552/aac960] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
OBJECTIVE An ability to map seizure-generating brain tissue, i.e. the seizure onset zone (SOZ), without recording actual seizures could reduce the duration of invasive EEG monitoring for patients with drug-resistant epilepsy. A widely-adopted practice in the literature is to compare the incidence (events/time) of putative pathological electrophysiological biomarkers associated with epileptic brain tissue with the SOZ determined from spontaneous seizures recorded with intracranial EEG, primarily using a single biomarker. Clinical translation of the previous efforts suffers from their inability to generalize across multiple patients because of (a) the inter-patient variability and (b) the temporal variability in the epileptogenic activity. APPROACH Here, we report an artificial intelligence-based approach for combining multiple interictal electrophysiological biomarkers and their temporal characteristics as a way of accounting for the above barriers and show that it can reliably identify seizure onset zones in a study cohort of 82 patients who underwent evaluation for drug-resistant epilepsy. MAIN RESULTS Our investigation provides evidence that utilizing the complementary information provided by multiple electrophysiological biomarkers and their temporal characteristics can significantly improve the localization potential compared to previously published single-biomarker incidence-based approaches, resulting in an average area under ROC curve (AUC) value of 0.73 in a cohort of 82 patients. Our results also suggest that recording durations between 90 min and 2 h are sufficient to localize SOZs with accuracies that may prove clinically relevant. SIGNIFICANCE The successful validation of our approach on a large cohort of 82 patients warrants future investigation on the feasibility of utilizing intra-operative EEG monitoring and artificial intelligence to localize epileptogenic brain tissue. Broadly, our study demonstrates the use of artificial intelligence coupled with careful feature engineering in augmenting clinical decision making.
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Affiliation(s)
- Yogatheesan Varatharajah
- Electrical and Computer Engineering, University of Illinois, Urbana, IL 61801, United States of America
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23
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Weiss SA, Berry B, Chervoneva I, Waldman Z, Guba J, Bower M, Kucewicz M, Brinkmann B, Kremen V, Khadjevand F, Varatharajah Y, Guragain H, Sharan A, Wu C, Staba R, Engel J, Sperling M, Worrell G. Visually validated semi-automatic high-frequency oscillation detection aides the delineation of epileptogenic regions during intra-operative electrocorticography. Clin Neurophysiol 2018; 129:2089-2098. [PMID: 30077870 DOI: 10.1016/j.clinph.2018.06.030] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Revised: 06/13/2018] [Accepted: 06/28/2018] [Indexed: 10/28/2022]
Abstract
OBJECTIVE To test the utility of a novel semi-automated method for detecting, validating, and quantifying high-frequency oscillations (HFOs): ripples (80-200 Hz) and fast ripples (200-600 Hz) in intra-operative electrocorticography (ECoG) recordings. METHODS Sixteen adult patients with temporal lobe epilepsy (TLE) had intra-operative ECoG recordings at the time of resection. The computer-annotated ECoG recordings were visually inspected and false positive detections were removed. We retrospectively determined the sensitivity, specificity, positive and negative predictive value (PPV/NPV) of HFO detections in unresected regions for determining post-operative seizure outcome. RESULTS Visual validation revealed that 2.81% of ripple and 43.68% of fast ripple detections were false positive. Inter-reader agreement for false positive fast ripple on spike classification was good (ICC = 0.713, 95% CI: 0.632-0.779). After removing false positive detections, the PPV of a single fast ripple on spike in an unresected electrode site for post-operative non-seizure free outcome was 85.7 [50-100%]. Including false positive detections reduced the PPV to 64.2 [57.8-69.83%]. CONCLUSIONS Applying automated HFO methods to intraoperative electrocorticography recordings results in false positive fast ripple detections. True fast ripples on spikes are rare, but predict non-seizure free post-operative outcome if found in an unresected site. SIGNIFICANCE Semi-automated HFO detection methods are required to accurately identify fast ripple events in intra-operative ECoG recordings.
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Affiliation(s)
- Shennan Aibel Weiss
- Dept. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA.
| | - Brent Berry
- Department of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Inna Chervoneva
- Dept. of Pharmacology & Experimental Therapeutics, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Zachary Waldman
- Dept. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Jonathan Guba
- Dept. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Mark Bower
- Department of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Michal Kucewicz
- Department of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Benjamin Brinkmann
- Department of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Vaclav Kremen
- Department of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Fatemeh Khadjevand
- Department of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Yogatheesan Varatharajah
- Department of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Hari Guragain
- Department of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Ashwini Sharan
- Dept. of Neurosurgery, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Chengyuan Wu
- Dept. of Neurosurgery, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Richard Staba
- Dept. of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Jerome Engel
- Dept. of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Michael Sperling
- Dept. of Neurology, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Gregory Worrell
- Department of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
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Khadjevand F, Cimbalnik J, Worrell GA. Progress and Remaining Challenges in the Application of High Frequency Oscillations as Biomarkers of Epileptic Brain. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2017. [PMID: 29532041 DOI: 10.1016/j.cobme.2017.09.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
High-frequency oscillations (HFOs: 100 - 600 Hz) have been widely proposed as biomarkers of epileptic brain tissue. In addition, HFOs over a broader range of frequencies spanning 30 - 2000 Hz are potential biomarkers of both physiological and pathological brain processes. The majority of the results from humans with focal epilepsy have focused on HFOs recorded directly from the brain with intracranial EEG (iEEG) in the high gamma (65 - 100 Hz), ripple (100 - 250 Hz), and fast ripple (250 - 600 Hz) frequency ranges. These results are supplemented by reports of HFOs recorded with iEEG in the low gamma (30 - 65Hz) and very high frequency (500 - 2000 Hz) ranges. Visual detection of HFOs is laborious and limited by poor inter-rater agreement; and the need for accurate, reproducible automated HFOs detection is well recognized. In particular, the clinical translation of HFOs as a biomarker of the epileptogenic brain has been limited by the ability to reliably detect and accurately classify HFOs as physiological or pathological. Despite these challenges, there has been significant progress in the field, which is the subject of this review. Furthermore, we provide data and corresponding analytic code in an effort to promote reproducible research and accelerate clinical translation.
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Affiliation(s)
- Fatemeh Khadjevand
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, 200 First St SW, Rochester MN, 55905, USA
| | - Jan Cimbalnik
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, 200 First St SW, Rochester MN, 55905, USA.,International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Gregory A Worrell
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, 200 First St SW, Rochester MN, 55905, USA.,Department of Biomedical Engineering and Physiology, Mayo Clinic, 200 First St SW, Rochester MN, 55905, USA
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