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Al-Bakri AF, Martinek R, Pelc M, Zygarlicki J, Kawala-Sterniuk A. Implementation of a Morphological Filter for Removing Spikes from the Epileptic Brain Signals to Improve Identification Ripples. SENSORS (BASEL, SWITZERLAND) 2022; 22:7522. [PMID: 36236621 PMCID: PMC9571066 DOI: 10.3390/s22197522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/20/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
Epilepsy is a very common disease affecting at least 1% of the population, comprising a number of over 50 million people. As many patients suffer from the drug-resistant version, the number of potential treatment methods is very small. However, since not only the treatment of epilepsy, but also its proper diagnosis or observation of brain signals from recordings are important research areas, in this paper, we address this very problem by developing a reliable technique for removing spikes and sharp transients from the baseline of the brain signal using a morphological filter. This allows much more precise identification of the so-called epileptic zone, which can then be resected, which is one of the methods of epilepsy treatment. We used eight patients with 5 KHz data set and depended upon the Staba 2002 algorithm as a reference to detect the ripples. We found that the average sensitivity and false detection rate of our technique are significant, and they are ∼94% and ∼14%, respectively.
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Affiliation(s)
- Amir F. Al-Bakri
- Department of Biomedical Engineering, College of Engineering, University of Babylon, Hillah 51001, Iraq
| | - Radek Martinek
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava–Poruba, Czech Republic
| | - Mariusz Pelc
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
- School of Computing and Mathematical Sciences, University of Greenwich, Park Row, London SE10 9LS, UK
| | - Jarosław Zygarlicki
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
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Nunez MD, Charupanit K, Sen-Gupta I, Lopour BA, Lin JJ. Beyond rates: time-varying dynamics of high frequency oscillations as a biomarker of the seizure onset zone. J Neural Eng 2022; 19:10.1088/1741-2552/ac520f. [PMID: 35120337 PMCID: PMC9258635 DOI: 10.1088/1741-2552/ac520f] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 02/04/2022] [Indexed: 11/11/2022]
Abstract
Objective. High frequency oscillations (HFOs) recorded by intracranial electrodes have generated excitement for their potential to help localize epileptic tissue for surgical resection. However, the number of HFOs per minute (i.e. the HFO 'rate') is not stable over the duration of intracranial recordings; for example, the rate of HFOs increases during periods of slow-wave sleep. Moreover, HFOs that are predictive of epileptic tissue may occur in oscillatory patterns due to phase coupling with lower frequencies. Therefore, we sought to further characterize between-seizure (i.e. 'interictal') HFO dynamics both within and outside the seizure onset zone (SOZ).Approach. Using long-term intracranial EEG (mean duration 10.3 h) from 16 patients, we automatically detected HFOs using a new algorithm. We then fit a hierarchical negative binomial model to the HFO counts. To account for differences in HFO dynamics and rates between sleep and wakefulness, we also fit a mixture model to the same data that included the ability to switch between two discrete brain states that were automatically determined during the fitting process. The ability to predict the SOZ by model parameters describing HFO dynamics (i.e. clumping coefficients and coefficients of variation) was assessed using receiver operating characteristic curves.Main results. Parameters that described HFO dynamics were predictive of SOZ. In fact, these parameters were found to be more consistently predictive than HFO rate. Using concurrent scalp EEG in two patients, we show that the model-found brain states corresponded to (1) non-REM sleep and (2) awake and rapid eye movement sleep. However the brain state most likely corresponding to slow-wave sleep in the second model improved SOZ prediction compared to the first model for only some patients.Significance. This work suggests that delineation of SOZ with interictal data can be improved by the inclusion of time-varying HFO dynamics.
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Affiliation(s)
- Michael D. Nunez
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands,Department of Biomedical Engineering, University of California, Irvine CA, USA,Corresponding author (Michael D. Nunez), (Beth A. Lopour)
| | - Krit Charupanit
- Department of Biomedical Engineering, University of California, Irvine CA, USA,Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
| | - Indranil Sen-Gupta
- Neurology, University of California Irvine Medical Center, Orange CA, USA
| | - Beth A. Lopour
- Department of Biomedical Engineering, University of California, Irvine CA, USA,Corresponding author (Michael D. Nunez), (Beth A. Lopour)
| | - Jack J. Lin
- Department of Neurology, University of California, Irvine CA, USA
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Xiang J, Maue E, Fan Y, Qi L, Mangano FT, Greiner H, Tenney J. Kurtosis and skewness of high-frequency brain signals are altered in paediatric epilepsy. Brain Commun 2020; 2:fcaa036. [PMID: 32954294 PMCID: PMC7425348 DOI: 10.1093/braincomms/fcaa036] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 02/19/2020] [Accepted: 03/02/2020] [Indexed: 01/15/2023] Open
Abstract
Intracranial studies provide solid evidence that high-frequency brain signals are a new biomarker for epilepsy. Unfortunately, epileptic (pathological) high-frequency signals can be intermingled with physiological high-frequency signals making these signals difficult to differentiate. Recent success in non-invasive detection of high-frequency brain signals opens a new avenue for distinguishing pathological from physiological high-frequency signals. The objective of the present study is to characterize pathological and physiological high-frequency signals at source levels by using kurtosis and skewness analyses. Twenty-three children with medically intractable epilepsy and age-/gender-matched healthy controls were studied using magnetoencephalography. Magnetoencephalographic data in three frequency bands, which included 2–80 Hz (the conventional low-frequency signals), 80–250 Hz (ripples) and 250–600 Hz (fast ripples), were analysed. The kurtosis and skewness of virtual electrode signals in eight brain regions, which included left/right frontal, temporal, parietal and occipital cortices, were calculated and analysed. Differences between epilepsy and controls were quantitatively compared for each cerebral lobe in each frequency band in terms of kurtosis and skewness measurements. Virtual electrode signals from clinical epileptogenic zones and brain areas outside of the epileptogenic zones were also compared with kurtosis and skewness analyses. Compared to controls, patients with epilepsy showed significant elevation in kurtosis and skewness of virtual electrode signals. The spatial and frequency patterns of the kurtosis and skewness of virtual electrode signals among the eight cerebral lobes in three frequency bands were also significantly different from that of the controls (2–80 Hz, P < 0.001; 80–250 Hz, P < 0.00001; 250–600 Hz, P < 0.0001). Compared to signals from non-epileptogenic zones, virtual electrode signals from epileptogenic zones showed significantly altered kurtosis and skewness (P < 0.001). Compared to normative data from the control group, aberrant virtual electrode signals were, for each patient, more pronounced in the epileptogenic lobes than in other lobes(kurtosis analysis of virtual electrode signals in 250–600 Hz; odds ratio = 27.9; P < 0.0001). The kurtosis values of virtual electrode signals in 80–250 and 250–600 Hz showed the highest sensitivity (88.23%) and specificity (89.09%) for revealing epileptogenic lobe, respectively. The combination of virtual electrode and kurtosis/skewness measurements provides a new quantitative approach to distinguishing pathological from physiological high-frequency signals for paediatric epilepsy. Non-invasive identification of pathological high-frequency signals may provide novel important information to guide clinical invasive recordings and direct surgical treatment of epilepsy.
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Affiliation(s)
- Jing Xiang
- MEG Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.,Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Ellen Maue
- MEG Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.,Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Yuyin Fan
- MEG Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.,Department of Pediatric Neurology, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Lei Qi
- MEG Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.,Department of Neurosurgery, Beijing Fengtai Hospital, Beijing 100071, China
| | - Francesco T Mangano
- Division of Neurosurgery, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Hansel Greiner
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Jeffrey Tenney
- MEG Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.,Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
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