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Shanir PPM, Khan KA, Khan YU, Farooq O, Adeli H. Automatic Seizure Detection Based on Morphological Features Using One-Dimensional Local Binary Pattern on Long-Term EEG. Clin EEG Neurosci 2018; 49:351-362. [PMID: 29214865 DOI: 10.1177/1550059417744890] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Epileptic neurological disorder of the brain is widely diagnosed using the electroencephalography (EEG) technique. EEG signals are nonstationary in nature and show abnormal neural activity during the ictal period. Seizures can be identified by analyzing and obtaining features of EEG signal that can detect these abnormal activities. The present work proposes a novel morphological feature extraction technique based on the local binary pattern (LBP) operator. LBP provides a unique decimal value to a sample point by weighing the binary outcomes after thresholding the neighboring samples with the present sample point. These LBP values assist in capturing the rising and falling edges of the EEG signal, thus providing a morphologically featured discriminating pattern for epilepsy detection. In the present work, the variability in the LBP values is measured by calculating the sum of absolute difference of the consecutive LBP values. Interquartile range is calculated over the preprocessed EEG signal to provide dispersion measure in the signal. For classification purpose, K-nearest neighbor classifier is used, and the performance is evaluated on 896.9 hours of data from CHB-MIT continuous EEG database. Mean accuracy of 99.7% and mean specificity of 99.8% is obtained with average false detection rate of 0.47/h and sensitivity of 99.2% for 136 seizures.
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Affiliation(s)
- P P Muhammed Shanir
- 1 Department of Electrical and Electronics Engineering, Thangal Kunju Musaliar College of Engineering, Kollam, Kerala, India.,2 Department of Electrical Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| | - Kashif Ahmad Khan
- 3 School of Electrical and Electronics Engineering, Lovely Professional University, Phagwara, Punjab, India
| | - Yusuf Uzzaman Khan
- 2 Department of Electrical Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| | - Omar Farooq
- 4 Department of Electronics Engineering, Zakir Husain College of Engineering and Technology, AMU Aligarh, Aligarh, Uttar Pradesh, India
| | - Hojjat Adeli
- 5 College of Engineering, The Ohio State University, Columbus, OH, USA
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Jozwiak S, Becker A, Cepeda C, Engel J, Gnatkovsky V, Huberfeld G, Kaya M, Kobow K, Simonato M, Loeb JA. WONOEP appraisal: Development of epilepsy biomarkers-What we can learn from our patients? Epilepsia 2017; 58:951-961. [PMID: 28387933 PMCID: PMC5806696 DOI: 10.1111/epi.13728] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/20/2017] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Current medications for patients with epilepsy work in only two of three patients. For those medications that do work, they only suppress seizures. They treat the symptoms, but do not modify the underlying disease, forcing patients to take these drugs with significant side effects, often for the rest of their lives. A major limitation in our ability to advance new therapeutics that permanently prevent, reduce the frequency of, or cure epilepsy comes from a lack of understanding of the disease coupled with a lack of reliable biomarkers that can predict who has or who will get epilepsy. METHODS The main goal of this report is to present a number of approaches for identifying reliable biomarkers from observing patients with brain disorders that have a high probability of producing epilepsy. RESULTS A given biomarker, or more likely a profile of biomarkers, will have both a quantity and a time course during epileptogenesis that can be used to predict who will get the disease, to confirm epilepsy as a diagnosis, to identify coexisting pathologies, and to monitor the course of treatments. SIGNIFICANCE Additional studies in patients and animal models could identify common and clinically valuable biomarkers to successfully translate animal studies into new and effective clinical trials.
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Affiliation(s)
- Sergiusz Jozwiak
- Department of Child Neurology, Medical University of Warsaw, Poland
- Department of Child Neurology, The Children’s Memorial Health Institute, Warsaw, Poland
| | - Albert Becker
- Section for Translational Epilepsy Research, Department of Neuropathology, University of Bonn Medical Center, Bonn, Germany
| | - Carlos Cepeda
- IDDRC, Semel Institute for Neuroscience & Human Behavior, University of California Los Angeles, Los Angeles, CA, USA
| | - Jerome Engel
- Departments of Neurology, Neurobiology, and Psychiatry & Biobehavioral Sciences and the Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Vadym Gnatkovsky
- Unit of Epilepsy and Experimental Neurophysiology, Fondazione Istituto Neurologico Carlo Besta, Milan, Italy
| | - Gilles Huberfeld
- Sorbonne and UPMC University, AP-HP, Department of Neurophysiology, UPMC and La Pitié-Salpêtrière Hospital, Paris, France
- INSERM U1129, Paris Descartes University, PRES Sorbonne Paris, Cité, Paris, CEA, France
| | - Mehmet Kaya
- Department of Physiology, Koc University School of Medicine, Rumelifeneri Yolu, Sariver, Istanbul, Turkey
| | - Katja Kobow
- Department of Neuropathology, University Hospital Erlangen, Erlangen, Germany
| | - Michele Simonato
- Department of Medical Sciences, University of Ferrara and Division of Neuroscience, University Vita-Salute San Raffaele, Milan, Italy
| | - Jeffrey A. Loeb
- Department of Neurology and Rehabilitation, The University of Illinois at Chicago, Chicago, IL
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Spyrou L, Martín-Lopez D, Valentín A, Alarcón G, Sanei S. Detection of Intracranial Signatures of Interictal Epileptiform Discharges from Concurrent Scalp EEG. Int J Neural Syst 2016; 26:1650016. [DOI: 10.1142/s0129065716500167] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Interictal epileptiform discharges (IEDs) are transient neural electrical activities that occur in the brain of patients with epilepsy. A problem with the inspection of IEDs from the scalp electroencephalogram (sEEG) is that for a subset of epileptic patients, there are no visually discernible IEDs on the scalp, rendering the above procedures ineffective, both for detection purposes and algorithm evaluation. On the other hand, intracranially placed electrodes yield a much higher incidence of visible IEDs as compared to concurrent scalp electrodes. In this work, we utilize concurrent scalp and intracranial EEG (iEEG) from a group of temporal lobe epilepsy (TLE) patients with low number of scalp-visible IEDs. The aim is to determine whether by considering the timing information of the IEDs from iEEG, the resulting concurrent sEEG contains enough information for the IEDs to be reliably distinguished from non-IED segments. We develop an automatic detection algorithm which is tested in a leave-subject-out fashion, where each test subject’s detection algorithm is based on the other patients’ data. The algorithm obtained a [Formula: see text] accuracy in recognizing scalp IED from non-IED segments with [Formula: see text] accuracy when trained and tested on the same subject. Also, it was able to identify nonscalp-visible IED events for most patients with a low number of false positive detections. Our results represent a proof of concept that IED information for TLE patients is contained in scalp EEG even if they are not visually identifiable and also that between subject differences in the IED topology and shape are small enough such that a generic algorithm can be used.
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Affiliation(s)
| | - David Martín-Lopez
- Department of Clinical Neuroscience, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, UK
- Department of Clinical Neurophysiology, King’s College Hospital NHS FT, London, UK
- Department of Clinical Neurophysiology, Ashford and St Peter’s Hospital NHS FT, Chertsey, UK
- Departamento de Fisiología, Facultad de Medicina, Universidad Complutense, Madrid, Spain
| | - Antonio Valentín
- Department of Clinical Neuroscience, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, UK
- Department of Clinical Neurophysiology, King’s College Hospital NHS FT, London, UK
- Departamento de Fisiología, Facultad de Medicina, Universidad Complutense, Madrid, Spain
| | - Gonzalo Alarcón
- Department of Clinical Neuroscience, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, UK
- Department of Clinical Neurophysiology, King’s College Hospital NHS FT, London, UK
- Comprehensive Epilepsy Center Neuroscience Institute, Academic Health Systems, Hamad Medical Corporation, Doha, Qatar
| | - Saeid Sanei
- Department of Computer Science, University of Surrey, UK
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Cogan D, Birjandtalab J, Nourani M, Harvey J, Nagaraddi V. Multi-Biosignal Analysis for Epileptic Seizure Monitoring. Int J Neural Syst 2016; 27:1650031. [PMID: 27389004 DOI: 10.1142/s0129065716500313] [Citation(s) in RCA: 82] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Persons who suffer from intractable seizures are safer if attended when seizures strike. Consequently, there is a need for wearable devices capable of detecting both convulsive and nonconvulsive seizures in everyday life. We have developed a three-stage seizure detection methodology based on 339 h of data (26 seizures) collected from 10 patients in an epilepsy monitoring unit. Our intent is to develop a wearable system that will detect seizures, alert a caregiver and record the time of seizure in an electronic diary for the patient's physician. Stage I looks for concurrent activity in heart rate, arterial oxygenation and electrodermal activity, all of which can be monitored by a wrist-worn device and which in combination produce a very low false positive rate. Stage II looks for a specific pattern created by these three biosignals. For the patients whose seizures cannot be detected by Stage II, Stage III detects seizures using limited-channel electroencephalogram (EEG) monitoring with at most three electrodes. Out of 10 patients, Stage I recognized all 11 seizures from seven patients, Stage II detected all 10 seizures from six patients and Stage III detected all of the seizures of two out of the three patients it analyzed.
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Affiliation(s)
- Diana Cogan
- 1 Quality of Life Technology Laboratory, Department of Electrical Engineering, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080, USA
| | - Javad Birjandtalab
- 1 Quality of Life Technology Laboratory, Department of Electrical Engineering, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080, USA
| | - Mehrdad Nourani
- 1 Quality of Life Technology Laboratory, Department of Electrical Engineering, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080, USA
| | - Jay Harvey
- 2 Neurology Consultants of Dallas, PA, 12221 Merit Drive, Suite 350, Dallas, TX 75230, USA
| | - Venkatesh Nagaraddi
- 2 Neurology Consultants of Dallas, PA, 12221 Merit Drive, Suite 350, Dallas, TX 75230, USA
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Jin H, Li W, Dong C, Wu J, Zhao W, Zhao Z, Ma L, Ma F, Chen Y, Liu Q. Hippocampal deep brain stimulation in nonlesional refractory mesial temporal lobe epilepsy. Seizure 2016; 37:1-7. [DOI: 10.1016/j.seizure.2016.01.018] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Revised: 01/28/2016] [Accepted: 01/29/2016] [Indexed: 11/28/2022] Open
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