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Aaftink D, Reijneveld JC, de Lange F, Sander JW, Thijs RD. Grading objective diagnostic components in paroxysmal events: One-year follow-up at a tertiary epilepsy center. Epilepsia 2024. [PMID: 39056373 DOI: 10.1111/epi.18062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 07/02/2024] [Accepted: 07/02/2024] [Indexed: 07/28/2024]
Abstract
OBJECTIVE This study was undertaken to develop a model and perform a preliminary internal validation study of the Scale for Objective Diagnostic Components of Paroxysmal Events (STAMP). METHODS We developed STAMP, which builds on the International League Against Epilepsy task force scale for functional seizures with additional categories for epileptic seizures and syncope. We included 200 consecutive referrals to a Dutch tertiary epilepsy center to evaluate seizurelike events. We recorded demographic and clinical data and collected the clinical evaluation at referral and after 3, 6, 9, and 12 months of follow-up. We ascertained the STAMP at each time point and evaluated factors predicting an improvement in STAMP grade during follow-up. RESULTS Of the 200 referrals at baseline, 131 were classified as having epileptic seizures, 17 as functional seizures, and three as syncope, and 49 were unclassifiable. STAMP grade at baseline was 4 (absent) in 56 individuals, 3 (circumstantial) in 78, 2 (clinically established) in six, and 1 (documented) in 11. Over time, 62 cases STAMP grades improved, and 23 remained unclassifiable. A refinement of STAMP grade during follow-up was due to successful event recordings in 34 people (30 video-electroencephalographic [EEG] recordings, four tilt table testing), home videos or clinician-witnessed events in 13, and identification of interictal EEG or magnetic resonance imaging abnormalities in seven. An improved STAMP grade after 12 months of follow-up was significantly more likely in those with higher event frequency, unclassifiable events, longer event duration, and a shorter time since the first event and less likely in those with a history suggestive of seizures. SIGNIFICANCE This epilepsy service evaluation underscores the crucial role of event recording in improving diagnostic certainty. STAMP may be used to monitor diagnostic performance over time but requires further validation.
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Affiliation(s)
- Daniel Aaftink
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
- Medische Kliniek Velsen, Velsen-Noord, the Netherlands
| | - Jaap C Reijneveld
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
| | - Frederik de Lange
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Josemir W Sander
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
- UCL Queen Square Institute of Neurology and Chalfont Centre for Epilepsy, London, UK
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Roland D Thijs
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
- UCL Queen Square Institute of Neurology and Chalfont Centre for Epilepsy, London, UK
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
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Patterson V, Glass DH, Kumar S, El-Sadig SM, Mohamed I, El-Amin R, Singh M. Construction and validation of an algorithm to separate focal and generalised epilepsy using clinical variables: A comparison of machine learning approaches. Epilepsy Behav 2024; 155:109793. [PMID: 38669972 DOI: 10.1016/j.yebeh.2024.109793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 03/19/2024] [Accepted: 04/13/2024] [Indexed: 04/28/2024]
Abstract
PURPOSE Epilepsy type, whether focal or generalised, is important in deciding anti-seizure medication (ASM). In resource-limited settings, investigations are usually not available, so a clinical separation is required. We used a naïve Bayes approach to devise an algorithm to do this, and compared its accuracy with algorithms devised by five other machine learning methods. METHODS We used data on 28 clinical variables from 503 patients attending an epilepsy clinic in India with defined epilepsy type, as determined by an epileptologist with access to clinical, imaging, and EEG data. We adopted a machine learning approach to select the most relevant variables based on mutual information, to train the model on part of the data, and then to evaluate it on the remaining data (testing set). We used a naïve Bayes approach and compared the results in the testing set with those obtained by several other machine learning algorithms by measuring sensitivity, specificity, accuracy, area under the curve, and Cohen's kappa. RESULTS The six machine learning methods produced broadly similar results. The best naïve Bayes algorithm contained eleven variables, and its accuracy was 92.2% in determining epilepsy type (sensitivity 92.0%, specificity 92.7%). An algorithm incorporating the best eight of these variables was only slightly less accurate - 91.0% (sensitivity 89.6%, and specificity 95.1%) - and easier for clinicians to use. CONCLUSION A clinical algorithm with eight variables is effective and accurate at separating focal from generalised epilepsy. It should be useful in resource-limited settings, by epilepsy-inexperienced doctors, to help determine epilepsy type and therefore optimal ASMs for individual patients, without the need for EEG or neuroimaging.
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Affiliation(s)
| | | | - Shambhu Kumar
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | | | - Inaam Mohamed
- Department of Paediatrics, University of Khartoum, Khartoum, Sudan
| | - Rahba El-Amin
- Department of Medicine, University of Khartoum, Khartoum, Sudan
| | - Mamta Singh
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
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Mora S, Turrisi R, Chiarella L, Consales A, Tassi L, Mai R, Nobili L, Barla A, Arnulfo G. NLP-based tools for localization of the epileptogenic zone in patients with drug-resistant focal epilepsy. Sci Rep 2024; 14:2349. [PMID: 38287042 PMCID: PMC10825198 DOI: 10.1038/s41598-024-51846-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 01/10/2024] [Indexed: 01/31/2024] Open
Abstract
Epilepsy surgery is an option for people with focal onset drug-resistant (DR) seizures but a delayed or incorrect diagnosis of epileptogenic zone (EZ) location limits its efficacy. Seizure semiological manifestations and their chronological appearance contain valuable information on the putative EZ location but their interpretation relies on extensive experience. The aim of our work is to support the localization of EZ in DR patients automatically analyzing the semiological description of seizures contained in video-EEG reports. Our sample is composed of 536 descriptions of seizures extracted from Electronic Medical Records of 122 patients. We devised numerical representations of anamnestic records and seizures descriptions, exploiting Natural Language Processing (NLP) techniques, and used them to feed Machine Learning (ML) models. We performed three binary classification tasks: localizing the EZ in the right or left hemisphere, temporal or extra-temporal, and frontal or posterior regions. Our computational pipeline reached performances above 70% in all tasks. These results show that NLP-based numerical representation combined with ML-based classification models may help in localizing the origin of the seizures relying only on seizures-related semiological text data alone. Accurate early recognition of EZ could enable a more appropriate patient management and a faster access to epilepsy surgery to potential candidates.
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Affiliation(s)
- Sara Mora
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, 16145, Genoa, Italy.
| | - Rosanna Turrisi
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, 16145, Genoa, Italy
- MaLGa Machine Learning Genoa Center, University of Genoa, 16146, Genoa, Italy
| | - Lorenzo Chiarella
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, 16132, Genoa, Italy
- Child Neuropsychiatry Unit, IRCCS Istituto Giannina Gaslini, Member of the European Reference Network EpiCARE, 16147, Genoa, Italy
| | - Alessandro Consales
- Division of Neurosurgery, IRCCS Istituto Giannina Gaslini, 16147, Genoa, Italy
| | - Laura Tassi
- "Claudio Munari" Epilepsy Surgery Center, Niguarda Hospital, 20162, Milan, Italy
| | - Roberto Mai
- "Claudio Munari" Epilepsy Surgery Center, Niguarda Hospital, 20162, Milan, Italy
| | - Lino Nobili
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, 16132, Genoa, Italy
- Child Neuropsychiatry Unit, IRCCS Istituto Giannina Gaslini, Member of the European Reference Network EpiCARE, 16147, Genoa, Italy
| | - Annalisa Barla
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, 16145, Genoa, Italy
- MaLGa Machine Learning Genoa Center, University of Genoa, 16146, Genoa, Italy
| | - Gabriele Arnulfo
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, 16145, Genoa, Italy
- Neuroscience Center, Helsinki Institute of Life Science (HiLife), University of Helsinki, 00014, Helsinki, Finland
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Knight A, Gschwind T, Galer P, Worrell GA, Litt B, Soltesz I, Beniczky S. Artificial intelligence in epilepsy phenotyping. Epilepsia 2023:10.1111/epi.17833. [PMID: 37983589 PMCID: PMC11102939 DOI: 10.1111/epi.17833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 11/17/2023] [Indexed: 11/22/2023]
Abstract
Artificial intelligence (AI) allows data analysis and integration at an unprecedented granularity and scale. Here we review the technological advances, challenges, and future perspectives of using AI for electro-clinical phenotyping of animal models and patients with epilepsy. In translational research, AI models accurately identify behavioral states in animal models of epilepsy, allowing identification of correlations between neural activity and interictal and ictal behavior. Clinical applications of AI-based automated and semi-automated analysis of audio and video recordings of people with epilepsy, allow significant data reduction and reliable detection and classification of major motor seizures. AI models can accurately identify electrographic biomarkers of epilepsy, such as spikes, high-frequency oscillations, and seizure patterns. Integrating AI analysis of electroencephalographic, clinical, and behavioral data will contribute to optimizing therapy for patients with epilepsy.
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Affiliation(s)
| | - Tilo Gschwind
- Department of Neurosurgery, Stanford University, Stanford, USA
| | - Peter Galer
- Center for Neuroengineering and Therapeutics; Department of Bioengineering; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, USA
| | | | - Brian Litt
- Center for Neuroengineering and Therapeutics; Department of Bioengineering; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, USA
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, USA
| | - Sándor Beniczky
- Danish Epilepsy Centre Filadelfia, Dianalund, Aarhus University Hospital and Aarhus University, Aarhus, Denmark
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El-Sadig SM, El-Amin R, Mohamed I, Kumar S, Singh M, Glass DH, Patterson V. An epilepsy type algorithm developed in India is accurate in Sudan: A prospective validation study. Seizure 2023; 111:187-190. [PMID: 37678076 DOI: 10.1016/j.seizure.2023.08.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/29/2023] [Accepted: 08/31/2023] [Indexed: 09/09/2023] Open
Abstract
PURPOSE The effects of epilepsy are worse in lower- and middle-income countries (LMICs) where most people with epilepsy live, and where most are untreated. Correct treatment depends on determining whether focal or generalised epilepsy is present. EEG and MRI are usually not available to help so an entirely clinical method is required. We applied an eight-variable algorithm, which had been derived from 503 patients from India using naïve-Bayesian methods, to an adult Sudanese cohort with epilepsy. METHODS There were 150 consecutive adult patients with known epilepsy type as defined by two neurologists who had access to clinical information, EEG and neuroimaging ("the gold standard"). We used seven of the eight variables, together with their likelihood ratios, to calculate the probability of focal as opposed to generalised epilepsy in each patient and compared that to the "gold standard". Sensitivity, specificity, accuracy, and Cohen's kappa statistic were calculated. RESULTS Mean age was 28 years (range 17-49) and 53% were female. The accuracy of an algorithm comprising seven of the eight variables was 92%, with sensitivity of 99% and specificity of 72% for focal epilepsy. Cohen's kappa was 0.773, indicating substantial agreement. Ninety-four percent of patients had probability scores either less than 0.1 (generalised) or greater than 0.9 (focal). CONCLUSION The results confirm the high accuracy of this algorithm in determining epilepsy type in Sudan. They suggest that, in a clinical condition like epilepsy, where a history is crucial, results in one continent can be applied to another. This is especially important as untreated epilepsy and the epilepsy treatment gap are so widespread. The algorithm can be applied to patients giving an individual probability score which can help determine the appropriate anti-seizure medication. It should give epilepsy-inexperienced doctors confidence in managing patients with epilepsy.
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Affiliation(s)
| | - Rahba El-Amin
- Department of Medicine, University of Khartoum, Khartoum, Sudan
| | - Inaam Mohamed
- Department of Paediatrics, University of Khartoum, Khartoum, Sudan
| | - Shambhu Kumar
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Mamta Singh
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - David H Glass
- School of Computing, Ulster University, Belfast, United Kingdom
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Asadi-Pooya AA, Brigo F, Lattanzi S, Blumcke I. Adult epilepsy. Lancet 2023; 402:412-424. [PMID: 37459868 DOI: 10.1016/s0140-6736(23)01048-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/15/2023] [Accepted: 05/19/2023] [Indexed: 07/31/2023]
Abstract
Epilepsy is a common medical condition that affects people of all ages, races, social classes, and geographical regions. Diagnosis of epilepsy remains clinical, and ancillary investigations (electroencephalography, imaging, etc) are of aid to determine the type, cause, and prognosis. Antiseizure medications represent the mainstay of epilepsy treatment: they aim to suppress seizures without adverse events, but they do not affect the underlying predisposition to generate seizures. Currently available antiseizure medications are effective in around two-thirds of patients with epilepsy. Neurosurgical resection is an effective strategy to reach seizure control in selected individuals with drug-resistant focal epilepsy. Non-pharmacological treatments such as palliative surgery (eg, corpus callosotomy), neuromodulation techniques (eg, vagus nerve stimulation), and dietary interventions represent therapeutic options for patients with drug-resistant epilepsy who are not suitable for resective brain surgery.
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Affiliation(s)
- Ali A Asadi-Pooya
- Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; Jefferson Comprehensive Epilepsy Center, Department of Neurology, Thomas Jefferson University, Philadelphia, PA, USA.
| | - Francesco Brigo
- Department of Neurology, Hospital of Merano (SABES-ASDAA), Merano, Italy; Lehrkrankenhaus der Paracelsus Medizinischen Privatuniversität, Salzburg, Austria
| | - Simona Lattanzi
- Neurological Clinic, Department of Experimental and Clinical Medicine, Marche Polytechnic University, Ancona, Italy
| | - Ingmar Blumcke
- Institute of Neuropathology, University Hospitals Erlangen, Erlangen, Germany; Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
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Terman SW. Choose Your Own Adventure: Expert Advice at Your Fingertips. Epilepsy Curr 2022; 22:46-47. [PMID: 35233199 PMCID: PMC8832356 DOI: 10.1177/15357597211062385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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Asadi-Pooya AA, Beniczky S, Rubboli G, Sperling MR, Rampp S, Perucca E. The EpiPick algorithm to select appropriate antiseizure medications in patients with epilepsy: Validation studies and updates. Epilepsia 2021; 63:254-255. [PMID: 34797915 DOI: 10.1111/epi.17129] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 11/08/2021] [Indexed: 11/29/2022]
Affiliation(s)
- Ali A Asadi-Pooya
- Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.,Jefferson Comprehensive Epilepsy Center, Department of Neurology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre (member of the ERN EpiCARE), Dianalund, Denmark.,Department of Clinical Medicine, Aarhus University and Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Guido Rubboli
- Department of Neurology, Danish Epilepsy Centre (member of the ERN EpiCARE), Dianalund, Denmark.,University of Copenhagen, Copenhagen, Denmark
| | - Michael R Sperling
- Jefferson Comprehensive Epilepsy Center, Department of Neurology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Stefan Rampp
- Department of Neurosurgery, University Hospital Erlangen, Erlangen, Germany.,Department of Neurosurgery, University Hospital Halle, Halle, Germany
| | - Emilio Perucca
- Department of Neuroscience, Monash University, Melbourne, Victoria, Australia
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