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Nguyen E, Li J, Nguyen DK, Bou Assi E. Patient Safety in Canadian Epilepsy Monitoring Units: A Survey of Current Practices. Can J Neurol Sci 2024; 51:238-245. [PMID: 37160380 DOI: 10.1017/cjn.2023.58] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
BACKGROUND Guidelines on epilepsy monitoring unit (EMU) standards have been recently published. We aimed to survey Canadian EMUs to describe the landscape of safety practices and compare these to the recommendations from the new guidelines. METHODS A 34-item survey was created by compiling questions on EMU structure, patient monitoring, equipment, personnel, standardized protocol use, and use of injury prevention tools. The questionnaire was distributed online to 24 Canadian hospital centers performing video-EEG monitoring (VEM) in EMUs. Responses were tabulated and descriptively summarized. RESULTS In total, 26 EMUs responded (100% response rate), 50% of which were adult EMUs. EMUs were on average active for 23.4 years and had on average 3.6 beds. About 81% of respondents reported having a dedicated area for VEM, and 65% reported having designated EMU beds. Although a video monitoring station was available in 96% of EMUs, only 48% of EMUs provided continuous observation of patients (video and/or physical). A total of 65% of EMUs employed continuous heart monitoring. The technologist-to-patient ratio was 1:1-2 in 52% of EMUs during the day. No technologist supervision was most often reported in the evening and at night. Nurse-to-EMU-patient ratio was mostly 1:1-4 independent of the time of day. Consent forms were required before admission in 27% of EMUs. CONCLUSION Canadian EMUs performed decently in terms of there being dedicated space for VEM, continuous heart monitoring, and adequate nurse-to-patient ratios. Other practices were quite variable, and adjustments should be made on a case-by-case basis to adhere to the latest guidelines.
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Affiliation(s)
- Emmanuelle Nguyen
- Centre de recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Jimmy Li
- Centre de recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Neurology Division, Centre Hospitalier de l'Université de Sherbrooke (CHUS), Sherbrooke, QC, Canada
| | - Dang Khoa Nguyen
- Centre de recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Department of Neuroscience, Université de Montréal, Montreal, QC, Canada
- Neurology Division, Centre Hospitalier de l'Université de Montréal (CHUM), Montreal, QC, Canada
| | - Elie Bou Assi
- Centre de recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Department of Neuroscience, Université de Montréal, Montreal, QC, Canada
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Bagić AI, Ahrens SM, Chapman KE, Bai S, Clarke DF, Eisner M, Fountain NB, Gavvala JR, Rossi KC, Herman ST, Ostendorf AP. Epilepsy monitoring unit practices and safety among NAEC epilepsy centers: A census survey. Epilepsy Behav 2024; 150:109571. [PMID: 38070408 DOI: 10.1016/j.yebeh.2023.109571] [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: 10/26/2023] [Revised: 11/27/2023] [Accepted: 11/28/2023] [Indexed: 01/14/2024]
Abstract
OBJECTIVE An epilepsy monitoring unit (EMU) is a specialized unit designed for capturing and characterizing seizures and other paroxysmal events with continuous video electroencephalography (vEEG). Nearly 260 epilepsy centers in the United States are accredited by the National Association of Epilepsy Centers (NAEC) based on adherence to specific clinical standards to improve epilepsy care, safety, and quality. This study examines EMU staffing, safety practices, and reported outcomes. METHOD We analyzed NAEC annual report data and results from a supplemental survey specific to EMU practices reported in 2019 from 341 pediatric or adult center directors. Data on staffing, resources, safety practices and complications were collated with epilepsy center characteristics. We summarized using frequency (percentage) for categorical variables and median (inter-quartile range) for continuous variables. We used chi-square or Fisher's exact tests to compare staff responsibilities. RESULTS The supplemental survey response rate was 100%. Spell classification (39%) and phase 1 testing (28%) were the most common goals of the 91,069 reported admissions. The goal ratio of EEG technologist to beds of 1:4 was the most common during the day (68%) and off-hours (43%). Compared to residents and fellows, advanced practice providers served more roles in the EMU at level 3 or pediatric-only centers. Status epilepticus (SE) was the most common reported complication (1.6% of admissions), while cardiac arrest occurred in 0.1% of admissions. SIGNIFICANCE EMU staffing and safety practices vary across US epilepsy centers. Reported complications in EMUs are rare but could be further reduced, such as with more effective treatment or prevention of SE. These findings have potential implications for improving EMU safety and quality care.
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Affiliation(s)
- Anto I Bagić
- University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), Department of Neurology, Pittsburgh, PA, USA.
| | - Stephanie M Ahrens
- Department of Pediatrics, Division of Neurology, Nationwide Children's Hospital and The Ohio State University College of Medicine, Columbus, OH, USA.
| | - Kevin E Chapman
- Barrow Neurologic Institute at Phoenix Children's Hospital, Phoenix, AZ, USA.
| | - Shasha Bai
- Pediatric Biostatistics Core, Emory University School of Medicine, Atlanta, GA, USA.
| | - Dave F Clarke
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, TX, USA.
| | - Mariah Eisner
- Biostatistics Resource at Nationwide Children's Hospital, Columbus, OH, USA.
| | - Nathan B Fountain
- Department of Neurology, University of Virginia Health Sciences Center, Charlottesville, VA, USA.
| | - Jay R Gavvala
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA.
| | - Kyle C Rossi
- Beth Israel Deaconess Medical Center and Harvard Medical School, Department of Neurology, Division of Epilepsy, Boston, MA, USA.
| | | | - Adam P Ostendorf
- Department of Pediatrics, Division of Neurology, Nationwide Children's Hospital and The Ohio State University College of Medicine, Columbus, OH, USA.
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Wong S, Simmons A, Rivera-Villicana J, Barnett S, Sivathamboo S, Perucca P, Kwan P, Kuhlmann L, Vasa R, O'Brien TJ. EEG based automated seizure detection - A survey of medical professionals. Epilepsy Behav 2023; 149:109518. [PMID: 37952416 DOI: 10.1016/j.yebeh.2023.109518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/29/2023] [Accepted: 11/02/2023] [Indexed: 11/14/2023]
Abstract
Diagnosing and managing seizures presents substantial challenges for clinicians caring for patients with epilepsy. Although machine learning (ML) has been proposed for automated seizure detection using EEG data, there is little evidence of these technologies being broadly adopted in clinical practice. Moreover, there is a noticeable lack of surveys investigating this topic from the perspective of medical practitioners, which limits the understanding of the obstacles for the development of effective automated seizure detection. Besides the issue of generalisability and replicability seen in a small amount of studies, obstacles to the adoption of automated seizure detection remain largely unknown. To understand the obstacles preventing the application of seizure detection tools in clinical practice, we conducted a survey targeting medical professionals involved in the management of epilepsy. Our study aimed to gather insights on various factors such as the clinical utility, professional sentiment, benchmark requirements, and perceived barriers associated with the use of automated seizure detection tools. Our key findings are: I) The minimum acceptable sensitivity reported by most of our respondents (80%) seems achievable based on studies reported from most currently available ML-based EEG seizure detection algorithms, but replication studies often fail to meet this minimum. II) Respondents are receptive to the adoption of ML seizure detection tools and willing to spend time in training. III) The top three barriers for usage of such tools in clinical practice are related to availability, lack of training, and the blackbox nature of ML algorithms. Based on our findings, we developed a guide that can serve as a basis for developing ML-based seizure detection tools that meet the requirements of medical professionals, and foster the integration of these tools into clinical practice.
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Affiliation(s)
- Sheng Wong
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia.
| | - Anj Simmons
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | | | - Scott Barnett
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | - Shobi Sivathamboo
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia; Department of Neurology, Alfred Health, Melbourne, Victoria, Australia; Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia; Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Piero Perucca
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia; Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia; Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia; Epilepsy Research Centre, Department of Medicine (Austin Health), The University of Melbourne, Heidelberg, Victoria, Australia; Bladin-Berkovic Comprehensive Epilepsy Program, Austin Health, Heidelberg, Victoria, Australia
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia; Department of Neurology, Alfred Health, Melbourne, Victoria, Australia; Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia; Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia; Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia
| | - Rajesh Vasa
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | - Terence J O'Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia; Department of Neurology, Alfred Health, Melbourne, Victoria, Australia; Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia; Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia
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Peh WY, Thangavel P, Yao Y, Thomas J, Tan YL, Dauwels J. Six-Center Assessment of CNN-Transformer with Belief Matching Loss for Patient-Independent Seizure Detection in EEG. Int J Neural Syst 2023; 33:2350012. [PMID: 36809996 DOI: 10.1142/s0129065723500120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Neurologists typically identify epileptic seizures from electroencephalograms (EEGs) by visual inspection. This process is often time-consuming, especially for EEG recordings that last hours or days. To expedite the process, a reliable, automated, and patient-independent seizure detector is essential. However, developing a patient-independent seizure detector is challenging as seizures exhibit diverse characteristics across patients and recording devices. In this study, we propose a patient-independent seizure detector to automatically detect seizures in both scalp EEG and intracranial EEG (iEEG). First, we deploy a convolutional neural network with transformers and belief matching loss to detect seizures in single-channel EEG segments. Next, we extract regional features from the channel-level outputs to detect seizures in multi-channel EEG segments. At last, we apply post-processing filters to the segment-level outputs to determine seizures' start and end points in multi-channel EEGs. Finally, we introduce the minimum overlap evaluation scoring as an evaluation metric that accounts for minimum overlap between the detection and seizure, improving upon existing assessment metrics. We trained the seizure detector on the Temple University Hospital Seizure (TUH-SZ) dataset and evaluated it on five independent EEG datasets. We evaluate the systems with the following metrics: sensitivity (SEN), precision (PRE), and average and median false positive rate per hour (aFPR/h and mFPR/h). Across four adult scalp EEG and iEEG datasets, we obtained SEN of 0.617-1.00, PRE of 0.534-1.00, aFPR/h of 0.425-2.002, and mFPR/h of 0-1.003. The proposed seizure detector can detect seizures in adult EEGs and takes less than 15[Formula: see text]s for a 30[Formula: see text]min EEG. Hence, this system could aid clinicians in reliably identifying seizures expeditiously, allocating more time for devising proper treatment.
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Affiliation(s)
- Wei Yan Peh
- Interdisciplinary Graduate School (IGS), Nanyang Technological University, Singapore 639798, Singapore
| | - Prasanth Thangavel
- Interdisciplinary Graduate School (IGS), Nanyang Technological University, Singapore 639798, Singapore
| | - Yuanyuan Yao
- Katholieke Universiteit Leuven, Oude Markt 13, 3000 Leuven, Belgium
| | - John Thomas
- Montreal Neurological Institute, McGill University, Montreal QC H3A 2B4, Canada
| | - Yee-Leng Tan
- National Neuroscience Institute, Singapore 308433, Singapore
| | - Justin Dauwels
- Department of Microelectronics, Delft, University of Technology, 2628 CD Delft, Netherlands
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Safety Evaluation of Employing Temporal Interference Transcranial Alternating Current Stimulation in Human Studies. Brain Sci 2022; 12:brainsci12091194. [PMID: 36138930 PMCID: PMC9496688 DOI: 10.3390/brainsci12091194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/31/2022] [Accepted: 09/02/2022] [Indexed: 01/20/2023] Open
Abstract
Temporal interference transcranial alternating current stimulation (TI-tACS) is a new technique of noninvasive brain stimulation. Previous studies have shown the effectiveness of TI-tACS in stimulating brain areas in a selective manner. However, its safety in modulating human brain neurons is still untested. In this study, 38 healthy adults were recruited to undergo a series of neurological and neuropsychological measurements regarding safety concerns before and after active (2 mA, 20/70 Hz, 30 min) or sham (0 mA, 0 Hz, 30 min) TI-tACS. The neurological and neuropsychological measurements included electroencephalography (EEG), serum neuron-specific enolase (NSE), the Montreal Cognitive Assessment (MoCA), the Purdue Pegboard Test (PPT), an abbreviated version of the California Computerized Assessment Package (A-CalCAP), a revised version of the Visual Analog Mood Scale (VAMS-R), a self-assessment scale (SAS), and a questionnaire about adverse effects (AEs). We found no significant difference between the measurements of the active and sham TI-tACS groups. Meanwhile, no serious or intolerable adverse effects were reported or observed in the active stimulation group of 19 participants. These results support that TI-tACS is safe and tolerable in terms of neurological and neuropsychological functions and adverse effects for use in human brain stimulation studies under typical transcranial electric stimulation (TES) conditions (2 mA, 20/70 Hz, 30 min).
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DeStefano S, Pellinen J, Sillau S, Buchhalter J. Standardization of seizure response times and data collection in an epilepsy monitoring unit. Epilepsy Res 2022; 180:106869. [PMID: 35101657 DOI: 10.1016/j.eplepsyres.2022.106869] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 12/30/2021] [Accepted: 01/24/2022] [Indexed: 11/18/2022]
Abstract
OBJECTIVE We sought to improve seizure response times in the epilepsy monitoring unit, improve the accuracy and reliability of seizure response time data collection, and develop a standardized and automated approach for seizure response data collection in the EMU. METHODS We used Quality Improvement (QI) methodology to understand the EMU workflow involved in responding to seizures (a process map); to create a theory of change that stated the desired aim, potential drivers/barriers and interventions (i.e., key driver diagram) and perform iterative interventions to address some of the drivers plan-do-study-act (PDSA) cycles. We performed three PDSA cycles with a focus on improving the seizure alert system in our EMU. Adjustments were made to the methodology as it became clear that this was a systems issue, and our project would need to focus on improving the system rather than iteratively improving a functioning (stable) system. RESULTS Over a 6-month period, 252 seizure response times were recorded and analyzed. We performed 3 interventions. The first was initiating twice monthly meetings with nursing and EEG techs to discuss the project and provide feedback on response times. The second was the implementation of a new Hill-Rom seizure alert system to reduce alert times and automate data tracking. The third was implementing a new alert deactivation system to reduce variability in the data. Following these 3 interventions, variation, and data collection methods were improved while also maintaining improvements in seizure response times. SIGNIFICANCE We identified and implemented an alert system in our EMU which led to more efficient and accurate data collection while maintaining improved response times that resulted from the first intervention. This lays the groundwork for future QI initiatives and has created a framework for standardizing seizure response time recording and data collection that can be replicated at other centers with similar infrastructure, personnel and workflows.
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Affiliation(s)
- Samuel DeStefano
- University of Colorado School of Medicine, Department of Neurology, Aurora, CO, United States.
| | - Jacob Pellinen
- University of Colorado School of Medicine, Department of Neurology, Aurora, CO, United States
| | - Stefan Sillau
- University of Colorado School of Medicine, Department of Neurology, Aurora, CO, United States
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Automated seizure detection in an EMU setting: Are software packages ready for implementation? Seizure 2022; 96:13-17. [PMID: 35042003 DOI: 10.1016/j.seizure.2022.01.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 01/10/2022] [Accepted: 01/12/2022] [Indexed: 11/22/2022] Open
Abstract
PURPOSE We assessed whether automated detection software, combined with live observation, enabled reliable seizure detection using three commercial software packages: Persyst, Encevis and BESA. METHODS Two hundred and eighty-six prolonged EEG records of individuals aged 16-86 years, collected between August 2019 and January 2020, were retrospectively processed using all three packages. The reference standard included all seizures mentioned in the clinical report supplemented with true detections made by the software and not previously detected by clinical physiologists. Sensitivity was measured for offline review by clinical physiologists and software seizure detection, both in combination with live monitoring in an EMU setting, for all three software packages at record and seizure level. RESULTS The database contained 249 seizures in 64 records. The sensitivity of seizure detection was 98% for Encevis and Persyst, and 95% for BESA, when a positive results was defined as detection at least one of the seizures occurring within an individual record. When positivity was defined as recognition of all seizures, sensitivity was 93% for Persyst, 88% for Encevis and 84% for BESA. Clinical physiologists' review had a sensitivity of 100% at record level and 98% at seizure level. The median false positive rate per record was 1.7 for Persyst, 2.4 for BESA and 5.5 for Encevis per 24 h. CONCLUSION Automated seizure detection software does not perform as well as technicians do. However, it can be used in an EMU setting when the user is aware of its weaknesses. This assessment gives future users helpful insight into these strengths and weaknesses. The Persyst software performs best.
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Ruiz Marín M, Villegas Martínez I, Rodríguez Bermúdez G, Porfiri M. Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings. iScience 2021; 24:101997. [PMID: 33490905 PMCID: PMC7811137 DOI: 10.1016/j.isci.2020.101997] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 10/23/2020] [Accepted: 12/23/2020] [Indexed: 11/23/2022] Open
Abstract
Automated seizure detection in long-term video-EEG recordings is far from being integrated into common clinical practice. Here, we leverage classical and state-of-the-art complexity measures to robustly and automatically detect seizures from scalp recordings. Brain activity is scored through eight features, encompassing traditional time domain and novel measures of recurrence. A binary classification algorithm tailored to treat unbalanced dataset is used to determine whether a time window is ictal or non-ictal from its features. The application of the algorithm on a cohort of ten adult patients with focal refractory epilepsy indicates sensitivity, specificity, and accuracy of 90%, along with a true alarm rate of 95% and less than four false alarms per day. The proposed approach emphasizes ictal patterns against noisy background without the need of data preprocessing. Finally, we benchmark our approach against previous studies on two publicly available datasets, demonstrating the good performance of our algorithm. Complexity measures are formulated to enhance classical time-domain statistics of EEG The detection algorithm does not need ad-hoc data preprocessing to address artifacts Focal seizures are detected 95% of the time with less than four false alarms per day The approach offers a visual representation of a seizure as a time-evolving network
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Affiliation(s)
- Manuel Ruiz Marín
- Department of Quantitative Methods, Law and Modern Languages, Technical University of Cartagena (UPCT), Cartagena, Murcia 30201, Spain
- Bio-Health Institute (IMIB-Arrixaca), Health Science Campus, Murcia, CP 30120, Spain
- Corresponding author
| | - Irene Villegas Martínez
- Department of Projects and Innovation, Health Service of Murcia (SMS), Murcia, Spain
- Bio-Health Institute (IMIB-Arrixaca), Health Science Campus, Murcia, CP 30120, Spain
- Corresponding author
| | | | - Maurizio Porfiri
- Department of Quantitative Methods, Law and Modern Languages, Technical University of Cartagena (UPCT), Cartagena, Murcia 30201, Spain
- Department of Mechanical and Aerospace Engineering, and Department of Biomedical Engineering New York University Tandon School of Engineering (NYU), Brooklyn, NY, USA
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Abstract
BACKGROUND Intervention time (IT) in response to seizures and adverse events (AEs) have emerged as key elements in epilepsy monitoring unit (EMU) management. We performed an audit of our EMU, focusing on IT and AEs. METHODS We performed a retrospective study on all clinical seizures of admissions over a 1-year period at our Canadian academic tertiary care center's EMU. This EMU was divided in two subunits: a daytime three-bed epilepsy department subunit (EDU) supervised by EEG technicians and a three-bed neurology ward subunit (NWU) equipped with video-EEG where patients were transferred to for nights and weekends, under nursing supervision. Among 124 admissions, 58 were analyzed. A total of 1293 seizures were reviewed to determine intervention occurrence, IT, and AE occurrence. Seizures occurring when the staff was present at bedside at seizure onset were analyzed separately. RESULTS Median IT was 21.0 (11.0-40.8) s. The EDU, bilateral tonic-clonic seizures (BTCS), and the presence of a warning signal were associated with increased odds of an intervention taking place. The NWU, BTCS, and seizure rank (seizures were chronologically ordered by the patient for each subunit) were associated with longer ITs. Bedside staff presence rate was higher in the EDU than in the NWU (p < 0.001). AEs occurred in 19% of admissions, with no difference between subunits. AEs were more frequent in BTCS than in other seizure types (p = 0.001). CONCLUSION This study suggests that close monitoring by trained staff members dedicated to EMU patients is key to optimize safety. AE rate was high, warranting corrective measures.
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Reus EEM, Visser GH, Cox FME. Using sampled visual EEG review in combination with automated detection software at the EMU. Seizure 2020; 80:96-99. [PMID: 32554293 DOI: 10.1016/j.seizure.2020.06.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 05/27/2020] [Accepted: 06/01/2020] [Indexed: 11/18/2022] Open
Abstract
PURPOSE Complete visual review of prolonged video-EEG recordings at an EMU (Epilepsy Monitoring Unit) is time consuming and can cause problems in times of paucity of educated personnel. In this study we aimed to show non inferiority for electroclinical diagnosis using sampled review in combination with EEG analysis softreferware (P13 software, Persyst Corporation), in comparison to complete visual review. METHOD Fifty prolonged video-EEG recordings in adults were prospectively evaluated using sampled visual EEG review in combination with automated detection software of the complete EEG record. Visually assessed samples consisted of one hour during wakefulness, one hour during sleep, half an hour of wakefulness after wake-up and all clinical events marked by the individual and/or nurses. The final electro-clinical diagnosis of this new review approach was compared with the electro-clinical diagnosis after complete visual review as presently used. RESULTS The electro-clinical diagnosis based on sampled visual review combined with automated detection software did not differ from the diagnosis based on complete visual review. Furthermore, the detection software was able to detect all records containing epileptiform abnormalities and epileptic seizures. CONCLUSION Sampled visual review in combination with automated detection using Persyst 13 is non-inferior to complete visual review for electroclinical diagnosis of prolonged video-EEG at an EMU setting, which makes this approach promising.
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Affiliation(s)
- Elisabeth E M Reus
- Department of Clinical Neurophysiology, Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands.
| | - Gerhard H Visser
- Department of Clinical Neurophysiology, Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
| | - Fieke M E Cox
- Department of Clinical Neurophysiology, Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
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Liu ZT, Xie Q, Wu M, Cao WH, Li DY, Li SH. Electroencephalogram Emotion Recognition Based on Empirical Mode Decomposition and Optimal Feature Selection. IEEE Trans Cogn Dev Syst 2019. [DOI: 10.1109/tcds.2018.2868121] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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