1
|
Veciana de Las Heras M, Sala-Padro J, Pedro-Perez J, García-Parra B, Hernández-Pérez G, Falip M. Utility of Quantitative EEG in Neurological Emergencies and ICU Clinical Practice. Brain Sci 2024; 14:939. [PMID: 39335433 PMCID: PMC11430096 DOI: 10.3390/brainsci14090939] [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: 06/28/2024] [Revised: 08/22/2024] [Accepted: 09/11/2024] [Indexed: 09/30/2024] Open
Abstract
The electroencephalogram (EEG) is a cornerstone tool for the diagnosis, management, and prognosis of selected patient populations. EEGs offer significant advantages such as high temporal resolution, real-time cortical function assessment, and bedside usability. The quantitative EEG (qEEG) added the possibility of long recordings being processed in a compressive manner, making EEG revision more efficient for experienced users, and more friendly for new ones. Recent advancements in commercially available software, such as Persyst, have significantly expanded and facilitated the use of qEEGs, marking the beginning of a new era in its application. As a result, there has been a notable increase in the practical, real-world utilization of qEEGs in recent years. This paper aims to provide an overview of the current applications of qEEGs in daily neurological emergencies and ICU practice, and some elementary principles of qEEGs using Persyst software in clinical settings. This article illustrates basic qEEG patterns encountered in critical care and adopts the new terminology proposed for spectrogram reporting.
Collapse
Affiliation(s)
- Misericordia Veciana de Las Heras
- Neurology Service, Neurophysiology Department, Hospital Universitari de Bellvitge-IDIBELL, Universitat de Barcelona, 08908 L'Hospitalet de Llobregat, Barcelona, Spain
| | - Jacint Sala-Padro
- Neurology Service, Epilepsy Unit, Hospital Universitari de Bellvitge-IDIBELL, Universitat de Barcelona, 08908 L'Hospitalet de Llobregat, Barcelona, Spain
| | - Jordi Pedro-Perez
- Neurology Service, Neurophysiology Department, Hospital Universitari de Bellvitge-IDIBELL, Universitat de Barcelona, 08908 L'Hospitalet de Llobregat, Barcelona, Spain
| | - Beliu García-Parra
- Neurology Service, Neurophysiology Department, Hospital Universitari de Bellvitge-IDIBELL, Universitat de Barcelona, 08908 L'Hospitalet de Llobregat, Barcelona, Spain
| | - Guillermo Hernández-Pérez
- Neurology Service, Epilepsy Unit, Hospital Universitari de Bellvitge-IDIBELL, Universitat de Barcelona, 08908 L'Hospitalet de Llobregat, Barcelona, Spain
| | - Merce Falip
- Neurology Service, Epilepsy Unit, Hospital Universitari de Bellvitge-IDIBELL, Universitat de Barcelona, 08908 L'Hospitalet de Llobregat, Barcelona, Spain
| |
Collapse
|
2
|
Chung YG, Cho A, Kim H, Kim KJ. Single-channel seizure detection with clinical confirmation of seizure locations using CHB-MIT dataset. Front Neurol 2024; 15:1389731. [PMID: 38836000 PMCID: PMC11148866 DOI: 10.3389/fneur.2024.1389731] [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: 02/22/2024] [Accepted: 05/03/2024] [Indexed: 06/06/2024] Open
Abstract
Introduction Long-term electroencephalography (EEG) monitoring is advised to patients with refractory epilepsy who have a failure of anti-seizure medication and therapy. However, its real-life application is limited mainly due to the use of multiple EEG channels. We proposed a patient-specific deep learning-based single-channel seizure detection approach using the long-term scalp EEG recordings of the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset, in conjunction with neurologists' confirmation of spatial seizure characteristics of individual patients. Methods We constructed 18-, 4-, and single-channel seizure detectors for 13 patients. Neurologists selected a specific channel among four channels, two close to the behind-the-ear and two at the forehead for each patient, after reviewing the patient's distinctive seizure locations with seizure re-annotation. Results Our multi- and single-channel detectors achieved an average sensitivity of 97.05-100%, false alarm rate of 0.22-0.40/h, and latency of 2.1-3.4 s for identification of seizures in continuous EEG recordings. The results demonstrated that seizure detection performance of our single-channel approach was comparable to that of our multi-channel ones. Discussion We suggest that our single-channel approach in conjunction with clinical designation of the most prominent seizure locations has a high potential for wearable seizure detection on long-term EEG recordings for patients with refractory epilepsy.
Collapse
Affiliation(s)
- Yoon Gi Chung
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Anna Cho
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Hunmin Kim
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea
- Department of Pediatrics, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ki Joong Kim
- Department of Pediatrics, Seoul National University Children's Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| |
Collapse
|
3
|
Voigtlaender S, Pawelczyk J, Geiger M, Vaios EJ, Karschnia P, Cudkowicz M, Dietrich J, Haraldsen IRJH, Feigin V, Owolabi M, White TL, Świeboda P, Farahany N, Natarajan V, Winter SF. Artificial intelligence in neurology: opportunities, challenges, and policy implications. J Neurol 2024; 271:2258-2273. [PMID: 38367046 DOI: 10.1007/s00415-024-12220-8] [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: 12/20/2023] [Revised: 01/20/2024] [Accepted: 01/22/2024] [Indexed: 02/19/2024]
Abstract
Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization's Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. This scoping review of 66 original articles explores the value of AI in neurology and brain health, systematizing the landscape for emergent clinical opportunities and future trends across the care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation. AI's potential to advance personalized precision neurology and global brain health directives hinges on resolving core challenges across four pillars-models, data, feasibility/equity, and regulation/innovation-through concerted pursuit of targeted recommendations. Paramount actions include swift, ethical, equity-focused integration of novel technologies into clinical workflows, mitigating data-related issues, counteracting digital inequity gaps, and establishing robust governance frameworks balancing safety and innovation.
Collapse
Affiliation(s)
- Sebastian Voigtlaender
- Systems Neuroscience Division, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany
- Virtual Diagnostics Team, QuantCo Inc., Cambridge, MA, USA
| | - Johannes Pawelczyk
- Faculty of Medicine, Ruprecht-Karls-University, Heidelberg, Germany
- Graduate Center of Medicine and Health, Technical University Munich, Munich, Germany
| | - Mario Geiger
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- NVIDIA, Zurich, Switzerland
| | - Eugene J Vaios
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Philipp Karschnia
- Department of Neurosurgery, Ludwig-Maximilians-University and University Hospital Munich, Munich, Germany
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Merit Cudkowicz
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jorg Dietrich
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ira R J Hebold Haraldsen
- Department of Neurology, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Valery Feigin
- National Institute for Stroke and Applied Neurosciences, Auckland University of Technology, Auckland, New Zealand
| | - Mayowa Owolabi
- Center for Genomics and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria
- Neurology Unit, Department of Medicine, University of Ibadan, Ibadan, Nigeria
- Blossom Specialist Medical Center, Ibadan, Nigeria
- Lebanese American University of Beirut, Beirut, Lebanon
| | - Tara L White
- Department of Behavioral and Social Sciences, Brown University, Providence, RI, USA
| | | | | | | | - Sebastian F Winter
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
Janmohamed M, Nhu D, Shakathreh L, Gonen O, Kuhlman L, Gilligan A, Tan CW, Perucca P, O'Brien TJ, Kwan P. Comparison of Automated Spike Detection Software in Detecting Epileptiform Abnormalities on Scalp-EEG of Genetic Generalized Epilepsy Patients. J Clin Neurophysiol 2023:00004691-990000000-00110. [PMID: 37934089 DOI: 10.1097/wnp.0000000000001039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023] Open
Abstract
PURPOSE Despite availability of commercial EEG software for automated epileptiform detection, validation on real-world EEG datasets is lacking. Performance evaluation of two software packages on a large EEG dataset of patients with genetic generalized epilepsy was performed. METHODS Three epileptologists labelled IEDs manually of EEGs from three centres. All Interictal epileptiform discharge (IED) markings predicted by two commercial software (Encevis 1.11 and Persyst 14) were reviewed individually to assess for suspicious missed markings and were integrated into the reference standard if overlooked during manual annotation during a second phase. Sensitivity, precision, specificity, and F1-score were used to assess the performance of the software packages against the adjusted reference standard. RESULTS One hundred and twenty-five routine scalp EEG recordings from different subjects were included (total recording time, 310.7 hours). The total epileptiform discharge reference count was 5,907 (including spikes and fragments). Encevis demonstrated a mean sensitivity for detection of IEDs of 0.46 (SD 0.32), mean precision of 0.37 (SD 0.31), and mean F1-score of 0.43 (SD 0.23). Using the default medium setting, the sensitivity of Persyst was 0.67 (SD 0.31), with a precision of 0.49 (SD 0.33) and F1-score of 0.51 (SD 0.25). Mean specificity representing non-IED window identification and classification was 0.973 (SD 0.08) for Encevis and 0.968 (SD 0.07) for Persyst. CONCLUSIONS Automated software shows a high degree of specificity for detection of nonepileptiform background. Sensitivity and precision for IED detection is lower, but may be acceptable for initial screening in the clinical and research setting. Clinical caution and continuous expert human oversight are recommended with all EEG recordings before a diagnostic interpretation is provided based on the output of the software.
Collapse
Affiliation(s)
- Mubeen Janmohamed
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Duong Nhu
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Lubna Shakathreh
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Ofer Gonen
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Levin Kuhlman
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Amanda Gilligan
- Neurosciences Clinical Institute, Epworth Healthcare Hospital, Melbourne, Victoria, Australia
| | - Chang Wei Tan
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Piero Perucca
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
- Epilepsy Research Centre, Department of Medicine (Austin Health), The University of Melbourne, Melbourne, Victoria, Australia; and
- Bladin-Berkovic Comprehensive Epilepsy Program, Department of Neurology, Austin Health, Melbourne, 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
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
| |
Collapse
|
6
|
Villamar MF, Ayub N, Koenig SJ. Automated Seizure Detection in Patients with Cardiac Arrest: A Retrospective Review of Ceribell™ Rapid-EEG Recordings. Neurocrit Care 2023; 39:505-513. [PMID: 36788179 DOI: 10.1007/s12028-023-01681-w] [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] [Received: 08/10/2022] [Accepted: 01/23/2023] [Indexed: 02/16/2023]
Abstract
BACKGROUND In patients with cardiac arrest who remain comatose after return of spontaneous circulation, seizures and other abnormalities on electroencephalogram (EEG) are common. Thus, guidelines recommend urgent initiation of EEG for the evaluation of seizures in this population. Point-of-care EEG systems, such as Ceribell™ Rapid Response EEG (Rapid-EEG), allow for prompt initiation of EEG monitoring, albeit through a reduced-channel montage. Rapid-EEG incorporates an automated seizure detection software (Clarity™) to measure seizure burden in real time and alert clinicians at the bedside when a high seizure burden, consistent with possible status epilepticus, is identified. External validation of Clarity is still needed. Our goal was to evaluate the real-world performance of Clarity for the detection of seizures and status epilepticus in a sample of patients with cardiac arrest. METHODS This study was a retrospective review of Rapid-EEG recordings from all the patients who were admitted to the medical intensive care unit at Kent Hospital (Warwick, RI) between 6/1/2021 and 3/18/2022 for management after cardiac arrest and who underwent Rapid-EEG monitoring as part of their routine clinical care (n = 21). Board-certified epileptologists identified events that met criteria for seizures or status epilepticus, as per the 2021 American Clinical Neurophysiology Society's Standardized Critical Care EEG Terminology, and evaluated any seizure burden detections generated by Clarity. RESULTS In this study, 4 of 21 patients with cardiac arrest (19.0%) who underwent Rapid-EEG monitoring had multiple electrographic seizures, and 2 of those patients (9.5%) had electrographic status epilepticus within the first 24 h of the study. None of these ictal abnormalities were detected by the Clarity seizure detection system. Clarity showed 0% seizure burden throughout the entirety of all four Rapid-EEG recordings, including the EEG pages that showed definite seizures or status epilepticus. CONCLUSIONS The presence of frequent electrographic seizures and/or status epilepticus can go undetected by Clarity. Timely and careful review of all raw Rapid-EEG recordings by a qualified human EEG reader is necessary to guide clinical care, regardless of Clarity seizure burden measurements.
Collapse
Affiliation(s)
- Mauricio F Villamar
- Department of Neurology, The Warren Alpert Medical School of Brown University, Providence, RI, USA.
- Department of Medicine, Kent Hospital, Warwick, RI, USA.
| | - Neishay Ayub
- Department of Neurology, The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Seth J Koenig
- Department of Medicine, Kent Hospital, Warwick, RI, USA
| |
Collapse
|
7
|
Reus EEM, Visser GH, Sommers-Spijkerman MPJ, van Dijk JG, Cox FME. Automated spike and seizure detection: Are we ready for implementation? Seizure 2023; 108:66-71. [PMID: 37088057 DOI: 10.1016/j.seizure.2023.04.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/06/2023] [Accepted: 04/06/2023] [Indexed: 04/25/2023] Open
Abstract
OBJECTIVE Automated detection of spikes and seizures has been a subject of research for several decades now. There have been important advances, yet automated detection in EMU (Epilepsy Monitoring Unit) settings has not been accepted as standard practice. We intend to implement this software at our EMU and so carried out a qualitative study to identify factors that hinder ('barriers') and facilitate ('enablers') implementation. METHOD Twenty-two semi-structured interviews were conducted with 14 technicians and neurologists involved in recording and reporting EEGs and eight neurologists who receive EEG reports in the outpatient department. The study was reported according to the Consolidated Criteria for Reporting Qualitative Studies (COREQ). RESULTS We identified 14 barriers and 14 enablers for future implementation. Most barriers were reported by technicians. The most prominent barrier was lack of trust in the software, especially regarding seizure detection and false positive results. Additionally, technicians feared losing their EEG review skills or their jobs. Most commonly reported enablers included potential efficiency in the EEG workflow, the opportunity for quantification of EEG findings and the willingness to try the software. CONCLUSIONS This study provides insight into the perspectives of users and offers recommendations for implementing automated spike and seizure detection in EMUs.
Collapse
Affiliation(s)
- E E M Reus
- Stichting Epilepsie Instellingen Nederland (SEIN).
| | - G H Visser
- Stichting Epilepsie Instellingen Nederland (SEIN)
| | - M P J Sommers-Spijkerman
- Department of Rehabilitation, Physical Therapy Science and Sports, University Medical Center Utrecht, the Netherlands
| | - J G van Dijk
- Department of Neurology, Leiden University Medical Centre, Leiden, the Netherlands
| | - F M E Cox
- Stichting Epilepsie Instellingen Nederland (SEIN)
| |
Collapse
|