[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"trial:NCT06598189":3,"trial-entities:NCT06598189":183,"trial-summary:NCT06598189":187},{"id":4,"nct_id":4,"org_study_id":5,"brief_title":6,"official_title":7,"overall_status":8,"completion_date":9,"status_verified_date":10,"last_update_date":11,"start_date":12,"sponsor_name":13,"lead_sponsor_class":14,"has_dmc":15,"brief_summary":16,"detailed_description":17,"conditions":18,"keywords":21,"study_type":25,"primary_purpose":26,"phases":27,"enrollment_info":29,"interventions":32,"primary_outcomes":44,"secondary_outcomes":63,"sex":67,"minimum_age":68,"maximum_age":69,"healthy_volunteers":15,"eligibility_criteria":70,"std_ages":74,"locations":77,"central_contacts":108,"overall_officials":116,"references":119,"see_also_links":170},"NCT06598189","STUDY00001889","Ear-Seizure Detection (EarSD) Study","Real-time Seizure Detection, Classification, and Prediction Using a Low-Cost Low-Burden Ear-worn System","RECRUITING","2032-12","2025-10","2025-10-28","2025-04-03","Felicia Chu","OTHER",true,"The proposed study is an investigator-initiated study that aims to measure the accuracy of a wearable seizure detection and prediction device (Ear-Seizure Detection Device (EarSD)) by simultaneous recording with conventional video-EEG (Electroencephalogram) on patients with epileptic seizures in the Epilepsy Monitoring Unit of the hospital.","A wearable seizure detection and prediction device (EarSD) is worn by patients with epileptic seizures. In this study, the goal is to validate the accuracy of a newly developed portable seizure detection device by examining if the Ear-SD device can (1) provide more comfort, (2) be unobtrusive to the subject during daily activities, and (3) be able to provide additional insight on a patients' seizure control.",[19,20],"Seizures","Epilepsy",[22,23,24],"Seizure Detection","Central Nervous System Diseases","Nervous System Diseases","INTERVENTIONAL","DIAGNOSTIC",[28],"NA",{"count":30,"type":31},40,"ESTIMATED",[33,39],{"type":34,"name":35,"description":36,"armGroupLabels":37},"DEVICE","Ear-SD","The Ear-SD is a purely EEG recording device Continuous Electroencephalogram (cEEG), Electromyogram (EMG), Electrooculogram (EOG), Photoplethysmogram (PPG), Electrodermoactivity (EDA), and Inertial Measurement Unit (IMU). The Ear-SD device rests on the ears and connects to the scalp by two sticker electrodes.",[38],"Ear-Worn Group",{"type":40,"name":41,"description":42,"armGroupLabels":43},"DIAGNOSTIC_TEST","Electroencephalogram","Standard 21-channel scalp-continuous electroencephalogram (cEEG) with video recording and electrocardiogram (ECG)",[38],[45,49,52,55,59],{"measure":46,"description":47,"timeFrame":48},"Seizure Recording Criteria 1","Recordings of Bioelectrical signal of subjects with the wearable device and simultaneous continuous EEG data is collected for the duration of hospitalization of participants. Outcome measures reported include number of seizure events per participant.","Through study completion, an average of 7 Days",{"measure":50,"description":51,"timeFrame":48},"Seizure Recording Criteria 2","Recordings of Bioelectrical signal of subjects with the wearable device and simultaneous continuous EEG data is collected for the duration of hospitalization of participants. Outcome measures reported include average duration of each seizure in minutes and seconds and total recording time in hours aggregated to arrive at one reported value seizure classification.",{"measure":53,"description":54,"timeFrame":48},"Seizure Recording Criteria 3","Recordings of Bioelectrical signal of subjects with the wearable device and simultaneous continuous EEG data is collected for the duration of hospitalization of participants. Outcome measures reported include reported value seizure classification. Seizure classification includes Unclassified (UC), Focal Onset Aware (FOA), Focal Onset Impaired (FOIA), Focal to Bilateral Tonic-Clonic (FBTC).",{"measure":56,"description":57,"timeFrame":58},"Data Interpretation","EarSD extracted EEG signals from the log file plotted alongside EDF files from cEEG are measured and compared to detect seizure onset and offset times for data interpretation. Two-minute segments of cEEG European Data Format (EDF) consisting of non-seizure signals from periods before and after the seizures, and non-seizure signals from periods of daily activities like talking, eating, and walking are involved in the comparison to detect seizure onset and offset times. Prediction measurement of Seizure Sensitivity (SS) and False Positivity Rate per hour (FPR\u002Fh) are measured from the recorded data signals. Seizure Sensitivity (SS) is the ratio between the (number of predicted seizures)\u002F(total number of seizures) = (number of true alarms)\u002F(total number of seizures). FPR\u002Fh is the number of alarms that do not correspond to seizures raised in one hour. FPR\u002Fh = ((Number of false alarms\u002FInterictal Duration) - (Number of False Alarms × Refractory period)).","up to 2 years",{"measure":60,"description":61,"timeFrame":62},"Seizure Accuracy\u002FPrediction","EarSD recordings from each electrode are separated and filtered to eliminate noise and artifact and results in 12 output signals (6 signals\u002Fear) for comparison against cEEG EDF files for accuracy and precision. Mean, standard and average deviation, skewness, kurtosis, lowest and highest value, and the root mean square amplitude are measured from the dataset and are normalized between 0 and 1 then passed into the seizure detection and prediction Machine Learning (ML) model. ML model consisting of algorithms using deep neural networks (DNN), recurrent neural networks (RNNs) and Long Short-Term Memory networks (LSTM), classifies whether the signals are a seizure signal vs non-seizure signal, the focal type (left side\u002Fright side) and predicts the accuracy of seizures a minute ahead with the goal of achieving 96 percent or better accuracy and reducing the number of false positives.","up to 5 years",[64],{"measure":65,"description":66,"timeFrame":48},"Qualitative Satisfaction Survey","At the end of the study, patients' experience and perception of the EarSD device are collected using a paper-based 7-question survey measured on a 5-point Likert scale ranging from Strongly Disagree to Strongly Agree. A maximum total point score of 35 represents a better reported satisfactory score from participants and having a good experience with the device and its comfortability for daily activities. The survey is a self-administered report, and participants will be asked about the comfortability and perceived utility of the device.","ALL","18 Years",null,{"inclusion":71,"exclusion":72,"raw_text":73},[],[],"Inclusion Criteria:\n\n1. Age ≥ 18 years.\n2. Patients admitted to UMass Memorial Epilepsy Monitoring Unit (EMU) for long term video-EEG monitoring as part of standard care of both focal and generalized epilepsy.\n3. Willing to wear the wearable device.\n4. Ability to provide informed consent\n\nExclusion Criteria:\n\n1. Subjects wearing other ear devices such as hearing aids.\n2. Inability or unwillingness to provide informed consent.\n3. Irritation of the skin where the device is to be placed.\n4. Patients with intracranial electrodes placement.\n5. Prisoners\n6. Cognitive impaired individuals\n7. Pregnant Women\n8. Children (Age 0-17)",[75,76],"ADULT","OLDER_ADULT",[78,100],{"facility":79,"status":8,"city":80,"state":81,"zip":82,"country":83,"contacts":84,"geoPoint":97},"Ummmc-Memorial Campus","Worcester","Massachusetts","01655","United States",[85,90,94],{"name":86,"role":87,"phone":88,"email":89},"Stephanie Stephens, BS","CONTACT","(508) 856-3939","stephanie.stephens1@umassmed.edu",{"name":91,"role":87,"phone":92,"email":93},"Charles Hill, BS","(508) 856 4667","charles.hill6@umassmed.edu",{"name":95,"role":96},"Felicia Chu, MD","PRINCIPAL_INVESTIGATOR",{"lat":98,"lon":99},42.26259,-71.80229,{"facility":101,"status":8,"city":80,"state":81,"zip":82,"country":83,"contacts":102,"geoPoint":107},"Ummmc-University Campus",[103,105,106],{"name":86,"role":87,"phone":104,"email":89},"508) 856-3939",{"name":91,"role":87,"phone":92,"email":93},{"name":95,"role":96},{"lat":98,"lon":99},[109,113],{"name":110,"role":87,"phone":111,"email":112},"Stephanie Stephens","508-856-3939","Stephanie.Stephens1@umassmed.edu",{"name":114,"role":87,"email":115},"Charles Hill","Charles.hill6@umassmed.edu",[117],{"name":95,"affiliation":118,"role":96},"UMass Neurology Department",[120,124,127,130,132,134,137,140,143,146,149,151,154,156,159,161,163,165,167],{"pmid":121,"type":122,"citation":123},"32090969","BACKGROUND","Barranco R, Caputo F, Molinelli A, Ventura F. Review on post-mortem diagnosis in suspected SUDEP: Currently still a difficult task for Forensic Pathologists. J Forensic Leg Med. 2020 Feb;70:101920. doi: 10.1016\u002Fj.jflm.2020.101920. Epub 2020 Feb 5.",{"pmid":125,"type":122,"citation":126},"28139449","Blachut B, Hoppe C, Surges R, Elger C, Helmstaedter C. Subjective seizure counts by epilepsy clinical drug trial participants are not reliable. Epilepsy Behav. 2017 Feb;67:122-127. doi: 10.1016\u002Fj.yebeh.2016.10.036. Epub 2017 Jan 28.",{"pmid":128,"type":122,"citation":129},"4521092","Prior PF, Virden RS, Maynard DE. An EEG device for monitoring seizure discharges. Epilepsia. 1973 Dec;14(4):367-72. doi: 10.1111\u002Fj.1528-1157.1973.tb03975.x. No abstract available.",{"type":122,"citation":131},"Manabe, H., Fukumoto, M., & Yagi, T. (2015a). Conductive rubber electrodes for earphone-based eye gesture input interface. Personal and Ubiquitous Computing, 19(1), 143-154. doi:10.1007\u002Fs00779-014-0818-8",{"type":122,"citation":133},"A. H. Shoeb and J. Guttag, \"Application of Machine Learning To Epileptic Seizure Detection,\" in 2010 International Conference on Machine Learning (ICML), Jun. 2010. [Online]. Available: https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FApplication-of-Machine-Learning-ToEpileptic-Shoeb-Guttag\u002F57e4afe9ca74414fa02f2e0a929b64dc9a03334d.",{"pmid":135,"type":122,"citation":136},"20659825","Zandi AS, Javidan M, Dumont GA, Tafreshi R. Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform. IEEE Trans Biomed Eng. 2010 Jul;57(7):1639-51. doi: 10.1109\u002FTBME.2010.2046417.",{"pmid":138,"type":122,"citation":139},"20594899","Doyle OM, Temko A, Marnane W, Lightbody G, Boylan GB. Heart rate based automatic seizure detection in the newborn. Med Eng Phys. 2010 Oct;32(8):829-39. doi: 10.1016\u002Fj.medengphy.2010.05.010. Epub 2010 Jul 1.",{"pmid":141,"type":122,"citation":142},"23939031","Jansen K, Varon C, Van Huffel S, Lagae L. Peri-ictal ECG changes in childhood epilepsy: implications for detection systems. Epilepsy Behav. 2013 Oct;29(1):72-6. doi: 10.1016\u002Fj.yebeh.2013.06.030. Epub 2013 Aug 10.",{"pmid":144,"type":122,"citation":145},"34240748","Vandecasteele K, De Cooman T, Chatzichristos C, Cleeren E, Swinnen L, Macea Ortiz J, Van Huffel S, Dumpelmann M, Schulze-Bonhage A, De Vos M, Van Paesschen W, Hunyadi B. The power of ECG in multimodal patient-specific seizure monitoring: Added value to an EEG-based detector using limited channels. Epilepsia. 2021 Oct;62(10):2333-2343. doi: 10.1111\u002Fepi.16990. Epub 2021 Jul 9.",{"pmid":147,"type":122,"citation":148},"29873829","Beniczky S, Conradsen I, Wolf P. Detection of convulsive seizures using surface electromyography. Epilepsia. 2018 Jun;59 Suppl 1:23-29. doi: 10.1111\u002Fepi.14048.",{"type":122,"citation":150},"C. Bagavathi, S. M, S. M. Nair, and S. R, \"Novel Epileptic Detection System using Portable EMG-based Assistance,\" in 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), May 2022, pp. 1762-1765. [Online]. Available: https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9793109.",{"pmid":152,"type":122,"citation":153},"37370634","Djemal A, Bouchaala D, Fakhfakh A, Kanoun O. Wearable Electromyography Classification of Epileptic Seizures: A Feasibility Study. Bioengineering (Basel). 2023 Jun 9;10(6):703. doi: 10.3390\u002Fbioengineering10060703.",{"type":122,"citation":155},"S. Ganesan, T. A. A. Victoire, and R. Ganesan, \"EDA based automatic detection of epileptic seizures using wireless system,\" in 2011 International Conference on Electronics, Communication and Computing Technologies, Sep. 2011, pp. 47-52. [Online]. Available: https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F6077068.",{"pmid":157,"type":122,"citation":158},"22432935","Poh MZ, Loddenkemper T, Reinsberger C, Swenson NC, Goyal S, Sabtala MC, Madsen JR, Picard RW. Convulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor. Epilepsia. 2012 May;53(5):e93-7. doi: 10.1111\u002Fj.1528-1167.2012.03444.x. Epub 2012 Mar 20.",{"type":122,"citation":160},"Z. Liang and T. Nishimura, \"Are wearable EEG devices more accurate than fitness wristbands for home sleep Tracking? Comparison of consumer sleep trackers with clinical devices,\" in 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE), Oct. 2017, pp. 1-5. [Online]. Available: https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F8229188.",{"type":122,"citation":162},"ANSI\u002FAAMI ES60601-1:2005, Medical electrical equipment-Part 1: General requirements for basic safety and essential performance. (2005). Association for the Advancement of Medical Instrumentation.",{"type":122,"citation":164},"DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration Center for Devices and Radiological Health, \"Guidance Document Device: Electrocardiograph Surface Electrode Tester\". (1997).",{"type":122,"citation":166},"IEEE International Committee on Electromagnetic Safety on Non-Ionizing Radiation, \"IEEE Std C95.6TM-2002: IEEE Standard for Safety Levels with Respect to Human Exposure to Electromagnetic Fields. (2002).",{"pmid":168,"type":122,"citation":169},"38454117","Costa G, Teixeira C, Pinto MF. Comparison between epileptic seizure prediction and forecasting based on machine learning. Sci Rep. 2024 Mar 7;14(1):5653. doi: 10.1038\u002Fs41598-024-56019-z.",[171,174,177,180],{"label":172,"url":173},"Epilepsy, accessed: 2023-11-02","https:\u002F\u002Fwww.who.int\u002Fnews-room\u002Ffact-sheets\u002Fdetail\u002Fepilepsy",{"label":175,"url":176},"\"Embrace2 Seizure Monitoring \\| Smarter Epilepsy Management \\| Embrace Watch,\" accessed:2023-11-02.","https:\u002F\u002Fwww.empatica.com\u002Fembrace2\u002F",{"label":178,"url":179},"O. Medical, \"Visensia,\" 2023. \\[Online\\].","https:\u002F\u002Fwww.obsmedical.com\u002Fvisensia-the-safety-index\u002F",{"label":181,"url":182},"B. Company Inc., \"BrainScope,\" Nov. 2023. \\[Online\\].","https:\u002F\u002Fwww.brainscope.com",{"nct_id":4,"conditions":184,"biomarkers":186},[185],"Seizure Disorder",[],{"nct_id":4,"found":15,"summary":188,"prompt_version":198},{"design":189,"status":190,"heading":191,"summary":192,"follow_up":193,"word_count":194,"commitments":195,"compensation":196,"drugs_mentioned":197},"This interventional study plans to enroll 40 participants. It is testing the Ear-SD device against standard video-EEG monitoring.","completed","Ear-Seizure Detection (EarSD) Study for Seizure Monitoring","This study is testing a new wearable device called Ear-SD. This device records different body signals like brain waves (EEG), muscle activity (EMG), and eye movements (EOG) to see if it can accurately detect and predict seizures. Researchers want to see if the Ear-SD device is comfortable, easy to use during daily activities, and provides helpful information about seizure control. You might be able to join if you are 18 or older, are admitted to the UMass Memorial Epilepsy Monitoring Unit for long-term video-EEG monitoring, and are willing to wear the device. The study aims to enroll 40 participants, but its current recruitment status is unclear.","The primary endpoints are measured through study completion, an average of 7 days.",106,"You would wear the Ear-SD device while undergoing standard video-EEG monitoring in the Epilepsy Monitoring Unit for an average of 7 days.","Not stated in the trial record.",[35],"v2"]