SeizuresEarSD001NCT06598189
Ear-Seizure Detection (EarSD) Study
A study testing Ear-SD and Electroencephalogram in people with Seizures and Epilepsy, run by Felicia Chu at 2 sites in United States.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- What is being tested?
- Ear-SD and Electroencephalogram
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 40 participants
- Where
- 2 sitesUnited States
- Study dates
- 3 Apr 2025to Dec 2032
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Age ≥ 18 years.
- 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.
- Willing to wear the wearable device.
- Ability to provide informed consent
Who cannot join
- Subjects wearing other ear devices such as hearing aids.
- Inability or unwillingness to provide informed consent.
- Irritation of the skin where the device is to be placed.
- Patients with intracranial electrodes placement.
- Prisoners
- Cognitive impaired individuals
- Pregnant Women
- Children (Age 0-17)
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Nimmy Francis contact
United States2 sites, 2 recruiting
- Recruiting
Ummmc-Memorial Campus
Worcester, Massachusetts, 01655
Stephanie Stephens, BS, (508) 856-3939, stephanie.stephens1@umassmed.edu
Nimmy Francis, BS, (508) 856 4667, Nimmy.Francis@umassmemorial.org
- Recruiting
Ummmc-University Campus
Worcester, Massachusetts, 01655
Stephanie Stephens, BS, 508) 856-3939, stephanie.stephens1@umassmed.edu
Nimmy Francis, BS, (508) 856 4667, Nimmy.Francis@umassmemorial.org
About this study
Official title: Real-time Seizure Detection, Classification, and Prediction Using a Low-Cost Low-Burden Ear-worn System
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.
Read the detailed description
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.
- Being tested
- Ear-SD, Electroencephalogram
- Keywords
- Seizure Detection, Central Nervous System Diseases, Nervous System Diseases
- Medical subject headings
- Seizures, Epilepsy, Central Nervous System Diseases, Nervous System Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Ear-SDDevice
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.
ElectroencephalogramDiagnostic test
Standard 21-channel scalp-continuous electroencephalogram (cEEG) with video recording and electrocardiogram (ECG)
| Study group | Role | What they receive |
|---|---|---|
| Ear-Worn Group | Experimental | All consented patients admitted to the Epilepsy Monitoring Unit (EMU) who are on continuous EEG (cEEG) will wear the ear-worn seizure detection device (EarSD) and there will be no randomization.
The Ear-SD Device will be simultaneously worn by EMU patients on continuous video 21 electrode EEG (International 10-20 system) and single channel electrocardiogram (ECG). Daily skin assessment will be conducted and electrodes will be replaced as needed. At the end of the study, a self-reported short qualitative survey will be conducted to assess the overall experience of the enrolled subjects. The EarSD device and electrodes will be removed at the end of the study with the last skin examination. Receives: Device: Ear-SD; Diagnostic Test: Electroencephalogram |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Through study completion, an average of 7 Days
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.
Measured: Through study completion, an average of 7 Days
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).
Measured: Through study completion, an average of 7 Days
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/h) are measured from the recorded data signals. Seizure Sensitivity (SS) is the ratio between the (number of predicted seizures)/(total number of seizures) = (number of true alarms)/(total number of seizures). FPR/h is the number of alarms that do not correspond to seizures raised in one hour. FPR/h = ((Number of false alarms/Interictal Duration) - (Number of False Alarms × Refractory period)).
Measured: up to 2 years
Seizure Accuracy/Prediction
EarSD recordings from each electrode are separated and filtered to eliminate noise and artifact and results in 12 output signals (6 signals/ear) 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/right 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.
Measured: up to 5 years
Secondary outcomes (1)
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.
Measured: Through study completion, an average of 7 Days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Sep 2024
- Study start3 Apr 2025
- Primary completionDec 2028 (estimated)
- Study completionDec 2032 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (19)Papers the sponsor has linked to this study.
- Other trials for SeizuresStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 40 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Felicia Chu
- Sponsor type
- University, hospital or other
- Collaborators
- University of Massachusetts, Amherst
- Responsible party
- Sponsor-investigator, Felicia Chu, University of Massachusetts, Worcester
- Investigators
- Felicia Chu, MD, Principal investigator, UMass Neurology Department
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06598189
- Sponsor's protocol ID
- STUDY00001889
- Acronym
- EarSD001
- Status last verified
- Oct 2026
- First submitted
- 23 Aug 2024
- Last updated
- 14 Oct 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- Yes: The collected de-identified Individual Participant Data (IPD) will be shared with our UMass Amherst collaborators which will include EEG monitoring data, EarSD Device monitoring data, and de-identified collected RedCap Database (start and end of monitoring, replacement of electrodes times, and short qualitative survey). Collected de-identified data of EarSD and cEEG monitoring will go feature extraction and subsequent statistical analysis will be performed by UMass Amherst. The dataset will not be published online or shared with other researchers or presented in a conference or in manuscripts publication. Only a demographic overview of the sample population and results of machine learning algorithms will be submitted for publication. IPD will not be shared or published in any of the articles or papers.
Publications
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/j.jflm.2020.101920. Epub 2020 Feb 5.
PubMed 32090969Blachut 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/j.yebeh.2016.10.036. Epub 2017 Jan 28.
PubMed 28139449Prior PF, Virden RS, Maynard DE. An EEG device for monitoring seizure discharges. Epilepsia. 1973 Dec;14(4):367-72. doi: 10.1111/j.1528-1157.1973.tb03975.x. No abstract available.
PubMed 4521092Manabe, 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/s00779-014-0818-8
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://www.semanticscholar.org/paper/Application-of-Machine-Learning-ToEpileptic-Shoeb-Guttag/57e4afe9ca74414fa02f2e0a929b64dc9a03334d.
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/TBME.2010.2046417.
PubMed 20659825Doyle 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/j.medengphy.2010.05.010. Epub 2010 Jul 1.
PubMed 20594899Jansen 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/j.yebeh.2013.06.030. Epub 2013 Aug 10.
PubMed 23939031Vandecasteele 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/epi.16990. Epub 2021 Jul 9.
PubMed 34240748Beniczky S, Conradsen I, Wolf P. Detection of convulsive seizures using surface electromyography. Epilepsia. 2018 Jun;59 Suppl 1:23-29. doi: 10.1111/epi.14048.
PubMed 29873829C. 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://ieeexplore.ieee.org/document/9793109.
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/bioengineering10060703.
PubMed 37370634S. 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://ieeexplore.ieee.org/document/6077068.
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/j.1528-1167.2012.03444.x. Epub 2012 Mar 20.
PubMed 22432935Z. 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://ieeexplore.ieee.org/abstract/document/8229188.
Study at a glance
From the registry record, last updated by the sponsor on 14 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Real-time Seizure Detection, Classification, and Prediction Using a Low-Cost Low-Burden Ear-worn System
- ClinicalTrials.gov ID
- NCT06598189
- Acronym / sponsor ID
- EarSD001 / STUDY00001889
- Keywords
- Seizure Detection, Central Nervous System Diseases, Nervous System Diseases
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 40 participants (estimated)
- Lead sponsor
- Felicia ChuUniversity, hospital or other
- Collaborators
- University of Massachusetts, Amherst
- Study start
- 3 Apr 2025 (actual)
- Primary completion
- Dec 2028 (estimated)
- Study completion
- Dec 2032 (estimated)
- First posted
- 19 Sep 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Ear-Worn Group | Experimental | Device: Ear-SD, Diagnostic Test: ElectroencephalogramAll consented patients admitted to the Epilepsy Monitoring Unit (EMU) who are on continuous EEG (cEEG) will wear the ear-worn seizure detection device (EarSD) and there will be no randomization. The Ear-SD Device will be simultaneously worn by EMU patients on continuous video 21 electrode EEG (International 10-20 system) and single channel electrocardiogram (ECG). Daily skin assessment will be conducted and electrodes will be replaced as needed. At the end of the study, a self-reported short qualitative survey will be conducted to assess the overall experience of the enrolled subjects. The EarSD device and electrodes will be removed at the end of the study with the last skin examination. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Age ≥ 18 years.
- 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.
- Willing to wear the wearable device.
- Ability to provide informed consent
Key exclusion criteria (8 listed)
- Subjects wearing other ear devices such as hearing aids.
- Inability or unwillingness to provide informed consent.
- Irritation of the skin where the device is to be placed.
- Patients with intracranial electrodes placement.
- Prisoners
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Ear-SDThe 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. | Device | Ear-Worn Group |
| ElectroencephalogramStandard 21-channel scalp-continuous electroencephalogram (cEEG) with video recording and electrocardiogram (ECG) | Diagnostic test | Ear-Worn Group |
Primary outcome
Seizure Recording Criteria 1
Time frame: Through study completion, an average of 7 Days
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.
Seizure Recording Criteria 2
Time frame: Through study completion, an average of 7 Days
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.
Seizure Recording Criteria 3
Time frame: Through study completion, an average of 7 Days
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).
Data Interpretation
Time frame: up to 2 years
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/h) are measured from the recorded data signals. Seizure Sensitivity (SS) is the ratio between the (number of predicted seizures)/(total number of seizures) = (number of true alarms)/(total number of seizures). FPR/h is the number of alarms that do not correspond to seizures raised in one hour. FPR/h = ((Number of false alarms/Interictal Duration) - (Number of False Alarms × Refractory period)).
Seizure Accuracy/Prediction
Time frame: up to 5 years
EarSD recordings from each electrode are separated and filtered to eliminate noise and artifact and results in 12 output signals (6 signals/ear) 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/right 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.
1 secondary outcome
Qualitative Satisfaction Survey
Time frame: Through study completion, an average of 7 Days
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Oct 2026.
- Background: PubMed 32090969Barranco 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/j.jflm.2020.101920. Epub 2020 Feb 5.
- Background: PubMed 28139449Blachut 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/j.yebeh.2016.10.036. Epub 2017 Jan 28.
- Background: PubMed 4521092Prior PF, Virden RS, Maynard DE. An EEG device for monitoring seizure discharges. Epilepsia. 1973 Dec;14(4):367-72. doi: 10.1111/j.1528-1157.1973.tb03975.x. No abstract available.
- BackgroundManabe, 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/s00779-014-0818-8
- BackgroundA. 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://www.semanticscholar.org/paper/Application-of-Machine-Learning-ToEpileptic-Shoeb-Guttag/57e4afe9ca74414fa02f2e0a929b64dc9a03334d.
- Background: PubMed 20659825Zandi 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/TBME.2010.2046417.
- Background: PubMed 20594899Doyle 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/j.medengphy.2010.05.010. Epub 2010 Jul 1.
- Background: PubMed 23939031Jansen 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/j.yebeh.2013.06.030. Epub 2013 Aug 10.
- Background: PubMed 34240748Vandecasteele 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/epi.16990. Epub 2021 Jul 9.
- Background: PubMed 29873829Beniczky S, Conradsen I, Wolf P. Detection of convulsive seizures using surface electromyography. Epilepsia. 2018 Jun;59 Suppl 1:23-29. doi: 10.1111/epi.14048.
- BackgroundC. 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://ieeexplore.ieee.org/document/9793109.
- Background: PubMed 37370634Djemal 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/bioengineering10060703.
- Epilepsy, accessed: 2023-11-02Link provided by the sponsor.
- "Embrace2 Seizure Monitoring \| Smarter Epilepsy Management \| Embrace Watch," accessed:2023-11-02.Link provided by the sponsor.
- O. Medical, "Visensia," 2023. \[Online\].Link provided by the sponsor.
- B. Company Inc., "BrainScope," Nov. 2023. \[Online\].Link provided by the sponsor.
- Individual participant dataSharing planned: Yes. The collected de-identified Individual Participant Data (IPD) will be shared with our UMass Amherst collaborators which will include EEG monitoring data, EarSD Device monitoring data, and de-identified collected RedCap Database (start and end of monitoring, replacement of electrodes times, and short qualitative survey). Collected de-identified data of EarSD and cEEG monitoring will go feature extraction and subsequent statistical analysis will be performed by UMass Amherst. The dataset will not be published online or shared with other researchers or presented in a conference or in manuscripts publication. Only a demographic overview of the sample population and results of machine learning algorithms will be submitted for publication. IPD will not be shared or published in any of the articles or papers.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Nimmy Francis contact
Felicia Chu, MD principal investigator
UMass Neurology Department
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Ear-SD and Electroencephalogram in people with Seizures and Epilepsy, run by Felicia Chu at 2 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 8 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 14 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Stephanie Stephens (508-856-3939, Stephanie.Stephens1@umassmed.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Seizure Recording Criteria 1 (time frame: Through study completion, an average of 7 Days). The study lists 5 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06598189, which also lists 19 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 Oct 2026.