Ear-Seizure Detection (EarSD) Study

NCT06598189 | Recruiting DMC FDA Device

• 1 other identifier: STUDY00001889
• Study Type: interventional
• Target: 40
• Locations: 1 country
• Sites: 2

Brief 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 Monitoring Unit of the hospital.

Conditions

Seizures; Epilepsy

Keywords

Seizure Detection; Central Nervous System Diseases; Nervous System Diseases

Outcome Measures

Primary Outcomes (5)

• 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

• 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.

  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).

  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)).

  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.

  up to 5 years

Secondary Outcomes (1)

• Qualitative Satisfaction Survey

  Through study completion, an average of 7 Days

Study Arms (1)

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.

Device: Ear-SD; Diagnostic Test: Electroencephalogram

Interventions

Ear-SD DEVICE

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.

Ear-Worn Group

Electroencephalogram DIAGNOSTIC_TEST

Standard 21-channel scalp-continuous electroencephalogram (cEEG) with video recording and electrocardiogram (ECG)

Ear-Worn Group

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• 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)

Sponsors & Collaborators

• Felicia Chu: lead
• University of Massachusetts, Amherst: collaborator

Study Sites (2)

Ummmc-Memorial Campus

Worcester, Massachusetts, 01655, United States

RECRUITING

Ummmc-University Campus

Worcester, Massachusetts, 01655, United States

RECRUITING

Related Publications (19)

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• 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/s00779-014-0818-8

  BACKGROUND

• 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.

  BACKGROUND

• 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.

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• 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/epi.16990. Epub 2021 Jul 9.

  PMID: 34240748 BACKGROUND

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  PMID: 29873829 BACKGROUND

• 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://ieeexplore.ieee.org/document/9793109.

  BACKGROUND

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  PMID: 37370634 BACKGROUND

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  BACKGROUND

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• 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://ieeexplore.ieee.org/abstract/document/8229188.

  BACKGROUND

• ANSI/AAMI ES60601-1:2005, Medical electrical equipment-Part 1: General requirements for basic safety and essential performance. (2005). Association for the Advancement of Medical Instrumentation.

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• DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration Center for Devices and Radiological Health, "Guidance Document Device: Electrocardiograph Surface Electrode Tester". (1997).

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• 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).

  BACKGROUND

• 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/s41598-024-56019-z.

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Related Links

• Epilepsy, accessed: 2023-11-02
• "Embrace2 Seizure Monitoring \| Smarter Epilepsy Management \| Embrace Watch," accessed:2023-11-02.
• O. Medical, "Visensia," 2023. \[Online\].
• B. Company Inc., "BrainScope," Nov. 2023. \[Online\].

MeSH Terms

Conditions

Seizures; Epilepsy; Central Nervous System Diseases; Nervous System Diseases

Interventions

Electroencephalography

Condition Hierarchy (Ancestors)

Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Brain Diseases

Intervention Hierarchy (Ancestors)

Diagnostic Techniques, Neurological; Diagnostic Techniques and Procedures; Diagnosis; Electrodiagnosis

Study Officials

• Felicia Chu, MD

  UMass Neurology Department

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Stephanie Stephens

CONTACT

508-856-3939; Stephanie.Stephens1@umassmed.edu

Charles Hill

CONTACT

Charles.hill6@umassmed.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Assistant Professor

Study Record Dates

• First Submitted: August 23, 2024
• First Posted: September 19, 2024
• Study Start: April 3, 2025
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): December 1, 2032
• Last Updated: October 28, 2025

Record last verified: 2025-10

Data Sharing

• IPD Sharing: Will share
• Shared Documents: SAP, ANALYTIC CODE
• Time Frame: Data will become available after participants have completed the study. And data have been analyzed through the seizure detection Algorithm. Duration 3-5 years; relative to the time when summary data are published or otherwise made available (starting 4-6 months after publication).
• Access Criteria: Access criteria will include organizations that will review the algorithm-building efficacy: UMass Amherst collaborators/listed staff and UMass Chan research staff. The Institutional Review Board (IRB) will review the requests and approve review according to their policies.

Locations

US (2)