Ear-Seizure Detection (EarSD) Study (EarSD001)

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.

Study Overview

• Status: Recruiting
• Conditions: Epilepsy; Seizures
• Intervention / Treatment: Device: Ear-SD; Diagnostic test: Electroencephalogram

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 Type: Interventional
• Enrollment (Estimated): 40
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Stephanie Stephens; Phone Number: 508-856-3939; Email: Stephanie.Stephens1@umassmed.edu
• Study Contact Backup: Name: Charles Hill; Email: Charles.hill6@umassmed.edu

Study Locations

• United States
  • Massachusetts
    • Worcester, Massachusetts, United States, 01655
      • Recruiting
      • Ummmc-Memorial Campus
      • Contact: Charles Hill, BS; Phone Number: (508) 856 4667; Email: charles.hill6@umassmed.edu
      • Principal Investigator: Felicia Chu, MD
      • Contact: Stephanie Stephens, BS; Phone Number: (508) 856-3939; Email: stephanie.stephens1@umassmed.edu
    • Worcester, Massachusetts, United States, 01655
      • Recruiting
      • Ummmc-University Campus
      • Contact: Charles Hill, BS; Phone Number: (508) 856 4667; Email: charles.hill6@umassmed.edu
      • Principal Investigator: Felicia Chu, MD
      • Contact: Stephanie Stephens, BS; Phone Number: 508) 856-3939; Email: stephanie.stephens1@umassmed.edu

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

1. Age ≥ 18 years.
2. 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.
3. Willing to wear the wearable device.
4. Ability to provide informed consent

Exclusion Criteria:

1. Subjects wearing other ear devices such as hearing aids.
2. Inability or unwillingness to provide informed consent.
3. Irritation of the skin where the device is to be placed.
4. Patients with intracranial electrodes placement.
5. Prisoners
6. Cognitive impaired individuals
7. Pregnant Women
8. Children (Age 0-17)

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm

Intervention / Treatment

Experimental: Ear-Worn Group; 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; 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.; Diagnostic test: Electroencephalogram; Standard 21-channel scalp-continuous electroencephalogram (cEEG) with video recording and electrocardiogram (ECG)

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	Through study completion, an average of 7 Days

Collaborators and Investigators

• Sponsor: Felicia Chu
• Collaborators: University of Massachusetts, Amherst
• Investigators: Principal Investigator: Felicia Chu, MD, UMass Neurology Department

Publications and helpful links

General Publications

• Beniczky 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.
• 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.
• 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.
• 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/j.yebeh.2016.10.036. Epub 2017 Jan 28.
• Prior 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.
• 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
• 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.
• 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/j.medengphy.2010.05.010. Epub 2010 Jul 1.
• 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/j.yebeh.2013.06.030. Epub 2013 Aug 10.
• 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.
• 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.
• 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://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.
• 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.
• 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.
• DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration Center for Devices and Radiological Health, "Guidance Document Device: Electrocardiograph Surface Electrode Tester". (1997).
• 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).
• 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.

Helpful 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].

Study record dates

Study Major Dates

• Study Start (Actual): April 3, 2025
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): December 1, 2032

Study Registration Dates

• First Submitted: August 23, 2024
• First Submitted That Met QC Criteria: September 13, 2024
• First Posted (Actual): September 19, 2024

Study Record Updates

• Last Update Posted (Estimated): October 28, 2025
• Last Update Submitted That Met QC Criteria: October 24, 2025
• Last Verified: October 1, 2025

More Information

Terms related to this study

• Keywords: Nervous System Diseases; Central Nervous System Diseases; Seizure Detection
• Additional Relevant MeSH Terms: Neurologic Manifestations; Brain Diseases; Pathological Conditions, Signs and Symptoms; Signs and Symptoms; Epilepsy; Nervous System Diseases; Central Nervous System Diseases; Seizures; Diagnostic Techniques and Procedures; Diagnosis; Diagnostic Techniques, Neurological; Electrodiagnosis; Electroencephalography
• Other Study ID Numbers: STUDY00001889

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: 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.
• IPD Sharing 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).
• IPD Sharing 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.
• IPD Sharing Supporting Information Type: SAP; ANALYTIC_CODE

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: No