Real-time Seizure Detection, Classification, and Prediction Using a Low-Cost Low-Burden Ear-worn System
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 40
- 试验地点
- 4
- 主要终点
- Seizure Accuracy/Prediction
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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
排除标准
- •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.
- •Cognitive impaired individuals
- •Pregnant Women
- •Children (Age 0-17)
结局指标
主要结局
Seizure Accuracy/Prediction
时间窗: 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.
Seizure Recording Criteria 1
时间窗: 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
时间窗: 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
时间窗: 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
时间窗: 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)).
次要结局
- Qualitative Satisfaction Survey(Through study completion, an average of 7 Days)
研究者
Felicia Chu
Assistant Professor
University of Massachusetts, Worcester