Comparison between Scalp EEG and Behind-the-Ear EEG for Development of a Wearable Seizure Detection System for Patients with Focal Epilepsy

被引:84
|
作者
Gu, Ying [1 ,2 ]
Cleeren, Evy [3 ]
Dan, Jonathan [4 ]
Claes, Kasper [5 ]
Van Paesschen, Wim [3 ]
Van Huffel, Sabine [1 ,2 ]
Hunyadi, Borbala [1 ,2 ]
机构
[1] Katholieke Univ Leuven, Dept Elect Engn ESAT, STADIUS Ctr Dynam Syst Signal Proc & Data Analyt, B-3001 Leuven, Belgium
[2] IMEC, B-3001 Leuven, Belgium
[3] Univ Hosp Leuven, Lab Epilepsy Res, B-3000 Leuven, Belgium
[4] Byteflies, B-2600 Antwerp, Belgium
[5] UCB, B-1070 Brussels, Belgium
来源
SENSORS | 2018年 / 18卷 / 01期
基金
欧洲研究理事会;
关键词
seizure detection; epilepsy; EEG; EOG; wearable sensor; SVM; CLASSIFICATION; INFORMATION; ALGORITHM; BRAIN; ONSET;
D O I
10.3390/s18010029
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
A wearable electroencephalogram (EEG) device for continuous monitoring of patients suffering from epilepsy would provide valuable information for the management of the disease. Currently no EEG setup is small and unobtrusive enough to be used in daily life. Recording behind the ear could prove to be a solution to a wearable EEG setup. This article examines the feasibility of recording epileptic EEG from behind the ear. It is achieved by comparison with scalp EEG recordings. Traditional scalp EEG and behind-the-ear EEG were simultaneously acquired from 12 patients with temporal, parietal, or occipital lobe epilepsy. Behind-the-ear EEG consisted of cross-head channels and unilateral channels. The analysis on Electrooculography (EOG) artifacts resulting from eye blinking showed that EOG artifacts were absent on cross-head channels and had significantly small amplitudes on unilateral channels. Temporal waveform and frequency content during seizures from behind-the-ear EEG visually resembled that from scalp EEG. Further, coherence analysis confirmed that behind-the-ear EEG acquired meaningful epileptic discharges similarly to scalp EEG. Moreover, automatic seizure detection based on support vector machine (SVM) showed that comparable seizure detection performance can be achieved using these two recordings. With scalp EEG, detection had a median sensitivity of 100% and a false detection rate of 1.14 per hour, while, with behind-the-ear EEG, it had a median sensitivity of 94.5% and a false detection rate of 0.52 per hour. These findings demonstrate the feasibility of detecting seizures from EEG recordings behind the ear for patients with focal epilepsy.
引用
收藏
页数:17
相关论文
共 19 条
  • [1] Real-Time Seizure Detection Using Behind-the-Ear Wearable System
    Lehnen, Jamie
    Venkatesh, Pooja
    Yao, Zhuoran
    Aziz, Abdul
    Nguyen, Phuc V. P.
    Harvey, Jay
    Alick-Lindstrom, Sasha
    Doyle, Alex
    Podkorytova, Irina
    Perven, Ghazala
    Hays, Ryan
    Zepeda, Rodrigo
    Das, Rohit R.
    Ding, Kan
    JOURNAL OF CLINICAL NEUROPHYSIOLOGY, 2025, 42 (02) : 118 - 125
  • [2] The Power of ECG in Semi-Automated Seizure Detection in Addition to Two-Channel behind-the-Ear EEG
    Bhagubai, Miguel
    Vandecasteele, Kaat
    Swinnen, Lauren
    Macea, Jaiver
    Chatzichristos, Christos
    De Vos, Maarten
    Van Paesschen, Wim
    BIOENGINEERING-BASEL, 2023, 10 (04):
  • [3] Ear-EEG-based sleep scoring in epilepsy: A comparison with scalp-EEG
    Jorgensen, Sofie D.
    Zibrandtsen, Ivan C.
    Kjaer, Troels W.
    JOURNAL OF SLEEP RESEARCH, 2020, 29 (06)
  • [4] Semi-supervised automatic seizure detection using personalized anomaly detecting variational autoencoder with behind-the-ear EEG
    You, Sungmin
    Cho, Baek Hwan
    Shon, Young-Min
    Seo, Dae-Won
    Kim, In Young
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2022, 213
  • [5] On-Chip Mental Stress Detection: Integrating a Wearable Behind-The-Ear EEG Device With Embedded Tiny Neural Network
    Mai, Ngoc-Dau
    Chung, Wan-Young
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2025, 29 (03) : 1872 - 1885
  • [6] Unsupervised automatic seizure detection for focal-onset seizures recorded with behind-the-ear EEG using an anomaly-detecting generative adversarial network
    You, Sungmin
    Cho, Baek Hwan
    Yook, Soonhyun
    Kim, Joo Young
    Shon, Young-Min
    Seo, Dae-Won
    Kim, In Young
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2020, 193
  • [7] An intelligent epilepsy seizure detection system using adaptive mode decomposition of EEG signals
    Kumar, Gulshan
    Chander, Subhash
    Almadhor, Ahmad
    PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE, 2022, 45 (01) : 261 - 272
  • [8] Development of a wearable system with In-Ear EEG electrodes for the monitoring of brain activities: An application to epilepsy
    Juez, Jose
    Henao, David
    Segura, Fredy
    Gomez, Rodrigo
    Quyen, Michel Le Van
    Valderrama, Mario
    2021 IEEE 2ND INTERNATIONAL CONGRESS OF BIOMEDICAL ENGINEERING AND BIOENGINEERING (CI-IB&BI 2021), 2021,
  • [9] Assessment of a scalp EEG-based automated seizure detection system
    Kelly, K. M.
    Shiau, D. S.
    Kern, R. T.
    Chien, J. H.
    Yang, M. C. K.
    Yandora, K. A.
    Valeriano, J. P.
    Halford, J. J.
    Sackellares, J. C.
    CLINICAL NEUROPHYSIOLOGY, 2010, 121 (11) : 1832 - 1843
  • [10] Detection and Removal of Muscle artifacts from Scalp EEG Recordings in Patients with Epilepsy
    Anastasiadou, Maria
    Hadjipapas, Avgis
    Christodoulakis, Manolis
    Papathanasiou, Eleftherios S.
    Papacostas, Savvas S.
    Mitsis, Georgios D.
    2014 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE), 2014, : 291 - 296