TY - JOUR
T1 - Get rid of the beat in mobile EEG applications
T2 - A framework towards automated cardiogenic artifact detection and removal in single-channel EEG
AU - Chiu, Neng Tai
AU - Huwiler, Stephanie
AU - Ferster, M. Laura
AU - Karlen, Walter
AU - Wu, Hau Tieng
AU - Lustenberger, Caroline
N1 - Funding Information:
This work was conducted as part of the SleepLoop Flagship of Hochschulmedizin Zürich and funded in part by the Schweizerische Hirn Stiftung and the Swiss National Science Foundation (P3P3PA_171525 and PZ00P3_179795 to CL). The authors thank all the participants for taking part in the study and R. Büchi, N. Demarmels, G. Hoppeler, J. Kurz, and E. Silberschmidt for performing the recruitment and data collection. SleepLoop consortium members provided uncountable discussions and feedback. This work and the Taiwan Integrated Database for Intelligent Sleep (TIDIS) project are partially supported by Ministry of Science and Technology 109-2119-M-002-014-, NCTS Taiwan. Neng-Tai Chiu is partially supported by the Undergraduate Summer Research Program hosted by National Center for Theoretical Sciences, Mathematics Division, Taiwan.
Publisher Copyright:
© 2021 The Author(s)
PY - 2022/2
Y1 - 2022/2
N2 - Brain activity recordings outside clinical or laboratory settings using mobile EEG systems have gained popular interest allowing for realistic long-term monitoring and eventually leading to identification of possible biomarkers for diseases. The less obtrusive, minimized systems (e.g., single-channel EEG, no ECG reference) have the drawback of artifact contamination with varying intensity that are particularly difficult to identify and remove. We developed brMEGA, the first open-source algorithm for automated detection and removal of cardiogenic artifacts using non-linear time-frequency analysis and machine learning to (1) detect whether and where cardiogenic artifacts exist, and (2) remove those artifacts. We compare our algorithm against visual artifact identification and a previously established approach and validate it in one real and semi-real datasets. We demonstrated that brMEGA successfully identifies and substantially removes cardiogenic artifacts in single-channel EEG recordings. Moreover, recovery of cardiogenic artifacts, if present, gives the opportunity for future extraction of heart rate features without ECG measurement.
AB - Brain activity recordings outside clinical or laboratory settings using mobile EEG systems have gained popular interest allowing for realistic long-term monitoring and eventually leading to identification of possible biomarkers for diseases. The less obtrusive, minimized systems (e.g., single-channel EEG, no ECG reference) have the drawback of artifact contamination with varying intensity that are particularly difficult to identify and remove. We developed brMEGA, the first open-source algorithm for automated detection and removal of cardiogenic artifacts using non-linear time-frequency analysis and machine learning to (1) detect whether and where cardiogenic artifacts exist, and (2) remove those artifacts. We compare our algorithm against visual artifact identification and a previously established approach and validate it in one real and semi-real datasets. We demonstrated that brMEGA successfully identifies and substantially removes cardiogenic artifacts in single-channel EEG recordings. Moreover, recovery of cardiogenic artifacts, if present, gives the opportunity for future extraction of heart rate features without ECG measurement.
KW - Automated artifact removal
KW - Cardiogenic artifact
KW - Electroencephalogram
KW - Machine learning
KW - Mobile technology
UR - http://www.scopus.com/inward/record.url?scp=85118473745&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85118473745&partnerID=8YFLogxK
U2 - 10.1016/j.bspc.2021.103220
DO - 10.1016/j.bspc.2021.103220
M3 - Article
AN - SCOPUS:85118473745
SN - 1746-8094
VL - 72
JO - Biomedical Signal Processing and Control
JF - Biomedical Signal Processing and Control
M1 - 103220
ER -