학술논문

Variation Minimization Based Electrocardiogram Artifacts Removal for Local Field Potentials From Neurostimulator
Document Type
Periodical
Source
IEEE Transactions on Neural Systems and Rehabilitation Engineering IEEE Trans. Neural Syst. Rehabil. Eng. Neural Systems and Rehabilitation Engineering, IEEE Transactions on. 32:94-101 2024
Subject
Bioengineering
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
Satellite broadcasting
Electrodes
Recording
Electrocardiography
Sensors
Hospitals
Diseases
Local field potential (LFP)
template subtraction
artifact removal
sensing-enabled neurostimulator
Language
ISSN
1534-4320
1558-0210
Abstract
Local field potential (LFP) recorded by sensing-enabled neurostimulators provided chronic observation of deep brain activities for the research of brain disorders. However, the contamination from the electrocardiogram (ECG) deteriorated the extraction of effective information from LFP. This study proposed a novel algorithm based on minimizing the variance combining template subtraction to improve the performance of ECG artifact removal for LFP. Four patients with implanted electrodes were recruited, and eight real LFP records were collected from their left and right hemispheres, respectively. The results showed that the proposed method improved the accuracy of artifact peak detection in LFP, and the subsequent signal quality after template subtraction compared to the traditional Pan-Tompkins (PT) method. The outcome of this study benefited the LFP-based brain research, promoting the application of sensing-enabled neurostimulators in more areas.