Physiological interference reduction for near infrared spectroscopy brain activity measurement based on recursive least squares adaptive filtering and least squares support vector machines

Computer Assisted Surgery

Ou Bai¹  Yan Zhang²  Qisong Wang²  Jinwei Sun²  Dan Liu²  Peter Rolfe²  Xin Liu³ 

[1] Department of Electrical and Computer Engineering, Florida International University, Miami, US;School of Electrical Engineering and Automaton, Harbin Institute of Technology, Harbin, China;School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, China;

关键词: Near infrared spectroscopy;    physiological interference;    adaptive filtering;    recursive least squares;    least squares support vector machine;   

DOI  :  10.1080/24699322.2018.1557901

来源: publisher

【 摘 要 】

Near infrared spectroscopy is the promising and noninvasive technique that can be used to detect the brain functional activation by monitoring the concentration alternations in the haemodynamic concentration. The acquired NIRS signals are commonly contaminated by physiological interference caused by breathing and cardiac contraction. Though the adaptive filtering method with least mean squares algorithm or recursive least squares algorithm based on multidistance probe configuration could improve the quality of evoked brain activity response, both methods can only remove the physiological interference occurred in superficial layers of the head tissue. To overcome the shortcoming, we combined the recursive least squares adaptive filtering method with the least squares support vector machine to suppress physiological interference both in the superficial layers and deeper layers of the head tissue. The quantified results based on performance measures suggest that the estimation performances of the proposed method for the evoked haemodynamic changes are better than the traditional recursive least squares method.

【 授权许可】

CC BY