Adaptive Hemodynamic Signal Estimation Using Kalman Estimator

Abstract

Functional Near Infrared Spectroscopy (fNIRS) is a technology that measures changes in the oxygenation level of blood present in brain whenever an activity is performed by human being. It is a non-invasive technique and uses near infrared light to detect changes in the concentration of two chromophores i.e., oxygenated and deoxygenated haemoglobin. During the recording, information related to neural activity in fNIRS signals gets compromised. This is due to the interference of noises from the environment outside as well as inside the human body. External noises can be light in the room and powerline noise. Internal noises are physiological noises such as cardiac, respiratory and mayer waves. Therefore, during analysis, it is required to remove these noises first and then extract main activity signal i.e., hemodynamic signal. Many techniques and methods have been proposed and practiced up to this date. Among them the most popular technique is General Linear Modelling (GLM). GLM models the signal by breaking it down into sum of all components present in the signal along with an error term. Previous studies and research that have used GLM for the reconstruction of activity signal used single frequency value for each noise but in reality, the frequency for each noise varies with the level of activity performed by the subject. This can lead to less accurate reconstruction of activity signal. In this study, this problem is kept under consideration and a method is developed to keep account for all the values of frequency that can corrupt fNIRS signal. Ranges of frequencies are considered instead of single values. These frequency ranges are first extracted using Continuous Wavelet Transform (CWT) and their possible magnitudes are estimated using Kalman filter. Similarly, activity signal is extracted from fNIRS signal using Discrete Wavelet Transform (DWT) and then its magnitude is estimated using Kalman filter. Output of these two steps is fed to GLM for reconstruction of possible hemodynamic signal. Results from this method are compared with the results of conventional GLM and significant improvement is observed both visually and statistically.