Improvement in Recovery of Hemodynamic Responses by Extended Kalman Filter with Non-Linear State-Space Model and Short Separation Measurement

Sunghee Dong, Jichai Jeong

Research output: Contribution to journalArticle

Abstract

Objective: The purpose of this study is to describe the noise reduction in the hemodynamic responses, obtained by functional near-infrared spectroscopy (fNIRS), using the proposed extended Kalman filter (EKF) with a non-linear state-space model, aided by the short separation (SS) measurement. Methods: The authors used the simulated data by adding the synthetic hemodynamic response function (HRF) to the multi-distance four-channel fNIRS signals obtained during the resting state. EKF was used to estimate the non-linear state-space model designed based on Balloon model. The SS channel was used as a regressor that is sensitive only to superficial noises. The whole segments were grouped by the existence of motion artifacts (MAs) to investigate the improvement by EKF compared to the linear Kalman filter (LKF) and adaptive filter (AF) in extracting neural-evoked hemodynamic. Results: Kalman-based approaches were better than AF in reducing noises. Using EKF, the averages of the decreased errors and increased correlation between the recovered and true HRF were 34 % in oxy-hemoglobin and 62 % in deoxy-hemoglobin concentrations in segments having MAs, compared with LKF. In the MA-free condition, EKF is more robust to the poor quality of signals in noise reduction than LKF. Conclusion: The proposed non-linear Kalman approach is better in noise reduction than AF and LKF especially in noisy deoxy-hemoglobin concentrations, and less affected by the conditions of measurements and contaminations by MAs. Significance: The proposed method can be used for reducing superficial noises and MAs from fNIRS signals as an upgraded alternative to existing adaptive filters.

Original languageEnglish
JournalIEEE Transactions on Biomedical Engineering
DOIs
Publication statusAccepted/In press - 2018 Jan 1

Fingerprint

Space Simulation
Hemodynamics
Extended Kalman filters
Noise
Adaptive filters
Artifacts
Kalman filters
Near infrared spectroscopy
Hemoglobin
Recovery
Noise abatement
Near-Infrared Spectroscopy
Hemoglobins
Balloons
Contamination

Keywords

  • Biomedical measurement
  • Brain modeling
  • extended Kalman filter
  • functional near-infrared spectroscopy
  • Hemodynamics
  • Kalman filters
  • Noise measurement
  • noise reduction
  • non-linear state-space model
  • short separation measurement
  • Signal to noise ratio
  • State-space methods

ASJC Scopus subject areas

  • Biomedical Engineering

Cite this

@article{f3b92e29441c49bea9f6afcd6e4a8b3d,
title = "Improvement in Recovery of Hemodynamic Responses by Extended Kalman Filter with Non-Linear State-Space Model and Short Separation Measurement",
abstract = "Objective: The purpose of this study is to describe the noise reduction in the hemodynamic responses, obtained by functional near-infrared spectroscopy (fNIRS), using the proposed extended Kalman filter (EKF) with a non-linear state-space model, aided by the short separation (SS) measurement. Methods: The authors used the simulated data by adding the synthetic hemodynamic response function (HRF) to the multi-distance four-channel fNIRS signals obtained during the resting state. EKF was used to estimate the non-linear state-space model designed based on Balloon model. The SS channel was used as a regressor that is sensitive only to superficial noises. The whole segments were grouped by the existence of motion artifacts (MAs) to investigate the improvement by EKF compared to the linear Kalman filter (LKF) and adaptive filter (AF) in extracting neural-evoked hemodynamic. Results: Kalman-based approaches were better than AF in reducing noises. Using EKF, the averages of the decreased errors and increased correlation between the recovered and true HRF were 34 {\%} in oxy-hemoglobin and 62 {\%} in deoxy-hemoglobin concentrations in segments having MAs, compared with LKF. In the MA-free condition, EKF is more robust to the poor quality of signals in noise reduction than LKF. Conclusion: The proposed non-linear Kalman approach is better in noise reduction than AF and LKF especially in noisy deoxy-hemoglobin concentrations, and less affected by the conditions of measurements and contaminations by MAs. Significance: The proposed method can be used for reducing superficial noises and MAs from fNIRS signals as an upgraded alternative to existing adaptive filters.",
keywords = "Biomedical measurement, Brain modeling, extended Kalman filter, functional near-infrared spectroscopy, Hemodynamics, Kalman filters, Noise measurement, noise reduction, non-linear state-space model, short separation measurement, Signal to noise ratio, State-space methods",
author = "Sunghee Dong and Jichai Jeong",
year = "2018",
month = "1",
day = "1",
doi = "10.1109/TBME.2018.2884169",
language = "English",
journal = "IEEE Transactions on Biomedical Engineering",
issn = "0018-9294",
publisher = "IEEE Computer Society",

}

TY - JOUR

T1 - Improvement in Recovery of Hemodynamic Responses by Extended Kalman Filter with Non-Linear State-Space Model and Short Separation Measurement

AU - Dong, Sunghee

AU - Jeong, Jichai

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Objective: The purpose of this study is to describe the noise reduction in the hemodynamic responses, obtained by functional near-infrared spectroscopy (fNIRS), using the proposed extended Kalman filter (EKF) with a non-linear state-space model, aided by the short separation (SS) measurement. Methods: The authors used the simulated data by adding the synthetic hemodynamic response function (HRF) to the multi-distance four-channel fNIRS signals obtained during the resting state. EKF was used to estimate the non-linear state-space model designed based on Balloon model. The SS channel was used as a regressor that is sensitive only to superficial noises. The whole segments were grouped by the existence of motion artifacts (MAs) to investigate the improvement by EKF compared to the linear Kalman filter (LKF) and adaptive filter (AF) in extracting neural-evoked hemodynamic. Results: Kalman-based approaches were better than AF in reducing noises. Using EKF, the averages of the decreased errors and increased correlation between the recovered and true HRF were 34 % in oxy-hemoglobin and 62 % in deoxy-hemoglobin concentrations in segments having MAs, compared with LKF. In the MA-free condition, EKF is more robust to the poor quality of signals in noise reduction than LKF. Conclusion: The proposed non-linear Kalman approach is better in noise reduction than AF and LKF especially in noisy deoxy-hemoglobin concentrations, and less affected by the conditions of measurements and contaminations by MAs. Significance: The proposed method can be used for reducing superficial noises and MAs from fNIRS signals as an upgraded alternative to existing adaptive filters.

AB - Objective: The purpose of this study is to describe the noise reduction in the hemodynamic responses, obtained by functional near-infrared spectroscopy (fNIRS), using the proposed extended Kalman filter (EKF) with a non-linear state-space model, aided by the short separation (SS) measurement. Methods: The authors used the simulated data by adding the synthetic hemodynamic response function (HRF) to the multi-distance four-channel fNIRS signals obtained during the resting state. EKF was used to estimate the non-linear state-space model designed based on Balloon model. The SS channel was used as a regressor that is sensitive only to superficial noises. The whole segments were grouped by the existence of motion artifacts (MAs) to investigate the improvement by EKF compared to the linear Kalman filter (LKF) and adaptive filter (AF) in extracting neural-evoked hemodynamic. Results: Kalman-based approaches were better than AF in reducing noises. Using EKF, the averages of the decreased errors and increased correlation between the recovered and true HRF were 34 % in oxy-hemoglobin and 62 % in deoxy-hemoglobin concentrations in segments having MAs, compared with LKF. In the MA-free condition, EKF is more robust to the poor quality of signals in noise reduction than LKF. Conclusion: The proposed non-linear Kalman approach is better in noise reduction than AF and LKF especially in noisy deoxy-hemoglobin concentrations, and less affected by the conditions of measurements and contaminations by MAs. Significance: The proposed method can be used for reducing superficial noises and MAs from fNIRS signals as an upgraded alternative to existing adaptive filters.

KW - Biomedical measurement

KW - Brain modeling

KW - extended Kalman filter

KW - functional near-infrared spectroscopy

KW - Hemodynamics

KW - Kalman filters

KW - Noise measurement

KW - noise reduction

KW - non-linear state-space model

KW - short separation measurement

KW - Signal to noise ratio

KW - State-space methods

UR - http://www.scopus.com/inward/record.url?scp=85057787030&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85057787030&partnerID=8YFLogxK

U2 - 10.1109/TBME.2018.2884169

DO - 10.1109/TBME.2018.2884169

M3 - Article

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

SN - 0018-9294

ER -