Detecting heart rate from fNIRS based brain signals
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1
Drexel University, School of Biomedical Engineering, Science and Health Systems, United States
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2
University of Pennsylvania, Department of Family and Community Health, United States
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3
Children's Hospital of Philadelphia, The Division of General Pediatrics, United States
Functional near-infrared spectroscopy (fNIRS) is a noninvasive and portable optical brain imaging technique that detects changes in cerebral blood oxygenation related to human brain functions. It uses near infrared light to measure changes in oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) levels due to the hemodynamic response. It has been utilized for study diverse array of brain function in healthy and clinical populations, from babies to elderly, and cognitive, affective to motor function.
fNIRS signals collected from the brain contains multiple components specifically physiological correlates such as respiration and heart rate (HR). In fact, near infrared based sensors have been widely used for heart rate detection in clinical settings such as finger pulse oximetry. However, in neuroimaging studies, physiological components are considered noise, and they discarded during preprocessing using specially designed temporal, spatial or frequency filters. Accessing systemic response could be useful in neuroimaging settings and extracting them reliably from cortical fNIRS data would eliminate any additional dedicated sensor.
In this work, our objective is to provide an overview of methods for extracting heart rate related signal from brain based fNIRS data. Common methodologies from the literatures will highlighted and assessed for practical field use and for experiments in real world settings. By accurately analyzing RR-intervals in HR related signal, detected variation can be used to measure autonomic nervous system. This variation, called Heart Rate Variability (HRV), is a valuable biomarker for understating human state in healthy and diverse clinical conditions. Identification of robust algorithms that can be generalized for detecting heartbeats, and calculating HR and HRV can potentially be used: i) to improve signal to noise ratio in functional neuroimaging research and ii) to complement brain data to help understand human workload while engaged with a machine or computerized tasks.
Keywords:
fNIRS,
HR,
HRV analysis,
prefrontal cortex (PFC),
hemodynamic response,
Biomedical Signal Processing
Conference:
2nd International Neuroergonomics Conference, Philadelphia, PA, United States, 27 Jun - 29 Jun, 2018.
Presentation Type:
Poster Presentation
Topic:
Neuroergonomics
Citation:
Kurt
AS and
Ayaz
H
(2019). Detecting heart rate from fNIRS based brain signals.
Conference Abstract:
2nd International Neuroergonomics Conference.
doi: 10.3389/conf.fnhum.2018.227.00026
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Received:
19 Feb 2018;
Published Online:
27 Sep 2019.
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Correspondence:
Dr. Hasan Ayaz, Drexel University, School of Biomedical Engineering, Science and Health Systems, Philadelphia, United States, hasan.ayaz@drexel.edu