Short-term Heart Rate Variability Detection Based On Functional Near Infrared Spectroscopy Measured From The Prefrontal Cortex

Heart rate variability(HRV)refers to the variation of heart rate intervals over time.HRV is regulated by the higher centers of the brain,as well as the sympathetic and parasympathetic nervous systems.It is related to heart health,regulation ability of the autonomic nervous systems,cognitive load and emotional state.In practical applications,HRV measurement is widely used for disease diagnosis and assessment of psychological/cognitive loads(i.e.,fatigue during driving).The analysis of RR intervals(RRI)obtained from electrocardiography(ECG)is the gold standard of HRV measurement.Besides,pulse rate variability(PRV)measured with Photoplethysmography(PPG)is often used as an approximation or estimate of HRV.Previous studies have demonstrated that PRV derived from PPG measurements is in high agreement with HRV measured from ECG in healthy young subjects at rest.Functional near-infrared spectroscopy(fNIRS)measures the dynamic changes of oxyhemoglobin(Oxy-Hb)and deoxyhemoglobin(Deoxy-Hb)concentrations in tissues.In recent years,it has been widely used for noninvasive observation and research on functional activities of the brain.The main purpose of this dissertation is to verify the feasibility and accuracy of shortterm HRV estimation using the fNIRS signals collected in conventional brain functional imaging experiments.The changes in blood flow/oxygenation measured with fNIRS not only reflect neural activities in the brain region(s)of interest,but also contain information about heart beat,respiration and other physiological homeostasis processes.The signals from the latter sources are exactly the same as the signal sources in the PPG measurement.Therefore,fNIRS brain functional imaging signals,in principle,can also be used to estimate HRV,alike to the PPG signals.If so,this will allows HRV estimation directly from the data acquired in fNIRS brain functional imaging experiments without the use of extra ECG equipment,and enable simultaneous measurements of HRV changes accompanying the alterations in brain state.The first part of the dissertation discusses the data processing piplines for estimating the short-term HRV from fNIRS data,and determines the workflow of data collection,analysis and processing and the final HRV estimation.Secondly,using simultaneously collected fNIRS and ECG data,the feasibility and accuracy of fNIRS in measuring shortterm HRV under the resting state and a state of online video game playing are analyzed by taking the short-term HRV measured from the ECG data as the gold standard.The results demonstrate that there is a high degree of consistency between the short-term HRV estimations from the fNIRS and ECG data,except for the inherent limitations caused by the low sampling rate of the fNIRS measurement.These results verify the feasibility of using fNIRS as an alternative of ECG to measure short-term HRV,and provide a new direction for the development of wearable devices in related fields.In the last part,some prospects for using fNIRS data to analyze the interaction between the heart and brain under task conditions are discussed.