Noncontact Vital Signs Detection

Human health condition can be accessed by measurement of vital signs, i.e., respiratory rate (RR), heart rate (HR), blood oxygen level, temperature and blood pressure. Due to drawbacks of contact sensors in measurement, non-contact sensors such as imaging photoplethysmogram (IPPG) and Doppler radar system have been proposed for cardiorespiratory rates detection by researchers.The UWB pulse Doppler radars provide high resolution range-time-frequency information. It is bestowed with advantages of low transmitted power, through-wall capabilities, and high resolution in localization. However, the poor signal to noise ratio (SNR) makes it challenging for UWB radar systems to accurately detect the heartbeat of a subject. To solve the problem, phased-methods have been proposed to extract the phase variations in the reflected pulses modulated by human tiny thorax motions. Advance signal processing method, i.e., state space method, can not only be used to enhance SNR of human vital signs detection, but also enable the micro-Doppler trajectories extraction of walking subject from UWB radar data.Stepped Frequency Continuous Wave (SFCW) radar is an alternative technique useful to remotely monitor human subject activities. Compared with UWB pulse radar, it relieves the stress on requirement of high sampling rate analog-to-digital converter (ADC) and possesses higher signal-to-noise-ratio (SNR) in vital signs detection. However, conventional SFCW radar suffers from long data acquisition time to step over many frequencies. To solve this problem, multi-channel SFCW radar has been proposed to step through different frequency bandwidths simultaneously. Compressed sensing (CS) can further reduce the data acquisition time by randomly stepping through 20% of the original frequency steps.In this work, SFCW system is implemented with low cost, off-the-shelf surface mount components to make the radar sensors portable. Experimental results collected from both pulse and SFCW radar systems have been validated with commercial contact sensors and satisfactory results are shown

Noncontact measurement of emotional and physiological changes in heart rate from a webcam

Heart rate, measured in beats per minute (BPM), can be used as an index of an individual's physiological state. Each time the heart beats, blood is expelled and travels through the body. This blood flow can be detected in the face using a standard webcam that is able to pick up subtle changes in color that cannot be seen by the naked eye. Due to the light absorption spectrum of blood, we are able to detect differences in the amount of light absorbed by the blood traveling just below the skin (i.e., photoplethysmography). By modulating emotional and physiological stress -- i.e., viewing arousing images and sitting vs. standing, respectively -- to elicit changes in heart rate, we explored the feasibility of using a webcam as a psychophysiological measurement of autonomic activity. We found a high level of agreement between established physiological measures, electrocardiogram (ECG), and blood pulse oximetry, and heart rate estimates obtained from the webcam. We thus suggest webcams can be used as a non-invasive and readily available method for measuring psychophysiological changes, easily integrated into existing stimulus presentation software and hardware setups

Robust and Analytical Cardiovascular Sensing

The photoplethysmogram (PPG) is a noninvasive cardiovascular signal related to the pulsatile volume of blood in tissue. The PPG is user-friendly and has the potential to be measured remotely in a contactless manner using a regular RGB camera. In this dissertation, we study the modeling and analytics of PPG signal to facilitate its applications in both robust and remote cardiovascular sensing. In the first part of this dissertation, we study the remote photoplethysmography (rPPG) and present a robust and efficient rPPG system to extract pulse rate (PR) and pulse rate variability (PRV) from face videos. Compared with prior art, our proposed system can achieve accurate PR and PRV estimates even when the video contains significant subject motion and environmental illumination change. In the second part of the dissertation, we present a novel frequency tracking algorithm called Adaptive Multi-Trace Carving (AMTC) to address the micro signal extraction problems. AMTC enables an accurate detection and estimation of one or more subtle frequency components in a very low signal-to-noise ratio condition. In the third part of the dissertation, the relation between electrocardiogram (ECG) and PPG is studied and the waveform of ECG is inferred via the PPG signals. In order to address this cardiovascular inverse problem, a transform is proposed to map the discrete cosine transform coefficients of each PPG cycle to those of the corresponding ECG cycle. As the first work to address this biomedical inverse problem, this line of research enables a full utilization of the easy accessibility of PPG and the clinical authority of ECG for better preventive healthcare

A Statistical Framework for Non-Contact Heart Rate Estimation via Photoplethysmogram Imaging

Although medical progress and increased health awareness over the last 60 years have reduced death rates from cardiovascular disease by more than 75%, cardiovascular disease remains one of the leading causes of death, hospitalization, and cause of prescription drug use. Resting heart rate can act as an independent risk factor in cardiovascular mortality, while more detailed blood volume waveforms can offer insight on blood pressure, blood oxygenation, respiration rate, and cognitive stress. Electrocardiograms (ECGs) are widely used in the clinical setting due to their accurate measurement of heart rate and detailed capture of heart muscle depolarization, making them useful in diagnosis of specific cardiovascular conditions. However, the discomfort caused by the required adhesive patches, as well as the relatively high cost of ECG machines, introduces the need for an alternative system when only the resting heart rate is required. Photoplethysmography (PPG), the optical acquisition of blood volume pulse over time, offers one such solution. The pulse oximeter, a device which clips onto a thin extremity and measures the amount of transmitted light over time, is widely used in a clinical setting for heart rate and oxygen saturation measurements in cases where ECG is unnecessary or unavailable. Recently, a technique has been demonstrated to construct a blood volume pulse signal without the need for contact, offering a more sanitary and comfortable alternative to pulse oximetry. This technique relies on camera systems and is known as PPG imaging (PPGI). However, the accuracy of PPGI methods suffers in realistic environments with error incurred by motion, illumination variation, and natural fluctuation of the heart rate. For this reason, a statistical framework which aims to offer higher accuracy in realistic scenarios is proposed. The initial step in the framework is to construct a PPG waveform, a time series correlated to hemoglobin concentration. Here, an importance-weighted Monte Carlo sampling strategy is used to construct a PPG waveform from many time series observations. Once the PPG waveform is established, a continuous wavelet transform is applied, using the so-called pulselet as the mother wavelet, to create a response map in the time-frequency domain. The average of frequencies corresponding to the maximum response over time is used as the heart rate estimation. To verify the efficacy of the proposed framework, tests were run on two data sets; the first consists of broadband red-green-blue (RGB) colour channel video data and the second contains single channel near infrared video data. In the first case, improvements over state-of-the-art methods were shown, however; in the second case, no statistically significant improvement was observed

High-Field Functional MRI from the Perspective of Single Vessels in Rats and Humans

Functional MRI (fMRI) has been employed to map brain activity and connectivity based on the neurovascular coupled hemodynamic signal. However, in most cases of fMRI studies, the cerebral vascular hemodynamic signal has been imaged in a spatially smoothed manner due to the limit of spatial resolution. There is a need to improve the spatiotemporal resolution of fMRI to map dynamic signal from individual venule or individual arteriole directly. Here, the thesis aims to provide a vascular-specific view of hemodynamic response during active state or resting state. To better characterize the temporal features of task-related fMRI signal from different vascular compartments, we implemented a line-scanning method to acquire vessel-specific blood-oxygen-level-dependent (BOLD) / cerebral-blood-volume (CBV) fMRI signal at 100-ms temporal resolution with sensory or optogenetic stimulation. Furthermore, we extended the line-scanning method with multi-echo scheme to provide vessel-specific fMRI with the higher contrast-to-noise ratio (CNR), which allowed us to directly map the distinct evoked hemodynamic signal from arterioles and venules at different echo time (TE) from 3 ms to 30 ms. The line-scanning fMRI methods acquire single k-space line per TR under a reshuffled k space acquisition scheme which has the limitation of sampling the fMRI signal in real-time for resting-state fMRI studies. To overcome this, we implemented a balanced Steady-state free precession (SSFP) to map task-related and resting-state fMRI (rsfMRI) with high spatial resolution in anesthetized rats. We reveal venule-dominated functional connectivity for BOLD fMRI and arteriole-dominated functional connectivity for CBV fMRI. The BOLD signal from individual venules and CBV signal from individual arterioles show correlations at an ultra-slow frequency (< 0.1 Hz), which are correlated with the intracellular calcium signal measured in neighboring neurons. In complementary data from awake human subjects, the BOLD signal is spatially correlated among sulcus veins and specified intracortical veins of the visual cortex at similar ultra-slow rhythms. This work provides a high-resolution fMRI approach to resolve brain activation and functional connectivity at the level of single vessels, which opened a new avenue to investigate brian functional connectivity at the scale of vessels

Speech Processes for Brain-Computer Interfaces

Speech interfaces have become widely used and are integrated in many applications and devices. However, speech interfaces require the user to produce intelligible speech, which might be hindered by loud environments, concern to bother bystanders or the general in- ability to produce speech due to disabilities. Decoding a usera s imagined speech instead of actual speech would solve this problem. Such a Brain-Computer Interface (BCI) based on imagined speech would enable fast and natural communication without the need to actually speak out loud. These interfaces could provide a voice to otherwise mute people. This dissertation investigates BCIs based on speech processes using functional Near In- frared Spectroscopy (fNIRS) and Electrocorticography (ECoG), two brain activity imaging modalities on opposing ends of an invasiveness scale. Brain activity data have low signal- to-noise ratio and complex spatio-temporal and spectral coherence. To analyze these data, techniques from the areas of machine learning, neuroscience and Automatic Speech Recog- nition are combined in this dissertation to facilitate robust classification of detailed speech processes while simultaneously illustrating the underlying neural processes. fNIRS is an imaging modality based on cerebral blood flow. It only requires affordable hardware and can be set up within minutes in a day-to-day environment. Therefore, it is ideally suited for convenient user interfaces. However, the hemodynamic processes measured by fNIRS are slow in nature and the technology therefore offers poor temporal resolution. We investigate speech in fNIRS and demonstrate classification of speech processes for BCIs based on fNIRS. ECoG provides ideal signal properties by invasively measuring electrical potentials artifact- free directly on the brain surface. High spatial resolution and temporal resolution down to millisecond sampling provide localized information with accurate enough timing to capture the fast process underlying speech production. This dissertation presents the Brain-to- Text system, which harnesses automatic speech recognition technology to decode a textual representation of continuous speech from ECoG. This could allow to compose messages or to issue commands through a BCI. While the decoding of a textual representation is unparalleled for device control and typing, direct communication is even more natural if the full expressive power of speech - including emphasis and prosody - could be provided. For this purpose, a second system is presented, which directly synthesizes neural signals into audible speech, which could enable conversation with friends and family through a BCI. Up to now, both systems, the Brain-to-Text and synthesis system are operating on audibly produced speech. To bridge the gap to the final frontier of neural prostheses based on imagined speech processes, we investigate the differences between audibly produced and imagined speech and present first results towards BCI from imagined speech processes. This dissertation demonstrates the usage of speech processes as a paradigm for BCI for the first time. Speech processes offer a fast and natural interaction paradigm which will help patients and healthy users alike to communicate with computers and with friends and family efficiently through BCIs

Photoplethysmography-Based Biomedical Signal Processing

In this dissertation, photoplethysmography-based biomedical signal processing methods are developed and analyzed. The developed methods solve problems concerning the estimation of the heart rate during physical activity and the monitoring of cardiovascular health. For the estimation of heart rate during physical activity, two methods are presented that are very accurate in estimating the instantaneous heart rate at the wrist and, at the same time, are computationally efficient so that they can easily be integrated into wearables. In the context of cardiovascular health monitoring, a method for the detection of atrial fibrillation using the video camera of a smartphone is proposed that achieves a high detection rate of atrial fibrillation (AF) on a clinical pre-study data set. Further monitoring of cardiovascular parameters includes the estimation of blood pressure (BP), pulse wave velocity (PWV), and vascular age index (VAI), for which an approach is presented that requires only a single photoplethysmographic (PPG) signal. Heart rate estimation during physical activity using PPG signals constitutes an important research focus of this thesis. In this work, two computationally efficient algorithms are presented that estimate the heart rate from two PPG signals using a three axis accelerometer. In the first approach, adaptive filters are applied to estimate motion artifacts that severely deteriorate the signal quality. The non-stationary relationship between the measured acceleration signals and the artifacts is modeled as a linear system. The outputs of the adaptive filters are combined to further enhance the signal quality and a constrained heart rate tracker follows the most probable high energy continuous line in the spectral domain. The second approach is modest in computational complexity and very fast in execution compared to existing approaches. It combines correlation-based fundamental frequency indicating functions and spectral combination to enhance the correlated useful signal and suppress uncorrelated noise. Additional harmonic noise damping further reduces the impact of strong motion artifacts and a spectral tracking procedure uses a linear least squares prediction. Both approaches are modest in computational complexity and especially the second approach is very fast in execution, as it is shown on a widely used benchmark data set and compared to state-of-the-art methods. The second research focus and a further major contribution of this thesis lies in the monitoring of the cardiovascular health with a single PPG signal. Two methods are presented, one for detection of AF and one for the estimation of BP, PWV, and VAI. The first method is able to detect AF based on a smartphone filming the finger placed on the video camera. The algorithm transforms the video into a PPG signal and extracts features which are then used to discriminate between AF and normal sinus rhythm (NSR). Perfect detection of AF is already achieved on a data set of 326 measurements (including 20 with AF) that were taken at a clinical pre-study using an appropriate pair of features whereby a decision is formed through a simple linear decision equation. The second method aims at estimating cardiovascular parameters from a single PPG signal without the conventional use of an additional electrocardiogram (ECG). The proposed method extracts a large number of features from the PPG signal and its first and second order difference series, and reconstructs missing features by the use of matrix completion. The estimation of cardiovascular parameters is based on a nonlinear support vector regression (SVR) estimator and compared to single channel PPG based estimators using a linear regression model and a pulse arrival time (PAT) based method. If the training data set contains the person for whom the cardiovascular parameters are to be determined, the proposed method can provide an accurate estimate without further calibration. All proposed algorithms are applied to real data that we have either recorded ourselves in our biomedical laboratory, that have been recorded by a clinical research partner, or that are freely available as benchmark data sets