Background Elevated resting heart rate (RHR) is known to be associated with reduced survival but inconsistencies remain, including lack of significance in most studies of healthy women, lack of independence from systolic blood pressure (SBP) in some, and the suggestion that RHR is merely functioning as a marker of physical inactivity or other comorbidities. We aimed to clarify these inconsistencies. Methods We analyzed the effect of RHR on end points in the National FINRISK Study; a representative, prospective study using Cox proportional hazards model. Ten-thousand five-hundred nineteen men and 11,334 women were included, excluding those with preexisting coronary heart disease, angina, heart failure, or on antihypertensive therapy. Results The hazard ratios for cardiovascular disease (CVD) mortality for each 15 beats/min increase in RHR were 1.24 (1.11-1.40) in men and 1.32 (1.08-1.60) in women, adjusted for age, gender, total cholesterol, physical activity (categorical), SBP, body mass index, and high-density lipoprotein cholesterol. This relationship remained significant after exclusion of those with comorbidities and events occurring within first 2 years of observation. Relationship with coronary mortality was stronger and with total mortality was slightly weaker. Inclusion of nonfatal end points weakened the relationship. Conclusions A strong, graded, independent relationship between RHR and incident CVD was demonstrated. This was consistent in healthy men and women. We have clarified that the relationship is independent of SBP and that the temporal sequence would be compatible with a causal relationship. New findings include independence from both a validated measure of physical activity and comorbidities and the demonstration of a stronger effect for fatal than nonfatal events, supporting increased arrhythmogenicity of one of the mechanisms.

Elevated resting heart rate is an independent risk factor for cardiovascular disease in healthy men and women

A novel optical fiber sensor using a mesh microbend optical fiber sensor to measure the perioperative heart rate (HR) and respiratory rate (RR) frequency signals was developed by our team. The feasibility of the sensor was evaluated in 10 infants in the perioperative period. We used traditional algorithms, such as Fast Fourier Transformation (FFT) and Wavelet Transformation (WT) to remove the noise and extract the features of the acquired HR and RR signals. However, the nonlinear fitting abilities of those traditional algorithms failed to completely remove the noise hence it was difficult to extract the features effectively. In this paper, we propose a deep learning model EMD-LSTM-CNN (ELC) to process both HR and RR based on Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), and Empirical Modal Decomposition (EMD) methods. The trend term is extracted by EMD from HR and RR. The CNN and LSTM are applied to extract features and process them respectively. The experimental results show that the deep learning model has a better result compared with the traditional FFT and WT algorithms. The proposed model demonstrates compliance with the current standard physiological monitoring method in measuring non-stationary vibration signals such as HR and RR, which promises potential clinical applications in the future.

Assessment of Heart Rate and Respiratory Rate for Perioperative Infants Based on ELC Model

Remote photoplethysmography (rPPG) is an attractive video-based technique to monitor heart rate (HR) information in telehealth screening. The subjects are required to stay stationary in the remote screening scenario and ambient lights become the main interference source of measuring HR with rPPG. In this paper, we introduce a novel approach robust against spatially uneven illumination interference in rPPG by combining ensemble empirical mode decomposition (EEMD) with multiset canonical correlation analysis (MCCA). We adopt the following procedures to ensure that the pulses dominate the correlations across multiple signal sets while the illumination noises are as diverse as possible. Specifically, a group of optimal regions of interest (ROIs) are selected according to the quality indicators defined from the green channel in each candidate ROI. A multi-channel signal set is then constructed by decomposing the green signal with EEMD. Only those intrinsic mode functions (IMFs) with the dominant frequencies falling into the interested HR range are utilized as the input of MCCA. The canonical variables (CVs) with the highest cross correlations are derived as the underlying candidate pulses. Finally, fast Fourier transform (FFT) is employed to calculate the dominant frequency and the target HR is determined based on both the quasi-periodic property and the continuity of HR. The proposed EEMD-MCCA method is validated on both the in-house BSIPL-RPPG and the public COHFACE databases, which achieves superior performance over several typical rPPG methods. This study will provide a promising tool for realistic rPPG applications in telehealth screening.

Remote Photoplethysmography With an EEMD-MCCA Method Robust Against Spatially Uneven Illuminations

Recent studies have shown that changes in human facial skin color due to varying blood flows can be recorded on videos by consumer-level cameras and, interestingly, can be used to measure pulse rates in a noncontact manner. However, it remains unclear whether the video-based pulse rate detection is feasible when videos are recorded in unstable lighting conditions. To address unstable illumination, a novel algorithm named adaptive spatiotemporal homomorphic filtering (ASTHF) is proposed in this article, with a focus on the separation of pulse signals and illumination-induced noise. This method first employs the homomorphic filtering framework to make illumination-induced noise in a video linearly separable. Subsequently, it incorporates a spatiotemporal filter into the framework, aiming to remove illumination-induced noise from both the time domain and the space domain of the video. To adapt to diverse illumination in the real world, the filter parameters are determined adaptively according to the sparsity in frequency spectrum of pulse signals. ASTHF is tested on two data sets, including 406 video samples. The test results show that ASTHF has stronger robustness to unstable illumination than state-of-the-art methods, improving the Pearson’s correlation coefficient by 5% in a challenging scenario compared with the best contrasting method. The proposed method demonstrates promise to achieve noncontact pulse rate detection from facial videos involving unsteady illumination.

Detecting Pulse Rates From Facial Videos Recorded in Unstable Lighting Conditions: An Adaptive Spatiotemporal Homomorphic Filtering Algorithm

The measurement of physiological parameters is fundamental to assess the health status of an individual. The contactless monitoring of vital signs may provide benefits in various fields of application, from healthcare and clinical setting to occupational and sports scenarios. Recent research has been focused on the potentiality of camera-based systems working in the visible range (380–750 nm) for estimating vital signs by capturing subtle color changes or motions caused by physiological activities but invisible to human eyes. These quantities are typically extracted from videos framing some exposed body areas (e.g., face, torso, and hands) with adequate post-processing algorithms. In this review, we provided an overview of the physiological and technical aspects behind the estimation of vital signs like respiratory rate, heart rate, blood oxygen saturation, and blood pressure from digital images as well as the potential fields of application of these technologies. Per each vital sign, we provided the rationale for the measurement, a classification of the different techniques implemented for post-processing the original videos, and the main results obtained during various applications or in validation studies. The available evidence supports the premise of digital cameras as an unobtrusive and easy-to-use technology for physiological signs monitoring. Further research is needed to promote the advancements of the technology, allowing its application in a wide range of population and everyday life, fostering a biometrical holistic of the human body (BHOHB) approach.

Contactless Vital Signs Monitoring From Videos Recorded With Digital Cameras: An Overview

Unobtrusive monitoring of vital signs is relevant for both medical (patient monitoring) and non-medical applications (e.g., stress and fatigue monitoring). In this paper, we focus on the use of imaging photoplethysmography (iPPG). High frame rate videos were acquired by using a monochrome camera and an optical band-pass filter ( $\text{560}\pm \text{20}$ nm). To enhance iPPG signal, we investigated the use of independent component analysis (ICA) pre-processing applied to iPPG signal from different regions of the face. Methodology was tested on $\text{30}$ healthy volunteers. Heart rate (HR) and standard time and frequency domain descriptors of heart rate variability (HRV), simultaneously extracted from videos and ECG data, were compared. A mean absolute error (MAE) about 3.812 ms was observed for normal-to-normal intervals with or without ICA pre-processing. Smaller MAE values of frequency domain descriptors were observed when ICA pre-processing was used. The impact of both video frame rate and video signal interval were also analyzed. All the results support the conclusion that proposed ICA pre-processing can effectively improve the HR and HRV assessment from iPPG.

Heart Rate and Heart Rate Variability From Single-Channel Video and ICA Integration of Multiple Signals

It is recognized that stress conditions play an important role in the definition of individual wellness and represent a major risk factor for most non-communicable diseases. Most studies focus on the evaluation of response to maximal stress conditions while a few of them reports results about the detection/monitoring of response to mild stimulations. In this study, we investigate the capability of some physiological signs and indicators (including Heart Rate, Heart Rate Variability, Respiratory Rate, Galvanic Skin Response) to recognize stress in response to moderate cognitive activation in daily life settings. To achieve this goal, we built up an unobtrusive platform to collect signals from healthy volunteers (10 subjects) undergoing cognitive activation via Stroop Color Word Test. We integrated our dataset with data from the Stress Recognition in the Automobile Drivers dataset. Following data harmonization, signal recordings in both datasets were split into five-minute blocks and a set of 12 features was extracted from each block. A feature selection was implemented by two complementary approaches: Sequential Forward Feature Selection (SFFS) and Auto-Encoder (AE) neural networks. Finally, we explored the use of Self-Organizing Map (SOM) to provide a flexible representation of an individual status. From the initial feature set we have determined, by SFFS analysis, that 2 of them (median Respiratory Rate and number peaks in Galvanic Skin Response signals) can discriminate activation statuses from resting ones. In addition, AE experiments also support that two features can suffice for recognition. Finally, we showed that SOM can provide a comprehensive but compact description of activation statuses allowing a fine prototypical representation of individual status.

Recognition of Stress Activation by Unobtrusive Multi Sensing Setup

This study is part of a research project aiming to build a model for quantifying an individual wellness status through unobtrusive measurements of psychophysical parameters and self-reported data. In particular, we focus on the evaluation of the individual response to mild stress stimuli. The experimental setup included: EG05000 Medlab Five Channel Module, Gigabit Ethernet camera, BioHarness 3 Zephyr chest belt, and Muse 2 Headband. Experimental results show increased heart rate and respiration rate, and changes of the brain activity in the stress condition. This is consistent with a “fight or flight” response in accordance with literature. Therefore, the methodology applied in this study can be used to monitor the individual wellness status in conditions of mild stress stimuli.

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Veronica Chiara Zuccala

Papers

Heart Rate and Heart Rate Variability From Single-Channel Video and ICA Integration of Multiple Signals

Recognition of Stress Activation by Unobtrusive Multi Sensing Setup

Unobtrusive Monitoring of ECG and EEG During Mild Stress Stimuli