HRV based health&sport markers using video from the face

Noise Reduction Technique for Single-Color Video Plethysmography Using Singular Spectrum Analysis

Recently, a contactless method for measuring a biological signal using a video camera has garnered attention. Especially, video plethysmography, a technique for obtaining a pulse wave from a video, is useful for managing the health of people on a daily basis. However, any body movement of a person subjected to the measurement leads to the generation of irregular noise in video plethysmography and reduces the accuracy of the recorded biological information, e.g., heart rate, during the measurement. Blind source separation is a popular technique for eliminating noise from the results of video plethysmography comprising different multiple-color channels. However, it is difficult to apply this technique to a single-color video such as a near-infrared video. Herein, a new method that combines singular spectrum analysis with the circular autocorrelation function is introduced to eliminate irregular noise in single-color video plethysmography. Applying the proposed method on videos collected from 39 individuals improved the estimation accuracy of instantaneous heart rate by approximately 44% over a conventional method using a linear filter. Furthermore, the proposed method also enabled more precise estimations of the heart rate than that achieved using multi-color video plethysmography.

Availability and performance of image-based, non-contact methods of monitoring heart rate, blood pressure, respiratory rate, and oxygen saturation: a systematic review

OBJECTIVE

Over the last 15 years, developments in camera technology have coincided with increased availability and affordability. This has led to an increasing interest in using these technologies in healthcare settings. Image-based monitoring methods potentially allow multiple vital signs to be measured concurrently using a non-contact sensor. We have undertaken a systematic review of the current availability and performance of these monitoring methods.

APPROACH

A multiple database search was conducted using MEDLINE, Embase, CINAHL, Cochrane Library, OpenGrey, IEEE Xplore Library and ACM Digital Library to July 2018. We included studies comparing image-based heart rate, respiratory rate, oxygen saturation and blood pressure monitoring methods against one or more validated reference device(s). Each included study was assessed using the modified GRRAS criteria for reporting bias.

MAIN RESULTS

Of 30 279 identified studies, 161 were included in the final analysis. Twenty studies (20/161, 12%) were carried out on patients in clinical settings, while the remainder were conducted in academic settings using healthy volunteer populations. The 18-40 age group was best represented across the identified studies. One hundred and twenty studies (120/161, 75%) estimated heart rate, followed by 62 studies (62/161, 39%) estimating respiratory rate. Fewer studies focused on oxygen saturation (11/161, 7%) or blood pressure (6/161, 4%) estimation. Fifty-one heart rate studies (51/120, 43%) and 24 respiratory rate studies (24/62, 39%) used Bland-Altman analysis to report their results. Of the heart rate studies, 28 studies (28/51, 55%) showed agreement within industry standards of [Formula: see text]5 beats per minute. Only two studies achieved this within clinical settings. Of the respiratory rate studies, 13 studies (13/24, 54%) showed agreement within industry standards of [Formula: see text]3 breaths per minute, but only one study achieved this in a clinical setting. Statistical analysis was heterogeneous across studies with frequent inappropriate use of correlation. The majority of studies (99/161, 61%) monitored subjects for under 5 min. Three studies (3/161, 2%) monitored subjects for over 60 min, all of which were conducted in hospital settings.

SIGNIFICANCE

Heart rate and respiratory rate monitoring using video images is currently possible and performs within clinically acceptable limits under experimental conditions. Camera-derived estimates were less accurate in the proportion of studies conducted in clinical settings. We would encourage thorough reporting of the population studied, details of clinically relevant aspects of methodology, and the use of appropriate statistical methods in future studies. Systematic review registration: PROSPERO CRD42016029167 Protocol: https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-017-0615-3.

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

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 ([Formula: see text] 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 [Formula: see text] 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.

Pulse rate variability analysis by video using face detection and tracking algorithms

Heart rate variability (HRV) is a physiological parameter which has increased its importance in recent years due to its relationship with the autonomic nervous system (ANS) and cardiovascular disorders. In this work the pulse rate variability (PRV) is measured as a surrogate measurement of HRV using only a video of the face of the subject. Because of pulse rate signals obtained by video are largely affected by the person movements and the analyzed region of interest (ROI), we propose the application of face detection and tracking algorithms as a solution to the artifacts produced by these factors. We compare the obtained results of the proposed method with a pulse sensor signal of a reference system. The statistical analysis results show good agreement between both methods.