In this paper, classical time- and frequency-domain variability indexes obtained by pulse rate variability (PRV) series extracted from video-photoplethysmography signals (vPPG) were compared with heart rate variability (HRV) parameters extracted from ECG signals. The study focuses on the analysis of the changes observed during a rest-to-stand manoeuvre (a mild sympathetic stimulus) performed on 60 young, normal subjects (age: [Formula: see text] years). The objective is to evaluate if video-derived PRV indexes may replace HRV in the assessment of autonomic responses to external stimulation. Video recordings were performed with a GigE Sony XCG-C30C camera and analyzed offline to extract the vPPG signal. A new method based on zero-phase component analysis (ZCA) was employed in combination with a fully-automatic method for detection and tracking of region of interest (ROI) located on the forehead, the cheek and the nose. Results show an overall agreement between time and frequency domain indexes computed on HRV and PRV series. However, some differences exist between resting and standing conditions. During rest, all the indexes computed on HRV and PRV series were not statistically significantly different (p > 0.05), and showed high correlation (Pearson's r > 0.90). The agreement decreases during standing, especially for the high-frequency, respiration-related parameters such as RMSSD (r = 0.75), pNN50 (r = 0.68) and HF power (r = 0.76). Finally, the power in the LF band (n.u.) was observed to increase significantly during standing by both HRV ([Formula: see text] versus [Formula: see text] (n.u.); rest versus standing) and PRV ([Formula: see text] versus [Formula: see text](n.u.); rest versus standing) analysis, but such an increase was lower in PRV parameters than that observed by HRV indexes. These results provide evidence that some differences exist between variability indexes extracted from HRV and video-derived PRV, mainly in the HF band during standing. However, despite these differences video-derived PRV indexes were able to evince the autonomic responses expected by the sympathetic stimulation induced by the rest-to-stand manoeuvre.
In this paper, common time- and frequency-domain variability indexes obtained by pulse rate variability (PRV) series extracted from video-photoplethysmographic signal (vPPG) were compared with heart rate variability (HRV) parameters calculated from synchronized ECG signals. The dual focus of this study was to analyze the effect of different video acquisition frame-rates starting from 60 frames-per-second (fps) down to 7.5 fps and different video compression techniques using both lossless and lossy codecs on PRV parameters estimation. Video recordings were acquired through an off-the-shelf GigE Sony XCG-C30C camera on 60 young, healthy subjects (age 23±4 years) in the supine position. A fully automated, signal extraction method based on the Kanade-Lucas-Tomasi (KLT) algorithm for regions of interest (ROI) detection and tracking, in combination with a zero-phase principal component analysis (ZCA) signal separation technique was employed to convert the video frames sequence to a pulsatile signal. The frame-rate degradation was simulated on video recordings by directly sub-sampling the ROI tracking and signal extraction modules, to correctly mimic videos recorded at a lower speed. The compression of the videos was configured to avoid any frame rejection caused by codec quality leveling, FFV1 codec was used for lossless compression and H.264 with variable quality parameter as lossy codec. The results showed that a reduced frame-rate leads to inaccurate tracking of ROIs, increased time-jitter in the signals dynamics and local peak displacements, which degrades the performances in all the PRV parameters. The root mean square of successive differences (RMSSD) and the proportion of successive differences greater than 50 ms (PNN50) indexes in time-domain and the low frequency (LF) and high frequency (HF) power in frequency domain were the parameters which highly degraded with frame-rate reduction. Such a degradation can be partially mitigated by up-sampling the measured signal at a higher frequency (namely 60 Hz). Concerning the video compression, the results showed that compression techniques are suitable for the storage of vPPG recordings, although lossless or intra-frame compression are to be preferred over inter-frame compression methods. FFV1 performances are very close to the uncompressed (UNC) version with less than 45% disk size. H.264 showed a degradation of the PRV estimation directly correlated with the increase of the compression ratio.
Video photoplethysmography (videoPPG) has emerged as area of great interest thanks to the possibility of remotely assessment of cardiovascular parameters, as heart rate (HR), respiration rate (RR) and heart rate variability (HRV). The present article proposes a fully automated method based on chrominance model, that selects for each subject the best region of interest (ROI) to detect and evaluate the accuracy of beat detection and interbeat intervals (IBI) measurements. The experimental recordings were conducted on 26 subjects which underwent a rest-to-stand maneuver. The results show that the accuracy of beat detection is slightly better during supine position (95%) compared to the standing one (92%), due to the maintenance of the balance that introduces larger motion artifact in the signal dynamic. The error in the measurement (expressed as mean±sd) of instantaneous heart rate is of +0.04 ±3.29 bpm in rest and +0.01±4.26 bpm in stand.
Aim of this study is to extract near-continuously respiratory rate by a contactless method. An industrial camera was used to record subjects face. Video data were processed offline to derive the video-photoplethysmographic (videoPPG) signal. Three features were extracted from videoPPG and finger PPG signal: pulse rate variability (PRV), pulse amplitude variability (PAV) and pulse width variability (PWV). A combination of these methods has been exploited to estimate the respiratory rate for each time window of 5 second. The results showed relative error with median around 0.5% and interquartile range of 5% both for finger PPG and videoPPG system.
Although there is growing interest in estimating cardiovascular information using contactless video plethysmography (VP), an in-depth validation of time-varying, nonlinear dynamics of the related pulse rate variability is still missing. In this study we estimate the heartbeat through VP and standard ECG, and employ inhomogeneous point-process nonlinear models to assess instantaneous heart rate variability measures defined in the time, frequency, and bispectral domains. Experimental data were gathered from 60 young healthy subjects (age: 24±3 years) undergoing postural changes (rest-to-stand maneuver). Video recordings are processed using our recently proposed method based on zero-phase component analysis. Results show that, at a group level, there is an overall agreement between linear and nonlinear indices computed from ECG and VP during resting state conditions. However, significant differences are found, especially in the bispectral domain, when considering data gathered while standing. Although significant differences exist between cardiovascular estimates from VP and ECG, results can be considered very promising as instantaneous sympatho-vagal changes were correctly identified. More research is indeed needed to improve on the precise estimation of nonlinear sympatho-vagal interactions.
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