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.
Objective: Indoor microclimate may affect students’ wellbeing, cardiac autonomic control and cognitive performance with potential impact on learning capabilities. To assess the effects of classroom temperature variations on the autonomic profile and students’ cognitive capabilities. Approach: Twenty students attending Humanitas University School, (14M, age 21 ± 3 years) underwent a single-lead ECG continuous recording by a portable device during a 2 h lecture when classroom temperature was set ‘neutral’ (20 °C–22 °C, Day 1) and when classroom temperature was set to 24 °C–26 °C (Day 2). ECGs were sent by telemetry to a server for off-line analysis. Spectral analysis of RR variability provided indices of cardiac sympathetic (LFnu), vagal (HF, HFnu) and cardiac sympatho-vagal modulation (LF/HF). Symbolic analysis of RR variability provided the percentage of sequences of three heart periods with no significant change in RR interval (0V%) and with two significant variations (2V%) reflecting cardiac sympathetic and vagal modulation, respectively. Students’ cognitive performance (memory, verbal comprehension and reasoning) was assessed at the end of each lecture using the Cambridge Brain Sciences cognitive evaluation tool. Main results: Classroom temperature and CO2 were assessed every 5 min. Classroom temperatures were 22.4 °C ± 0.1 °C (Day 1) and 26.2 °C ± 0.1 °C (Day 2). Student’s thermal comfort was lower during Day 2 compared to Day 1. HR, LF/HF and 0V% were greater during Day 2 (79.5 ± 12.1 bpm, 6.9 ± 7.1 and 32.8% ± 10.3%) than during Day 1 (72.6 ± 10.8 bpm, 3.4 ± 3.7, 21.4% ± 9.2%). Conversely, 2V% was lower during Day 2 (23.1% ± 8.1%) than during Day 1 (32.3% ± 11.4%). Short-term memory, verbal ability and the overall cognitive C-score scores were lower during Day 2 (10.3 ± 0.3; 8.1 ± 1.2 and 10.9 ± 2.0) compared to Day 1 (11.7 ± 2.1; 10.7 ± 1.7 and 12.6 ± 1.8). Significance: During Day 2, a shift of the cardiac autonomic control towards a sympathetic predominance was observed compared to Day 1, in the presence of greater thermal discomfort. Furthermore, during Day 2 reduced cognitive performances were found.
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.
Quantitative analysis of Tumor Microenvironment (TME) provides prognostic and predictive information in several human cancers but, with few exceptions, it is not performed in daily clinical practice since it is extremely time-consuming. We recently showed that the morphology of Tumor Associated Macrophages (TAMs) correlates with outcome in patients with Colo-Rectal Liver Metastases (CLM). However, as for other TME components, recognizing and characterizing hundreds of TAMs in a single histopathological slide is unfeasible. To fasten this process, we explored a deep-learning based solution. We tested three Convolutional Neural Networks (CNNs), namely UNet, SegNet and DeepLab-v3, with three different segmentation strategies, semantic segmentation, pixel penalties and instance segmentation. The different experiments are compared according to the Intersection over Union (IoU), a metric describing the similarity between what CNN predicts as TAM and the ground truth, and the Symmetric Best Dice (SBD), which indicates the ability of CNN to separate different TAMs. UNet and SegNet showed intrinsic limitations in discriminating single TAMs (highest SBD 61.34±2.21), whereas DeepLab-v3 accurately recognized TAMs from the background (IoU 89.13±3.85) and separated different TAMs (SBD 79.00±3.72). This deep-learning pipeline to recognize TAMs in digital slides will allow the characterization of TAM-related metrics in the daily clinical practice, allowing the implementation of prognostic tools.
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