No abstract

This work compares several fiducial points to detect the arrival of a new pulse in a photoplethysmographic signal using the built-in camera of smartphones or a photoplethysmograph. Also, an optimization process for the signal preprocessing stage has been done. Finally we characterize the error produced when we use the best cutoff frequencies and fiducial point for smartphones and photopletysmograph and compare if the error of smartphones can be reasonably be explained by variations in pulse transit time. The results have revealed that the peak of the first derivative and the minimum of the second derivative of the pulse wave have the lowest error. Moreover, for these points, high pass filtering the signal between 0.1 to 0.8 Hz and low pass around 2.7 Hz or 3.5 Hz are the best cutoff frequencies. Finally, the error in smartphones is slightly higher than in a photoplethysmograph.

This work proposes a systematic procedure to report the differences between heart rate variability time series obtained from alternative measurements reporting the spread and mean of the differences as well as the agreement between measuring procedures and quantifying how stationary, random and normal the differences between alternative measurements are. A description of the complete automatic procedure to obtain a differences time series (DTS) from two alternative methods, a proposal of a battery of statistical tests, and a set of statistical indicators to better describe the differences in RR interval estimation are also provided. Results show that the spread and agreement depend on the choice of alternative measurements and that the DTS cannot be considered generally as a white or as a normally distributed process. Nevertheless, in controlled measurements the DTS can be considered as a stationary process.

The aim of this work is to characterize the differences in the respiratory rhythm obtained through three video based methods by comparing the obtained respiratory signals with the one obtained with the gold standard method in adult population. The analysed methods are an RGB camera, a depth camera and a thermal camera while the gold standard is an inductive thorax plethysmography system (Respiband system from BioSignals Plux).21 healthy subjects where measured, performing 4 tests for each subject. The respiratory rhythm and its variability was obtained from the four respiratory signals (3 video methods and gold standard). The signal acquisition was performed with custom and proprietary algorithms. To characterize the respiratory rhythm and its variability obtained with the different video sources and gold standard, the instantaneous frequency, Bland-Altman plots and standard deviation of the error between video methods and the gold standard have been computed.The depth and RGB camera present high agreement with no statistical differences between them, with errors when comparing with the gold standard in the range of mHz. The thermal camera performs poorly if compared with the two other methods, nevertheless it cannot be discarded directly because some errors produced by the subjects head movement could not be corrected.From these results we conclude that the depth and RGB camera, and their respective acquisition algorithms, can be used in controlled conditions to measure respiration rhythm and its variability. The thermal camera on the other hand, although it can not be discarded directly, performed poorly if compared with the other two methods. Further studies are needed to confirm that these methods can be used in real life conditions.

The aim of this paper is to present a smartphone based system for real-time pulse-to-pulse (PP) interval time series acquisition by frame-to-frame camera image processing. The developed smartphone application acquires image frames from built-in rear-camera at the maximum available rate (30 Hz) and the smartphone GPU has been used by Renderscript API for high performance frame-by-frame image acquisition and computing in order to obtain PPG signal and PP interval time series. The relative error of mean heart rate is negligible. In addition, measurement posture and the employed smartphone model influences on the beat-to-beat error measurement of heart rate and HRV indices have been analyzed. Then, the standard deviation of the beat-to-beat error (SDE) was 7.81 ± 3.81 ms in the worst case. Furthermore, in supine measurement posture, significant device influence on the SDE has been found and the SDE is lower with Samsung S5 than Motorola X. This study can be applied to analyze the reliability of different smartphone models for HRV assessment from real-time Android camera frames processing.