Measurement of heart rate variability using off-the-shelf smart phones

Abstract

Background The cardiac parameters, such as heart rate (HR) and heart rate variability (HRV), are very important physiological data for daily healthcare. Recently, the camera-based photoplethysmography techniques have been proposed for HR measurement. These techniques allow us to estimate the HR contactlessly with low-cost camera. However, the previous works showed limit success for estimating HRV because the R–R intervals, the primary data for HRV calculation, are sensitive to noise and artifacts. Methods This paper proposed a non-contact method to extract the blood volume pulse signal using a chrominance-based method followed by a proposed CWT-based denoising technique. The R–R intervals can then be obtained by finding the peaks in the denoised signal. In this paper, we taped 12 video clips using the frontal camera of a smart phone with different scenarios to make comparisons among our method and the other alternatives using the absolute errors between the estimated HRV metrics and the ones obtained by an ECG-accurate chest band. Results As shown in experiments, our algorithm can greatly reduce absolute errors of HRV metrics comparing with the related works using RGB color signals. The mean of absolute errors of HRV metrics from our method is only 3.53 ms for the static-subject video clips. Conclusions The proposed camera-based method is able to produce reliable HRV metrics which are close to the ones measured by contact devices under different conditions. Thus, our method can be used for remote health monitoring in a convenient and comfortable way.

Two-dimensional XD-GRASP provides better image quality than conventional 2D cardiac cine MRI for patients who cannot suspend respiration

“…The ability to obtain a cardiac motion-related signal during image acquisition allows us to further obtain a surrogate signal for the successive RR intervals from the cardiac motion signal [30]; that surrogate signal can be used to sort patients into non-arrhythmic patients and arrhythmic patients, using the global variation of RR intervals [31, 32]. We can then incorporate this data into the reconstruction process, effectively using a total of 5 dimensions (2 spatial and 3 physiologic, including an additional dimension corresponding to the preceding RR interval for each cardiac cycle acquired), with an associated improvement in the reconstructed image quality.…”

Efficient Real-Time Camera Based Estimation of Heart Rate and Its Variability

“…Heart rate variability calculation Multiple metrics can be computed to express the measure of heart rate variability in different units. In this work, we focus on one of the most popular time-domain metric for summarizing HRV called the root mean square of successive differences (RMSSD) [10,13,18], expressed in units of time. As the name suggests, this is computed by calculating the root mean square of time difference between adjacent IBIs: The example from VicarPPG dataset shows the RoI overlaid on the face.…”

“…HR (bpm) 0.7 ± 0.72 4.57 ± 8.87 0.16 ± 0.11 0.77 ± 0.76 0.12 ± 0.06 0.18 ± 0.26 0.288 ± 0.56 0.32 ± 0.7 HRV RMSSD (ms)10.54 ± 8.20 7.15 ± 5.39 10.59 ± 7.73 27.55 ± 15.56 11.98 ± 7.67 14.02± 8.46 12.07 ± 6.63 23.82± 13.59 …”

Enhancing the Robustness of Smartphone Photoplethysmography: A Signal Quality Index Approach

“…In addition, there have been several reported techniques for increasing the accuracy of smartphone PPG, such as point-of-interest selection [27], bandpass filtering [28], adaptive signal thresholding [29], motion detection techniques [30][31][32], interpolation techniques [33], and signal decomposition methods [34][35][36]. Bioengineering studies indicate that the average HR [37] and HRV measured using smartphone PPG are comparable with those measured using gold standard ECGs [21,28,[38][39][40].Although it is a promising solution for practical data collection and has an accuracy that has been well proved in several experiments, using smartphone PPG to measure HRV has received limited research attention in applied disciplines such as medicine or psychology [41]; a possible explanation is the lack of robustness in practical scenarios. When smartphone users measure their heartbeats outside a laboratory environment, slight hand movements or ambient light changes can corrupt the PPG signals.…”