A methodology to quantify the differences between alternative methods of heart rate variability measurement.

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.

Accuracy of heart rate variability estimation by photoplethysmography using an smartphone: Processing optimization and fiducial point selection.

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.

Real time heart rate variability assessment from Android smartphone camera photoplethysmography: Postural and device influences.

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.

New indices for quantification of the power spectrum of heart rate variability time series without the need of any frequency band definition.

This paper presents a new family of indices for the frequency domain analysis of heart rate variability time series that do not need any frequency band definition. After proper detrending of the time series, a cumulated power spectrum is obtained and frequencies that contain a certain percentage of the power below them are identified, so median frequency, bandwidth and a measure of the power spectrum asymmetry are proposed to complement or improve the classical spectral indices as the ratio of the powers of LF and HF bands (LF/HF). In normal conditions the median frequency provides similar information as the classical indices, while the bandwidth and asymmetry can be complementary measures of the physiological state of the tested subject. The proposed indices seem to be a good choice for tracking changes in the power spectrum in exercise stress, and they can guide in the determination of frequency band limits in other animal species.

The effect of electrocardiographic lead choice on RR time series.

Results of heart rate variability analysis depend on the quality of the initial RR time series that is measured only in one lead of the ECG. This work shows that RR time series can subtly change from lead to lead so the choice of the analyzed lead is another source of uncertainty. The standard deviation of the differences of two RR time series obtained from different leads can change from 0.5 ms to more than 20 ms depending on the amount of noise, the morphological changes of the QRS complexes, the strategies of fiducial point determination and the measured subject. This source of uncertainty is in healthy subjects greater than that associated to the sampling frequency of the ECG for sampling frequencies greater than 400 Hz.

Drowsiness detection by thoracic effort signal analysis in real driving environments.

Detection of drowsiness while driving is a leading objective in advanced driver assistance systems. This work presents a new index to assess the alertness state of drivers based on the respiratory dynamics derived from an inductive band. More than 100 hours of driving in real environments from 13 healthy subjects were analyzed. The proposed method has a sensitivity of 93.7% and specificity of 86.3% in detecting full awake drivers while it has a sensitivity of 83.1% and specificity of 95.3% in detecting drowsy drivers. The results show that the proposed index may be promising to assess the alertness state of real drivers.

Bias and uncertainty in heart rate variability spectral indices due to the finite ECG sampling frequency.

Spectral analysis of the heart rate variability is becoming a usual tool as a marker of the autonomic nervous system. The final output of the spectral analysis is a set of indices that are always estimators due to technical limitations. In this work, the bias and the uncertainty in the VLF, LF, HF and LF/HF indices due to the finite sampling frequency of the ECG are analysed. The results indicate that for low sampling frequency (125 Hz), the bias and uncertainty in the HF and LF/HF indices can blur the results of the analysis, especially if the RR time series has low variability. The HF index is overestimated and, accordingly, the LF/HF index is underestimated. Then, results from RR time series with low sampling frequency must be used with care. The uncertainty of the spectral indices is proportional to the inverse of the sampling frequency and the bias is proportional to the inverse of the square sampling frequency.

A new index for the analysis of heart rate variability dynamics: characterization and application.

A new index for the analysis of the heart rate variability (HRV) dynamics is presented. The proposed index (acceleration change index (ACI)) characterizes the sign of the differences of a time series. A theoretical study shows an expression that relates ACI and the autocorrelation function of the time series. This formula is tested and validated with different simulated time series (uncorrelated noise, sinusoidal and fractional Brownian motion). Next, ACI is applied to RR time series from healthy subjects showing that ACI decreases with periodic controlled breathing, increases during exercise, and it has a lower value at night than during the day. In a preliminary study, ACI has been shown to be lower in healthy subjects than in patients who had suffered a myocardial infarction one month previously. ACI can be employed as a fast and robust new marker of the HRV dynamics.