## 1 Introduction
One possible explanation can be the relation of RRI and the pulse-to-pulse interval (PPI) as detailed in our earlier publication [5]. The difference of consecutive pulse arrival times  
Figure 1: Poincaré plot of B_SZ_29031990 illustrates the more symmetrical distribution of the deceleration and acceleration of the consecutive RR-interval at 1:1 compared to 1:0 and 1:2 inspiration/expiration (i/e) ratio. (**A**) 1:0, (**B**) 1:2 and (**C**) 1:1 breathing. The graph represents the subsequent RR intervals (Y-axis; RRIn+1) against the current RR intervals (X-axis; RRIn). Porta-index (PI), Guzik-index (GI), under the identity line the heart rate accelerates (ACCELERATION), above the identity line decelerates (DECELERATION). (PAT, see later in the Introduction) oscillating with breathing is added to the RRIs, resulting in greater fluctuation of PPIs. In consideration of technical factors, including appropriate sampling frequency, [PERSON] et al. investigated the minimally required temporal resolution of the PPG signals for accurate PRV parameters among healthy volunteers. Their results showed the mean RRI is the most resistant to the low sampling frequency (or high sampling interval), as low as 303 ms sampling may be sufficient to maintain accuracy within the five-minute analysis. In the case of SDNN and RMSSD, a minimum of 50 Hz sampling frequency is required, which can be slightly decreased by interpolation [7]. Another current research paper highlights the importance of the signal-to-noise ratio (SNR) prior to digitization versus the sampling noise due to the low sampling rate [23]. In order to reduce the timing uncertainty of the reference point, it is essential to select the appropriate filtering technique, which minimizes the confounding noise or baseline wandering. [PERSON] and co-workers extracted several time-domain, frequency-domain, and Poincare plot parameters from simulated PPG signals prior to and following noise contamination and subsequent filtering by the combination of several IIR and FIR low pass and high pass filters. They found a better reproducibility using elliptic IIR filters and equiripple, or Parks-McClellan FIR filters when compared to Butterworth, Hamming window, constrained least squares and least squares filtering. They also suggest a lower low cut-off frequency, except for those PPG signals in which the motion artifacts' baseline wandering and respiratory noise appear together. In this case, a higher frequency low-pass is required to achieve better results [24]. Several studies focused on selecting the most reliable reference point of the PPG signal, which also affects the accuracy of PRV parameters. [PERSON] et al. [9] found time domain parameters reliable from simulated PPG signals based on the valley point among five complex interbeat-interval detection algorithms. Other authors [25] proved the superiority of middle-amplitude, first derivative peak and the tangent intersection points versus the peak and foot fiducial points in a human study during the head-up tilt test. In addition to technical and environmental factors influencing the quality of the PPG signal and derived PRV parameters, certain physiological factors such as instantaneous blood pressure, arterial stiffness, and vasomotor activity among others can distort the shape of the peripheral pulse wave, resulting in a constant or variable shift of the reference points [19]. The PAT is the time interval between the defined reference point of the ventricular complex in the ECG signal and a corresponding fiducial point of the simultaneous PPG wave. This time interval consists of the pre-ejection period (PEP), which is the delay between the left ventricular depolarization and the contraction (also known as the electromechanical coupling), and the pulse transit time (PTT) which is the span of time consumed for the ejected blood to travel from the aortic valve to the PPG sensor [26].