## 1 Introduction  
The dynamic technological progress in the wearables market opens new fields in well-being and outpatient monitoring. These gadgets are able to collect and evaluate several biological signals over the long-term. The ease of use and convenience of these devices have promoted their popularity at the same time wearables have attracted the attention of both researchers and industry [1]. Photoplethysmography (PPG) is a popular optical method to detect the variations in the light intensity related to the blood volume changes and the tilting of red blood cells due to the blood flow [2]. The PPG sensor consists of a light source and a photodiode. Most wearables (for example, smart watches) are equipped with a reflective PPG-unit, which operates in most cases with a green light, that penetrates through the epidermal and papillary dermal layers of the skin, but not deeper, as in the case of red or near-infrared light [3]. The signal-to-noise ratio is better for the green types compared to the others. In the reflective PPG, the photodiode is positioned next to the light source, on the same side of the tissue or body part and detects the reflected light. On the contrary, in consideration of transmissive PPG devices, the light source and the photosensor are separated by the tissue or body part. These types use mostly red- or infrared light [2; 3]. When compared to the ECG, the main advantage of the reflective PPG technique is the single contact surface and the lack of cables, electrodes and potential skin irritation. Recent publications report on the possibility of remote PPG [2]. [PERSON] and co-workers extracted biological signals (heart rate, breathing rate, and pulse transit time) from images captured by a commercial digital camera [4]. The most conventional use of the PPG in clinical practice is to measure blood oxygen saturation and monitor heart rate (HR) [5; 6]. Due to its advantages described above, several investigations have focused on improving the signal quality [7; 8; 9; 10], in order to obtain reliable biological data including pulse rate variability (PRV) parameters [11]. The autonomic nervous system can be examined non-invasively through the heart rate variability (HRV). In addition to sympathetic and parasympathetic innervation, numerous mechanisms (autocrine, paracrine, endocrine and mechanical effects) influence the activity of the sinus node (SN) including numerous feedback-loops. The SN summarizes these effects, which are manifested in the instantaneous heart rate. Consequently, the succeeding R-R intervals (RRIs) are not identical and show a beat-to-beat variability. The most spectacular fluctuations are related to the respiration [12]. The asymmetry in the accelerations and decelerations of the heart rate can be described by the parameters of heart rate asymmetry (HRA), which is also related to the autonomic modulation of the cardiovascular system [13; 14]. The Poincare plot illustrates the actual RRI against the preceding RRI of the tachogram, resulting in a cloud (Figure 1). In addition to visual assessment, the heart rate asymmetry parameters quantize the evident asymmetry of the cloud [15]: the Guzik-index (GI) [16] is proportional to the contribution of the decelerating cloud to the global variability, related to expiration. Whereas the Porta-index (PI) [17] is proportionate to the number of points in the acceleration cloud, associated to inspiration. In the last decades, several studies focused on the comparison of the PPG-based HRV calculation to the \"gold standard\" ECG-based method [18; 19]. Many of them concluded that the PRV is not a surrogate of HRV [19; 20; 21], at the same time others found HRV and PRV equivalent in certain conditions [7; 22]. In most cases, the short-term variability (or beat-to-beat variability) parameters such as RMSSD (root mean square of successive RR-differences) or frequency domain parameters were overestimated from the PPG signal [20].