Heart rate variability (HRV) has been studied for decades in clinical environments. Currently, the exponential growth of wearable devices in health monitoring is leading to new challenges that need to be solved. These devices have relatively poor signal quality and are affected by numerous motion artifacts, with data loss being the main stumbling block for their use in HRV analysis. In the present paper, it is shown how data loss affects HRV metrics in the time domain and frequency domain and Poincaré plots. A gap-filling method is proposed and compared to other existing approaches to alleviate these effects, both with simulated (16 subjects) and real (20 subjects) missing data. Two different data loss scenarios have been simulated: (i) scattered missing beats, related to a low signal to noise ratio; and (ii) bursts of missing beats, with the most common due to motion artifacts. In addition, a real database of photoplethysmography-derived pulse detection series provided by Apple Watch during a protocol including relax and stress stages is analyzed. The best correction method and maximum acceptable missing beats are given. Results suggest that correction without gap filling is the best option for the standard deviation of the normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD) and Poincaré plot metrics in datasets with bursts of missing beats predominance ($p<0.05$), whereas they benefit from gap-filling approaches in the case of scattered missing beats ($p<0.05$). Gap-filling approaches are also the best for frequency-domain metrics ($p<0.05$). The findings of this work are useful for the design of robust HRV applications depending on missing data tolerance and the desired HRV metrics.

Obstructive sleep apnea (OSA) is a high-prevalence disease in the general population, often underdiagnosed. The gold standard in clinical practice for its diagnosis and severity assessment is the polysomnography, although in-home approaches have been proposed in recent years to overcome its limitations. Today's ubiquitously presence of wearables may become a powerful screening tool in the general population and pulse-oximetry-based techniques could be used for early OSA diagnosis. In this work, the peripheral oxygen saturation together with the pulse-to-pulse interval (PPI) series derived from photoplethysmography (PPG) are used as inputs for OSA diagnosis. Different models are trained to classify between normal and abnormal breathing segments (binary decision), and between normal, apneic and hypopneic segments (multiclass decision). The models obtained 86.27% and 73.07% accuracy for the binary and multiclass segment classification, respectively. A novel index, the cyclic variation of the heart rate index (CVHRI), derived from PPI's spectrum, is computed on the segments containing disturbed breathing, representing the frequency of the events. CVHRI showed strong Pearson's correlation (r) with the apnea-hypopnea index (AHI) both after binary (r=0.94, p $< $ 0.001) and multiclass (r=0.91, p $< $ 0.001) segment classification. In addition, CVHRI has been used to stratify subjects with AHI higher/lower than a threshold of 5 and 15, resulting in 77.27% and 79.55% accuracy, respectively. In conclusion, patient stratification based on the combination of oxygen saturation and PPI analysis, with the addition of CVHRI, is a suitable, wearable friendly and low-cost tool for OSA screening at home.

Diminished stress reactivity is frequently reported in individuals with depression and anxiety. Smartphone-camera photoplethysmography (SCPPG) could offer an innovative, objective, and ambulatory metric for monitoring these conditions. This study aims to evaluate the use of SCPPG to monitor anxiety and depression by analyzing stress responses. Specifically, it examines the autonomic nervous system through heart rate variability using pulse rate variability (PRV) metrics derived from SCPPG. The study involved 79 participants, including patients diagnosed with generalized anxiety disorder and major depressive disorder (n = 22), as well as a control group (n = 57). SCPPG signals were compared with those from a validated device during a stress-inducing protocol, consisting of baseline, stress tests (Trail Making Test and Stroop Test), and recovery phases. Pearson’s correlation and Bland-Altman analysis were used to assess the agreement. The results indicate a high correlation (r ≥ 0.96, p < 0.001) between PRV metrics derived from SCPPG and those from reference devices. Additionally, exhibited minimal bias (Δ ≤ 2%) with the exception of RMSSD (Δ = 12%). Notably, SCPPG reliably detects stress reactivity differences between patient and control groups across all PRV metrics (p < 0.05). The study highlights the significance of SCPPG in understanding and personalizing mental health treatments, considering factors such as stress reactivity and recovery. Future research directions include longitudinal studies and improving SCPPG accuracy, particularly for patients with tremors or during dynamic tasks.