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.

A novel method for tracking the tidal volume (TV) from electrocardiogram (ECG) is presented. The method is based on the amplitude of ECG-derived respiration (EDR) signals. Three different morphology-based EDR signals and three different amplitude estimation methods have been studied, leading to a total of 9 amplitude-EDR (AEDR) signals per ECG channel. The potential of these AEDR signals to track the changes in TV was analyzed. These methods do not need a calibration process. In addition, a personalized-calibration approach for TV estimation is proposed, based on a linear model that uses all AEDR signals from a device. All methods have been validated with two different ECG devices: a commercial Holter monitor, and a custom-made wearable armband. The lowest errors for the personalized-calibration methods, compared to a reference TV, were -3.48% [-17.41% / 12.93%] (median [first quartile / third quartile]) for the Holter monitor, and 0.28% [-10.90% / 17.15%] for the armband. On the other hand, medians of correlations to the reference TV were higher than 0.8 for uncalibrated methods, while they were higher than 0.9 for personal-calibrated methods. These results suggest that TV changes can be tracked from ECG using either a conventional (Holter) setup, or our custom-made wearable armband. These results also suggest that the methods are not as reliable in applications that induce small changes in TV, but they can be potentially useful for detecting large changes in TV, such as sleep apnea/hypopnea and/or exacerbations of a chronic respiratory disease.

Obstructive sleep apnea (OSA) is believed to contribute significantly to atrial fibrillation (AF) development in certain patients. Recent studies indicate a rising risk of AF with increasing OSA severity. However, the commonly used apnea-hypopnea index in clinical practice may not adequately account for the potential cardiovascular risks associated with OSA.1) Objective:to propose and explore a novel method for assessing OSA severity considering potential connection to cardiac arrhythmias.2) Method:the approach utilizes cross-recurrence features to characterize OSA and AF by considering the relationships among oxygen desaturation, pulse arrival time, and heart-beat intervals. Multinomial logistic regression models were trained to predict four levels of OSA severity and four groups related to heart rhythm issues. The rank biserial correlation coefficient,$r_{rb}$, was used to estimate effect size for statistical analysis. The investigation was conducted using the MESA database, which includes polysomnography data from 2055 subjects.3) Results:a derived cross-recurrence-based index showed a significant association with a higher OSA severity (p$< $0.01) and the presence of AF (p$< $0.01). Additionally, the proposed index had a significantly larger effect,$r_{rb}$, than the conventional apnea-hypopnea index in differentiating increasingly severe heart rhythm issue groups: 0.14$>$0.06, 0.33$>$0.10, and 0.41$>$0.07.4) Significance:the proposed method holds relevance as a supplementary diagnostic tool for assessing the authentic state of sleep apnea in clinical practice.

A first study on the potential of electrocardiogram(ECG)-derived respiration (EDR) signals based on QRS slopes and R-wave angles for sleep apnea detection is been presented. This EDR techniques have been previously validated with a wearable ECG armband for respiratory rate estimation. Furthermore, the amplitude of the oscillations in these EDR signals was observed to be related to the tidal volume in a previous pilot study.The hypothesis of this work is that that relation can be exploited for sleep apnea detection. A public data set (Physionet Apnea-ECG) composed of 105 polysomnography recordings was analyzed. A linear discriminant analysis was used using features related to the amplitude of the EDR oscillations.The classifier obtained an area under the curve from 0.74 to 0.82 when using the different analyzed features sets, suggesting that the relation between tidal volume and the amplitude of the EDR signals based on QRS slopes and R-wave angles can be exploited for sleep apnea detection. These results allow us to think of a sleep apnea screening tool based on the wearable armband and these EDR techniques, which would have both social and economic advantages.

The main aim of this work is to study the effect of the sampling rate of the photoplethysmographic (PPG) signal for pulse rate variability (PRV) analysis. Forehead and finger PPG signals were recorded at 1000 Hz during a rest state, with red and infrared wavelengths, simultaneously with the electrocardiogram (ECG). The PPG sampling rate has been reduced by decimation, obtaining signals at 500 Hz, 250 Hz, 125 Hz, 100 Hz, 50 Hz and 25 Hz. Five fiducial points were computed: apex, up-slope, medium, line-medium and medium interpolate point. The medium point is located in the middle of the up-slope of the pulse. The medium interpolate point is a new proposal as fiducial point that consider the abrupt up-slope of the PPG pulse, so it can be recovered by linear interpolation when the sampling rate is reduced. The error performed in the temporal location of the fiducial points was computed. Pulse period time interval series were obtained from all PPG signals and fiducial points, and compared with the RR intervals obtained from the ECG. Heart rate variability and PRV signals were estimated and classical time and frequency domain indices were computed. The results showed that the medium interpolate point of the PPG pulse was the most accurate fiducial point under different PPG morphologies and sensor locations, when sampling rate was reduced. Being able to reduce the sampling rate to 50 Hz without causing significant changes in time and frequency indices, when medium interpolate point was used as fiducial point.

The main aim of this work is to identify alterations in the morphology of the pulse photoplethysmogram (PPG) signal, due to the exposure of the subjects to a hyperbaric environment. Additionally, their Pulse Rate Variability (PRV) is analysed to characterise the response of their Autonomic Nervous System (ANS). To do that, 28 volunteers are introduced into a hyperbaric chamber and five sequential stages with different atmospheric pressures from 1 atm to 5 atm are performed. In this work, nineteen morphological parameters of the PPG signal are analysed: the pulse amplitude; eight parameters related to pulse width; eight parameters related to pulse area; and the two two pulse slopes. Also, classical time and frequency parameters of PRV are computed. Notable widening of the pulses width is observed in the stages analysed. The PPG area increases with pressure, with no significant changes when the initial pressure is recovered. These changes in PPG waveform may be caused by an increase in the systemic vascular resistance as a consequence of of vasoconstriction in the extremities, suggesting a sympathetic activation. However, the PRV results show an augmented parasympathetic activity and a reduction in the parameters that characterise the sympathetic response. So, only a sympathetic activation is detected in the peripheral region, as reflected by PPG morphology. The information regarding the ANS and the cardiovascular response that can be extracted from the PPG signal, as well as its compatibility with wet conditions make this signal the most suitable for studying the physiological response in hyperbaric environments.

The main aim of this paper was to characterize the Autonomic Nervous System response in hyperbaric environments using electrocardiogram (ECG) and pulse-photoplethysmogram (PPG) signals. To that end, 26 subjects were introduced into a hyperbaric chamber and five stages with different atmospheric pressures (1 atm; descent to 3 and 5 atm; ascent to 3 and 1 atm) were recorded. Respiratory information was extracted from the ECG and PPG signals and a combined respiratory rate was studied. This information was also used to analyze Heart Rate Variability (HRV) and Pulse Rate Variability (PRV). The database was cleaned by eliminating those cases where the respiratory rate dropped into the low frequency band (LF: 0.04-0.15 Hz) and those in which there was a discrepancy between the respiratory rates estimated using the ECG and PPG signals. Classical temporal and frequency indices were calculated in such cases. The ECG results showed a time-related dependency, with the heart rate and sympathetic markers (normalized power in LF and LF/HF ratio) decreasing as more time was spent inside the hyperbaric environment. A dependence between the atmospheric pressure and the parasympathetic response, as reflected in the high-frequency band power (HF: 0.15-0.40 Hz), was also found, with power increasing with atmospheric pressure. The combined respiratory rate also reached a maximum in the deepest stage; thus, highlighting a significant difference between this stage and the first one. The PPG data gave similar findings and also allowed the oxygen saturation to be computed; therefore, we propose the use of this signal for future studies in hyperbaric environments.

Our long-term goal is the development of an automatic identifier of attentional states. In order to accomplish it, we should first be able to identify different states based on physiological signals. So, the first aim of this paper is to identify the most appropriate features to detect a subject's high performance state. For that, a database of electrocardiographic (ECG) and photoplethysmographic (PPG) signals is recorded in two unequivocally defined states (rest and attention task) from up to 50 subjects as a sample of the population. Time and frequency parameters of heart/pulse rate variability have been computed from the ECG/PPG signals, respectively. Additionally, the respiratory rate has been estimated from both signals and also six morphological parameters from PPG. In total, 26 features are obtained for each subject. They provide information about the autonomic nervous system and the physiological response of the subject to an attention demand task. Results show an increase of sympathetic activation when the subjects perform the attention test. The amplitude and width of the PPG pulse were more sensitive than the classical sympathetic markers ([Formula: see text] and [Formula: see text]) for identifying this attentional state. State classification accuracy reaches a mean of [Formula: see text], a maximum of [Formula: see text], and a minimum of 85%, in the 100 classifications made by only selecting four parameters extracted from the PPG signal (pulse amplitude, pulsewidth, pulse downward slope, and mean pulse rate). These results suggest that attentional states could be identified by PPG.

OBJECTIVE: This paper proposes an approach to better estimate the sympathovagal balance (SB) and the respiratory sinus arrhythmia (RSA) after separating respiratory influences from the heart rate (HR). METHODS: The separation is performed using orthogonal subspace projections and the approach is first tested using simulated HR and respiratory signals with different spectral properties. Then, RSA and SB are estimated during autonomic blockade and stress using the proposed approach and the classical heart rate variability (HRV) analysis. Both real- and ECG-derived respiration (EDR) are used and the reliability of the EDR is evaluated. RESULTS: Mean absolute percentage errors lower than [Formula: see text] were obtained after removing previously known respiratory signals from simulated HR. The proposed indices were able to improve the quantification of SB during autonomic withdrawal. In the stress data, differences ( ) among relaxed and stressful phases were found with the proposed approach, using both the real respiration and the EDR, but they disappeared when using the classical HRV. CONCLUSION: A better assessment of the autonomic nervous system' response to pharmacological blockade and stress can be achieved after removing respiratory influences from HR, and this can be done using either the real respiration or the EDR. SIGNIFICANCE: This work can be used to better identify vagal withdrawal and increased sympathetic activation when the classical HRV analysis fails due to the respiratory influences on HR. Furthermore, it can be computed using only the ECG, which is an advantage when developing wearable systems with limited number of sensors.

Asthma is a chronic lung disease that usually develops during childhood. Despite that symptoms can almost be controlled with medication, early diagnosis is desirable in order to reduce permanent airway obstruction risk. It has been suggested that abnormal parasympathetic nervous system (PSNS) activity might be closely related with the pathogenesis of asthma, and that this PSNS activity could be reflected in cardiac vagal control. In this work, an index to characterize the spectral distribution of the high frequency (HF) component of heart rate variability (HRV), named peakness ($\wp$), is proposed. Three different implementations of $\wp$, based on electrocardiogram (ECG) recordings, impedance pneumography (IP) recordings and a combination of both, were employed in the characterization of a group of preschool children classified attending to their risk of developing asthma. Peakier components were observed in the HF band of those children classified as high-risk ( $p < 0.005$), who also presented reduced sympathvoagal balance. Results suggest that high-risk of developing asthma might be related with a lack of adaptability of PSNS.

Respiratory rate and heart rate variability (HRV) are studied as stress markers in a database of young healthy volunteers subjected to acute emotional stress, induced by a modification of the Trier Social Stress Test. First, instantaneous frequency domain HRV parameters are computed using time-frequency analysis in the classical bands. Then, the respiratory rate is estimated and this information is included in HRV analysis in two ways: 1) redefining the high-frequency (HF) band to be centered at respiratory frequency; 2) excluding from the analysis those instants where respiratory frequency falls within the low-frequency (LF) band. Classical frequency domain HRV indices scarcely show statistical differences during stress. However, when including respiratory frequency information in HRV analysis, the normalized LF power as well as the LF/HF ratio significantly increase during stress ( p-value 0.05 according to the Wilcoxon test), revealing higher sympathetic dominance. The LF power increases during stress, only being significantly different in a stress anticipation stage, while the HF power decreases during stress, only being significantly different during the stress task demanding attention. Our results support that joint analysis of respiration and HRV obtains a more reliable characterization of autonomic nervous response to stress. In addition, the respiratory rate is observed to be higher and less stable during stress than during relax ( p-value 0.05 according to the Wilcoxon test) being the most discriminative index for stress stratification (AUC = 88.2 % ).

A technique for ambulatory diagnosis of the obstructive sleep apnea syndrome (OSAS) in children based on pulse photoplethysmographic (PPG) signal is presented. Decreases in amplitude fluctuations of the PPG signal (DAP) events have been proposed as OSAS discriminator, since they are related to vasoconstriction associated to apnea. Heart rate variability (HRV) analysis during these DAP events has been proposed to discriminate between DAP events related or unrelated to an apneic event. The use of HRV requires electrocardiogram (ECG) as an additional recording, meaning a disadvantage that takes more relevance in sleep studies context where the number of sensors is tried to be minimized in order not to affect the physiological sleep. This study proposes the use of pulse rate variability (PRV) extracted from the PPG signal instead of HRV. Polysomnographic registers from 21 children (aged 4.47 ±2.04 years) were studied. The subject classification based on DAP events and PRV analysis obtained an accuracy of 86.67% which represents an improvement of 6.67% with respect to the HRV analysis. These results suggest that PRV can be used in apnea detectors based on DAP events, to discriminate apneic from nonapneic events avoiding the need for ECG recordings.