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Objective. Most current algorithms for detecting atrial fibrillation (AF) rely on heart rate variability (HRV), and only a few studies analyse the variability of photopletysmography (PPG) waveform. This study aimed to compare morphological features of the PPG curve in patients with AF to those presenting a normal sinus rhythm (NSR) and evaluate their usefulness in AF detection. Approach. 10 min PPG signals were obtained from patients with persistent/paroxysmal AF and NSR. Nine morphological parameters (1/ΔT), Pulse Width [PW], augmentation index [AI], b/a, e/a, [b-e]/a, crest time [CT], inflection point area [IPA], Area and five HRV parameters (heart rate [HR], Shannon entropy [ShE], root mean square of the successive differences [RMSSD], number of pairs of consecutive systolic peaks [R–R] that differ by more than 50 ms [NN50], standard deviation of the R–R intervals [SDNN]) were calculated. Main results. Eighty subjects, including 33 with AF and 47 with NSR were recruited. In univariate analysis five morphological features (1/ΔT, p < 0.001; b/a, p < 0.001; [b-e]/a, p < 0.001; CT, p = 0.011 and Area, p < 0.001) and all HRV parameters (p = 0.01 for HR and p < 0.001 for others) were significantly different between the study groups. In the stepwise multivariate model (Area under the curve [AUC] = 0.988 [0.974–1.000]), three morphological parameters (PW, p < 0.001; e/a, p = 0.011; (b-e)/a, p < 0.001) and three of HRV parameters (ShE, p = 0.01; NN50, p < 0.001, HR, p = 0.01) were significant. Significance. There are significant differences between AF and NSR, PPG waveform, which are useful in AF detection algorithm. Moreover adding those features to HRV-based algorithms may improve their specificity and sensitivity.

Background: In patients with Duchenne Muscular Dystrophy (DMD), the respiratory system determines the quality and length of life; therefore, the search for easy and safe everyday monitoring of the pulmonary function is currently extremely important, particularly in the COVID-19 pandemic. The aim of the study was to evaluate the influence of a three-month home electronic spirometry (e-spirometry) monitoring of the pulmonary function and strength of respiratory muscles as well as the patients’ benefits from this telemetric program. Methods: Twenty-one boys with DMD (aged 7–22; non-ambulatory-11) received a remote electronic spirometer for home use with a special application dedicated for patients and connected with a doctor platform. Control of the hospital spirometry (forced vital capacity-FVC, forced expiratory volume in 1 second-FEV1, peak expiratory flow-PEF) and respiratory muscle strength (maximal inspiratory-MIP and expiratory pressures-MEP) before and after the three-month monitoring were performed as well telemonitoring benefit survey. Results: A total of 1403 measurements were performed; 15 of the participants were able to achieve correct attempts. There were no differences between the hospital and the home spirometry results as well as between respiratory muscle strength during v1 vs. v2 visits for the whole study group (all parameters p > 0.05); the six participants achieved increased value of FVC during the study period. There was a positive correlation between ΔFVC and the number of assessments during the home spirometry (r = 0.7, p < 0.001). Differences between FVC and MIPcmH2O (r = 0.58; p = 0.01), MEPcmH2O (r = 0.75; p < 0.001) was revealed. The mean general satisfaction rating of the telemonitoring was 4.46/5 (SD 0.66) after one month and 4.91/5 (SD 0.28) after three months. The most reported benefit of the home monitoring was the improvement in breathing (38% of participants after one month, 52% after three months of telemonitoring). Forgetting about the procedures was the most common reason for irregular measurements; the participants reported also increased motivation but less time to perform tests. Conclusions: The study indicates high compliance of the home telemonitoring results with the examination in the hospital. Benefits from home spirometry were visible for all participants; the most important benefit was breathing improvement. The remote home spirometry is usable for everyday monitoring of the pulmonary function in DMD patients as well can be also treated as respiratory muscle training.

SARS-Cov-2 infection, due to inflammation processes, can affect autonomic nervous system and heart rate variability (HRV) even after disease. Previous studies showed significant changes in HRV parameters in severe (including fatal) infection of SARS-Cov-2. However, HRV analysis for the asymptomatic or mild-symptomatic Covid-19 patients have not been reported. In this study, we suggested that there is an influence of a SARS-Cov-2 infection on the HRV in such patients after weeks form disease.Sixty-five ECG Holter recordings from young (mean age 22.6 ± 3.4 years), physically fit male subjects after 4-6 weeks from the second negative test (considered to be the beginning of recovery) and twenty-six control male subjects (mean age 23.2 ± 2.9 years) were considered in the study. Night-time RR time series were extracted from ECG signals. Selected linear, frequency as well as nonlinear HRV parameters were calculated. We found significant differences in Porta’s symbolic analysis parameters V0 and V2 (p2 (p

Background: Duchenne muscular dystrophy (DMD) is the most common, progressive, irreversible muscular dystrophy. Pulmonary function is crucial for duration of life in this disease. Currently, the European Respiratory Society is focused on digital health, seeking innovations that will be realistic for digital respiratory medicine to support professionals and patients during the COVID-19 pandemic. Aims: The aim of this study was to investigate whether it is possible to monitor pulmonary function at home using an individual electronic spirometry system in boys with Duchenne muscular dystrophy. Materials and methods: In this observational, prospective study, conducted from March 2021 to June 2021, twenty boys with DMD (aged 8–16) were enrolled. The patients were recruited from the Rare Disease Centre, University Clinical Centre, of Gdańsk, Poland. Medical history and anthropometric data were collected, and spirometry (Jaeger, Germany) was performed in all patients at the start of the study. Each patient received an electronic individual spirometer (AioCare) and was asked to perform spirometry on their own every day, morning and evening, at home for a period of 4 weeks. The number of measurements, correctness of performing measurements, forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), and peak expiratory flow (PEF) were evaluated. Results: Finally, 14 out of 20 boys enrolled in the study with a mean age of 12.5 years (7 non-ambulatory) applied and received a home spirometer (AioCare). A total of 283 measurements were performed by all patients at home for 4 weeks. Half of the patients were able to perform measurements correctly. There were no significant differences between mean values of FVC, FE1, PEF between home and hospital spirometry (p &gt, 0.05) expect PEF pv% (p &lt, 0.00046). Patients with higher FEV1 (p = 0.0387) and lower BMI (p = 0.0494) were more likely to take home spirometer measurements. The mean general satisfaction rating of home-spirometry was 4.33/5 (SD 0.78), the mean intelligibility rating was 4.83/5 (SD 0.58). Reasons for irregular measurements were: forgetting (43%), lack of motivation (29%), difficulty (14%), lack of time (14%). Conclusion: Home electronic monitoring of pulmonary function in patients with DMD is possible to implement in daily routines at home. This protocol should be introduced as early as possible in patients 7–8 years old with good, preserved lung function. Patients accept this form of medical care but require more education about the benefits of e-monitoring. There is a need to implement a system to remind patients of the use of electronic medical devices at home, e.g., via SMS (short message service).

Introduction Fractional flow reserve (FFR) is the gold standard for functional assessment of intermediate lesions. However, assessing a stenosis with pressure wire prolongs the procedure, increases costs and carries a risk of procedure-related adverse events. Quantitative flow ratio (QFR) is a wire-free method for detection of significant ischemia based on 3D reconstruction of angiographic images and TIMI frame count. Aim To evaluate the influence of laboratory and clinical variables on QFR-FFR mismatch. Material and methods We retrospectively computed QFR (Medis Suite XA/QAngio XA 3D/QFR, Medis/Netherlands) in suitable cases with corresponding FFR (PressureWire, Abbott, US). Uni-/multivariate analysis was performed to identify clinical and biochemical predictors of QFR-FFR mismatch. Results Two hundred six lesions (196 patients, 76% male, mean age: 66.4 ±10.1 years) were included. Chronic kidney disease (CKD) and insulin-treated diabetes mellitus (ITDM) were associated with significantly larger differences between QFR and FFR values (-0.062 ±0.031 vs. -0.025 ±0.068; p = 0.027 and -0.059 ±0.07 vs. -0.027 ±0.074; p = 0.039; respectively). CKD was associated with a decrease of diagnostic efficiency (AUC = 0.67, 95% CI: 0.46-0.88 vs. AUC = 0.89, 95% CI: 0.84-0.94, p = 0.05). For biochemical variables only weak Spearman correlations were identified for hemoglobin concentration (r = -0.18) and hematocrit levels (r = -0.18). Conclusions CKD may impair the QFR diagnostic accuracy. Larger, prospective studies are needed to further explore this potential relationship.

Ambient air pollution impairs lung development in children, particularly in industrialized areas. The air quality in Zabrze, a city located in the Upper Silesian Industrial Region of Poland, is among the worst in Europe. We compared lung function and the frequency of respiratory or allergic symptoms between children living in Zabrze and those living in Gdynia, a city on the Baltic coast, which has the best long-term air quality in Poland. We enrolled children aged 9–15 years from both cities who were able to perform a spirometry. The following spirometry variables were measured for all participants: forced vital capacity (FVC), forced expiratory volume during the first second of expiration (FEV1), FEV1/FVC index, and peak expiratory flow (PEF). The frequencies of respiratory or allergic symptoms were taken from a survey completed by the participants’ parents. In total, 258 children from Gdynia and 512 children from Zabrze were examined. The mean values of FVC, FEV1, and PEF were significantly greater among children in Gdynia than those reported in Zabrze (p ≤ 0.032), and the frequencies of seasonal rhinorrhea (p = 0.015) or coughing episodes (p = 0.022) were significantly higher in Zabrze than in Gdynia. In conclusion, lung function was significantly impaired in children living in Zabrze, an area which is associated with poor air quality. Strategies to improve air quality in the Silesia region are urgently needed.

Self-management is an appealing strategy for prevention of asthma exacerbations. This study aimed to evaluate the feasibility and safety of a portable spirometer for unsupervised home spirometry measurements among patients with asthma.A multi-center, prospective, single-arm, open study recruited 86 patients with controlled or partly controlled asthma (41 women, 38.6 ± 10.4 y/o and 45 men, 36.2 ± 12.1 y/o). After a training session, patients performed daily spirometry at home with the AioCareOf 78 patients with complete data, 67 (86%) achieved the primary endpoint. Seventy-five (96%) participants used the device correctly once or more, and 10 (13%) patients succeeded every single day over the three-week follow-up. The rate of acceptable spirometry examinations differed between the sites (Self-monitoring of asthma with a connected mobile spirometer is feasible, safe and satisfactory for patients with asthma. It remains to be established whether unsupervised home spirometry measurements may improve early diagnosis and outcomes of self-management in cases of exacerbation or loss of asthma control.Highlights BoxThis study aimed to evaluate the ability of patients with asthma to perform high-quality daily spirometry examinations at home with using the AioCare

The growing demand for diagnosing of cardiovascular diseases leads to the development of new solutions for automatic classification of recorded ECG signals. Creating a robust and fast algorithm for automatic classification of ECG signal is crucial to improve the quality of healthcare, especially in countries where a lack of experienced specialists is an issue or the healthcare system is overloaded. The aim of the PhysioNet/Computing in Cardiology Challenge 2020 is to create an algorithm for classification of 12-lead ECGs based on ECG signals from multiple databases across the world. The shared training set consisted of 43,101 ECG recordings lasting from 5 to 1800 seconds. We (BioS Team) proposed the machine learning algorithm based on convolutional neural networks. The ECG signals were pre-processed using moving median filters to remove high-frequency noise and baseline wandering. We developed simply convolutional neural network consisting of four main convolutional blocks and one fully connected layer. We achieved a challenge validation score of 0.349, and full test score of 0.279, placing us 14 out of 41 in the official ranking.

U-shaped patterns are acceleration-deceleration periods in RR interval series. These relatively short time events (average duration 29.8±4.1 s) are the most laminar structures in the night-time recordings. The aim of this study is a analysis of sleep events occurring during U-shaped patterns in polysomnography (PSG) recordings obtained from Sleep Heart Health Study database. 500 PSG recordings were analyzed. U-shaped patterns were detected and categorized based on sleep stages, body position, respiratory events and EEG arousals. 4202 U-shaped patterns were found in 463 recordings. The majority of U-shaped patterns coincide with EEG arousals (73.9%). 48% of the patterns occurred at sleep phase changes. Most of the U-shaped patterns were associated with the Wake phase (61%). U-shaped patterns occurred at the position changes in 22.6% of cases. Analysis of respiratory events showed that U-shaped patterns occurred during hypopnea in 32.3% of the cases, CSA - 1.7%, OSA - 0.8% and desaturation - 18.6%. The quantitative analysis of PSG recordings is a first step to discover the origin of the phenomenon of U-shaped patterns. These first observations show that the U-shaped patterns are strongly associated with EEG arousals and may play role in sleep regulation.

Objective: U-shaped patterns, characteristic periods of time observed in tachograms, are a specific subgroup among very low frequency components characterized by relatively short periods of smooth accelerations followed by decelerations of heart rhythm. In this study, we characterize this phenomenon and its effect on heart rate variability (HRV) parameters. Approach: We calculated linear (the mean and standard deviation of RR intervals, RMSSD, pNN50 and the power of the frequency components) and nonlinear (V0, V1 and V2 Porta’s symbolic analysis, Shannon and Sample entropy, Guzik’s and Porta’s asymmetry indexes, the exponents α1 and α2 of detrended fluctuation analysis and the Hurst surface h(q,s) of multiscale multifractal analysis (MMA)) HRV parameters for 65 RR interval night-time series (39 females, 37.5(11.3) years old and 26 males, 41.7(16.5) years old; all without organic heart diseases). All parameters were calculated for original data and for the three kinds of test data in which the following parts of the time series were replaced by 1/f noise: (A) the U-shape patterns annotated in a given data set, (B) randomly chosen windows of similar size as the U-shaped patterns, (C) acceleration-deceleration events shorter than U-shaped patterns. Main results: We found that the U-shaped patterns, as the most persistent structures in RR night-time intervals series, affect the long-range correlation properties (measured by α2). We also found that the U-shaped patterns importantly strictly affect the shape of h(q,s) surface at different scales s. Removing the U-shaped patterns results in the shape of the h(q,s) surface losing the properties characteristic for healthy heart rhythm. The largest quantitative effect of U-shaped patterns was obtained for the power of the VLF component. The mean percentage difference of the VLF component between the original data and the A to C type test data were 19.4%, −4.3% and 5.3%, respectively. Significance: Although percentage contribution of U-shaped patterns is small compared to the whole night-time series (on the average 3.1%(1.7%) with a standard deviation of 1.7%), these patterns have a considerable impact on the HRV parameters describing the VLF, persistency, nonlinear correlations and multifractal properties.

Objectives Mobile phone-linked portable spirometers are light-weight, easy to use and low cost, with new software to facilitate data collection. In this study we investigated the feasibility of the AioCare® mobile spirometry in primary care. Methods In this nationwide, cross-sectional study, AioCare® spirometers (HealthUp, Poland) were distributed among primary healthcare centres across Poland. Operators (primary care professionals) received a 2-h training session, after which spirometry was performed in patients attending routine visits with respiratory symptoms or risk factors for obstructive airway diseases. Spirometry was considered technically correct when at least three manoeuvres met ERS/ATS acceptability and repeatability criteria. The most common spirometry errors were assessed and stepwise logistic regression was applied to identify factors associated with technically correct spirometry. Airway obstruction was defined as FEV1/FVC below the lower limit of normal. A restrictive pattern was defined as FVC below the lower limit of normal. Results Between 1 September 2018 and 1 September 2019, 10,936 spirometry examinations were performed in 9855 patients by 673 operators. 5347 (49%) spirometry examinations met both acceptability and repeatability criteria. The most common error was plateau error (17.7%). Operator age >40 years (OR 1.49, 95% CI 1.35–1.64) and repetition of the examination at the same visit (OR 1.90, 95% CI 1.66–2.16) increased the likelihood of a technically correct examination. Airway obstruction was found in 17% of correctly performed spirometry examinations. Conclusions Our nationwide study suggests that use of the AioCare® mobile spirometer in primary care could be feasible. More intensive and continual training should be implemented to improve the quality of spirometry examinations.

We give a short introduction to cough detection efforts that were undertaken during the last decade and we describe the solution for automatic cough detection developed for the AioCare portable spirometry system. In contrast to more popular analysis of sound and audio recordings, we fully based our approach on airflow signals only. As the system is intended to be used in a large variety of environments and different patients, we trained and validated the algorithm using AioCare-collected data and the large database of spirometry curves from the NHANES database by the American National Center for Health Statistics. We trained different classifiers, such as logistic regression, feed-forward artificial neural network, support vector machine, and random forest to choose the one with the best performance. The ANN solution was selected as the final classifier. The classification results on the test set (AioCare data) are: 0.86 (sensitivity), 0.91 (specificity), 0.91 (accuracy) and 0.88 (F1 score). The classification methodology developed in this study is robust for detecting cough events during spirometry measurements. As far as we know, the solution presented in this work is the first fully reproducible description of the automatic cough detection algorithm based totally on airflow signals and the first cough detection implemented in a commercial spirometry system that is to be published., Comment: 18 pages, original work. Few improvements and some additional analysis added in this version

AIMS: To evaluate diagnostic accuracy of quantitative flow ratio (QFR). A novel method was used for non-invasive functional assessment of intermediate coronary lesions. Fractional flow reserve (FFR) is the gold standard for functional assessment of intermediate lesions. However, interrogating a stenosis with pressure wire prolongs the procedure, increases costs and carries a risk of procedure-related adverse events. QFR is a wire-free method for computation of FFR based on 3D reconstruction of angiographic images and modified TIMI frame count.METHODS AND RESULTS: We retrospectively computed QFR (Medis Suite XA/QAngio XA 3D/QFR, Medis/Netherlands) in suitable cases with corresponding FFR (PressureWire™, Abbott, US/). Four QFR measures were tested against FFR: (1) fixed-flow QFR (fQFR), (2) vessel QFR (vQFR), (3) lesion QFR (lQFR) and (4) index QFR (iQFR). 857 lesions (740 patients) were reviewed, 306 (268 patients) met technical inclusion criteria for QFR (two optimal angiographic projections > 25° apart; no ostial location, no overlapping/shortening, frame-rate ≥ 15 fps). Mean angiographic percentage diameter stenosis was 51.3 ± 10.18%. Wire-based FFR ≤ 0.80 was found in 130 lesions (42.5%). Strong Pearson correlation was identified for iQFR (r = 0.85), fQFR (r = 0.73), vQFR (r = 0.78) and lQFR (r = 0.70). The optimal QFR decision values corresponding to FFR = 0.80 were iQFR = 0.79 (AUC = 0.94), fQFR = 0.73 (AUC = 0.87), vQFR = 0.77 (AUC = 0.90), and lQFR = 0.83 (AUC = 0.82). Sensitivity and specificity > 95% were identified for iQFR ≤ 0.74 (n = 89, 29%) and > 0.83 (n = 116, 38%), respectively.CONCLUSIONS: The QFR value at the pressure transducer position (iQFR) was the best corresponding QFR model. iQFR is characterised by high diagnostic accuracy and used in a hybrid model with FFR which may reduce the number of procedures requiring pressure-wire by two-thirds.

Atrial fibrillation (AF) is one of the most common sustained cardiac arrhythmia, occurring in 1–2% of the general population. Significant mortality and morbidity is related to occurrence of AF arrhythmia due to high risk of hospitalization, stroke, heart failure and coronary artery disease, etc. In many cases AF may not produce any symptoms and may go unnoticed by a patient, which is why there is a high importance to develop methods of detecting this heart disorder. Creating an algorithm for AF and other arrhythmias classification of short-term single lead ECG signals was the aim of the PhysioNet Challenge 2017. The database was composed by over 8.5 thousand ECG recordings (between 10 sec and 60 sec length) measured by AliveCor device, provided by organizers. We prepared an alternative hybrid approach for QRS detection in order to obtain RR time intervals. It consists of two complementary methods in hierarchical order: one based on nonlinear transformation and first-order Gaussian differentiator as superior and another one proposed in sample entry as inferior. We introduce the machine learning algorithm in order to classify whether it is normal sinus rhythm, AF or an alternative heart rhythm using features considered regularity of RR time intervals and morphology of the ECG signal. The separate part of the algorithm based on beat averaging method is dedicated for preceding extraction of too noisy recordings from the input to the classifier. The best overall F1 score we achieved in the official phase of the PhysioNet Challenge 2017 was 0.77 (0.86 for normal, 0.78 for AF and 0.66 for other rhythms).

Introduction: Heart rate variability (HRV) fluctuations have a wide frequency band and are a result of multiple contributing factors. In this study, we focused on laminar, U-shaped accelerations of heart rate — approximately 20–40 seconds long — in the nighttime recordings. Methods: The influence of U-shape accelerations on HRV parameters was evaluated based on night-time 34 RR interval time series obtained from healthy adults. We performed calculations on recordings without top 5% intervals (in terms of RR duration), without bottom 5% intervals and on recordings with randomly shuffled RR intervals. For each recording, the VLF frequency band, Shannon and Sample Entropy, standard deviation, mean, RMSSD, Multiscale Multifractal Analysis (MMA), and symbolic analysis parameters were calculated using signals with and without modifications. Results: U-shape accelerations accounted for 5.3±3.3% of the total recordings duration. There were significant differences between the signals without modification and without the smallest 5% intervals (thus without majority of U-shape periods) of the values of HF, LF and VLF frequency bands power, Shannon and Sample Entropy, standard deviation and symbolic analysis parameters (p

Phonocardiography (PCG) is the one of noninvasive ways to diagnose condition of human heart. The mechanics of heart muscle contractions and closure of the heart valves generates vibrations audible as sounds and murmurs, which can be analysed by qualified cardiologists. Developing an accurate algorithm to determine whether patients' heart works properly or should be referred to an expert for further diagnosis would significantly improve the quality of healthcare system. It would allow to perform less unnecessary, expensive and time consuming examinations. The analysed data consisted of PCG recordings from the training set provided by the organizers of the PhysioNet Challenge 2016. Its length variedfrom several to 120 seconds. We propose the machine learning algorithm based on neural networks. The segmentation of the PCG signals is performed with algorithm based on Hidden Markov Model. Whereas, the features necessary to define whether the signal looks normal or should be further analysed were carefully chosen by our team and belonged to time domain, ordinate axis or frequency domain group. The great emphasis was put on the statistical features representing the characteristics of the signal. Their optimal values were found during the process of learning of our algorithm. The best overall score we achieved in the official phase of the PhysioNet Challenge 2016 is 0.79 with specificity 0.76 and sensitivity 0.81.

Fractional flow reserve (FFR) is the gold standard for functional assessment of intermediate lesions. However, interrogating a stenosis with pressure-wire prolongs the procedure, increases costs and carries a risk of procedure-related adverse events. Quantitative Flow Ratio (QFR) is a wire-free

With the increasing quality and costs of health services a lot of attention is put to provide excellent care on the ICU. However, the amount of false alarms of cardiac episodes still outnumbers the true ones significantly. The advanced analysis of multiple signals registered by monitoring system might enable reduction of the false alarms.

Current gold-standard algorithms for heart beat detection do not work properly in the case of high noise levels and do not make use of multichannel data collected by modern patient monitors. The main idea behind the method presented in this paper is to detect the most prominent part of the QRS complex, i.e. the RS slope. We localize the RS slope based on the consistency of its characteristics, i.e. adequate, automatically determined amplitude and duration. It is a very simple and non-standard, yet very effective, solution. Minor data pre-processing and parameter adaptations make our algorithm fast and noise-resistant. As one of a few algorithms in the PhysioNet/Computing in Cardiology Challenge 2014, our algorithm uses more than two channels (i.e. ECG, BP, EEG, EOG and EMG). Simple fundamental working rules make the algorithm universal: it is able to work on all of these channels with no or only little changes. The final result of our algorithm in phase III of the Challenge was 86.38 (88.07 for a 200 record test set), which gave us fourth place. Our algorithm shows that current standards for heart beat detection could be improved significantly by taking a multichannel approach. This is an open-source algorithm available through the PhysioNet library.

We propose a model for heart rate variability (HRV) of a healthy individual during sleep with the assumption that the heart rate variability is predominantly a random process. Autonomic nervous system activity has different properties during different sleep stages, and this affects many physiological systems including the cardiovascular system. Different properties of HRV can be observed during each particular sleep stage. We believe that taking into account the sleep architecture is crucial for modeling the human nighttime HRV. The stochastic model of HRV introduced by Kantelhardt et al. was used as the initial starting point. We studied the statistical properties of sleep in healthy adults, analyzing 30 polysomnographic recordings, which provided realistic information about sleep architecture. Next, we generated synthetic hypnograms and included them in the modeling of nighttime RR interval series. The results of standard HRV linear analysis and of nonlinear analysis (Shannon entropy, Poincaré plots, and multiscale multifractal analysis) show that-in comparison with real data-the HRV signals obtained from our model have very similar properties, in particular including the multifractal characteristics at different time scales. The model described in this paper is discussed in the context of normal sleep. However, its construction is such that it should allow to model heart rate variability in sleep disorders. This possibility is briefly discussed.