Search Results (21)

Background: Cardiac autonomic neuropathy (CAN) is a common yet frequently underdiagnosed complication of diabetes. While our previous study demonstrated the utility of multiscale cross-approximate entropy (MS-CXApEn) in detecting early CAN, the present study further investigates the use of frequency-domain coherence analysis between systolic blood pressure (SBP) and R-R intervals (RRI) and evaluates the effects of insulin treatment on autonomic function in diabetic rats. Methods: At the onset of diabetes induced by streptozotocin (STZ), rats were assessed for cardiovascular autonomic function both before and after insulin treatment. Spectral and coherence analyses were performed to evaluate baroreflex function and autonomic regulation. Parameters assessed included low-frequency power (LFP) and high-frequency power (HFP) of heart rate variability, coherence between SBP and RRI at low and high-frequency bands (LF_Coh and HF_Coh), spontaneous and phenylephrine-induced baroreflex sensitivity (BRS_spn and BRS_phe), HRV components derived from fast Fourier transform, and MS-CXApEn at multiple scales. Results: Compared to normal controls (LF_Coh: 0.14 ± 0.07, HF_Coh: 0.19 ± 0.06), early diabetic rats exhibited a significant reduction in both LF_Coh (0.08 ± 0.04, p < 0.05) and HF_Coh (0.16 ± 0.10, p > 0.05), indicating impaired autonomic modulation. Insulin treatment led to a recovery of LF_Coh (0.11 ± 0.04) and HF_Coh (0.24 ± 0.12), though differences remained statistically insignificant (p > 0.05 vs. normal). Additionally, low-frequency LFP increased at the onset of diabetes and decreased after insulin therapy in most rats significantly, while MS-CXApEn at all scale levels increased in the early diabetic rats, and MS-CXApEn_large declined following hyperglycemia correction. The BRS_spn and BRS_phe showed no consistent trend. Conclusions: Coherence analysis provides valuable insights into autonomic dysfunction in early diabetes. The significant reduction in LF_Coh in early diabetes supports its role as a potential marker for CAN. Although insulin treatment partially improved coherence, the lack of full recovery suggests persistent autonomic impairment despite glycemic correction. These findings underscore the importance of early detection and long-term management strategies for diabetic CAN. Full article

Background: Wearable technologies have been developed to measure physiological parameters conveniently. To consider the new measurement device valid, the crucial point is to assess its reliability with the gold standard. The study aimed to assess the validity of the Pneumonitor (PM, fs = 250 Hz) for acquisition of 5 min RR intervals (RRi) for analysis of heart rate asymmetry (HRA) in relation to the electrocardiography (ECG, fs = 1000 Hz) in a group of 19 pediatric cardiac patients. Association between HRA and respiratory rate (RespRate) was verified. Methods: The validation comprised Bland–Altman analysis, intraclass correlation coefficient, and Student’s t-test. Results: Sufficient agreement between 10 from 16 HRA parameters was observed. Different HRA parameters values calculated based on RRi from both devices were related to different results of correlation analysis between two parameters and RespRate. Conclusions: The PM might be considered valid for recording RRi, which are then processed to calculate selected HRA parameters in a group of pediatric cardiac patients in rest condition. However, RRi recorded using devices with fs < 250 Hz may be not adequate for reliable HRA analysis. Full article

Sleep disorders can have harmful consequences in both the short and long term. They can lead to attention deficits, as well as cardiac, neurological and behavioral repercussions. One of the most widely used methods for assessing sleep disorders is polysomnography (PSG). A major challenge associated with this method is all the cables needed to connect the recording devices, making the examination more intrusive and usually requiring a clinical environment. This can have potential consequences on the test results and their accuracy. One simple way to assess the state of the central nervous system (CNS), a well-known indicator of sleep disorder, could be the use of a portable medical device. With this in mind, we implemented a simple model using both the RR interval (RRI) and its second derivative to accurately predict the awake and napping states of a subject using a feature classification model. For training and validation, we used a database providing measurements from nine healthy young adults (six men and three women), in which heart rate variability (HRV) associated with light-on, light-off, sleep onset and sleep offset events. Results show that using a 30 min RRI time series window suffices for this lightweight model to accurately predict whether the patient was awake or napping. Full article

Background: As technology becomes more sophisticated, more accessible methods of interpretating Big Data become essential. We have continued to develop Complexity and Entropy in Physiological Signals (CEPS) as an open access MATLAB^® GUI (graphical user interface) providing multiple methods for the modification and analysis of physiological data. Methods: To demonstrate the functionality of the software, data were collected from 44 healthy adults for a study investigating the effects on vagal tone of breathing paced at five different rates, as well as self-paced and un-paced. Five-minute 15-s recordings were used. Results were also compared with those from shorter segments of the data. Electrocardiogram (ECG), electrodermal activity (EDA) and Respiration (RSP) data were recorded. Particular attention was paid to COVID risk mitigation, and to parameter tuning for the CEPS measures. For comparison, data were processed using Kubios HRV, RR-APET and DynamicalSystems.jl software. We also compared findings for ECG RR interval (RRi) data resampled at 4 Hz (4R) or 10 Hz (10R), and non-resampled (noR). In total, we used around 190–220 measures from CEPS at various scales, depending on the analysis undertaken, with our investigation focused on three families of measures: 22 fractal dimension (FD) measures, 40 heart rate asymmetries or measures derived from Poincaré plots (HRA), and 8 measures based on permutation entropy (PE). Results: FDs for the RRi data differentiated strongly between breathing rates, whether data were resampled or not, increasing between 5 and 7 breaths per minute (BrPM). Largest effect sizes for RRi (4R and noR) differentiation between breathing rates were found for the PE-based measures. Measures that both differentiated well between breathing rates and were consistent across different RRi data lengths (1–5 min) included five PE-based (noR) and three FDs (4R). Of the top 12 measures with short-data values consistently within ± 5% of their values for the 5-min data, five were FDs, one was PE-based, and none were HRAs. Effect sizes were usually greater for CEPS measures than for those implemented in DynamicalSystems.jl. Conclusion: The updated CEPS software enables visualisation and analysis of multichannel physiological data using a variety of established and recently introduced complexity entropy measures. Although equal resampling is theoretically important for FD estimation, it appears that FD measures may also be usefully applied to non-resampled data. Full article

As heart rate variability (HRV) studies become more and more prevalent in clinical practice, one of the most common and significant causes of errors is associated with distorted RR interval (RRI) data acquisition. The nature of such artifacts can be both mechanical as well as software based. Various currently used noise elimination in RRI sequences methods use filtering algorithms that eliminate artifacts without taking into account the fact that the whole RRI sequence time cannot be shortened or lengthened. Keeping that in mind, we aimed to develop an artifacts elimination algorithm suited to long-term (hours or days) sequences that does not affect the overall structure of the RRI sequence and does not alter the duration of data registration. An original adaptive smart time series step-by-step analysis and statistical verification methods were used. The adaptive algorithm was designed to maximize the reconstruction of the heart-rate structure and is suitable for use, especially in polygraphy. The authors submit the scheme and program for use. Full article

Individuals affected by COVID-19 have an alteration in autonomic balance, associated with impaired cardiac parasympathetic modulation and, consequently, a decrease in heart rate variability (HRV). This study examines the inter- and intrarater reliability of HRV) parameters derived from short-term recordings in individuals post-COVID. Sixty-nine participants of both genders post-COVID were included. The RR interval, the time elapsed between two successive R-waves of the QRS signal on the electrocardiogram (RRi), were recorded during a 10 min period in a supine position using a portable heart rate monitor (Polar^® V800 model). The data were transferred into Kubios^® HRV standard analysis software and analyzed within the stable sessions containing 256 sequential RRi. The intraclass correlation coefficient (ICC) ranged from 0.920 to 1.000 according to the intrarater analysis by Researcher 01 and 0.959 to 0.999 according to the intrarater by Researcher 02. The interrater ICC ranged from 0.912 to 0.998. The coefficient of variation was up to 9.23 for Researcher 01 intrarater analysis, 6.96 for Researcher 02 intrarater analysis and 8.83 for interrater analysis. The measurement of HRV in post-COVID-19 individuals is reliable and presents a small amount of error inherent to the method, supporting its use in the clinical environment and in scientific research. Full article

Background: Cardiovascular diseases are the world’s number one cause of death, with exceeding psychosocial stress load being considered a major risk factor. A stress management technique that has repeatedly shown positive effects on the cardiovascular system is the Transcendental Meditation (TM) technique. The present pilot study aimed to investigate the potential effect of TM on the recovery of cardiac patients. Objectives: We hypothesized that practicing TM in patients undergoing a 4-week cardiac rehabilitation program augments the recovery of cardiovascular parameters and reduces skeletal muscle tone after rehabilitation. Methods: Twenty cardiac patients were recruited and randomly assigned to either the control or the TM group. Cardiovascular parameters were assessed with the Task Force Monitor (TFM) and skeletal muscle contractile properties by Tensiomyography during a sit-stand test, performed at the beginning and end of a 4-week in-patient rehabilitation program. Results: Systolic blood pressure (SBP) was significantly lower after 4 weeks of cardiac rehabilitation, while the RR-interval (RRI) significantly increased. At the skeletal muscle level, the contraction time and maximal displacement increased, though only in the gastrocnemius medialis and biceps femoris muscles and not in vastus lateralis. Group interactions were not observed for hemodynamic parameters nor for muscle contractile properties. Discussion: Although significant improvements in hemodynamic and muscular parameters were observed after 4 weeks of rehabilitation, we could not provide evidence that TM improved rehabilitation after 4 weeks. TM may unfold its effects on the cardiovascular system in the longer term. Hence, future studies should comprise a long-term follow-up. Full article

This study investigates the use of atrioventricular (AV) synchronization as an important diagnostic criterion for atrial fibrillation and flutter (AF) using one to twelve ECG leads. Heart rate, lead-specific AV conduction time, and P-/f-wave amplitude were evaluated by three representative ECG metrics (mean value, standard deviation), namely RR-interval (RRi-mean, RRi-std), PQ-interval (PQi-mean, PQI-std), and PQ-amplitude (PQa-mean, PQa-std), in 71,545 standard 12-lead ECG records from the six largest PhysioNet CinC Challenge 2021 databases. Two rhythm classes were considered (AF, non-AF), randomly assigning records into training (70%), validation (20%), and test (10%) datasets. In a grid search of 19, 55, and 83 dense neural network (DenseNet) architectures and five independent training runs, we optimized models for one-lead, six-lead (chest or limb), and twelve-lead input features. Lead-set performance and SHapley Additive exPlanations (SHAP) input feature importance were evaluated on the test set. Optimal DenseNet architectures with the number of neurons in sequential [1st, 2nd, 3rd] hidden layers were assessed for sensitivity and specificity: DenseNet [16,16,0] with primary leads (I or II) had 87.9–88.3 and 90.5–91.5%; DenseNet [32,32,32] with six limb leads had 90.7 and 94.2%; DenseNet [32,32,4] with six chest leads had 92.1 and 93.2%; and DenseNet [128,8,8] with all 12 leads had 91.8 and 95.8%, indicating sensitivity and specificity values, respectively. Mean SHAP values on the entire test set highlighted the importance of RRi-mean (100%), RR-std (84%), and atrial synchronization (40–60%) for the PQa-mean (aVR, I), PQi-std (V2, aVF, II), and PQi-mean (aVL, aVR). Our focus on finding the strongest AV synchronization predictors of AF in 12-lead ECGs would lead to a comprehensive understanding of the decision-making process in advanced neural network classifiers. DenseNet self-learned to rely on a few ECG behavioral characteristics: first, characteristics usually associated with AF conduction such as rapid heart rate, enhanced heart rate variability, and large PQ-interval deviation in V2 and inferior leads (aVF, II); second, characteristics related to a typical P-wave pattern in sinus rhythm, which is best distinguished from AF by the earliest negative P-peak deflection of the right atrium in the lead (aVR) and late positive left atrial deflection in lateral leads (I, aVL). Our results on lead-selection and feature-selection practices for AF detection should be considered for one- to twelve-lead ECG signal processing settings, particularly those measuring heart rate, AV conduction times, and P-/f-wave amplitudes. Performances are limited to the AF diagnostic potential of these three metrics. SHAP value importance can be used in combination with a human expert’s ECG interpretation to change the focus from a broad observation of 12-lead ECG morphology to focusing on the few AV synchronization findings strongly predictive of AF or non-AF arrhythmias. Our results are representative of AV synchronization findings across a broad taxonomy of cardiac arrhythmias in large 12-lead ECG databases. Full article

The interest in photoplethysmography (PPG) for sleep monitoring is increasing because PPG may allow assessing heart rate variability (HRV), which is particularly important in breathing disorders. Thus, we aimed to evaluate how PPG wearable systems measure HRV during sleep at high altitudes, where hypobaric hypoxia induces respiratory disturbances. We considered PPG and electrocardiographic recordings in 21 volunteers sleeping at 4554 m a.s.l. (as a model of sleep breathing disorder), and five alpine guides sleeping at sea level, 6000 m and 6800 m a.s.l. Power spectra, multiscale entropy, and self-similarity were calculated for PPG tachograms and electrocardiography R–R intervals (RRI). Results demonstrated that wearable PPG devices provide HRV measures even at extremely high altitudes. However, the comparison between PPG tachograms and RRI showed discrepancies in the faster spectral components and at the shorter scales of self-similarity and entropy. Furthermore, the changes in sleep HRV from sea level to extremely high altitudes quantified by RRI and PPG tachograms in the five alpine guides tended to be different at the faster frequencies and shorter scales. Discrepancies may be explained by modulations of pulse wave velocity and should be considered to interpret correctly autonomic alterations during sleep from HRV analysis. Full article

Cardiac autonomic neuropathy (CAN) is a common complication of diabetes mellitus, and can be assessed using heart rate variability (HRV) and the correlations between systolic blood pressure (SBP) and ECG R-R intervals (RRIs), namely baroreflex sensitivity (BRS). In this study, we propose a novel parameter for the nonlinear association between SBP and RRIs based on multiscale cross-approximate entropy (MS-CXApEn). Sixteen male adult Wistar Kyoto rats were equally divided into two groups: streptozotocin-induced diabetes and age-matched controls. RRIs and SBP were acquired in control rats and the diabetic rats at the onset of hyperglycemia and insulin-treated euglycemia to determine HRV by the ratio of low-frequency to high-frequency power (LF/HF) and Poincaré plot as SSR (SD1/SD2), BRS, and MS-CXApEn. SSR and BRS were not significantly different among the three groups. The LF/HF was significantly higher in the hyperglycemic diabetics than those in the controls and euglycemic diabetic rats. MS-CXApEn was higher in the diabetic hyperglycemic rats than the control rats from scales 2 to 10, and approached the values of controls in diabetic euglycemic rats at scales 9 and 10. Conclusions: We propose MS-CXApEn as a novel parameter to quantify the dynamic nonlinear interactions between SBP and RRIs that reveals more apparent changes in early diabetic rats. Furthermore, changes in this parameter were related to correction of hyperglycemia and could be useful for detecting and assessing CAN in early diabetes. Full article

Camera-based remote photoplethysmography (rPPG) is a low-cost and casual non-contact heart rate measurement method suitable for telemedicine. Several factors affect the accuracy of measuring the heart rate and heart rate variability (HRV) using rPPG despite HRV being an important indicator for healthcare monitoring. This study aimed to investigate the appropriate setup for precise HRV measurements using rPPG while considering the effects of possible factors including illumination, direction of the light, frame rate of the camera, and body motion. In the lighting conditions experiment, the smallest mean absolute R–R interval (RRI) error was obtained when light greater than 500 lux was cast from the front (among the following conditions—illuminance: 100, 300, 500, and 700 lux; directions: front, top, and front and top). In addition, the RRI and HRV were measured with sufficient accuracy at frame rates above 30 fps. The accuracy of the HRV measurement was greatly reduced when the body motion was not constrained; thus, it is necessary to limit the body motion, especially the head motion, in an actual telemedicine situation. The results of this study can act as guidelines for setting up the shooting environment and camera settings for rPPG use in telemedicine. Full article

Heart rate variability, which is the fluctuation of the R-R interval (RRI) in electrocardiograms (ECG), has been widely adopted for autonomous evaluation. Since the HRV features that are extracted from RRI data easily fluctuate when arrhythmia occurs, RRI data with arrhythmia need to be modified appropriately before HRV analysis. In this study, we consider two types of extrasystoles—premature ventricular contraction (PVC) and premature atrial contraction (PAC)—which are types of extrasystoles that occur every day, even in healthy persons who have no cardiovascular diseases. A unified framework for ectopic RRI detection and a modification algorithm that utilizes an autoencoder (AE) type of neural network is proposed. The proposed framework consists of extrasystole occurrence detection from the RRI data and modification, whose targets are PVC and PAC. The RRI data are monitored by means of the AE in real time in the detection phase, and a denoising autoencoder (DAE) modifies the ectopic RRI caused by the detected extrasystole. These are referred to as AE-based extrasystole detection (AED) and DAE-based extrasystole modification (DAEM), respectively. The proposed framework was applied to real RRI data with PVC and PAC. The result showed that AED achieved a sensitivity of 93% and a false positive rate of 0.08 times per hour. The root mean squared error of the modified RRI decreased to 31% in PVC and 73% in PAC from the original RRI data by DAEM. In addition, the proposed framework was validated through application to a clinical epileptic seizure problem, which showed that it correctly suppressed the false positives caused by PVC. Thus, the proposed framework can contribute to realizing accurate HRV-based health monitoring and medical sensing systems. Full article

Obstructive sleep apnea (OSA) is highly associated with cardiovascular diseases, but most patients remain undiagnosed. Cyclic variation of heart rate (CVHR) occurs during the night, and R-R interval (RRI) analysis using a Holter electrocardiogram has been reported to be useful in screening for OSA. We investigated the usefulness of RRI analysis to identify OSA using the wearable heart rate sensor WHS-1 and newly developed algorithm. WHS-1 and polysomnography simultaneously applied to 30 cases of OSA. By using the RRI averages calculated for each time series, tachycardia with CVHR was identified. The ratio of integrated RRIs determined by integrated RRIs during CVHR and over all sleep time were calculated by our newly developed method. The patient was diagnosed as OSA according to the predetermined criteria. It correlated with the apnea hypopnea index and 3% oxygen desaturation index. In the multivariate analysis, it was extracted as a factor defining the apnea hypopnea index (r = 0.663, p = 0.003) and 3% oxygen saturation index (r = 0.637, p = 0.008). Twenty-five patients could be identified as OSA. We developed the RRI analysis using the wearable heart rate sensor WHS-1 and a new algorithm, which may become an expeditious and cost-effective screening tool for identifying OSA. Full article

Background and Objectives: The available research shows conflicting data on the heart rate variability (HRV) in metabolic syndrome (MetS) subjects. The discrepancy suggests a methodical shortcoming: due to the influence of physical activity, the standard measuring of HRV at rest is not comparable with HRV assessment based on 24 h Holter monitoring, which is preferred because of its comprehensiveness. To obtain a more reliable measure and to clarify to what extent HRV is altered in MetS, we assessed a 24 h HRV before and after the elimination of the influence of physical activity. Materials and Methods: We investigated 69 metabolic syndrome (MetS) and 37 control subjects, aged 50–55. In all subjects, 24 h monitoring of electrocardiogram, blood pressure, and actigraphy profiles were conducted. To eliminate the influence of day-time physical activity on RR intervals (RRI), a linear polynomial autoregressive model with exogenous terms (ARX) was used. Standard spectral RRI analysis was performed. Results: Subjects with MetS had blunted HRV; the diurnal SDNN index was reliably lower in the MetS group than in control subjects. The elimination of the influence of physical activity did not reveal a significant HRV change in long-term indices (SDNN, SDANN, and SD2), whilst adjacent RRI values (RMSSD, pNN50, and SD1) and SDNN index significantly increased (p < 0.001). An increase in the latter indices highlighted the HRV difference between the MetS and control groups; a significant (p < 0.001) decrease of all short-term HRV variables was found in the MetS group (p < 0.01), and low-frequency spectral components were less pronounced in the MetS group. Conclusion: The application of a polynomial autoregressive model in 24 h HRV assessment allowed for the exclusion of the influence of physical activity and revealed that MetS is associated with blunted HRV, which reflects mitigated parasympathetic tone. Full article

The paper presents an application of Transfer Entropy (TE) to the analysis of information transfer between biosignals (heart rate expressed as R-R intervals (RRI), blood pressure (sBP, dBP) and stroke volume (SV)) measured during head up tilt testing (HUTT) in patients with suspected vasovagal syndrome. The study group comprised of 80 patients who were divided into two groups: the HUTT(+) group consisting of 57 patients who developed syncope during the passive phase of the test and HUTT(−) group consisting of 23 patients who had a negative result of the passive phase and experienced syncope after provocation with nitroglycerin. In both groups the information transfer depends on the phase of the tilt test. In supine position the highest transfer occurred between driver RRI and other components. In upright position it is the driver sBP that plays the crucial role. The pre-syncope phase features the highest information transfer from driver SV to blood pressure components. In each group the comparisons of TE between different phases of HUT test showed significant differences for RRI and SV as drivers. Full article