Novel Detection and Progression Markers for Diabetes Based on Continuous Glucose Monitoring Data Dynamics.

PURPOSE: Develop a diabetes diagnostic tool based on two markers of continuous glucose monitoring (CGM) dynamics: CGM entropy rate (ER) and Poincaré plot (PP) ellipse area (S). METHODS: 5,754 daily CGM profiles from 843 individuals with type 1, type 2 diabetes, or healthy individuals with or without islet autoantibody status were used to compute two individual dynamic markers: ER (in bits per transition; BPT) of daily probability matrices describing CGM transitions between eight glycemic states, and the area S (mg2/dL2) of individual CGM PP ellipses using standard PP descriptors. The Youden's index was used to determine "optimal" cut-points for ER and S for health vs. diabetes (case 1); type 1 vs. type 2 (case 2); and low vs. high type 1 immunological risk (case 3). The markers' discriminative power was assessed through the area under the receiver operating characteristics curves (AUC). RESULTS: Optimal cut-off points were determined for ER and S for each of the three cases. ER and S discriminated case 1 with AUC = 0.98 (95% CI: 0.97-0.99) and AUC = 0.99 (95% CI: 0.99-1.00), respectively, (cut-offs ERcase1 = 0.76 BPT, Scase1 = 1993.91 mg2/dL2), case 2 with AUC = 0.81 (95% CI, 0.77-0.84) and AUC = 0.76 (95% CI, 0.72-0.81), respectively (ERcase2 = 1.00 BPT, Scase2 = 5112.98 mg2/dL2), and case 3 with AUC = 0.81 (95% CI, 0.77-0.84) and AUC = 0.76 (95% CI, 0.72-0.81), respectively (ERcase3 = 0.52 BPT, Scase3 = 923.65 mg2/dL2). CONCLUSIONS: CGM dynamics markers can be an alternative to fasting plasma glucose or glucose tolerance testing and identifying individuals at higher immunological risk of progressing to type 1 diabetes.

Examination of Cardiac Activity with ECG Monitoring Using Heart Rate Variability Methods.

The paper presents a system for analyzing cardiac activity with the possibility of continuous and remote monitoring. The created sensor mobile device monitors heart activity by means of the convenient and imperceptible registration of cardiac signals. At the same time, the behavior of the human body is also monitored through the accelerometer and gyroscope built into the device, thanks to which it is possible to signal in the event of loss of consciousness or fall (in patients with syncope). Conducting real-time cardio monitoring and the analysis of recordings using various mathematical methods (linear, non-linear, and graphical) enables the research, accurate diagnosis, timely assistance, and correct treatment of cardiovascular diseases. The paper examines the recordings of patients diagnosed with arrhythmia and syncope recorded by electrocardiography (ECG) sensors in real conditions. The obtained results are subjected to statistical analysis to determine the accuracy and significance of the obtained results. The studies show significant deviations in the patients with arrhythmia and syncope regarding the obtained values of the studied parameters of heart rate variability (HRV) from the accepted normal values (for example, the root mean square of successive differences between normal heartbeats (RMSSD) in healthy individuals is 24.02 ms, while, in patients with arrhythmia (6.09 ms) and syncope (5.21 ms), it is much lower). The obtained quantitative and graphic results identify some possible abnormalities and demonstrate disorders regarding the activity of the autonomic nervous system, which is directly related to the work of the heart.

Unravelling COVID-19 waves in Rio de Janeiro city: Qualitative insights from nonlinear dynamic analysis.

Since the COVID-19 pandemic was first reported in 2019, it has rapidly spread around the world. Many countries implemented several measures to try to control the virus spreading. The healthcare system and consequently the general quality of life population in the cities have all been significantly impacted by the Coronavirus pandemic. The different waves of contagious were responsible for the increase in the number of cases that, unfortunately, many times lead to death. In this paper, we aim to characterize the dynamics of the six waves of cases and deaths caused by COVID-19 in Rio de Janeiro city using techniques such as the Poincaré plot, approximate entropy, second-order difference plot, and central tendency measures. Our results reveal that by examining the structure and patterns of the time series, using a set of non-linear techniques we can gain a better understanding of the role of multiple waves of COVID-19, also, we can identify underlying dynamics of disease spreading and extract meaningful information about the dynamical behavior of epidemiological time series. Such findings can help to closely approximate the dynamics of virus spread and obtain a correlation between the different stages of the disease, allowing us to identify and categorize the stages due to different virus variants that are reflected in the time series.

Poincaré plot can help predict the curative effect of metoprolol for pediatric postural orthostatic tachycardia syndrome.

Purpose: To study whether a Poincaré plot can help predict the curative effect of metoprolol for postural orthostatic tachycardia syndrome (POTS) in children. Methods: Pediatric patients with POTS who were administered metoprolol were retrospectively included. The collected data included general data (sex, age, height, weight, and body mass index), the manifestations and treatment (baseline orthostatic intolerance symptom score and course of metoprolol treatment), vital signs (supine heart rate [HR], supine blood pressure, and increased HR during the standing test), HR variability indexes (standard deviation of normal-to-normal intervals [SDNN]; standard deviation of the averages of normal-to-normal intervals [SDANN]; mean standard deviation of the NN intervals for each 5-min segment [SDNNI]; root mean square of the successive differences [rMSSD]; percentage of adjacent NN intervals that differ by >50 ms [pNN50]; triangular index; ultra-low [ULF], very low [VLF], low [LF], and high frequency [HF]; total power [TP]; and LF/HF ratio), and graphical parameters of the Poincaré plot (longitudinal axis [L], transverse axis [T], and L/T). Receiver operator characteristic curves were used to calculate the predictive function of the indexes with significant differences between patients who responded and those who did not. The index combination with the highest predictive value was obtained through series-parallel analysis. Results: Overall, 40 responders and 23 non-responders were included. The L and T in the Poincaré plots and rMSSD, pNN50, HF, and TP of the HR variability data were significantly lower in participants who responded to metoprolol than in participants who did not (p < 0.001). The L/T of participants who responded to metoprolol was greater than that of non-responders (p < 0.001). Moreover, we noted a strong correlation between every two indexes among L, T, rMSSD, pNN50, HF, TP, and L/T (p < 0.05). T < 573.9 ms combined with L/T > 2.9 had the best performance for predicting the effectiveness of metoprolol, with a sensitivity of 85.0%, specificity of 82.6%, and accuracy of 84.1%. Conclusion: In the Poincaré plot, a T < 573.9 ms combined with an L/T > 2.9 helps predict good outcomes of using metoprolol to treat pediatric POTS.

Fractal complexity alternations in paroxysmal atrial fibrillation patients with and without recurrence after pulmonary vein isolation.

BACKGROUND: Pulmonary vein isolation (PVI) is a cornerstone therapy for paroxysmal atrial fibrillation (PAF). The variations in nonlinear heart rate variability (HRV) between patients with and without recurrences remain unclear. We aimed to characterize the nonlinear HRV before and after PVI in patients with and without recurrence. METHODS: Twenty-five drug-refractory PAF patients (56.0 ± 9.1 years old, 20 males) who received PVI were enrolled. Holter electrocardiography were performed before, 1-3, and 6-12 months after PVI. After 8.2 ± 2.5 months of follow-ups after PVI, patients were divided into two groups: the recurrence (n = 8) and non-recurrence (n = 17) groups. Linear and nonlinear HRV variables were analyzed, including the Poincaré Plot analysis and the Detrended Fluctuation Analysis (DFA). RESULTS: The non-recurrence group, but not the recurrence group, had decreased high-frequency component (HF), the root mean square of successive RR interval differences (RMSSD), and the Poincaré Plot index SD1 1-3 months after PVI and increased DFAslope2 6-12 months after PVI. The non-recurrence group's LF/HF ratio and DFAslope1 decreased significantly 1-3 and 6-12 months after PVI, respectively, whereas there was no significant change in the recurrence group after PVI. CONCLUSIONS: Significantly reduced vagal tone 1-3 months after PVI, increased long-term fractal complexity 6-12 months after PVI, and decreased sympathetic tone as well as short-term fractal complexity 1-3 and 6-12 months after PVI led to a better AF-free survival after PVI. These findings suggest that neuromodulation and heart rate dynamics play crucial roles in AF recurrence following PVI.

Comparison of oxygen supplementation in very preterm infants: Variations of oxygen saturation features and their application to hypoxemic episode based risk stratification.

Background: Oxygen supplementation is commonly used to maintain oxygen saturation (SpO2) levels in preterm infants within target ranges to reduce intermittent hypoxemic (IH) events, which are associated with short- and long-term morbidities. There is not much information available about differences in oxygenation patterns in infants undergoing such supplementations nor their relation to observed IH events. This study aimed to describe oxygenation characteristics during two types of supplementation by studying SpO2 signal features and assess their performance in hypoxemia risk screening during NICU monitoring. Subjects and methods: SpO2 data from 25 infants with gestational age <32 weeks and birthweight <2,000 g who underwent a cross over trial of low-flow nasal cannula (NC) and digitally-set servo-controlled oxygen environment (OE) supplementations was considered in this secondary analysis. Features pertaining to signal distribution, variability and complexity were estimated and analyzed for differences between the supplementations. Univariate and regularized multivariate logistic regression was applied to identify relevant features and develop screening models for infants likely to experience a critically high number of IH per day of observation. Their performance was assessed using area under receiver operating curves (AUROC), accuracy, sensitivity, specificity and F1 scores. Results: While most SpO2 measures remained comparable during both supplementations, signal irregularity and complexity were elevated while on OE, pointing to more volatility in oxygen saturation during this supplementation mode. In addition, SpO2 variability measures exhibited early prognostic value in discriminating infants at higher risk of critically many IH events. Poincare plot variability at lag 1 had AUROC of 0.82, 0.86, 0.89 compared to 0.63, 0.75, 0.81 for the IH number, a clinical parameter at observation times of 30 min, 1 and 2 h, respectively. Multivariate models with two features exhibited validation AUROC > 0.80, F1 score > 0.60 and specificity >0.85 at observation times ≥ 1 h. Finally, we proposed a framework for risk stratification of infants using a cumulative risk score for continuous monitoring. Conclusion: Analysis of oxygen saturation signal routinely collected in the NICU, may have extensive applications in inferring subtle changes to cardiorespiratory dynamics under various conditions as well as in informing clinical decisions about infant care.

Correlation among Poincare plot and traditional heart rate variability indices in adults with different risk levels of metabolic syndrome: a cross-sectional approach from Southern India.

OBJECTIVES: Heart rate variability (HRV) is an important marker of cardiac autonomic modulation. Metabolic syndrome (MetS) can alter cardiac autonomic modulation, raising the risk of cardiovascular disease (CVD). Poincaré plot analysis (PPA) is a robust scatter plot-based depiction of HRV and carries similar information to the traditional HRV measures. However, no prior studies have examined the relationship between PPA and traditional HRV measures among different risk levels of MetS. We evaluated the association between the Poincare plot and traditional heart rate variability indices among adults with different risk levels of MetS. METHODS: We measured anthropometric data and collected fasting blood samples to diagnose MetS. The MetS risk was assessed in 223 participants based on the number of MetS components and was classified as control (n=64), pre-MetS (n=49), MetS (n=56), and severe MetS (n=54). We calculated the Poincaré plot (PP) and traditional HRV measures from a 5 min HRV recording. RESULTS: Besides the traditional HRV measures, we found that various HRV indices of PPA showed significant differences among the groups. The severe MetS group had significantly lower S (total HRV), SD1 (short-term HRV), SD2 (long-term HRV), and higher SD2/SD1. The values of S, SD1, SD2, and SD2/SD1 were significantly correlated with most traditional HRV measures. CONCLUSIONS: We found gradual changes in HRV patterns as lower parasympathetic and higher sympathetic activity alongside the rising number of MetS components. The HRV indices of PPA integrating the benefits of traditional HRV indices distinguish successfully between different risk levels of MetS and control subjects.

Application of short-term analysis of skin temperature variability in prediction of survival in patients with cirrhosis.

Background: Liver cirrhosis is a complex disorder, involving several different organ systems and physiological network disruption. Various physiological markers have been developed for survival modelling in patients with cirrhosis. Reduction in heart rate variability and skin temperature variability have been shown to predict mortality in cirrhosis, with the potential to aid clinical prognostication. We have recently reported that short-term skin temperature variability analysis can predict survival independently of the severity of liver failure in cirrhosis. However, in previous reports, 24-h skin temperature recordings were used, which are often not feasible in the context of routine clinical practice. The purpose of this study was to determine the shortest length of time from 24-h proximal temperature recordings that can accurately and independently predict 12-month survival post-recording in patients with cirrhosis. Methods: Forty individuals diagnosed with cirrhosis participated in this study and wireless temperature sensors (iButtons) were used to record patients' proximal skin temperature. From 24-h temperature recordings, different length of recordings (30 min, 1, 2, 3 and 6 h) were extracted sequentially for temperature variability analysis using the Extended Poincaré plot to quantify both short-term (SD1) and long-term (SD2) variability. These patients were then subsequently followed for a period of 12 months, during which data was gathered concerning any cases of mortality. Results: Cirrhosis was associated with significantly decreased proximal skin temperature fluctuations among individuals who did not survive, across all durations of daytime temperature recordings lasting 1 hour or more. Survival analysis showcased 1-h daytime proximal skin temperature time-series to be significant predictors of survival in cirrhosis, whereby SD2, was found to be independent to the Model for End-Stage Liver Disease (MELD) score and thus, the extent of disease severity. As expected, longer durations of time-series were also predictors of mortality for the majority of the temperature variability indices. Conclusion: Crucially, this study suggests that 1-h proximal skin temperature recordings are sufficient in length to accurately predict 12-month survival in patients with cirrhosis, independent from current prognostic indicators used in the clinic such as MELD.

Detecting Metabolic Thresholds from Nonlinear Analysis of Heart Rate Time Series: A Review.

Heart rate time series are widely used to characterize physiological states and athletic performance. Among the main indicators of metabolic and physiological states, the detection of metabolic thresholds is an important tool in establishing training protocols in both sport and clinical fields. This paper reviews the most common methods, applied to heart rate (HR) time series, aiming to detect metabolic thresholds. These methodologies have been largely used to assess energy metabolism and to identify the appropriate intensity of physical exercise which can reduce body weight and improve physical fitness. Specifically, we focused on the main nonlinear signal evaluation methods using HR to identify metabolic thresholds with the purpose of identifying a method which can represent a useful tool for the real-time settings of wearable devices in sport activities. While the advantages and disadvantages of each method, and the possible applications, are presented, this review confirms that the nonlinear analysis of HR time series represents a solid, robust and noninvasive approach to assess metabolic thresholds.

ECG and Heart Rate Variability in Sleep-Related Breathing Disorders.

Here we discuss the current perspectives of comprehensive heart rate variability (HRV) analysis in electrocardiogram (ECG) signals as a non-invasive and reliable measure to assess autonomic function in sleep-related breathing disorders (SDB). It is a tool of increasing interest as different facets of HRV can be implemented to screen and diagnose SDB, monitor treatment efficacy, and prognose adverse cardiovascular outcomes in patients with sleep apnea. In this context, the technical aspects, pathophysiological features, and clinical applications of HRV are discussed to explore its usefulness in better understanding SDB.

Nonlinear Heart Rate Dynamics Before and After Paroxysmal Atrial Fibrillation Events.

Background: Heart rate complexity, derived from nonlinear heart rate variability (HRV), has been shown to help predict the outcomes of various diseases. Changes in heart rate complexity before and after paroxysmal atrial fibrillation (PAF) events are unclear. Objectives: To evaluate changes in heart rate complexity through nonlinear HRV before and after PAF events. Methods: We enrolled 65 patients (72 ± 12.34 years old, 31 females) with 99 PAF events who received 24-hour Holter recording, and analyzed nonlinear HRV variables including Poincaré plot analysis, sample entropy (SampEn), and multiscale entropy (MSE). HRV analyses were applied to a 20-minute window before the onset and after the termination of PAF events. HRV parameters were evaluated and compared based on eight different 5-minute time segments, as we divided each 20-minute window into four segments of 5 minutes each. Results: SampEn and MSE1~5 significantly decreased before the onset of PAF events, whereas SampEn, MSE1~5 and MSE6~20 significantly increased after the termination of PAF events. SD1 and SD2, which are nonlinear HRV parameters calculated via Poincaré plot analysis, did not significantly change before the PAF events, however they both decreased significantly after termination. Conclusions: Heart rate complexity significantly decreased before the initiation and increased after the termination of PAF events, which indicates the crucial role of nonlinear heart rate dynamics in the initiation and termination of PAF.

Poincaré Plot Nonextensive Distribution Entropy: A New Method for Electroencephalography (EEG) Time Series.

As a novel form of visual analysis technique, the Poincaré plot has been used to identify correlation patterns in time series that cannot be detected using traditional analysis methods. In this work, based on the nonextensive of EEG, Poincaré plot nonextensive distribution entropy (NDE) is proposed to solve the problem of insufficient discrimination ability of Poincaré plot distribution entropy (DE) in analyzing fractional Brownian motion time series with different Hurst indices. More specifically, firstly, the reasons for the failure of Poincaré plot DE in the analysis of fractional Brownian motion are analyzed; secondly, in view of the nonextensive of EEG, a nonextensive parameter, the distance between sector ring subintervals from the original point, is introduced to highlight the different roles of each sector ring subinterval in the system. To demonstrate the usefulness of this method, the simulated time series of the fractional Brownian motion with different Hurst indices were analyzed using Poincaré plot NDE, and the process of determining the relevant parameters was further explained. Furthermore, the published sleep EEG dataset was analyzed, and the results showed that the Poincaré plot NDE can effectively reflect different sleep stages. The obtained results for the two classes of time series demonstrate that the Poincaré plot NDE provides a prospective tool for single-channel EEG time series analysis.

Epileptic seizure prediction based on features extracted from lagged Poincaré plots.

OBJECTIVE: The present work proposes a new epileptic seizure prediction method based on lagged Poincaré plot analysis of heart rate (HR). METHODS: In this article, the Poincaré plots with six different lags (1-6) were constructed for four episodes of heart rate variability (HRV) before the seizures. Moreover, two features were extracted based on lagged Poincare plots, which include the angle between the time series and the ellipse density fitted to the RR points. RESULTS: The proposed method was applied to 16 epileptic patients with 170 seizures. The results included sensitivity of 80.42% for the angle feature and 75.19% for the density feature. The false-positive rate was 0.15/Hr, which indicates that the system has superiority over the random predictor. CONCLUSION: The proposed HRV-based epileptic seizure prediction method has the potential to be used in daily life because HR can be measured easily by using a wearable sensor.

Poincaré Plot Is Useful for Distinguishing Vasovagal Syncope From Postural Tachycardia Syndrome in Children.

Objectives: To explore the role of the Poincaré plot derived from a 24-hour Holter recording in distinguishing vasovagal syncope (VVS) from postural tachycardia syndrome (POTS) in pediatric patients. Materials and Methods: Pediatric patients with VVS or POTS, hospitalized in Peking University First Hospital between January 2012 and December 2018, were included in a derivation study. The transverse axis (T), longitudinal axis (L), T/L ratio, product T × L, distance between the origin and the proximal end of the longitudinal axis (pro-D), and distance between the origin and distal end of the longitudinal axis (dis-D) of the Poincaré plot were compared between the VVS and POTS groups, and the differential diagnostic performance of the above-mentioned graphic parameters was evaluated using receiver operating characteristic curve analysis. A validation study was conducted in pediatric patients hospitalized between January 2019 and December 2020. Results: In school-aged children, the T, L, T/L, T × L, and dis-D values of patients with VVS were greater than those of patients with POTS; in adolescents, the T, T/L, T × L, and pro-D values of patients with VVS were greater than those of patients with POTS. Using a T/L cut-off value of 0.3 to distinguish between the two diseases, the sensitivity and specificity were 91.0 and 90.5%, respectively, for the total participants; 91.6 and 88.9%, respectively, for the school-aged children; and 82.1 and 95.7%, respectively, for the adolescents. In the validation study, a T/L cut-off value of 0.3 yielded an accuracy, sensitivity, and specificity of 81.8, 87.2, and 77.6%, respectively, in the total participants; 76.5, 82.6, and 71.4%, respectively, in the school-aged children; and 89.2, 93.8, and 85.7%, respectively, in the adolescents, in distinguishing VVS from POTS validated by clinical diagnosis. Conclusions: The graphic parameters of the Poincaré plot are significantly different between VVS and POTS in pediatric patients, and the T/L of the Poincaré plot may be a useful measure to help differentiate VVS from POTS in children and adolescents.

Poincaré Plot Can Be a Useful Tool to Select Potential Responders to Metoprolol Therapy in Children with Vasovagal Syncope.

Purpose: To explore the value of the longitudinal axis/transverse axis ratio (L/T) of Poincaré plot in selecting children with vasovagal syncope (VVS) who were suitable for metoprolol therapy. Patients and Methods: Children with VVS hospitalized in Peking University First Hospital between January 2012 and June 2019 and treated with metoprolol were retrospectively included as the training set, and children with VVS hospitalized between July 2019 and December 2020 were included as the validation set. The sex, age at admission, height, weight, body mass index, course of disease, syncope symptom score before metoprolol treatment, treatment duration, supine heart rate (HR), supine systolic pressure, supine diastolic pressure, peak HR during the head-up tilt test (HUTT), changes of HR during HUTT, hemodynamic response during HUTT, left ventricular ejection fraction, left ventricular fractional shortening and the L/T of Poincaré plot were compared between responders and nonresponders in the training set. Logistic regression analysis was conducted to explore predictors. Receiver operating characteristic curve was utilized to determine the value of the predictors for selecting potential responders. Finally, the value of the predictors was further verified. Results: In the training set including 105 children, the L/T in responders was distinctly higher than that in nonresponders (P < 0.001), and there was no apparent difference between the two groups in other indexes. The L/T was statistically related to the efficacy of metoprolol (P < 0.001). The L/T >2.7 yielded a sensitivity of 88.2% and a specificity of 82.8% for indicating responders to metoprolol. Taking L/T >2.7 to select potential responders in another 43 children with VVS in the validation set, the sensitivity was 96.6%, specificity 71.4%, and accuracy 88.4%. Conclusion: The L/T of Poincaré plot >2.7 can be a useful tool to select potential responders to metoprolol therapy in children with VVS.

Nonlinear Dynamics of Reaction Time and Time Estimation during Repetitive Test.

(1) Background: In this research, we aimed to investigate a computational model of repetitive reaction time (RT) and virtual reaction time (VRT) testing. (2) Methods: The study involved 180 subjects (50 men, 130 women, mean age 31.61 ± 13.56 years). The data were statistically analyzed through the coefficient of variation (CV) and the Poincaré plot indicators. (3) Results: We obtained an excellent level of reliability for both sessions of testing and we put into evidence a relationship of association of the RT and VRT with the subjects' age, which was more pregnant for RT (p < 0.05). For both RT and VRT data series, we determined a consistent closer association between CV and the Poincaré plot descriptors SD1, SD2 (SD-standard deviation), and the area of the fitting ellipse (AFE) (p < 0.01). We reported an underestimation of the time interval of 2 s during the VRT session of testing, with an average value of CV of VRT, the equivalent of the Weber fraction, of 15.21 ± 8.82%. (4) Conclusions: The present study provides novel evidence that linear and nonlinear analysis of RT and VRT variability during serial testing bring complementary insights to the understanding of complex neurocognitive processes implied in the task execution.

Assessing rheoencephalography dynamics through analysis of the interactions among brain and cardiac networks during general anesthesia.

Cerebral blood flow (CBF) reflects the rate of delivery of arterial blood to the brain. Since no nutrients, oxygen or water can be stored in the cranial cavity due to space and pressure restrictions, a continuous perfusion of the brain is critical for survival. Anesthetic procedures are known to affect cerebral hemodynamics, but CBF is only monitored in critical patients due, among others, to the lack of a continuous and affordable bedside monitor for this purpose. A potential solution through bioelectrical impedance technology, also known as rheoencephalography (REG), is proposed, that could fill the existing gap for a low-cost and effective CBF monitoring tool. The underlying hypothesis is that REG signals carry information on CBF that might be recovered by means of the application of advanced signal processing techniques, allowing to track CBF alterations during anesthetic procedures. The analysis of REG signals was based on geometric features extracted from the time domain in the first place, since this is the standard processing strategy for this type of physiological data. Geometric features were tested to distinguish between different anesthetic depths, and they proved to be capable of tracking cerebral hemodynamic changes during anesthesia. Furthermore, an approach based on Poincaré plot features was proposed, where the reconstructed attractors form REG signals showed significant differences between different anesthetic states. This was a key finding, providing an alternative to standard processing of REG signals and supporting the hypothesis that REG signals do carry CBF information. Furthermore, the analysis of cerebral hemodynamics during anesthetic procedures was performed by means of studying causal relationships between global hemodynamics, cerebral hemodynamics and electroencephalogram (EEG) based-parameters. Interactions were detected during anesthetic drug infusion and patient positioning (Trendelenburg positioning and passive leg raise), providing evidence of the causal coupling between hemodynamics and brain activity. The provided alternative of REG signal processing confirmed the hypothesis that REG signals carry information on CBF. The simplicity of the technology, together with its low cost and easily interpretable outcomes, should provide a new opportunity for REG to reach standard clinical practice. Moreover, causal relationships among the hemodynamic physiological signals and brain activity were assessed, suggesting that the inclusion of REG information in depth of anesthesia monitors could be of valuable use to prevent unwanted CBF alterations during anesthetic procedures.

Single channel photoplethysmography-based obstructive sleep apnea detection and arrhythmia classification.

BACKGROUND: Simplified and easy-to-use monitoring approaches are crucial for the early diagnosis and prevention of obstructive sleep apnea (OSA) and its complications. OBJECTIVE: In this study, the OSA detection and arrhythmia classification algorithms based on single-channel photoplethysmography (PPG) are proposed for the early screening of OSA. METHODS: Thirty clinically diagnosed OSA patients participated in this study. Fourteen features were extracted from the PPG signals. The relationship between the number of features as inputs of the support vector machine (SVM) and performance of apnea events detection was evaluated. Also, a multi-classification algorithm based on the modified Hausdorff distance was proposed to recognize sinus rhythm and four arrhythmias highly related with SA. RESULTS: The feature set composed of meanPP, SDPP, RMSSD, meanAm, and meank1 could provide a satisfactory balance between the performance and complexity of the algorithm for OSA detection. Also, the arrhythmia classification algorithm achieves the average sensitivity, specificity and accuracy of 83.79%, 95.91% and 93.47%, respectively in the classification of all four types of arrhythmia and regular rhythm. CONCLUSION: Single channel PPG-based OSA detection and arrhythmia classification in this study can provide a feasible and promising approach for the early screening and diagnosis of OSA and OSA-related arrhythmias.

Research on physical activity variability and changes of metabolic profile in patients with prediabetes using Fitbit activity trackers data.

BACKGROUND: Monitoring physical activity with consumers wearables is one of the possibilities to control a patient's self-care and adherence to recommendations. However, clinically approved methods, software, and data analysis technologies to collect data and make it suitable for practical use for patient care are still lacking. OBJECTIVE: This study aimed to analyze the potential of patient physical activity monitoring using Fitbit physical activity trackers and find solutions for possible implementation in the health care routine. METHODS: Thirty patients with impaired fasting glycemia were randomly selected and participated for 6 months. Physical activity variability was evaluated and parameters were calculated using data from Fitbit Inspire devices. RESULTS: Changes in parameters were found and correlation between clinical data (HbA1c, lipids) and physical activity variability were assessed. Better correlation with variability than with body composition changes shows the potential to include nonlinear variability parameters analysing physical activity using mobile devices. Less expressed variability shows better relationship with control of prediabetic and lipid parameters. CONCLUSIONS: Evaluation of physical activity variability is essential for patient health, and these methods used to calculate it is an effective way to analyze big data from wearable devices in future trials.

An assessment of heart rate and blood pressure asymmetry in the diagnosis of vasovagal syncope in females.

Introduction: Heart Rate Asymmetry (HRA) describes a phenomenon of differences between accelerations and decelerations in human heart rate. Methods used for HRA assessment can be further implemented in the evaluation of asymmetry in blood pressure variations (Blood Pressure Asymmetry-BPA). Methods: We have analyzed retrospectively the series of heartbeat intervals extracted from ECG and beat-to-beat blood pressure signals from 16 vasovagal patients (age: 32.1 ± 13.3; BMI: 21.6 ± 3.8; all female) and 19 healthy subjects (age: 34.6 ± 7.6; BMI: 22.1 ± 3.4; all female) who have undergone tilt test (70°). Asymmetry was evaluated with Poincaré plot-based methods for 5 min recordings from supine and tilt stages of the test. The analyzed biosignals were heart rate (RR), diastolic (dBP) and systolic Blood Pressure (sBP) and Pulse Pressure (PP). In the paper we explored the differences between healthy and vasovagal women. Results: The changes of HRA indicators between supine and tilt were observed only in the control group (Porta Index p = 0.026 and Guzik Index p = 0.005). No significant differences in beat-to-beat variability (i.e. spread of points across the line of identity in Poincaré plot-SD1) of dBP was noted between supine and tilt in the vasovagal group (p = 0.433 in comparison to p = 0.014 in healthy females). Moreover, in vasovagal patients the PP was significantly different (supine: 41.47; tilt: 39.27 mmHg) comparing to healthy subjects (supine: 35.87; tilt: 33.50 mmHg) in supine (p = 0.019) and in tilt (p = 0.014). Discussion: Analysis of HRA and BPA represents a promising method for the evaluation of cardiovascular response to orthostatic stressors, however currently it is difficult to determine a subject's underlying health condition based only on these parameters.