Increased beat-to-beat heart rate variability (HRV) is a feature of patients with Parkinson’s disease (PD) who carry the G2019S mutation in the LRRK2 gene (LRRK2-PD). Since LRRK2 mutations have incomplete penetrance, HRV changes preceding PD conversion would likely be observed only in a subset of LRRK2 non-manifesting carriers (NMC). We aimed to assess HRV in a subgroup of NMC with distinctive characteristics of LRRK2-PD, identified through clustering analysis. HRV measures derived from 300 normal heartbeat intervals extracted from the electrocardiograms of 25 NMC, 32 related non-carriers (RNC), 27 unrelated healthy controls, and 14 patients with LRRK2-PD were analyzed. Clinical symptoms were evaluated using questionnaires and scales, and three NMC subgroups were identified using a k-means cluster analysis on the basis of the deceleration capacity of heart rate (DC) and Rényi entropy. Standard and advanced HRV measures were compared using multiple regression analysis, controlling for age, sex, and mean heart rate. Beat-to-beat HRV markers were significantly increased in a subgroup of seven NMC (NMC2, 28

Vasovagal syncope is a transient loss of consciousness, which can occur due to neurological, metabolic, psychiatric, and cardiac causes. Palpitations are characterized by abnormally rapid or irregular heartbeats and in some cases fainting. Determination of cause may be undertaken by patient history and physical examination including an ECG and head-up tilt testing (HUTT). However, HUTT bears some risks such as fainting and arrhythmia in syncope patients as well as being uncomfortable and unable to distinguish between palpitations and vasovagal syncope. Alternative diagnostic tools are therefore of interest and this work examines the usefulness of Heart Rate Variability (HRV) obtained from 24-hour ECG recordings. A range of entropy measures were calculated for each recording and Mann-Whitney tests were used to compare groups. Ré nyi entropy provides a significant result for positive exponents, for q=5, where the 2-group comparison has a p-value of 0.00031. Tsallis, Norm, SB, Beta, and Gamma entropies show similar results. These results suggest that short heart rate recordings, possibly during a clinic visit can differentiate between syncope and palpitations and hence HUTT is not required.

Point-of-Care Testing (POCT) have mainly addressed biochemical systems. This paper presents a POCT based on recording physiological data and the options for intervention. Smartphone or tablets provide an ever-increasing number of applications for measuring diverse biochemical as well as physiological variables. An important adjunct to this is that the POCT App should provide a means of intervention. Previous work on assessing the efficacy of the biofeedback using HeartMath device has mainly concentrated on the effect of heart rate response, measured as heart rate variability (HRV) with the aim of improving anxiety, depression and immune response. This study investigated the effect of diaphragmatic paced breathing (6 breaths/min) or a serious game-based balloon-game for guiding biofeedback compared to normal breathing on electroencephalograph (EEG) signal complexity. Signal characteristics were analyzed following pre-processing and using the Higuchi Fractal Dimension (HFD) from the EEG directly, HFD obtained after applying Hilbert-Huang Transform (HHT) and Sample Entropy (SE) from EEG directly. Six subjects participated in the repeated measures pilot study. EEG was recorded using the Thought Technology device with the scalp electrode located at Cz prior, during and after HeartMath biofeedback training. Using all three complexity measures, most or all participants showed the lowest signal complexity during paced breathing regardless of analysis method. Only HFD with HHT showed a significant statistical difference (p<0.05) between the three conditions when using a Friedman repeated measures test. The findings suggest that biofeedback may be efficacious for POCT in psychopathology to reduce complexity of EEG which is often higher in patients with anxiety, depression or schizophrenia.

We review the literature to argue the importance of the occurrence of crucial events in the dynamics of physiological processes. Crucial events are interpreted as short time intervals of turbulence, and the time distance between two consecutive crucial events is a waiting time distribution density with an inverse power law (IPL) index μ, with μ < 3 generating non-stationary behavior. The non-stationary condition is characterized by two regimes of the IPL index: (a) perennial non-stationarity, with 1 < μ < 2 and (b) slow evolution toward the stationary regime, with 2 < μ < 3. Human heartbeats and brain dynamics belong to the latter regime, with healthy physiological processes tending to be closer to the border with the perennial non-stationary regime with μ = 2. The complexity of cognitive tasks is associated with the mental effort required to address a difficult task, which leads to an increase of μ with increasing task difficulty. On this basis we explore the conjecture that disease evolution leads the IPL index μ moving from the healthy condition μ = 2 toward the border with Gaussian statistics with μ = 3, as the disease progresses. Examining heart rate time series of patients affected by diabetes-induced autonomic neuropathy of varying severity, we find that the progression of cardiac autonomic neuropathy (CAN) indeed shifts μ from the border with perennial variability, μ = 2, to the border with Gaussian statistics, μ = 3 and provides a novel, sensitive index for assessing disease progression. We find that at the Gaussian border, the dynamical complexity of crucial events is replaced by Gaussian fluctuation with long-time memory.

Purpose Cardiac autonomic dysfunction in idiopathic Parkinson's disease (PD) manifests as reduced heart rate variability (HRV). In the present study, we explored the deceleration capacity of heart rate (DC) in patients with idiopathic PD, an advanced HRV marker that has proven clinical utility. Methods Standard and advanced HRV measures derived from 7-min electrocardiograms in 20 idiopathic PD patients and 27 healthy controls were analyzed. HRV measures were compared using regression analysis, controlling for age, sex, and mean heart rate. Results Significantly reduced HRV was found only in the subcohort of PD patients older than 60 years. Low- frequency power and global HRV measures were lower in patients than in controls, but standard beat-to-beat HRV markers (i.e., rMSSD and high-frequency power) were not significantly different between groups. DC was significantly reduced in the subcohort of PD patients older than 60 years compared to controls. Conclusions Deceleration-related oscillations of HRV were significantly reduced in the older PD patients compared to healthy controls, suggesting that short-term DC may be a sensitive marker of cardiac autonomic dysfunction in PD. DC may be complementary to traditional markers of short-term HRV for the evaluation of autonomic modulation in PD. Further study to examine the association between DC and cardiac adverse events in PD is needed to clarify the clinical relevance of DC in this population.

Leucine-rich repeat kinase 2 (LRRK2) mutations are the most common known cause of both familial and sporadic Parkinson's disease (PD). On a single patient basis, LRRK2-associated PD (LRRK2-PD) and idiopathic PD (iPD) are indistinguishable. Recent evidence suggests that LRRK2-PD may be related to greater vagal activity. This study aimed to explore the potential of standard and novel heart rate variability (HRV) measures to distinguish LRRK2-PD from iPD patients. Support vector machine classifiers, based on HRV features, were used to discriminate between PD types. The combination of two classifiers reached 79% sensitivity and 86% specificity. Cardiac autonomic biomarkers may be useful to accurately distinguish individuals with LRRK2-PD from iPD.

This study examined the effectiveness of diaphragmatic paced breathing at (0.1Hz) or obtaining biofeedback via a computer-game compared to normal breathing on heart rate variability (HRV). The aim was to determine whether individual differences exist and allow best practice on an individualized basis for relaxation and stress reduction. Six subjects participated in the repeat measures pilot study. HRV data was recorded during every session using HeartMath and analysed using Kubios software. Paced breathing indicated a substantial decrease in heart rate. Maximum change of low frequency power (%) was seen for the balloon condition with lesser change during paced breathing, The most changes between rest and intervention was observed for the very low frequency power (%) with no substantial differences between paced breathing and the balloon intervention. This suggests that the balloon game may more effective when breathing at approximately 0.1Hz and initiates vagal predominance which has been associated with reduced stress.

A person’s health behavior plays a vital role in mitigating their risk of disease and promoting positive health outcomes. In recent years, mHealth systems have emerged to offer novel approaches for encouraging and supporting users in changing their health behavior. Mobile biosensors represent a promising technology in this regard; that is, sensors that collect physiological data (e.g., heart rate, respiration, skin conductance) that individuals wear, carry, or access during their normal daily activities. mHealth system designers have started to use the health information from physiological data to deliver behavior-change interventions. However, little research provides guidance about how one can design mHealth systems to use mobile biosensors for health behavior change. In order to address this research gap, we conducted an exploratory study. Following a hybrid approach that combines deductive and inductive reasoning, we integrated a body of fragmented literature and conducted 30 semi-structured interviews with mHealth stakeholders. From this study, we developed a theoretical framework and six general design guidelines that shed light on the theoretical pathways for how the mHealth interface can facilitate behavior change and provide practical design considerations.

The time series of interbeat intervals of the heart reveals much information about disease and disease progression. An area of intense research has been associated with cardiac autonomic neuropathy (CAN). In this work we have investigated the value of additional information derived from the magnitude, sign and acceleration of the RR intervals. When quantified using an entropy measure, these time series show statistically significant differences between disease classes of Normal, Early CAN and Definite CAN. In addition, pathophysiological characteristics of heartbeat dynamics provide information not only on the change in the system using the first difference but also the magnitude and direction of the change measured by the second difference (acceleration) with respect to sequence length. These additional measures provide disease categories to be discriminated and could prove useful for non-invasive diagnosis and understanding changes in heart rhythm associated with CAN.

Predicting adsorption energies of reaction intermediates is critical for determining catalytic reaction mechanisms. Here, we present three combined representations for predicting adsorption energies of carbon reforming species on transition-metal surfaces. Among the three combined representations, the Elemental Properties and Spectral London Axilrod-Teller-Muto (EP&SLATM) representation, which uses separate EP and SLATM representations for the surface and adsorbates, yields the lowest mean absolute error (MAE) of ∼0.18 eV with respect to density functional theory (DFT) adsorption formation energies for 68 adsorbates on four low-index metal facets (Cu(111), Pt(111), Pd(111), Ru(0001)). All three combined representations also have lower MAEs compared with linear scaling relations. Notably, two of the combined representations achieve their results using empirical/experimental molecular structures only (i.e., without recourse to structural optimization based on first-principles methods such as DFT). The combined representations enable improved efficiency for predicting heterogeneous catalytic mechanisms using machine learning approaches, largely bypassing expensive electronic structure calculations. Further, we show that the combined representations enable "cross-surface" training with regression and tree-based machine learning methods. That is, to predict adsorption formation energies on a particular catalyst metal, these methods only need a small amount of training samples (20%) on that metal.

Cardiac autonomic dysfunction manifests as reduced heart rate variability (HRV) in idiopathic Parkinson’s disease (PD), but no significant reduction has been found in PD patients who carry the LRRK2 mutation. Novel HRV features have not been investigated in these individuals. We aimed to assess cardiac autonomic modulation through standard and novel approaches to HRV analysis in individuals who carry the LRRK2 G2019S mutation. Short-term electrocardiograms were recorded in 14 LRRK2-associated PD patients, 25 LRRK2-non-manifesting carriers, 32 related non-carriers, 20 idiopathic PD patients, and 27 healthy controls. HRV measures were compared using regression modeling, controlling for age, sex, mean heart rate, and disease duration. Discriminant analysis highlighted the feature combination that best distinguished LRRK2-associated PD from controls. Beat-to-beat and global HRV measures were significantly increased in LRRK2-associated PD patients compared with controls (e.g., deceleration capacity of heart rate: p = 0.006) and idiopathic PD patients (e.g., 8th standardized moment of the interbeat interval distribution: p = 0.0003), respectively. LRRK2-associated PD patients also showed significantly increased irregularity of heart rate dynamics, as quantified by Rényi entropy, when compared with controls (p = 0.002) and idiopathic PD patients (p = 0.0004). Ordinal pattern statistics permitted the identification of LRRK2-associated PD individuals with 93% sensitivity and 93% specificity. Consistent results were found in a subgroup of LRRK2-non-manifesting carriers when compared with controls. Increased beat-to-beat HRV in LRRK2 G2019S mutation carriers compared with controls and idiopathic PD patients may indicate augmented cardiac autonomic cholinergic activity, suggesting early impairment of central vagal feedback loops in LRRK2-associated PD.

Remote photoplethysmography (rPPG) allows remote measurement of the heart rate using low-cost RGB imaging equipment. In this study, we review the development of the field of rPPG since its emergence in 2008. We also classify existing rPPG approaches and derive a framework that provides an overview of modular steps. Based on this framework, practitioners can use our classification to design algorithms for an rPPG approach that suits their specific needs. Researchers can use the reviewed and classified algorithms as a starting point to improve particular features of an rPPG algorithm.

Cardiac autonomic neuropathy (CAN) is a complication of diabetes with a long asymptomatic phase that is associated with high morbidity and mortality. Early identification of CAN in Type 1 diabetes mellitus (T1DM) may be possible using heart rate variability (HRV). However, the power of HRV analysis to identify CAN depends on the selection of suitable features that provide reliable information regarding cardiac autonomic regulation. Our aim was to compare the performance of Renyi entropy (RE) and permutation entropy (PE) for identification of T1DM patients with CAN. RE and PE measures from 235 data points and 5 min of cardiac interbeat interval (RR) sequences were analysed in 18 T1DM patients without CAN, 14 T1DM patients with CAN, and healthy controls matched for age and sex. RE was calculated for different orders alpha (-5, 5), pattern lengths lambda (2, 4, 8), and tolerance sigma. For PE analysis lambda was set to (3-4) and time delays tau to (1-10). A forward stepwise discriminant analysis was carried out for estimating the classification functions. Accuracy was estimated following a K-fold cross-validation (k = 14). RE calculated for RR sequences of lambda = 2, alpha > 0 showed the best performance for differentiating T1DM patients with CAN (p < 0.0001). PE measures showed better performance with ordinal patterns and tau = 4, 5 and 7 for differentiating patients with CAN. RE and PE provide complementary information achieving 100% classification accuracy (p < 0.0001 and p < 0.001, respectively). This approach might be promising as a sensitive and specific tool for CAN diagnosis in T1DM.