Short-term deceleration capacity of heart rate: a sensitive marker of cardiac autonomic dysfunction in idiopathic Parkinson's disease.

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.

Increased markers of cardiac vagal activity in leucine-rich repeat kinase 2-associated Parkinson's disease.

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Investigation of Linear and Nonlinear Properties of a Heartbeat Time Series Using Multiscale Rényi Entropy.

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.

Depression and cardiac dysautonomia in eating disorders.

PURPOSE: Individuals with eating disorder (ED) are at an increased risk of cardiac arrhythmias due to cardiac dysautonomia, which may be exacerbated if depression is also present. The aim of the study was to use heart rate analysis as a marker for cardiac dysautonomia in patients with eating disorders and depression as a comorbidity. METHODS: Clinical data, including presence of depression, was obtained from all participants. A three-lead ECG was used to determine interbeat intervals, and these were analyzed using time domain, frequency domain, and nonlinear heart rate variability measures. RESULTS: Thirty ED patients and 44 healthy controls participated in the research. The presence of depression was associated with additional decreased time domain (RMSSD 36.8 ± 26 vs. 22.9 ± 12.3; p < 0.05), frequency domain (HF power 788 ± 1075 vs. 279 ± 261; p < 0.05), and nonlinear domain (DFAα2 0.82 ± 0.1 vs. 0.97 ± 0.1; p < 0.01) which results in the ED group compared to patients with no depression. CONCLUSIONS: The presence of depression in ED patients decreased HRV even further compared to the non-depressed patient group and controls, suggesting that higher vigilance and a holistic treatment approach may be required for these patients to avoid cardiac arrhythmia complications.

Diffusion Entropy vs. Multiscale and Rényi Entropy to Detect Progression of Autonomic Neuropathy.

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.

Improved Representations of Heterogeneous Carbon Reforming Catalysis Using Machine Learning.

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.

Diagnostic Accuracy of Random ECG in Primary Care for Early, Asymptomatic Cardiac Autonomic Neuropathy.

AIMS: Cardiac autonomic reflex tests (CARTs) are time consuming and require patient cooperation for detecting cardiac autonomic neuropathy (CAN). Heart rate variability (HRV) analysis requires less patient cooperation and is quicker to complete. However the reliability of HRV results as a clinical tool, with respect to length of recording and accuracy of diagnosis is inconclusive. The current study investigated the reproducibility associated with varying length of recording for early CAN (eCAN) assessment. METHODS: Participants were 68 males, 72 females with average age of 55 for controls and 63 for early CAN. Inclusion criteria were that participants were medication free and presented with no comorbidities. ECGs of control and eCAN were recorded and heart rate changes analyzed with the fast Fourier transform (FFT) and Lomb-Scargle periodogram (LSP). Ten-second to 5-minute recordings were extracted from a 15-minute lead-II ECG and accuracy in assessment of eCAN determined. RESULTS: The eCAN group was older ( P < .001) and systolic blood pressure was higher ( P < .01). HDL-cholesterol was also higher in the eCAN group ( P < .05). HRV analysis showed that both FFT and LSP results were significantly different between eCAN and control down to a 10-second ECG length for low frequency (LSP: P = .013, FFT: P = .024) and high frequency (HF-LSP: P = .002, FFT: P = .002) power. eCAN assessment was optimal down to 90-second recordings with a sensitivity of 100% and specificity of 29.49%. CONCLUSION: HRV is suitable for clinical practice from ECG recordings of more than 90 seconds with high accuracy and repeatability within a session for each participant.

Socioeconomic status, age and heart rate variability in a Bangladeshi community.

Socioeconomic status (SES) is a risk factor for cardiovascular disease (CVD) and either low or high heart rate variability (HRV) at rest has been shown to predict cardiovascular morbidity and mortality. The study investigated the extent HRV features can predict SES. Four hundred and twenty eight people were randomly selected from the commercial districts (high SES) and slum areas (low SES) within Dhaka city. Demographic, clinical, and HRV features were recorded. Of the clinical variables age, waist circumference and diastolic blood pressure (p<;0.01) were significantly different. HRV feature extraction from heart rate recordings were analyzed using Kubios software. Age corrected results showed that high frequency power (Median (IQR): 112.1 (85.6) ms2 vs. 96.8 (97.6) ms2, p=0.02) and the correlation dimension (0.78 (0.82) vs. 0.51 (1.20), p<;0.0002) were significantly lower in the low SES group, whilst the high frequency peak was significantly higher (0.1500 (0.0004) Hz vs. 0.1503 (0.0066) Hz, p<;0.0001). Our results show that although the high SES group had a more sedentary behavior, the low SES group had a higher risk of cardiac arrhythmia even though they were generally younger with normal blood pressure and waist circumference.

Using support vector machine ensembles for target audience classification on Twitter.

The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space.

Complex nonlinear autonomic nervous system modulation link cardiac autonomic neuropathy and peripheral vascular disease.

BACKGROUND: Physiological interactions are abundant within, and between, body systems. These interactions may evolve into discrete states during pathophysiological processes resulting from common mechanisms. An association between arterial stenosis, identified by low ankle-brachial pressure index (ABPI) and cardiovascular disease (CVD) as been reported. Whether an association between vascular calcification-characterized by high ABPI and a different pathophysiology-is similarly associated with CVD, has not been established. The current study aims to investigate the association between ABPI, and cardiac rhythm, as an indicator of cardiovascular health and functionality, utilizing heart rate variability (HRV). METHODS AND RESULTS: Two hundred and thirty six patients underwent ABPI assessment. Standard time and frequency domain, and non-linear HRV measures were determined from 5-min electrocardiogram. ABPI data were divided into normal (n = 101), low (n = 67) and high (n = 66) and compared to HRV measures.(DFAα1 and SampEn were significantly different between the low ABPI, high ABPI and control groups (p < 0.05). CONCLUSION: A possible coupling between arterial stenosis and vascular calcification with decreased and increased HRV respectively was observed. Our results suggest a model for interpreting the relationship between vascular pathophysiology and cardiac rhythm. The cardiovascular system may be viewed as a complex system comprising a number of interacting subsystems. These cardiac and vascular subsystems/networks may be coupled and undergo transitions in response to internal or external perturbations. From a clinical perspective, the significantly increased sample entropy compared to the normal ABPI group and the decreased and increased complex correlation properties measured by DFA for the low and high ABPI groups respectively, may be useful indicators that a more holistic treatment approach in line with this more complex clinical picture is required.