The acceleration and deceleration patterns in heartbeat fluctuations distribute asymmetrically, which is known as heart rate asymmetry (HRA). It is hypothesized that HRA reflects the balancing regulation of the sympathetic and parasympathetic nervous systems. This study was designed to examine whether altered autonomic balance during exercise can lead to HRA changes. Sixteen healthy college students were enrolled, and each student undertook two 5-min ECG measurements: one in a resting seated position and another while walking on a treadmill at a regular speed of 5 km/h. The two measurements were conducted in a randomized order, and a 30-min rest was required between them. RR interval time series were extracted from the 5-min ECG data, and HRA (short-term) was estimated using four established metrics, that is, Porta's index (PI), Guzik's index (GI), slope index (SI), and area index (AI), from both raw RR interval time series and the time series after wavelet detrending that removes the low-frequency component of <~0.03 Hz. Our pilot data showed a reduced PI but unchanged GI, SI, and AI during walking compared to resting seated position based on the raw data. Based on the wavelet-detrended data, reduced PI, SI, and AI were observed while GI still showed no significant changes. The reduced PI during walking based on both raw and detrended data which suggests less short-term HRA may underline the belief that vagal tone is withdrawn during low-intensity exercise. GI may not be sensitive to short-term HRA. The reduced SI and AI based on detrended data suggest that they may capture both short- and long-term HRA features and that the expected change in short-term HRA is amplified after removing the trend that is supposed to link to long-term component. Further studies with more subjects and longer measurements are warranted to validate our observations and to examine these additional hypotheses.
Identifying prognostic factors by affordable tools is crucial for guiding gastric cancer (GC) treatments especially at earlier stages for timing interventions. The autonomic function that is clinically assessed by heart rate variability (HRV) is involved in tumorigenesis. This pilot study was aimed to examine whether nonlinear indices of HRV can be biomarkers of GC severity. Sixty-one newly-diagnosed GC patients were enrolled. Presurgical serum fibrinogen (FIB), carcinoembryonic antigen (CEA), and carbohydrate antigen 19-9 (CA199) were examined. Resting electrocardiogram (ECG) of 5-min was collected prior to surgical treatments to enable the HRV analysis. Twelve nonlinear HRV indices covering the irregularity, complexity, asymmetry, and temporal correlation of heartbeat fluctuations were obtained. Increased short-range temporal correlations, decreased asymmetry, and increased irregularity of heartbeat fluctuations were associated with higher FIB level. Increased irregularity and decreased complexity were also associated with higher CEA level. These associations were independent of age, sex, BMI, alcohol consumption, history of diabetes, left ventricular ejection fraction, and anemia. The results support the hypothesis that perturbations in nonlinear dynamical patterns of HRV predict increased GC severity. Replication in larger samples as well as the examination of longitudinal associations of HRV nonlinear features with cancer prognosis/survival are warranted.
ECG-derived respiration (EDR) is a low-cost and productive means for capturing respiratory activity. In particular, as the primary procedure in some cardiorespiratory-related studies, the quality of EDR is decisive for the performance of subsequent analyses.In this paper, we proposed a novel EDR method based on the feature derived from the first moment (mean frequency) of the power spectrum (FMS). After obtaining the EDR signal from the feature, we introduced the Interacting Multiple Model (IMM) smoother to enhance the similarity of the EDR signal to the reference respiration. The assessment of the approach consisted of two steps: 1) the performance of extracted feature was verified against R-peak misalignment and noise. 2) the enhancement of IMM smoother to EDR waveforms was evaluated based on waveform correlation and respiratory rate estimation. All the assessments were conducted under the Fantasia database and Drivers database.The FMS improved robustness against R peak offsets compared to most established feature-based EDR algorithms, but a slight 5% improvement of waveform correlation against RR interval-based feature under accurate R peaks. The IMM smoother performed similarly with the Kalman filter in the static database but realized the enhancement of some extent of the EDR waveform in the ambulatory database.The proposed method investigated frequency domain mapping of ECG morphological changes caused by respiratory modulation and explained the EDR signal as a non-stationary time series, which provided a direction of better fitting the natural respiration process and enhancing the EDR waveform.
ObjectiveTo investigate the effect of smoking on heart rate variability (HRV), QT interval, and QT dispersion. MethodsTwo hundred healthy persons who received physical examination in our hospital from January 2010 to December 2011 were divided into smoking group (n = 100) and non-smoking group (n = 100). These persons were monitored with 24-h dynamic electrocardiogram. Various types of arrhythmia were detected by man-machine interaction, and ectopic beats and artifacts were removed. Sinus rhythm was automatically detected, and SDNN, SDANN, SDNN index, rMSSD, and pNN50 were calculated. QT intervals were acquired by computer-aided measurement, and their mean value was obtained. QT dispersion was calculated. ResultsCompared with the non-smoking group, the smoking group showed significantly decreased SDNN, rMSSD, and pNN50 (P0.01-0.05) and significantly increased QT interval and QT dispersion (P0.05). ConclusionSmoking can significantly decrease HRV, prolong QT interval, and increase QT dispersion. That may be one of the mechanisms by which smoking increases the incidence of cardiovascular events.
The analyses of cardiac dynamics appear to be very promising in assessing cardiovascular health. Short-term analysis well meets the increasing clinical needs for point-of-care diagnosis, portable monitoring and personal healthcare. In one of our previous CinC articles, we have shown that there is inherent coupling between short-term heart rate variability (HRV) and diastolic period variability (DPV). Besides, the coupling is reduced at their small temporal scales in healthy aging subjects. We thus aimed to investigate the HRV-DPV coupling in heart failure (HF) patients in this work. Fifty healthy volunteers and 52 HF patients were studied. Multiscale multivariate fuzzy entropy (MMFE) analysis was performed in each bivariate signal (short-term HRV and simultaneous recorded DPV) to probe into their within- and cross-channel correlations. Results show that the coupling between short-term HRV and DPV is reduced at both small and large temporal scales in HF patients compared with healthy volunteers. It may indicate that the heart loses both the immediate mechanical response to the changes of heart period and its long-range compliance, which is very different from the effects of healthy aging. It thus shows great potential of short-term HRV-DPV coupling analysis in the noninvasive and nondestructive detection of HF. An increased specificity of the so-constructed HF detection indices should also be expected.
Abstract Background The 8th edition of AJCC/UICC TNM staging system (TNM system) and the previous nomograms have limitations, therefore we aimed to develop and validate nomograms incorporating routine hematological biomarkers with or without EBV DNA for overall survival (OS) and progression-free survival (PFS). We also evaluated the prognostic role of EBV DNA. Material and Methods 1203 patients at our hospital from 2013 to 2016 were retrospectively reviewed and divided into two parts (922 patients for primary cohort and 281 for validation cohort). Nomograms (nomogram with or without EBV DNA) were developed and compared with other models (TNM system alone, TNM system with EBV DNA), via comparison the prognostic role of EBV DNA was evaluated. Internal and external validation were performed. Risk stratification were conducted with recursive partitioning analysis. Results The nomograms with EBV DNA for OS and PFS included sex, age, T category, N category, EBV DNA, albumin, neutrophil to lymphocyte ratio and lactate dehydrogenase. The nomograms without EBV DNA for OS and PFS included the same variables but without EBV DNA. The C-index for nomogram with EBV DNA was 0.715 for OS and 0.705 for PFS. For nomogram without EBV DNA, it was 0.709 and 0.700, respectively. It was 0.639 and 0.636 for TNM system alone and 0.648, 0.646 respectively for TNM system with EBV DNA. The nomograms with or without EBV DNA had better performance than both the TNM system alone and TNM system with EBV DNA, while the TNM system with EBV DNA were better than TNM system alone. The validation cohort indicates great applicability of nomograms. The patients were stratified into 4 risk groups. Conclusion The nomograms with or without EBV DNA provide better prognostication than the TNM system and also the TNM system with EBV DNA. EBV DNA is valuable in predicting survival, but it is not suggested to incorporate EBV DNA alone to TNM system.
Abstract Classification and outcome prediction of intracerebral hemorrhage (ICH) is critical for improving the survival rate of patients. Early or delayed neurological deterioration is common in ICH patients, which may lead to changes in the autonomic nervous system (ANS). Therefore, we proposed a new framework for ICH classification and outcome prediction based on skin sympathetic nervous activity (SKNA) signals. A customized measurement device presented in our previous papers was used to collect data. 117 subjects (50 healthy control subjects and 67 ICH patients) were recruited for this study to obtain their five-minute ECG and SKNA signals. We extracted the signal’s time-domain, frequency-domain, and nonlinear features and analyzed their differences between healthy control subjects and ICH patients. Subsequently, we established the ICH classification and outcome evaluation model based on the eXtreme Gradient Boosting (XGBoost). In addition, HRV as an autonomic nerve assessment method was also included as a comparison method in this study. The results showed significant differences in most features of the SKNA signal between healthy control subjects and ICH patients. The ICH patients with good outcomes have a higher change rate and complexity of SKNA signal than those with bad outcomes. In addition, the accuracy of the model for ICH classification and outcome prediction based on the SKNA signal was more than 91% and 83%, respectively. The ICH classification and outcome prediction based on the SKNA signal proved to be a feasible method in this study. Furthermore, the features of change rate and complexity, such as entropy measures, can be used to characterize the difference in SKNA signals of different groups. The method can potentially provide a new tool for rapid classification and outcome prediction of ICH patients.
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