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This study aims at evaluating the potential of a wrist-type photoplethysmographic (PPG) device to discriminate between atrial fibrillation (AF) and other types of rhythm. Data from 17 patients undergoing catheter ablation of various arrhythmias were processed. ECGs were used as ground truth and annotated for the following types of rhythm: sinus rhythm (SR), AF, and ventricular arrhythmias (VA). A total of 381/1370/415 10-s epochs were obtained for the three categories, respectively. After pre-processing and removal of segments corresponding to motion artifacts, two different types of feature were derived from the PPG signals: the interbeat interval-based features and the wave-based features, consisting of complexity/organization measures that were computed either from the PPG waveform itself or from its power spectral density. Decision trees were used to assess the discriminative capacity of the proposed features. Three classification schemes were investigated: AF against SR, AF against VA, and AF against (SR&VA). The best results were achieved by combining all features. Accuracies of 98.1/95.9/95.0 %, specificities of 92.4/88.7/92.8 %, and sensitivities of 99.7/98.1/96.2 % were obtained for the three aforementioned classification schemes, respectively. Graphical Abstract Atrial fibrillation detection using PPG signals. [ABSTRACT FROM AUTHOR]

Anti-gravity treadmills facilitate locomotion by lower-body positive pressure (LBPP). Effects on cardiorespiratory regulation are unknown. Healthy men (30 ± 8 y, 178.3 ± 5.7 cm, 70.3 ± 8.0 kg; mean ± SD) stood upright (n = 10) or ran (n = 9) at 9, 11, 13, and 15 km.h-1 (5min stages) with LBPP (0, 15, 40 mmHg). Cardiac output (CO), stroke volume (SV), heart rate (HR), blood pressure (BP), peripheral resistance (PR), and oxygen uptake (VO2) were monitored continuously. During standing, LBPP increased SV [by +29 ± 13 (+41%) and +42 ± 15 (+60%) ml, at 15 and 40 mmHg, respectively (p < 0.05)] and decreased HR [by -15 ± 6 (-20%) and -22 ± 9 (-29%) bpm(p < 0.05)] resulting in a transitory increase in CO [by +1.6 ± 1.0 (+32%) and +2.0 ± 1.0 (+39%) l.min-1 (p < 0.05)] within the first seconds of LBPP. This was accompanied by a transitory decrease in end-tidal PO2 [by -5 ± 3 (-5%) and -10 ± 4 (-10%) mmHg (p < 0.05)] and increase in VO2 [by +66 ± 53 (+26%) and +116 ± 64 (+46%) ml.min-1 (p < 0.05)], suggesting increased venous return and pulmonary blood flow. The application of LBPP increased baroreflex sensitivity (BRS) [by +1.8 ± 1.6 (+18%) and +4.6 ± 3.7 (+47%) at 15 and 40 mmHg LBPP, respectively P < 0.05]. After reaching steady-state exercise CO vs. VO2 relationships remained linear with similar slope and intercept for each participant (mean R2 = 0.84 ± 0.13) whileMAP remained unchanged. It follows that (1) LBPP affects cardiorespiratory integration at the onset of exercise; (2) at a given LBPP, once reaching steady-state exercise, the cardiorespiratory load is reduced proportionally to the lower metabolic demand resulting from the body weight support; (3) the balance between cardiovascular response, oxygen delivery to the exercising muscles and blood pressure regulation is maintained at exercise steady-state; and (4) changes in baroreflex sensitivity may be involved in the regulation of cardiovascular parameters during LBPP. [ABSTRACT FROM AUTHOR]

Heart rate variability (HRV) is non-invasive and commonly used for monitoring responses to training loads, fitness, or overreaching in athletes. Yet, the recording duration for a series of RR-intervals varies from 1 to 15 min in the literature. The aim of the present work was to assess the minimum record duration to obtain reliable HRV results. RR-intervals from 159 orthostatic tests (7 min supine, SU, followed by 6 min standing, ST) were analyzed. Reference windows were 4 min in SU (min 3-7) and 4 min in ST (min 9-13). Those windows were subsequently divided and the analyses were repeated on eight different fractioned windows: the first min (0-1), the second min (1-2), the third min (2-3), the fourth min (3-4), the first 2 min (0-2), the last 2 min (2-4), the first 3 min (0-3), and the last 3 min (1-4). Correlation and Bland & Altman statistical analyses were systematically performed. The analysis window could be shortened to 0-2 instead of 0-4 for RMSSD only, whereas the 4-min window was necessary for LF and total power. Since there is a need for 1 min of baseline to obtain a steady signal prior the analysis window, we conclude that studies relying on RMSSD may shorten the windows to 3 min (= 1+2) in SU or seated position only and to 6 min (=1+2 min SU plus 1+2 min ST) if there is an orthostatic test. Studies relying on time- and frequency-domain parameters need a minimum of 5 min (=1+4) min SU or seated position only but require 10 min (=1+4 min SU plus 1+4 min ST) for the orthostatic test. [ABSTRACT FROM AUTHOR]

This paper introduces a new method for QT-interval estimation. It consists in a batch processing mode of the improved Woody’s method. Performance of this methodology is evaluated using synthetic data. In parallel, a new model of QT-interval dynamics behavior related to heart rate changes is presented. Since two kinds of QT response have been pointed out, the main idea is to split the modeling process into two steps: 1) the modeling of the fast adaptation, which is inspired by the electrical behavior at the cellular level relative to the electrical restitution curve, and 2) the modeling of the slow adaptation, inspired by experimental works at the cellular level. Both approaches are based on a low-complexity autoregressive process whose parameters are estimated using an unbiased estimator. This new modeling of QT adaptation, combined with the presented QT-estimation process, is applied to several ECG recordings with various heart rate variability dynamics. Its potential is then illustrated on ECG recorded during rest, atrial fibrillation episodes, and exercise. Meaningful results in agreement with physiological knowledge at the cellular level are obtained. [ABSTRACT FROM PUBLISHER]

Due to the much higher amplitude of the electrical activity of the ventricles in the surface electrocardiogram (ECG), its cancellation is crucial for the analysis and characterization of atrial fibrillation. In this paper, two different methods are proposed for this cancellation. The first one is an average beat subtraction type of method. Two sets of templates are created: one set for the ventricular depolarization waves and one for the ventricular repolarization waves. Next, spatial optimization (rotation and amplitude scaling) is applied to the QRS templates. The second method is a single beat method that cancels the ventricular involvement in each cardiac cycle in an independent manner. The estimation and cancellation of the ventricular repolarization is based on the concept of dominant T and U waves. Subsequently, the atrial activities during the ventricular depolarization intervals are estimated by a weighted sum of sinusoids observed in the cleaned up segments. ECG signals generated by a biophysical model as well as clinical ECG signals are used to evaluate the performance of the proposed methods in comparison to two standard ABS-based methods. [ABSTRACT FROM AUTHOR]

Recent advances in wearable technologies integrating multi-modal sensors have enabled the in-field monitoring of several physiological metrics. In sport applications, wearable devices have been widely used to improve performance while minimizing the risk of injuries and illness. The objective of this project is to estimate breathing rate (BR) from respiratory sinus arrhythmia (RSA) using heart rate (HR) recorded with a chest belt during physical activities, yielding additional physiological insight without the need of an additional sensor. Thirty-one healthy adults performed a run at increasing speed until exhaustion on an instrumented treadmill. RR intervals were measured using the Polar H10 HR monitoring system attached to a chest belt. A metabolic measurement system was used as a reference to evaluate the accuracy of the BR estimation. The evaluation of the algorithms consisted of exploring two pre-processing methods (band-pass filters and relative RR intervals transformation) with different instantaneous frequency tracking algorithms (short-term Fourier transform, single frequency tracking, harmonic frequency tracking and peak detection). The two most accurate BR estimations were achieved by combining band-pass filters with short-term Fourier transform, and relative RR intervals transformation with harmonic frequency tracking, showing 5.5% and 7.6% errors, respectively. These two methods were found to provide reasonably accurate BR estimation over a wide range of breathing frequency. Future challenges consist in applying/validating our approaches during in-field endurance running in the context of fatigue assessment. [ABSTRACT FROM AUTHOR]

Presents information on a study that examined a novel method for the blind reconstruction of the cardiac sympathetic nerve activity (CSNA) in the low-frequency (LF) band using only heart rate and arterial blood pressure. Experimental protocols and data acquisition; Description of the blind source separation; Application to the cardiovascular system.

Modern computer power allows development of models of the heart that may be helpful for the understanding of arrhythmia mechanisms if, based on realistic physiological parameters, such models can display phenomena difficult to study in nature. Therefore, a two-dimensional model of the cardiac tissue has been implemented, where the modeling of each cell is based on membrane ionic channels (Beeler-Reuter and Luo-Rudy models). In addition, an ECG was computed based on the ionic currents simulated. This model allows us to observe the propagation of the action potentials Vm across the cardiac tissue, the evolution of Vm for any of the cardiac cells, and the underlying ionic currents. The computation of the ECG makes it possible to relate this information with an often-used diagnostic tool. Simulations of normal and pathological phenomena such as functional and anatomic reentry have been performed. Our simulation results show that the applied computer model based on ionic currents seems accurate and realistic when compared with biological models and offers a new approach to study the origin, prevention, and termination of arrhythmias. [ABSTRACT FROM AUTHOR]

Easy-to-use and accurate heart rate variability (HRV) assessments are essential in athletes' follow-up, but artifacts may lead to erroneous analysis. Artifact detection and correction are the purpose of extensive literature and implemented in dedicated analysis programs. However, the effects of number and/or magnitude of artifacts on various time- or frequency-domain parameters remain unclear. The purpose of this study was to assess the effects of artifacts on HRV parameters. Root mean square of the successive differences (RMSSD), standard deviation of the normal to normal inter beat intervals (SDNN), power in the low- (LF) and high-frequency band (HF) were computed from two 4-min RR recordings in 178 participants in both supine and standing positions, respectively. RRs were modified by (1) randomly adding or subtracting 10, 30, 50 or 100 ms to the successive RRs; (2) a single artifact was manually inserted; (3) artifacts were automatically corrected from signal naturally containing artifacts. Finally, RR recordings were analyzed before and after automatic detection-correction of artifacts. Modifying each RR by 10, 30, 50 and 100 ms randomly did not significantly change HRV parameters (range -6%, +6%, supine). In contrast, by adding a single artifact, RMSSD increased by 413% and 269%, SDNN by 54% and 47% in supine and standing positions, respectively. LF and HF changed only between -3% and +8% (supine and standing) in the artifact condition. When more than 0.9% of the signal contained artifacts, RMSSD was significantly biased, whilst when more than 1.4% of the signal contained artifacts LF and HF were significantly biased. RMSSD and SDNN were more sensitive to a single artifact than LF and HF. This indicates that, when using RMSSD only, a single artifact may induce erroneous interpretation of HRV. Therefore, we recommend using both time- and frequencydomain parameters to minimize the errors in the diagnoses of health status or fatigue in athletes. [ABSTRACT FROM AUTHOR]