Yves Papelier

Bio: Yves Papelier is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Baroreflex & Heart rate. The author has an hindex of 12, co-authored 24 publications receiving 727 citations.

An Algorithm for Robust and Efficient Location of T-Wave Ends in Electrocardiograms

Abstract: The purpose of this paper is to propose a new algorithm for T-wave end location in electrocardiograms, mainly through the computation of an indicator related to the area covered by the T-wave curve. Based on simple assumptions, essentially on the concavity of the T-wave form, it is formally proved that the maximum of the computed indicator inside each cardiac cycle coincides with the T-wave end. Moreover, the algorithm is robust to acquisition noise, to wave form morphological variations and to baseline wander. It is also computationally very simple: the main computation can be implemented as a simple finite impulse response filter. When evaluated with the PhysioNet QT database in terms of the mean and the standard deviation of the T-wave end location errors, the proposed algorithm outperforms the other algorithms evaluated with the same database, according to the most recent available publications up to our knowledge

Spectral analysis of heart rate variability during exercise in trained subjects.

Abstract: Purpose:To investigate the effects of strenuous exercise on heart rate variability (HRV).Methods:We evaluated the effects of exercise intensity and duration on HRV indices in 14 healthy trained subjects. Each subject exercised for 3, 6, and 9 min at 60 and 70% of the power achieved at maxima

Heart rate variability during exercise performed below and above ventilatory threshold.

Abstract: Purpose To examine whether differences in heart rate variability (HRV) can distinguish sub- from supra-ventilatory-threshold exercise and whether the exercise duration at supra-threshold intensity alters cardiorespiratory synchronization. Methods Beat-to-beat RR interval, VO2, VCO2, VE, and blood lactate concentration of 11 healthy well-trained young subjects were collected during two exercise tests: 1) a moderate-intensity test: 15 min performed below the power at ventilatory threshold (pVT); and 2) a heavy-intensity test: above pVT until exhaustion. Fast Fourier transform, smoothed pseudo Wigner-Ville distribution, and complex demodulation were applied to RR time series. Results 1) Moderate exercise shows a prevalence of low-frequency (LF) spectral energy compared with the high-frequency (HF) one (LF = 80 +/- 10% vs HF = 20 +/- 10%, P Conclusion 1) HRV allows us to differentiate sub- from supra-ventilatory-threshold exercise and 2) exercise duration at supra-threshold intensity does not alter the cardiorespiratory synchronization.

Assessment of Ventilatory Thresholds from Heart Rate Variability in Well-Trained Subjects during Cycling

Abstract: The purpose of this study was to implement a new method for assessing the ventilatory thresholds from heart rate variability (HRV) analysis ECG, VO2, VCO2, and VE were collected from eleven well-trained subjects during an incremental exhaustive test performed on a cycle ergometer The "Short-Term Fourier Transform" analysis was applied to RR time series to compute the high frequency HRV energy (HF, frequency range: 015 - 2 Hz) and HF frequency peak (fHF) vs power stages For all subjects, visual examination of ventilatory equivalents, fHF, and instantaneous HF energy multiplied by fHF (HFfHF) showed two nonlinear increases The first nonlinear increase corresponded to the first ventilatory threshold (VT1) and was associated with the first HF threshold (T(RSA1) from fHF and HFT1 from HFfHF detection) The second nonlinear increase represented the second ventilatory threshold (VT2) and was associated with the second HF threshold (T(RSA2) from fHF and HFT2 from HFfHF detection) HFT1 , T(RSA1), HFT2, and T(RSA2) were, respectively, not significantly different from VT1 (VT1 = 219 +/- 45 vs HFT1 = 220 +/- 48 W, p = 0975; VT1 vs T(RSA1) = 213 +/- 56 W, p = 0662) and VT2 (VT2 = 293 +/- 45 vs HFT2 = 294 +/- - 48 W, p = 0956; vs T(RSA2) = 300 +/- 58 W, p = 0445) In addition, when expressed as a function of power, HFT1, T(RSA1), HFT2, and T(RSA2) were respectively correlated with VT1 (with HFT1 r2 = 094, p < 0001; with T(RSA1) r2 = 048, p < 005) and VT2 (with HFT2 r2 = 097, p < 0001; with T(RSA2 )r2 = 079, p < 0001) This study confirms that ventilatory thresholds can be determined from RR time series using HRV time-frequency analysis in healthy well-trained subjects In addition it shows that HFfHF provides a more reliable and accurate index than fHF alone for this assessment

Effect of heavy exercise on spectral baroreflex sensitivity, heart rate, and blood pressure variability in well-trained humans

Abstract: The aim of the study was to assess the instantaneous spectral components of heart rate variability (HRV) and systolic blood pressure variability (SBPV) and determine the low-frequency (LF) and high...

Heart rate variability in athletes.

Abstract: This review examines the influence on heart rate variability (HRV) indices in athletes from training status, different types of exercise training, sex and ageing, presented from both cross-sectional and longitudinal studies. The predictability of HRV in over-training, athletic condition and athletic performance is also included. Finally, some recommendations concerning the application of HRV methods in athletes are made.The cardiovascular system is mostly controlled by autonomic regulation through the activity of sympathetic and parasympathetic pathways of the autonomic nervous system. Analysis of HRV permits insight in this control mechanism. It can easily be determined from ECG recordings, resulting in time series (RR-intervals) that are usually analysed in time and frequency domains. As a first approach, it can be assumed that power in different frequency bands corresponds to activity of sympathetic (0.04-0.15 Hz) and parasympathetic (0.15-0.4 Hz) nerves. However, other mechanisms (and feedback loops) are also at work, especially in the low frequency band. During dynamic exercise, it is generally assumed that heart rate increases due to both a parasympathetic withdrawal and an augmented sympathetic activity. However, because some authors disagree with the former statement and the fact that during exercise there is also a technical problem related to the non-stationary signals, a critical look at interpretation of results is needed. It is strongly suggested that, when presenting reports on HRV studies related to exercise physiology in general or concerned with athletes, a detailed description should be provided on analysis methods, as well as concerning population, and training schedule, intensity and duration. Most studies concern relatively small numbers of study participants, diminishing the power of statistics. Therefore, multicentre studies would be preferable. In order to further develop this fascinating research field, we advocate prospective, randomised, controlled, long-term studies using validated measurement methods. Finally, there is a strong need for basic research on the nature of the control and regulating mechanism exerted by the autonomic nervous system on cardiovascular function in athletes, preferably with a multidisciplinary approach between cardiologists, exercise physiologists, pulmonary physiologists, coaches and biomedical engineers.

Heart rate variability biofeedback: how and why does it work?

Abstract: In recent years there has been substantial support for heart rate variability biofeedback (HRVB) as a treatment for a variety of disorders and for performance enhancement (Gevirtz, 2013). Since conditions as widely varied as asthma and depression seem to respond to this form of cardiorespiratory feedback training, the issue of possible mechanisms becomes more salient. The most supported possible mechanism is the strengthening of homeostasis in the baroreceptor (Vaschillo et al., 2002; Lehrer et al., 2003). Recently, the effect on the vagal afferent pathway to the frontal cortical areas has been proposed. In this article, we review these and other possible mechanisms that might explain the positive effects of HRVB.

The concept of maximal lactate steady state: a bridge between biochemistry, physiology and sport science.

Abstract: concentration (MLSSc) and work load (MLSSw) that can be maintained over time without a continual blood lactate accumulation. A close relationship between endurance sport performance and MLSSw has been reported and the average velocity over a marathon is just below MLSSw. This work rate delineates the lowto high-intensity exercises at which carbohydrates contribute more than 50% of the total energy need and at which the fuel mix switches (crosses over) from predominantly fat to predominantly carbohydrate. The rate of metabolic adenosine triphosphate (ATP) turnover increases as a direct function of metabolic power

Logistic Regression-HSMM-Based Heart Sound Segmentation

Abstract: The identification of the exact positions of the first and second heart sounds within a phonocardiogram (PCG), or heart sound segmentation, is an essential step in the automatic analysis of heart sound recordings, allowing for the classification of pathological events. While threshold-based segmentation methods have shown modest success, probabilistic models, such as hidden Markov models, have recently been shown to surpass the capabilities of previous methods. Segmentation performance is further improved when a priori information about the expected duration of the states is incorporated into the model, such as in a hidden semi-Markov model (HSMM). This paper addresses the problem of the accurate segmentation of the first and second heart sound within noisy real-world PCG recordings using an HSMM, extended with the use of logistic regression for emission probability estimation. In addition, we implement a modified Viterbi algorithm for decoding the most likely sequence of states, and evaluated this method on a large dataset of 10 172 s of PCG recorded from 112 patients (including 12 181 first and 11 627 second heart sounds). The proposed method achieved an average $F_{1}$ score of 95.63 $\,\pm \,$ 0.85%, while the current state of the art achieved 86.28 $\pm \,$ 1.55% when evaluated on unseen test recordings. The greater discrimination between states afforded using logistic regression as opposed to the previous Gaussian distribution-based emission probability estimation as well as the use of an extended Viterbi algorithm allows this method to significantly outperform the current state-of-the-art method based on a two-sided paired t-test.