Rolf Vetter

Wrist-located pulse detection using IR signals, activity and nonlinear artifact cancellation

We present a new integrated device for monitoring heart rate at the wrist using an optical measure. Motion robustness is obtained by using accurate motion reference signals of 3D low noise accelerometers together with dual channel optical sensing. Nonlinear modelling allows to remove the motion contributions in the optical signals and the spatial diversity of the sensors is used to remove reciprocal contributions in the two channels. Finally a statistical estimation, based on physiological properties of the heart, gives a robust estimation of the heart rate. Qualitative and quantitative performance evaluation of the performances on real signals clearly show that the proposed system gives an accurate estimation of the heart rate, even under intense physical activity.

Parametric estimation of pulse arrival time: a robust approach to pulse wave velocity

Pulse wave velocity (PWV) is a surrogate of arterial stiffness and represents a non-invasive marker of cardiovascular risk. The non-invasive measurement of PWV requires tracking the arrival time of pressure pulses recorded in vivo, commonly referred to as pulse arrival time (PAT). In the state of the art, PAT is estimated by identifying a characteristic point of the pressure pulse waveform. This paper demonstrates that for ambulatory scenarios, where signal-to-noise ratios are below 10 dB, the performance in terms of repeatability of PAT measurements through characteristic points identification degrades drastically. Hence, we introduce a novel family of PAT estimators based on the parametric modeling of the anacrotic phase of a pressure pulse. In particular, we propose a parametric PAT estimator (TANH) that depicts high correlation with the Complior(R) characteristic point D1 (CC = 0.99), increases noise robustness and reduces by a five-fold factor the number of heartbeats required to obtain reliable PAT measurements.

Observer of autonomic cardiac outflow based on blind source separation of ECG parameters

We present a novel method which provides an observer of the autonomic cardiac outflow using heartbeat intervals (RR) and QT intervals. The model of the observer is inferred from qualitative physiological knowledge. It consists in a problem of blind source separation of noisy mixtures which is resolved by a simple and robust algorithm. The robustness of the algorithm has been assessed by numerical simulations in adverse noisy environments. In clinical applications, we have validated the observer on subjects exposed to experimental conditions known to elicit sympathetic or parasympathetic response.

Observer of the human cardiac sympathetic nerve activity using noncausal blind source separation

We present a novel method for the blind reconstruction of the cardiac sympathetic nerve activity (CSNA) in the low-frequency (LF) band (0.04-0.15 Hz) using only heart rate and arterial blood pressure. The originality of the method consists in the application of blind source separation techniques to obtain an observer of CSNA. We show how this observer can be deduced from a linear model of the cardiovascular system by introduction of the fundamental assumptions about the independence of the cardiac sympathetic an parasympathetic outflow. In cardiovascular applications, the reliability of the observer has been assessed by verification of the fundamental assumption for the given data. A primer qualitative validation has been performed using the muscle sympathetic nerve activity as an indirect indicator of CSNA. Very satisfying and promising results have been obtained. Moreover, we have performed quantitative validations of the observer in various experimental conditions known to elicit selectively cardiac sympathetic or parasympathetic response. The experimental conditions include a supine-to-60/spl deg/ tilt test, indirect sympathetic stimulation/inhibition by medication, and sympathetic stimulation by isometric handgrip. We show that the observer allows to highlight changing levels of the cardiac sympathetic activity in the LF band in all these experimental conditions.

Wearable biosensing: signal processing and communication architectures issues

Mutation testing – a fault-based technique for software testing – is a computationally expensive approach. One of the powerful methods to improve the performance of mutation without reducing effectiveness is to employ parallel processing, where mutants and tests are executed in parallel. This approach reduces the total time needed to accomplish the mutation analysis. This paper proposes three strategies for parallel execution of mutants on multicore machines using the Parallel Computing Toolbox (PCT) with the Matlab Distributed Computing Server. It aims to demonstrate that the computationally intensive software testing schemes, such as mutation, can be facilitated by using parallel processing. The experiments were carried out on eight different Simulink models. The results represented the efficiency of the proposed approaches in terms of execution time during the testing process. Keywords—mutant execution, mutation testing, parallel processing, software testing.

Sub-signal extraction of RR time series using independent component analysis

We present a novel method for the decomposition of RR time series into independent sub-signals. The estimation of the optimal number of subsignals is based on principal component analysis together with an information theory selection criterion. The independent sub-signals are extracted by a blind source separation technique. We show on 8 subjects under various clinical conditions, that the sub-signals are related mainly to sympathetic and parasympathetic regulation mechanisms of the heart rate.

Estimation of a Runner’s Speed Based on Chest-belt Integrated Inertial Sensors (P27)

In long distance running, real time monitoring and performance optimization has been recently rendered possible through the commercialization of a large variety of running computers. Generally, such computers simultaneously monitor several parameters such as heart rate, running speed, stride frequency and stride length. More precisely, stride and speed information is obtained using foot located inertial sensors. Unfortunately, due to its leg extremity location, the foot inertial sensor presents some disadvantages: it may be perceived as cumbersome; it requires supplementary telecommunication facilities as well as local power supply; it may increase a runner’s energy expenditure even though it weights only a few tens of grams.

Closed loop heart beat interval modelization in humans using mean blood pressure, instantaneous lung volume and muscle sympathetic nerve activity

Considering a closed loop model of the cardiovascular system, the authors develop accurate multi-input linear and nonlinear approaches for estimation of the heart beat interval time series using mean arterial blood pressure (MABP), instantaneous lung volume (ILV) and muscle sympathetic nerve activity (MSNA). The nonlinear model proved to be more adequate compared to the linear one, with respiration being the most nonlinear component of the system.

Prosodic Speech Restoration Device: Glottal Excitation Restoration Using a Multi-resolution Approach

We describe a novel device for the restoration of authentic characteristics in pathological speech uttered by subjects with laryngeal disorders. The original speech signal is acquired and analyzed by the device and a speech signal with improved, healthy-like features is reconstructed. A concatenation of randomly chosen healthy reference patterns replaces the pathological excitation. In order to restore authentic features, intervals between subsequent reference patterns are obtained through a multi-resolution approach. Short-term pitch variability is reproduced through a statistical variation model. For middle-term pitch variability the correlation between pitch and signal envelope at the middle-term time scale is exploited. Long-term variability is obtained through adaptive wavetable oscillators; a novel, reliable and computationally efficient pitch estimation method. Two authentic features were used to assess performance, namely breathiness and prosody. Preliminary results indicate that breathiness of the restored signal is clearly reduced and prosody related features are improved.

Robust extraction of autonomous nervous profile using a non-invasive method

An improved algorithm for the non-invasive estimation of the autonomous nervous profile is presented. It is based on a previously published method using blind source separation on inter-beat intervals and systolic pressure time series. Improvements focus on robust extraction of cardiovascular parameters and management of singular solutions of blind source separation algorithm. Clinical validation has been performed successfully in 104 experiments on 38 subjects. The method has been finally applied to estimate the autonomic nervous profile during vasovagal syncope, and results have been compared to classical heart rate variability methods.