On-off closed-loop control of vagus nerve stimulation for the adaptation of heart rate

Vagus nerve stimulation (VNS) is a potential therapeutic approach in a number of clinical applications. Although VNS is commonly delivered in an open-loop approach, it is now recognized that closed-loop approaches may be necessary to optimize the therapy and minimize side effects of neuro-stimulation devices. In this paper, we describe a prototype system for real-time control of the instantaneous heart rate, working synchronously with the heart period. As a first step, an on-off control method has been integrated. The system is evaluated on one sheep with induced heart failure, showing the interest of the proposed approach.

Surface electrocardiogram reconstruction from intracardiac electrograms using a dynamic time delay artificial neural network

This study proposes a method to facilitate the remote follow up of patients suffering from cardiac pathologies and treated with an implantable device, by synthesizing a 12-lead surface ECG from the intracardiac electrograms (EGM) recorded by the device. Two methods (direct and indirect), based on dynamic time-delay artificial neural networks (TDNNs) are proposed and compared with classical linear approaches. The direct method aims to estimate 12 different transfer functions between the EGM and each surface ECG signal. The indirect method is based on a preliminary orthogonalization phase of the available EGM and ECG signals, and the application of the TDNN between these orthogonalized signals, using only three transfer functions. These methods are evaluated on a dataset issued from 15 patients. Correlation coefficients calculated between the synthesized and the real ECG show that the proposed TDNN methods represent an efficient way to synthesize 12-lead ECG, from two or four EGM and perform better than the linear ones. We also evaluate the results as a function of the EGM configuration. Results are also supported by the comparison of extracted features and a qualitative analysis performed by a cardiologist.

On-line apnea-bradycardia detection using hidden semi-Markov models

In this work, we propose a detection method that exploits not only the instantaneous values, but also the intrinsic dynamics of the RR series, for the detection of apnea-bradycardia episodes in preterm infants. A hidden semi-Markov model is proposed to represent and characterize the temporal evolution of observed RR series and different pre-processing methods of these series are investigated. This approach is quantitatively evaluated through synthetic and real signals, the latter being acquired in neonatal intensive care units (NICU). Compared to two conventional detectors used in NICU our best detector shows an improvement of around 13% in sensitivity and 7% in specificity. Furthermore, a reduced detection delay of approximately 3 seconds is obtained with respect to conventional detectors.

Respiratory and spontaneous arousals in patients with Sleep Apnea Hypopnea Syndrome

Sleep in patients with Sleep Apnea-Hypopnea Syndrome (SAHS) is frequently interrupted with arousals. Increased amounts of arousals result in shortening total sleep time and repeated sleep-arousal change can result in sleep fragmentation. According to the American Sleep Disorders Association (ASDA) an arousal is a marker of sleep disruption representing a detrimental and harmful feature for sleep. The nature of arousals and its role on the regulation of the sleep process raises controversy and has sparked the debate in the last years. In this work, we analyzed and compared the EEG spectral content of respiratory and spontaneous arousals on a database of 45 SAHS subjects. A total of 3980 arousals (1996 respiratory and 1984 spontaneous) were analyzed. The results showed no differences between the spectral content of the two kinds of arousals. Our findings raise doubt as to whether these two kinds of arousals are truly triggered by different organic mechanisms. Furthermore, they may also challenge the current beliefs regarding the underestimation of the importance of spontaneous arousals and their contribution to sleep fragmentation in patients suffering from SAHS.

Multiscale systems modeling of the tetralogy of Fallot

This paper provides a first description of a multiscale systems modeling approach applied to the congenital birth defect known as the tetralogy of Fallot. The multiscale approach adopted owes a lot to the effort of the world-wide physiome consortium and the work of research groups within the European Union on the Virtual Physiological Human. Both a spatial scale and time scale are used to establish the systems boundaries of the application. The tetralogy of Fallot includes up to four simultaneously occurring anatomic abnormalities that underpin the defect. The use of finite state machines and cellular automata pave the way to understand the processes in time and space that contribute to the defect.

A multiformalism and multiresolution modelling environment: application to the cardiovascular system and its regulation

The role of modelling and simulation in the systemic analysis of living systems is now clearly established. Emerging disciplines, such as systems biology, and worldwide research actions, such as the Physiome Project or the Virtual Physiological Human, are based on an intensive use of modelling and simulation methodologies and tools. One of the key aspects in this context is to perform an efficient integration of various models representing different biological or physiological functions, at different resolutions, spanning through different scales. This paper presents a multiformalism modelling and simulation environment (M2SL) that has been conceived to ease model integration. A given model is represented as a set of coupled and atomic model components that may be based on different mathematical formalisms with heterogeneous structural and dynamical properties. A co-simulation approach is used to solve these hybrid systems. The pioneering model of the overall regulation of the cardiovascular system proposed by Guyton and co-workers in 1972 has been implemented under M2SL and a pulsatile ventricular model based on a time-varying elastance has been integrated in a multi-resolution approach. Simulations reproducing physiological conditions and using different coupling methods show the benefits of the proposed environment.

Analysis of the QRS complex for apnea-bradycardia characterization in preterm infants

This work presents an analysis of the information content of new features derived from the electrocardiogram (ECG) for the characterization of apnea-bradycardia events in preterm infants. Automatic beat detection and segmentation methods have been adapted to the ECG signals from preterm infants, through the application of two evolutionary algorithms. ECG data acquired from 32 preterm infants with persistent apnea-bradycardia have been used for quantitative evaluation. The adaptation procedure led to an improved sensitivity and positive predictive value, and a reduced jitter for the detection of the R-wave, QRS onset, QRS offset, and iso-electric level. Additionally, time series representing the RR interval, R-wave amplitude and QRS duration, were automatically extracted for periods at rest, before, during and after apnea-bradycardia episodes. Significant variations (p<0.05) were observed for all time-series when comparing the difference between values at rest versus values just before the bradycardia event, with the difference between values at rest versus values during the bradycardia event. These results reveal changes in the R-wave amplitude and QRS duration, appearing at the onset and termination of apnea-bradycardia episodes, which could be potentially useful for the early detection and characterization of these episodes.

A tissue-level electromechanical model of the left ventricle: application to the analysis of intraventricular pressure

The ventricular pressure profile is characteristic of the cardiac contraction progress and is useful to evaluate the cardiac performance. In this contribution, a tissue-level electromechanical model of the left ventricle is proposed, to assist the interpretation of left ventricular pressure waveforms. The left ventricle has been modeled as an ellipsoid composed of twelve mechano-hydraulic sub-systems. The asynchronous contraction of these twelve myocardial segments has been represented in order to reproduce a realistic pressure profiles. To take into account the different energy domains involved, the tissue-level scale and to facilitate the building of a modular model, multiple formalisms have been used: Bond Graph formalism for the mechano-hydraulic aspects and cellular automata for the electrical activation. An experimental protocol has been defined to acquire ventricular pressure signals from three pigs, with different afterload conditions. Evolutionary Algorithms have been used to identify the model parameters in order to minimize the error between experimental and simulated ventricular pressure signals. Simulation results show that the model is able to reproduce experimental ventricular pressure. In addition, electro-mechanical activation times have been determined in the identification process. For example, the maximum electrical activation time is reached, respectively, 96.5, 139.3 and 131.5 ms for the first, second, and third pigs. These preliminary results are encouraging for the application of the model on non-invasive data like ECG, arterial pressure or myocardial strain.

Modelling temporal evolution of cardiac electrophysiological features using Hidden Semi-Markov Models

This paper presents a new method to analyse cardiac electrophysiological dynamics. It aims to classify or to cluster (i.e. to find natural groups) patients according to the dynamics of features extracted from their ECG. In this work, the dynamics of the features are modelled with Continuous Density Hidden Semi-Markovian Models (CDHSMM) which are interesting for the characterization of continuous multivariate time series without a priori information. These models can be easily used for classification and clustering. In this last case, a specific method, based on a fuzzy Expectation Maximisation (EM) algorithm, is proposed. Both tasks are applied to the analysis of ischemic episodes with encouraging results and a classification accuracy of 71%.

Application of multiple correspondence analysis to asses the relation between time after transplantation and sympathetic activity in cardiac transplant recipient

The aim of the present study was to evidence that the heart reinnervation can occur and it is related with the time after transplantation (evolution with time). Data were evaluated using Multiple Correspondence Analyses (MCA), which is the ideal method to study the relation, probably nonlinear, between the Time After Transplantation (TAT) and the probable restoration of normal heart rate responses of sinus node regulated by the autonomic nervous system. Twenty four nonrejecting transplant recipients (60 +/- 48 months after transplantation) and nine healthy subjects were studied by heart rate variability parameters. Results showed that sympathetic activity is restored some time after transplantation. Until 48 months, the recent HTR are in direct correlation to low values SD and LF and for the oldest transplant recipient, these parameters are similar to that observed in normal subjects.

A model of ventilation used to interpret newborn lamb respiratory signals

This paper presents a model-based method for the analysis of respiratory signals specifically adapted to newborns. The model includes a description of upper airways, distensible and lower airways, lungs, chest wall, alveoli, pleura and respiratory muscle activity. The model was used in stationary and non-stationary conditions to simulate respiratory signals obtained in one newborn lamb in various physiological conditions. An identification algorithm was used to adapt the model parameters to lamb data. Finally, the identified parameters were compared to values previously reported in adult humans.

Uncorrelated randomness of the heart rate is associated with sepsis in sick premature infants

BACKGROUND

Late-onset sepsis in the premature infant is frequently revealed by severe, unusual and recurrent bradycardias. In view of the high morbidity and mortality associated with infection, reliable markers are needed.

OBJECTIVES

It was the aim of this study to determine if heart rate (HR) behavior may help the diagnosis of infection in premature infants with such cardiac decelerations.

METHODS

Electrocardiogram recordings were collected in 51 premature infants with a postmenstrual age <33 weeks with frequent bradycardias. Newborns in the sepsis group (C-reactive protein increase and positive blood culture) were compared with a no-sepsis group (C-reactive protein <5 mg/l before and 24 h after recording and negative blood cultures) for their HR characteristics, i.e. RR series distribution (mean, median, skewness, kurtosis, sample asymmetry), magnitude of variability in time and frequency domain, fractal exponents (alpha(1), alpha(2)) and complexity measurements (approximate and sample entropy). Results are presented as the median (25%, 75%).

RESULTS

Gestational, chronological and postmenstrual age and gender were similar in the sepsis (n = 10) and no-sepsis group (n = 38). Three infants had an increase in C-reactive protein but negative cultures. Low entropy measurements [approximate entropy 0.4 (0.3, 0.5) vs. 0.8 (0.6, 1); p < 0.001] and long-range fractal exponent [alpha(2) 0.78 (0.71, 0.83) vs. 0.92 (0.8, 1.1); p < 0.05] were significantly associated with sepsis. No other HR characteristic was associated with sepsis. The decrease in 0.1 units of approximate entropy was associated with an over 2-fold increase in the odds of sepsis.

CONCLUSION

Late-onset sepsis is associated with uncorrelated randomness of the HR. This abnormal HR behavior may help to monitor premature infants presenting with frequent and severe bradycardias.

Exploring time series retrieved from cardiac implantable devices for optimizing patient follow-up

Current cardiac implantable devices (IDs) are equipped with a set of sensors that can provide useful information to improve patient follow-up and prevent health deterioration in the postoperative period. In this paper, data obtained from an ID with two such sensors (a transthoracic impedance sensor and an accelerometer) are analyzed in order to evaluate their potential application for the follow-up of patients treated with a cardiac resynchronization therapy (CRT). A methodology combining spatiotemporal fuzzy coding and multiple correspondence analysis (MCA) is applied in order to: 1) reduce the dimensionality of the data and provide new synthetic indexes based on the "factorial axes" obtained from MCA; 2) interpret these factorial axes in physiological terms; and 3) analyze the evolution of the patient's status by projecting the acquired data into the plane formed by the first two factorial axes named "factorial plane." In order to classify the different evolution patterns, a new similarity measure is proposed and validated on the simulated datasets, and then, used to cluster observed data from 41 CRT patients. The obtained clusters are compared with the annotations on each patient's medical record. Two areas on the factorial plane are identified, one being correlated with a health degradation of patients and the other with a stable clinical state.

Improving ECG beats delineation with an evolutionary optimization process

As in other complex signal processing tasks, ECG beat delineation algorithms are usually constituted of a set of processing modules, each one characterized by a certain number of parameters (filter cutoff frequencies, threshold levels, time windows, etc.). It is well recognized that the adjustment of these parameters is a complex task that is traditionally performed empirically and manually, based on the experience of the designer. In this paper, we propose a new automated and quantitative method to optimize the parameters of such complex signal processing algorithms. To solve this multiobjective optimization problem, an evolutionary algorithm (EA) is proposed. This method for parameter optimization is applied to a wavelet-transform-based ECG delineator that has previously shown interesting performance. An evaluation of the final delineator, using the optimal parameters, has been performed on the QT database from Physionet and results are compared with previous algorithms reported in the literature. The optimized parameters provide a more accurate delineation, with a global improvement of 7.7%, over all the criteria evaluated, and over the best results found in the literature, which is a proof of interest in the approach.

Clustering follow-up time-series recorded by cardiac implantable devices

Follow-up of patients treated by cardiac resynchronization therapy (CRT) is of great interest to prevent health deterioration in the postoperative period. In this purpose, data recorded in implantable devices (ID) can be informative. They are large, multivariate, evolutive with time, and then difficult to interpret. This study proposes a methodology, based on Multiple Correspondence Analysis (MCA) and fuzzy coding, to i) reduce the dimensionality of these data, ii) characterize the factorial axes by the variables recorded in the ID, iii) identify evolutions of patients in the factorial plane which are related to health deterioration. The results show that the first three axes of the MCA contain more than 90% of the total variance and are linked to the functional state of the patients. A simple clustering of the patients is performed and defines an area of the factorial plane, correlated with a degradation of patients' clinical state.

On the interest of computer modelling in the validation of ambulatory measurement of movement disorders in man

Continuous ambulatory monitoring is a promising technique for objective evaluation of movement disorders in man. Although technological progress make such devices currently available, no validated measurements exist. We propose an original approach to validate instrumental measurements in central neurological diseases, based upon the use of computer modeling of movement disorders. In such an approach, computer simulations of altered movement were used to validate accelerometric-derived measurements in spasticity and Parkinson's disease. We found that some measurements, derived from the frequency power spectrum of distal acceleration at the upper limb, may be suitable for evaluation of some motor disorders, as spasticity, parkinsonian rigidity and related motor disability. Direct applications are expected in ambulatory monitoring of neurological diseases.

Prolonged dynamic changes in autonomic heart rate modulation induced by acid laryngeal stimulation in non-sedated lambs

BACKGROUND

Exaggerated laryngeal chemoreflexes (LCR) have been implicated in the occurrence and prolongation of apnea in premature and term neonates.

OBJECTIVES

To test the hypothesis that the cardiovascular component of the LCR can occur and be prolonged in the absence of apnea.

METHODS

Bolus injections of 0.5 ml of citric acid (CitAc) and serum saline were administered onto the larynx in seven non-sedated lambs, aged 4-5 days, during quiet sleep. Blood pressure (BP) and heart rate (HR) variability measurements were taken and compared with results obtained prior to stimulation and with saline.

RESULTS

Apneas were rare and occurred consistently in the 32-beat period (10 s) following stimulation. The increase in BP and decrease in HR were consistent after CitAc and saline. All the studied components of HR variability significantly increased after CitAc, but not after saline. The low frequency estimates were significantly increased during a 480-beat period (133 +/- 6.6 s) following CitAc stimulation. Analysis of gains and respiration showed that bradycardia and HR variability changes were neither related to an apnea nor a vagal arterial baroreflex.

CONCLUSION

This long-lasting increase in HR variability indicates a prolonged disturbance of the cardiovascular regulatory systems, consisting in increased sympathetic and parasympathetic tones following CitAc stimulation.

A tissue-level model of the left ventricle for the analysis of regional myocardial function

This paper presents a model-based method for the analysis of regional myocardial strain, based on echocardiography and Tissue Doppler Imaging (TDI). A multi-formalism, tissue-level electromechanical model of the left ventricle is proposed. The parameters of the model are identified in order to reproduce regional strain signal morphologies obtained from a healthy subject and a patient presenting a dilated cardiomyopathy. The parameters identified for the DCM patient allow the localization of the failing myocardial segments and may be useful for a better design of cardiac resynchronization therapy on heart failure patients.

A bond graph model of the cardiovascular system

The study of the autonomic nervous system (ANS) function has shown to provide useful indicators for risk stratification and early detection on a variety of cardiovascular pathologies. However, data gathered during different tests of the ANS are difficult to analyse, mainly due to the complex mechanisms involved in the autonomic regulation of the cardiovascular system (CVS). Although model-based analysis of ANS data has been already proposed as a way to cope with this complexity, only a few models coupling the main elements involved have been presented in the literature. In this paper, a new model of the CVS, representing the ventricles, the circulatory system and the regulation of the CVS activity by the ANS, is presented. The models of the vascular system and the ventricular activity have been developed using the Bond Graph formalism, as it proposes a unified representation for all energetic domains, facilitating the integration of mechanic and hydraulic phenomena. In order to take into account the electro-mechanical behaviour of both ventricles, an electrophysiologic model of the cardiac action potential, represented by a set of ordinary differential equations, has been integrated. The short-term ANS regulation of heart rate, cardiac contractility and peripheral vasoconstriction is represented by means of continuous transfer functions. These models, represented in different continuous formalisms, are coupled by using a multi-formalism simulation library. Results are presented for two different autonomic tests, namely the Tilt Test and the Valsalva Manoeuvre, by comparing real and simulated signals.

Blind source separation for ambulatory sleep recording

This paper deals with the conception of a new system for sleep staging in ambulatory conditions. Sleep recording is performed by means of five electrodes: two temporal, two frontal and a reference. This configuration enables to avoid the chin area to enhance the quality of the muscular signal and the hair region for patient convenience. The electroencephalopgram (EEG), eletromyogram (EMG), and electrooculogram (EOG) signals are separated using the Independent Component Analysis approach. The system is compared to a standard sleep analysis system using polysomnographic recordings of 14 patients. The overall concordance of 67.2% is achieved between the two systems. Based on the validation results and the computational efficiency we recommend the clinical use of the proposed system in a commercial sleep analysis platform.

LF/(LF+HF) index in ventricular repolarization variability correlated and uncorrelated with heart rate variability

The purpose of this study, was to asses whether LF/(LF+HF) obtained from ventricular repolarization variability (VRV) reflects the state of sympathovagal balance. The VRV time series and heart rate variability (HRV) time series from seventy two electrocardiogram (ECG) records in four different autonomic nervous system (ANS) profiles (athletes, cardiac transplant patient, heart failure patients and normal subjects) were extracted. A dynamic linear parametric model was applied to separate the VRV in two parts, VRV correlated with HRV (VRV(r)) and VRV uncorrelated with HRV (VRV(u)). Spectral indices were obtained from HRV, VRV, VRV(u) and VRV(u) time series. Changes of these indicators from rest to tilt position were analyzed. Results showed that: i) only LF/(LF+HF) from HRV time series increases significantly from rest to tilt in all ANS profiles, this information could not be retrieved in the other three series (VRV, VRV (u) and VRV(u)) ii) LF/(LF+HF) index in HRV series are significantly different between normal subjects and heart failure patients, while cardiac transplant patients show a low coherence between HRV and VRV power spectra and iii) HF rhythm in VRV series seem to be related to the mechanical effect of respiration.

Evaluation of real-time QRS detection algorithms in variable contexts

A method is presented to evaluate the detection performance of real-time QRS detection algorithms to propose a strategy for the adaptive selection of ORS detectors, in variable signal contexts. Signal contexts are defined as different combinations of QRS morphologies and clinical noise. Four QRS detectors are compared in these contexts by means of a multivariate analysis. This evaluation strategy is general and can be easily extended to a larger number of detectors. A set of morphology contexts, corresponding to eight QRS morphologies (normal, PVC, premature atrial beat, paced beat, LBBB, fusion, RBBB, junctional premature beat), was extracted from 17 standard ECG records. For each morphology context, the set of extracted beats, ranging from 30 to 23000, was resampled to generate 50 realisations of 20 concatenated beats. These realisations were then used as input to the QRS detectors, without noise, and with three different types of additive clinical noise (electrode motion artifact, muscle artifact, baseline wander) at three signal-to-noise ratios (5 dB, -5 dB, -15 dB). Performance was assessed by the number of errors, which reflected both false alarms and missed beats. The results show that the evaluated detectors are indeed complementary. For example, the Pan-Tompkins detector is the best in most contexts but the Okada detector generates fewer errors in the presence of electrode motion artifact. These results will be particularly useful to the development of a real-time system that will be able to choose the best ORS detector according to the current context.

Reliability of heart rate variability in healthy older women at rest and during orthostatic testing

BACKGROUND AND AIMS

In the older population, the reliability of heart rate variability (HRV) has only been evaluated in a few studies, in the supine position, and covering a broad sample of age and patients of both sexes. To document the relevance of using HRV analysis in healthy older women, the aim of this study was to evaluate the reliability of HRV indexes during three classical tests.

METHODS

33 healthy women (66.9+/-0.7 years old) performed two test sessions. Each session consisted of an ECG recorded in the supine position, first with free breathing (Test 1), then with controlled breathing (Test 2), and in the upright position (Test 3). Time and frequency HRV indexes were obtained by processing the ECG signals. Reliability was assessed between sessions using Student's paired t-test, intraclass correlation coefficient (ICC) and coefficient of variation (CV).

RESULTS

There were no differences between the sessions. ICC showed good reliability for all HRV indexes. CV was low for absolute HRV indexes, except in Test 3 for parasympathetic indexes with modest CV. The CV of HRV ratio indexes were modest to high in all three tests.

CONCLUSIONS

Time and absolute frequency HRV indexes are reliable when testing healthy older women. Our results support the use of such indexes in gerontology research, to assess the effects of clinical or pharmacological interventions on the autonomic nervous system.

Multi-formalism modelling and simulation: application to cardiac modelling

Cardiovascular modelling has been a major research subject for the last decade. Different cardiac models have been developed at a cellular level as well as at the whole organ level. Most of these models are defined by a comprehensive cellular modelling using continuous formalisms or by a tissue-level modelling often based on discrete formalisms. Nevertheless, both views still suffer from difficulties that reduce their clinical applications: the first approach requires heavy computational resources while the second one is not able to reproduce certain pathologies. This paper presents an original methodology trying to gather advantages from both approaches, by means of a hybrid model mixing discrete and continuous formalisms. This method has been applied to define a hybrid model of cardiac action potential propagation on a 2D grid of endocardial cells, combining cellular automata and a set of cells defined by the Beeler-Reuter model. For simulations under physiological and ischemic conditions, results show that the action potential propagation as well as electrogram reconstructions are consistent with clinical diagnosis. Finally, the advantage of the proposed approach is discussed within the frame of cardiac modelling and simulation.

A computer model of rigidity and related motor dysfunction in Parkinson's disease

This work explores the involvement of spinal circuits in the generation of parkinsonian rigidity and related motor dysfunction. A computer model of spinal proprioceptive input processing, derived from previous work on spasticity modeling, was adapted to the simulation of parkinsonian rigidity. Model parameters were varied to generate simulations reproducing experimental data obtained using the pendulum test of the leg in 10 parkinsonian patients and 3 healthy subjects. Convenient reproductions of experimental traces in rigidity were obtained by the combination of a low reflex gain and a decrease in reflex threshold. These findings are consistent with studies reporting an increase of spinal interneuron excitability and proprioception deficits in Parkinson's disease (PD). Moreover, as the threshold parameter was much lowered, our model generated typical features of parkinsonian resting tremor, endorsing the hypothesis of a participation of a spinal oscillator in this disorder. Finally, tuning the reflex gain during simulations of rigidity resulted in the generation of active movement, opening some hypotheses on pathophysiology of motor dysfunction in PD, and notably, of akinesia. More generally, this work accredits the hypothesis of the involvement of an aperiodic, altered supra-spinal motor drive in PD, resulting in spinal dysfunction, through specific descending motor pathways. This may lead to a search for new (spinal) pharmacological targets in PD. It emphasizes further the value of computer modeling in understanding motor control in health and disease.

Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms

This paper proposes a novel approach to cardiac arrhythmia recognition from electrocardiograms (ECGs). ECGs record the electrical activity of the heart and are used to diagnose many heart disorders. The numerical ECG is first temporally abstracted into series of time-stamped events. Temporal abstraction makes use of artificial neural networks to extract interesting waves and their features from the input signals. A temporal reasoner called a chronicle recogniser processes such series in order to discover temporal patterns called chronicles which can be related to cardiac arrhythmias. Generally, it is difficult to elicit an accurate set of chronicles from a doctor. Thus, we propose to learn automatically from symbolic ECG examples the chronicles discriminating the arrhythmias belonging to some specific subset. Since temporal relationships are of major importance, inductive logic programming (ILP) is the tool of choice as it enables first-order relational learning. The approach has been evaluated on real ECGs taken from the MIT-BIH database. The performance of the different modules as well as the efficiency of the whole system is presented. The results are rather good and demonstrate that integrating numerical techniques for low level perception and symbolic techniques for high level classification is very valuable.

Model-based interpretation of cardiac beats by evolutionary algorithms: signal and model interaction

This paper presents a new approach for cardiac beat interpretation, based on a direct integration between a model and observed ECG signals. Physiological knowledge is represented by means of a semi-quantitative model of the cardiac electrical activity. The interpretation of cardiac beats is formalized as an optimization problem, by minimizing an error function defined between the model's output and the observations. Evolutionary algorithms (EAs) are used as the search technique in order to obtain the set of model parameters reproducing at best the observed phenomena. Examples of model adaptation to three different kinds of cardiac beats are presented. Preliminary results show the potentiality of this approach to reproduce and explain complex pathological disorders and to better localize their origin.

[Evaluation of pendulum testing of spasticity]

OBJECTIVES

To identify valid measurements of spasticity derived from the pendulum test of the leg in a representative population of spastic patients.

MATERIAL AND METHODS

Pendulum testing was performed in 15 spastic and 10 matched healthy subjects. The reflex-mediated torque evoked in quadriceps femoris, as well as muscle mechanical parameters (viscosity and elasticity), were calculated using mathematical modelling. Correlation with the two main measures derived from the pendulum test reported in the literature (the Relaxation Index and the area under the curve) was calculated in order to select the most valid.

RESULTS, DISCUSSION

Among mechanical parameters, only viscosity was found to be significantly higher in the spastic group. As expected, the computed integral of the reflex-mediated torque was found to be larger in spastics than in healthy subjects. A significant non-linear (logarithmic) correlation was found between the clinically-assessed muscle spasticity (Ashworth grading) and the computed reflex-mediated torque, emphasising the non-linear behaviour of this scale. Among measurements derived from the pendulum test which are proposed in the literature for routine estimation of spasticity, the Relaxation Index exhibited an unsuitable U-shaped pattern of variation with increasing reflex-mediated torque. On the opposite, the area under the curve revealed a linear regression, which is more convenient for routine estimation of spasticity.

CONCLUSION

The pendulum test of the leg is a simple technique for the assessment of spastic hypertonia. However, the measurement generally used in the literature (the Relaxation Index) exhibits serious limitations, and would benefit to be replaced by more valid measures, such as the area under the goniometric curve, especially for the assessment of therapeutics.

Numerically solving physiological models based on a polynomial approach

Much research effort has been directed in different physiological contexts towards describing realistic behaviors with differential equations. One observes obviously that more state-variables give the model more accuracy. Unfortunately, the computational cost involved is higher. A new algorithm is presented for simulating a model described by a system of differential equations in which efficiency may not be altered by its size. In order to do this, the method is based on a polynomial description of the state-variables' evolution and on a computation distributed control. Evaluations and results performed with classical models like Fitzhugh Nagumo or Hodgkin Huxley, allow validation of the method and exhibits its potential to decrease the computational costs.

A comprehensive model of spastic hypertonia derived from the pendulum test of the leg

We propose a comprehensive model of spastic hypertonia based on clinical neurophysiology and validated using experimental data obtained from the pendulum test of the leg in 8 healthy volunteers and 15 spastic patients. This nonlinear computational model includes mechanical parameters and a stretch reflex representation involving three neural parameters: a threshold coefficient, the gain of the stretch reflex, and a time lag accounting for the reflex loop latency and the electromechanical coupling delay. Variation of the threshold coefficient alone allowed an overall reproduction of experimental data obtained from spastic and healthy subjects. We propose that this parameter could represent the supraspinal drive, supposed to be preserved in control subjects and decreased in spastic patients. No subsequent variation of the reflex gain was required to simulate spastic traces. Adjustment of the time lag influenced the duration of the swinging phase and oscillatory phenomena possibly occurring during the pendulum test. It could be related to the involvement of either short- or long-latency stretch reflex loops. With respect to current neurophysiological concepts of motor control, this modeling approach may help in understanding mechanisms underlying spastic hypertonia, and in predicting the clinical effect of antispasticity agents.

Real-time ECG transmission via Internet for nonclinical applications

Telemedicine is producing a great impact in the monitoring of patients located in remote nonclinical environments such as homes, elder communities, gymnasiums, schools, remote military bases, ships, and the like. A number of applications, ranging from data collection, to chronic patient surveillance, and even to the control of therapeutic procedures, are being implemented in many parts of the world. As part of this growing trend, this paper discusses the problems in electrocardiogram (ECG) real-time data acquisition, transmission, and visualization over the Internet. ECG signals are transmitted in real time from a patient in a remote nonclinical environment to the specialist in a hospital or clinic using the current capabilities and availability of the Internet. A prototype system is composed of a portable data acquisition and preprocessing module connected to the computer in the remote site via its RS-232 port, a Java-based client-server platform, and software modules to handle communication protocols between data acquisition module and the patient's personal computer, and to handle client-server communication. The purpose of the system is the provision of extended monitoring for patients under drug therapy after infarction, data collection in some particular cases, remote consultation, and low-cost ECG monitoring for the elderly.

Atrial activity enhancement by Wiener filtering using an artificial neural network

This paper describes a novel technique for the cancellation of the ventricular activity for applications such as P-wave or atrial fibrillation detection. The procedure was thoroughly tested and compared with a previously published method, using quantitative measures of performance. The novel approach estimates, by means of a dynamic time delay neural network (TDNN), a time-varying, nonlinear transfer function between two ECG leads. Best results were obtained using an Elman TDNN with nine input samples and 20 neurons, employing a sigmoidal tangencial activation in the hidden layer and one linear neuron in the output stage. The method does not require a previous stage of QRS detection. The technique was quantitatively evaluated using the MIT-BIH arrhythmia database and compared with an adaptive cancellation scheme proposed in the literature. Results show the advantages of the proposed approach, and its robustness during noisy episodes and QRS morphology variations.

Overview of CARMEM: a new dynamic quantitative cardiac model for ECG monitoring and its adaptation to observed signals

Different approaches have been proposed in order to achieve knowledge integration for coronary care monitoring applications, usually in the form of expert systems. The clinical impact of these expert systems, which are based only on "shallow" knowledge, has not been remarkable due to the difficulties associated with the construction and maintenance of a complete knowledge base. Model-based systems represent an alternative to these problems because they allow efficient integration of the "deep" knowledge on the underlying physiological phenomena being monitored. In this work, a brief review of existing model-based systems for cardiac rhythm interpretation is presented, followed by the description of a new system for Cardiac Arrhythmia Recognition by Model-Based ECG Matching (CARMEM). Fundamental characteristics of CARMEM are presented; in particular, its ability to provide online parameter adaptation to simulate complex rhythms and to match observed ECG signals. The proposed model can be useful for the explanation of the origin of cardiac arrhythmias and contribute towards their robust characterization in the context of coronary care units.

Pediatric anesthesia monitoring with the help of EEG and ECG

This paper presents research regarding the monitoring of the brain and the adequacy of anesthesia during surgery. Particular variables are derived from EEG and ECG signals and are correlated to anesthetic gas (sevoflurane) concentration, in pediatric anesthesia. The methods used for parameter extraction are based on change detection theory and time-frequency representation. Preliminary results show that the expired anesthetic gas concentration modulates both the heart rate variability and the duration of the burst suppression. Monitors of the central nervous system and autonomic nervous system activities can be expected to be based on these variables.

Multisensor fusion for atrial and ventricular activity detection in coronary care monitoring

Information management for critical care monitoring is still a very difficult task. Medical staff is often overwhelmed by the amount of data provided by the increased number of specific monitoring devices and instrumentation, and the lack of an effective automated system. Specifically, a basic task such as arrhythmia detection still produce an important amount of undesirable alarms, due in part to the mechanistic approach of current monitoring systems. In this work, multisensor and multisource data fusion schemes to improve atrial and ventricular activity detection in critical care environments are presented. Applications of these schemes are quantitatively evaluated and compared with current methods, showing the potential advantages of data fusion techniques for event detection in noise corrupted signals.

Segmentation of depth-EEG seizure signals: method based on a physiological parameter and comparative study

The analysis of stereoelectroencephalographic (intracerebral recording) signals provides information on the electrical activity of brain structures implied in epileptic seizures. A simple nonparametric adaptive segmentation method, based on a physiologically relevant parameter, is presented and compared with three methods reported in the literature. The comparative frame allows us to objectively test methods for their performances on the same basis. Results show that the proposed method is robust with respect to the types of change studied and easier to conduct, even if it is less accurate about the estimation of instants of change than another method presented in this study. Signals are segmented throughout the duration of seizures without parameter readjustment and generate instants of change in accordance with those interactively delimited by the clinician.

Non-linear algorithms for processing biological signals

This paper illustrates different approaches to the analysis of biological signals based on non-linear methods. The performance of such approaches, despite the greater methodological and computational complexity is, in many instances, more successful compared to linear approaches, in enhancing important parameters for both physiological studies and clinical protocols. The methods introduced employ median filters for pattern recognition, adaptive segmentation, data compression, prediction and data modelling as well as multivariate estimators in data clustering through median learning vector quantizers. Another approach described uses Wiener-Volterra kernel technique to obtain a satisfactory estimation and causality test among EEG recordings. Finally, methods for the assessment of non-linear dynamic behaviour are discussed and applied to the analysis of heart rate variability signal. In this way invariant parameters are studied which describe non-linear phenomena in the modelling of the physiological systems under investigation.

Linear multivariate models for physiological signal analysis: theory

The general linear parametric multivariate modelling concept is presented. This model combines a variety of different kinds of multivariate linear models. The concept of partial spectral analysis is derived from the general model. Some emphasis is laid on the causality demands of the model, and it is shown that the classic strictly-causal structure must be abandoned in order to utilise the modelling in many practical situations. Two special sub-class models are described in detail: the multivariate autoregressive model and the multivariate dynamic adjustment model. Furthermore, time-varying modelling is considered. The modelling of the real system is presented on a general level as a system identification cycle. The application of the methods to real physiological data is presented in the companion paper.

Linear multivariate models for physiological signal analysis: applications

Some applications of linear multivariate modelling methods in the analysis of physiological signals are presented. These applications illustrate the methods in the analysis of cardiovascular dynamics, which has been one of the main application fields of the multivariate modelling during the last ten years. It is demonstrated that physiologically meaningful information about the causal interactions in the cardiovascular system can be drawn from the routinely available clinical signals. Both static and dynamic conditions are considered.