Vincent Jacquemet UdeM HSCM

Mission Themes Methodology Facilities Funding

M I S S I O N

Combine integrative biophysical modeling and signal processing techniques to improve diagnostic interpretation of cardiac bioelectric signals.

R E S E A R C H    T H E M E S

Modeling atrial electrophysiology and arrhythmias

Atrial fibrillation is the most frequent rhythm disorder in humans (nearly 250,000 patients in Canada). It often leads to severe complications such as heart failure and stroke. Diagnosis of this arrhythmia is mainly performed through the inspection of electrical signal recordings (electrograms and electrocardiograms). To develop and validate new diagnostic tools, it is necessary to understand the link between what the cardiologist observes (these electrical signals) and what is going wrong in the heart (the underlying cardiac pathology).

In parallel with the dramatic increase in computer power over the last few decades, computer models of cardiac electrical activity have evolved from small strings of cells to a detailed description of the whole heart. Integrating information from the molecular scale to the whole organ, our models can not only simulate arrhythmias and investigate mechanisms but also can evaluate diagnostic and therapeutic approaches. Used in combination with experimental and clinical research, computer modeling is expected to play an increasing role in the interpretation of biomedical measurements.

We create three-dimensional virtual models of the human atria based on anatomical, histological and electrophysiological data. In these models, conditions are set up that trigger and maintain an arrhythmia, as inspired by clinical observations and physiological hypotheses. A variety of conditions are simulated to reproduce different diseased states of increasing severity. The evolution of the electrical activity generated by the heart during an arrhythmia is simulated. Then, electrical signals obtained from computer simulations, animal experiments and patients can be analyzed and compared.

Collaborators: Drs Jean-Marc Vesin, Nathalie Virag, Lukas Kappenberger, Adriaan van Oosterom.

computer model of the atria



Electrocardiogram signal processing

The electrocardigram (ECG) remains the most commonly used noninvasive tool for diagnosing cardiac electrically manifest abnormalities such as arrhythmias. Diagnosis is performed by analyzing the different waveforms in the ECG (notably the QRS complex and T wave). In particular, careful monitoring of the T wave and the QT interval is important to ensure the safety of drug administration.

We are developing patient-specific approaches to estimate and monitor the rate-corrected QT interval in patients undergoing various protocols/therapies or with atrial arrhythmia. ECG are recorded using 2 to 24 hour long Holter monitoring.

Collaborators: Drs Réginald Nadeau, Marcio Sturmer, Giuliano Becker, Teresa Kus, A. Robert LeBlanc, Alain Vinet.

QT-RR analysis



Multichannel recordings of the intrinsic cardiac nervous system

Relations between the occurence of atrial arrhythmias and the intrinsic cardiac nervous system have been established. Notably, atrial fibrillation can be induced by electrical stimulation of mediastinal nerves in canine models.

The goal of this project is to understand the role of the intrinsic cardiac nervous system in the initiation of atrial arrhythmias using a dog model of neurogenically-induced atrial fibrillation. We hypothesized that the intrinsic cardiac nervous system processes local information to coordinate regional cardiac indices under the influence of central efferent neurons.

In this model, neural activity is continuously recorded during several hours using a 16-channel microelectrode array in the right atrial ganglionated plexus. Different physiological stimuli (mechanical, vascular, electrical, atrial arrhythmia) are applied and the neural response is analyzed and compared to control.

The new tools we are developping are relevant to mechanistic investigations and experimental assessment of therapeutic devices such as spinal cord/vagal nerve stimulators or pharmacological intervention.

Collaborators: Drs Éric Beaumont, Alain Vinet, J. Andrew Armour, Jeffrey L. Ardell.

Spike2 analysis

Multichannel spike train analysis



M E T H O D O L O G Y

  • Collaboration with experimentalists and clinicians: to ensure the physiological relevance of our work

  • Theoretical background: bioelectricity, biophysical modeling, partial differential equations, complex dynamical systems, advanced signal processing

  • Numerical analysis: finite elements, boundary elements, optimization

  • Programming: matlab (data analysis, visualization), C/C++ (simulation code)

  • Operating systems: Linux (simulations, data analysis), Windows (data analysis, office work)

  • High performance computing: Linux clusters at RQCHP



F A C I L I T I E S

  • Research Center at Hôpital du Sacré-Coeur: office space for students, desktop workstations

  • Physiology Department at Université de Montréal: Institute of Biomedical Engineering's computer lab



F U N D I N G

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