ScilabTEC 2015 - Inria
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Transcript of ScilabTEC 2015 - Inria
Cardiovascular wave analysis module for Scilab
Serge Steer
The heart : a pump
To o
rgan
sO
2
To the lungs
From the lungsO2
Fro
m o
rgan
sVentricular systole (contraction):● isovolumic contraction
➢ closes the atrioventricular valves ➢ opens sigmoid valves
● ejection
Ventricular diastole (relaxation)● isovolumic relaxation
➢ Opens the atrioventricular valves● filling
➢ Fast➢ Slow➢ Atrial systole
The heart : a regulated pump
Cellular oxygenation, in particular for brain
Long term control : hormones
Short term control : autonomic nervous system
Baroreceptor control loop: two antagonistic actions
- sympathetic = accelerator (fast)
- parasympathetic = inhibitor (slow)
Control also acts on arteries and veins
Sino atrial node
Cardiac electrical activity
The heart contraction is driven by electric depolarization of myocyte cells inducted by cardiac fibers.
Autonomous impulse generator
Cardiac fibers
Autonomic nervoussystem
Trans membrane voltage
DepolarizationSodium ions
Re polarizationPotassium ions Restauration of ionic balance
Cell membranes form electric dipoles
ECG
Electric dipoles generated by cells membranes of the heart produces an electrical potential at the surface of the thorax.
A set of electrodes, typically 3 to 10, are used to measure the electrical potential at different thorax locations.
The electrocardiogram (ECG) records voltage evolution deduced from the measured potentials
Cheap and non invasive
●
Typical ECG lead trace
P wave Atrial depolarization
QRS complex
Ventricular depolarization
T wave Ventricular repolarization
Applications of the ECG to cardiological diagnosis
● Arrhythmias detections● Disorders in the activation sequences● Increase in wall thickness or size of the atria
and ventricles ● Myocardial ischemia (coronary atherosclerosis)
and infarction ● ...
Heart rate and ECG morphology analysis provides cheap diagnostic elements
The Cardiovascular Wave Analysis module
Designed for – Long term ECG
– Multi-leads records
– Off line analysis
Consists of five classes of tools– Data acquisition
– Pretreatment
– Event detection
– Analysis
– Visualization
Data acquisitionSeveral file formats
● ISHNE Holter (Task Force standard)● TMS32 ( signals recorded using TMS ADC system and PortiLab)● WFDB (WaveForm DataBase used by Physionet)
Can be read and converted into a Scilab structure:
S=readTMS("ECG2.poly5",[25 27]);
viewECG(S(:,1))
Or converted to Scilab specific format (ecgs)
Easy for batch processing
Pretreatment● ECG subsampling● ECG FIR filtering ● Power line interference removal
● ECG detrending● Finding and removing outliers
viewECG([S(:,1) ECGSubstractPLI(S(:,1),50)])
Detections
● R peaks heart rate● T wave ends Q-T segment● All events
S=extractPartFromEcgsFile(“P5J0.ecgs",... 100,3000);S1=ECGDetrend(S);S1=ECGSubstractPLI(S1,50); L=ECGDetections(SynthesisECG(S1)); ECGShowDetections(S(:,1),L)
Analysis tools
Classical signal processing methods (heart rate spectral analysis)
Multichannel non stationary signal analysis
Baroreflex analysis (Arterial blood pressure effect on heart rate)
Complex demodulation (Breathing rate effect on heart rate)
Time frequency analysis
Time domain characteristics
Interactive toolsHRVAS (Heart Rate Variability Analysis System),
TimeFrequencyTool
The contribution of Scilab
● Signal processing and analysis● Data file handling● Graphics● Graphical user interface● Community● Free multi-plateforms software
Acknowledgments- Detection algorithms :
● Quighua Zhang (INRIA)
- Heart rate analysis algorithms: ● Alexandro Monti (INRIA)● John T. Ramshur (U. Menphis)
- Signal processing● D.E. Lake, J.R. Moorman and C. Hanqing● Wavelets :Christopher Torrence (Exelis), Gilbert. P. Compo (U. Colorado)● George B. Moody, Cambridge● Time Frequency toolbox: François Auger (CNRS), Holger Nahrstaedt (TU Berlin)
- Data● Physionet● François Cottin (U. Paris Sud)● Lisa Guigue ( INRIA)
- Testing● Claire Medigue (INRIA)● Lisa Guigue (INRIA)
- Atoms Module (http://atoms.scilab.org/toolboxes/CWA)● Dominique Callens (Scilab-Enterprises)
The end
Thanks