Annex thesis
Artificial neural network based alorithm for the analysis of velocity profiles in the middle cerebral artery obtained using transcranial Doppler.
The goal of this work was to develop an artificial neural network (ANN) based algorithm capable of accurate and reliable quantification of blood velocity profiles of the middle cerebral artery, obtained by transcranial Doppler. In particular, the algorithm was built for the determination of the time of occurrence and value of the minimal (diastolic) and maximal (systolic) velocity on a beat-by-beat basis.
Bilateral middle cerebral artery velocity profiles were recorded in 12 normal, healthy sub- jects, aged 33 ± 8 years. The subjects were non-smokers, presented no history of migraine and were under no medication. A Nicolet EME’s pioneer transcranial Doppler ultrasono- graph was used. The probes (2 MHz) were fixed using a head probe-holder and depth set to 56mm. The velocity profiles were recorded during 5 min (100 Hz sampling rate).
Examples extracted from these recordings were scored by a human operator, and were used to train 2 independent ANNs: one network was trained to recognize the onset of the rise in velocity after each heart beat, and the second network to recognize the maximal velocity. The algorithmic approach followed closely previous work on detecting events in respiratory volume signals
6, presented in detail in chapter 4.
The algorithm outputs the diastolic and systolic velocity (V dia and V sys ) for each indi- vidual heart-beat and the corresponding time T dia and T sys . The period and mean velocity are computed on a cycle per cycle basis. However, these parameters tell little about the velocity profile itself, about the steepness of the rise in velocity induced by each heart beat, or about the decay of the artery response to this repetitive stimuli. Three additional variables were introduced to quantify these effects, namely the percentage of rise time,
%T up = (T sys (i) − T dia (i))/(T sys (i + 1) − T sys (i)), the acceleration of rise (a rise ) - supposing a constant acceleration from V dia (i) to V sys (i) - and the rate of decay (a decay ) - assuming an exponential velocity decay from V sys (i) to V dia (i + 1) . These parameters are markers of elastic properties of the arteries in the vicinity of the section measured
7.
The algorithm was applied to the data set presented above
8, proving the feasibility and effectiveness of using an artificial neural network based algorithm to analyse velocity profile curves of the middle cerebral artery. The algorithm quantifies changes in the velocity curves and can be used to follow velocity profile changes on individual subjects (e.g during hyperventilation), or to compare different populations.
6
R.C. S´ a and Y. Verbandt. Automated breath detection on long-duration signals using feedforward backpropagation artificial neural networks. IEEE Trans Biomed Eng 49(10): 1130-1141, 2002.
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W.N. Nichols and M.F. O’Rourke. McDonald’s Blood flow in arteries (3rd edition). Edward Arnold Publishing, 1990.
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