# Automatic detection of microemboli by means of a synchronous linear prediction technique

Sébastien Ménigot, Latifa Dreibine, Nawal Meziati, Jean-Marc Girault

► To cite this version:

Sébastien Ménigot, Latifa Dreibine, Nawal Meziati, Jean-Marc Girault. Automatic detection of microemboli by means of a synchronous linear prediction technique. IEEE. IEEE International Ultrasonics Symposium (IUS), Sep 2009, Rome, Italy. IEEE, pp.2371 - 2374, 2009, 10.1109/ULT-SYM.2009.5441701. [hal-01075514](https://hal.science/hal-01075514)

HAL Id: hal-01075514

https://hal.science/hal-01075514

Submitted on 17 Oct 2014

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## 1. Introduction

Detection of microemboli is of great clinical importance to prevent cerebro-vascular events and to identify the causes of such events. As standard detection techniques implemented in the most commonly used systems cannot detect all of microemboli events whose energy is lower than the systolic energy, new techniques are proposed.

Joint used of synchronous and linear prediction techniques could detect very small microemboli. If we periodically take and compare the values of the energy of the prediction error (or autoregressive parameters) at different time points in the cardiac cycle, we can therefore detect the presence of non-periodic events such as microemboli.

In our study, we tested and compared our new technique to the standard technique (Fourier) using simulated and in vivo signals from patients with stenosis of high degrees of severity.

## 2. Materials

### TansCranial Doppler system (TCD)

* Velocity of blood flow through the brain's arteries

* Pulsed Doppler probe

* Examination in the temporal region

* used TCD : WakiTM (Atys Medical, Soucieu en Jarrest, France)

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### Doppler signals

* The Doppler signals are cyclo-stationary

* Synthetized Embolus Doppler signal proposed by Wedling

* In vivo Doppler signals from patients stenosis of degrees IV of severity

## 3. Methods

### Gold standard test

Manual embolus detection by audible detection and sonogram visualization

### standard technique

1. Computation of energy by Short Time Fourier Transform

2. Constant threshold $\lambda$ set between 3 to 9 dB above the maximal energy → reduce the false alarm probability

3. Embolus detection when energy upper than the threshold $\lambda$

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### Synchronous linear prediction technique (SLP)

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1. Computation of energy by Short Time Fourier Transform

2. Model of signal with 2nd-order AR model

$$x(n) = -a_{1}(n)x(n-1) - a_{2}(n)x(n-2) + \varepsilon(n)$$

3. Error Autocorrelation $\Gamma_{\varepsilon\varepsilon}(n) = \sum_{m=-\infty}^{+\infty} \varepsilon(m)\varepsilon^{*}(m-n)$

4. Synchronization with the cardiac cycle

5. Detection by a time-varying threshold $\lambda(t) = \mu(t) + \beta\sigma(t)$ with $\mu(t)$ the average and $\sigma(t)$ the standard deviation

## 4. Results and Discussion

### In simulation

<table><thead><tr><th rowspan="2"></th><th rowspan="2">Gold standard</th><th rowspan="2">Standard</th><th colspan="2">SLP</th></tr><tr><th>a<sub>1</sub></th><th>error</th></tr></thead><tbody><tr><th>embolus detection</th><td>100 %</td><td>100 %</td><td>100 %</td><td>100 %</td></tr><tr><th>false alarm rate</th><td>0 %</td><td>32.37 %</td><td>7.49 %</td><td>5.80 %</td></tr></tbody></table>

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### In vivo

<table><thead><tr><th></th><th>Gold standard</th><th>Standard</th><th>SLP</th></tr></thead><tbody><tr><th>embolus detection</th><td>100 %</td><td>67 %</td><td>100 %</td></tr><tr><th>false alarm detection</th><td>0 %</td><td>0 %</td><td>0 %</td></tr></tbody></table>

### Discussion

* Best detection with SLP : embolus can be detected even if it was inaudible up to now

* Synchronous error autocorrelation does not improve the detection: the error does not vary with the cardiac cycle

## 5. Conclusion

* Large microemboli are all detected

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* incorporate "on line" technique