Detection of Doppler Microembolic Signals Using High Order Statistics

Maroun Geryes (1, 2) , Sébastien Ménigot (1) , Walid Hassan (3) , Ali Mcheick (2) , Jamal Charara (2) , Jean-Marc Girault (1)

1 iBraiN - Imaging, Brain & Neuropsychiatry
2 Département de physique et d'électronique
3 Thomson Reuters

Robust detection of the smallest circulating cerebral micro-emboli is an efficient way of preventing strokes, which is second cause of mortality worldwide. Transcranial Doppler ultrasound is widely considered the most convenient system for the detection of micro-emboli. The most common standard detection is achieved through the Doppler energy signal and depends on an empirically set constant threshold. On the other hand, in the past few years, higher order statistics have been an extensive field of research as they represent descriptive statistics that can be used to detect signal outliers. In this study, we propose new types of micro-embolic detectors based on the windowed calculation of the third moment skewness and fourth moment kurtosis of the energy signal. During energy embolus-free periods the distribution of the energy is not altered and the skewness and kurtosis signals do not exhibit any peak values. In the presence of emboli, the energy distribution is distorted and the skewness and kurtosis signals exhibit peaks, corresponding to the latter emboli. Applied on real signals, the detection of micro-emboli through the skewness and kurtosis signals outperformed the detection through standard methods. The sensitivities and specificities reached 78% and 91%, and 80% and 90% for the skewness and kurtosis detectors respectively.

Mots clés: kurtosis; skewness; signal processing; micro-embolic detection; standard detection

Domaines: Traitement du signal et de l'image [eess.SP]; Ingénierie biomédicale; Acoustique [physics.class-ph]

Maroun Geryes, Sébastien Ménigot, Walid Hassan, Ali Mcheick, Jamal Charara, et al.. Detection of Doppler Microembolic Signals Using High Order Statistics. Computational and Mathematical Methods in Medicine, 2016, 2016, pp.3243290. ⟨10.1155/2016/3243290⟩. ⟨hal-01407735⟩