LAUSR

OBJECTIVE Moyamoya is a cerebrovascular disease with a high mortality rate. Early detection and mechanistic studies are necessary. METHODS Near-infrared spectroscopy (NIRS) was used to study the signals of the cerebral tissue oxygen saturation index (TOI) and the changes in oxygenated and deoxygenated haemoglobin concentrations (HbO and Hb) in 64 patients with moyamoya disease and 64 healthy volunteers. The wavelet transforms (WT) of TOI, HbO and Hb signals, as well as the wavelet phase coherence (WPCO) of these signals from the left and right frontal lobes of the same subject, were calculated. Features were extracted from the spontaneous oscillations of TOI, HbO and Hb in five physiological activity-related frequency segments. Machine learning models based on support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost) have been built to classify the two groups. RESULTS For 20-minute signals, the ten-fold cross-validation accuracies of SVM, RF and XGBoost were 87%, 85% and 85%, respectively. For 5-minute signals, the accuracies of the three methods were 88%, 88% and 84%, respectively. CONCLUSIONS The method proposed in this paper has potential for detecting and screening moyamoya with high proficiency. SIGNIFICANCE Evaluating the cerebral oxygenation with NIRS shows great potential in screening moyamoya diseases. This article is protected by copyright. All rights reserved.