查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Mental Health Diseases and Conditions-Anxiety Disorders is the subject of a report. According to new s reporting originating in Yantai, People's Republic of China, by NewsRx journal ists, research stated, "Perioperative anxiety and depression syndrome (PADS) is a common clinical concern among women with systemic tumors. Esketamine has been considered for its potential to alleviate anxiety and depressive symptoms." The news reporters obtained a quote from the research from Yantaishan Hospital, "However, its specific application and effectiveness in PADS among women with sy stemic tumors remain unclear. This study aimed to analyze the utility of Machine Learning (ML) algorithms based on electroencephalogram (EEG) signals in evaluat ing perioperative anxiety and depression in women with systemic tumors treated w ith Esketamine, utilizing a large-scale medical data background. A single-center , randomized, placebocontrolled (SC-RPC) trial design was adopted. A total of 1 12 female patients with systemic tumors and PADS who received Esketamine treatme nt were included as study participants. A moderate dose (0.7 mg/kg) of Esketamin e was administered through intravenous infusion over a duration of 60 minutes. E EG signals were collected from all patients, and the EEG signal features of indi viduals with depression were compared to those without depression. In this study , a Support Vector Machine (SVM)-K-Nearest Neighbour (KNN) hybrid classifier was constructed based on SVM and KNN algorithms. Using the EEG signals, the classif ier was utilized to assess the anxiety and depression status of the patients. Th e predictive performance of the classifier was evaluated using accuracy, sensiti vity, and specificity measures. The C2 correntropy feature of the delta rhythm i n the left-brain EEG signal was significantly higher in individuals with depress ion compared to those without depression (p <0.05). Moreove r, the C2 correntropy feature of the Alpha, Beta, and Gamma rhythms in the left- brain EEG signal was significantly lower in individuals with depression compared to those without depression (p <0.05). In the right brain EEG signal, the C2 correntropy feature of the delta rhythm was significantly hig her in individuals with depression (p <0.05), while the C2 correntropy feature of the alpha and gamma rhythms was significantly lower in in dividuals with depression compared to those without depression (p <0.05). Additionally, the C1 correntropy feature of the Gamma rhythm in the right brain EEG signal was significantly higher in individuals with depression compar ed to those without depression (p <0.05). The SVM classifie r achieved accuracy, sensitivity, and specificity of 98.23%, 98.10% , and 98.56%, respectively, in recognizing the left-brain EEG signa ls, with a correlation coefficient of 0.95. In recognizing the right brain EEG s ignals, the SVM classifier achieved accuracy, sensitivity, and specificity of 98 .74%, 98.43%, and 99.03%, respectively, w ith a correlation coefficient of 0.96. The improved SVM-KNN approach yielded an accuracy, recall, precision, F-score, area over the curve (AOC), and Receiver Op eration Characteristics (ROC) of 0.829, 0.811, 0.791, 0.853, 0.787, and 0.877, r espectively, in predicting anxiety. For predicting depression, the accuracy, rec all, precision, F-score, AOC, and ROC were 0.869, 0.842, 0.831, 0.893, 0.827, an d 0.917, respectively. Significant differences were observed in the brain EEG si gnals between individuals with depression and those without depression."
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