Table_2_Optimizing functional near-infrared spectroscopy (fNIRS) channels for schizophrenic identification during a verbal fluency task using metaheuristic algorithms.XLSX

ObjectiveWe aimed to reduce the complexity of the 52-channel functional near-infrared spectroscopy (fNIRS) system to facilitate its usage in discriminating schizophrenia during a verbal fluency task (VFT).
MethodsOxygenated hemoglobin signals obtained using 52-channel fNIRS from 100 patients with schizophrenia and 100 healthy controls during a VFT were collected and processed. Three features frequently used in the analysis of fNIRS signals, namely time average, functional connectivity, and wavelet, were extracted and optimized using various metaheuristic operators, i.e., genetic algorithm (GA), particle swarm optimization (PSO), and their parallel and serial hybrid algorithms. Support vector machine (SVM) was used as the classifier, and the performance was evaluated by ten-fold cross-validation.
ResultsGA and GA-dominant algorithms achieved higher accuracy compared to PSO and PSO-dominant algorithms. An optimal accuracy of 87.00% using 16 channels was obtained by GA and wavelet analysis. A parallel hybrid algorithm (the best 50% individuals assigned to GA) achieved an accuracy of 86.50% with 8 channels on the time-domain feature, comparable to the reported accuracy obtained using 52 channels.
ConclusionThe fNIRS system can be greatly simplified while retaining accuracy comparable to that of the 52-channel system, thus promoting its applications in the diagnosis of schizophrenia in low-resource environments. Evolutionary algorithm-dominant optimization of time-domain features is promising in this regard.

研究目的：本研究旨在简化52通道功能近红外光谱（functional near-infrared spectroscopy, fNIRS）系统的复杂度，以推动其在言语流畅性任务（verbal fluency task, VFT）中用于精神分裂症鉴别诊断的应用。
研究方法：本研究收集并处理了100例精神分裂症患者与100名健康对照者在完成言语流畅性任务（VFT）期间，通过52通道fNIRS采集的氧合血红蛋白信号。针对fNIRS信号分析中常用的三类特征——时域平均值、功能连接性与小波特征，分别采用多种元启发式算子进行提取与优化，包括遗传算法（genetic algorithm, GA）、粒子群优化（particle swarm optimization, PSO）及其并行、串行混合算法。以支持向量机（support vector machine, SVM）作为分类器，并通过十折交叉验证评估模型性能。
研究结果：相较于PSO及以PSO为主导的算法，GA及以GA为主导的算法可获得更高的分类准确率。采用GA结合小波分析时，仅使用16通道即可达到87.00%的最优准确率。针对时域特征，采用并行混合算法（将50%的最优个体分配至GA）时，仅需8通道即可实现86.50%的准确率，这一性能与原52通道系统的报道准确率相当。
研究结论：本研究可在保持与52通道系统相当的分类准确率的前提下，大幅简化fNIRS系统，从而推动其在资源匮乏环境下的精神分裂症诊断应用中发挥作用。基于进化算法主导的时域特征优化方案在该领域具有良好的应用前景。