Grupo de Ingeniería Biomédica, Departamento de Electrónica, Universidad de Alcalá, Alcalá de Henares, Spain.
Instituto de Investigación Hospital 12 de Octubre (i+12), Madrid, Spain.
PLoS One. 2019 Apr 4;14(4):e0214662. doi: 10.1371/journal.pone.0214662. eCollection 2019.
The aim of this study is to develop a computer-aided diagnosis system to identify subjects at differing stages of development of multiple sclerosis (MS) using multifocal visual-evoked potentials (mfVEPs). Using an automatic classifier, diagnosis is performed first on the eyes and then on the subjects.
MfVEP signals were obtained from patients with Radiologically Isolated Syndrome (RIS) (n = 30 eyes), patients with Clinically Isolated Syndrome (CIS) (n = 62 eyes), patients with definite MS (n = 56 eyes) and 22 control subjects (n = 44 eyes). The CIS and MS groups were divided into two subgroups: those with eyes affected by optic neuritis (ON) and those without (non-ON).
For individual eye diagnosis, a feature vector was formed with information about the intensity, latency and singular values of the mfVEP signals. A flat multiclass classifier (FMC) and a hierarchical classifier (HC) were tested and both were implemented using the k-Nearest Neighbour (k-NN) algorithm. The output of the best eye classifier was used to classify the subjects. In the event of divergence, the eye with the best mfVEP recording was selected.
In the eye classifier, the HC performed better than the FMC (accuracy = 0.74 and extended Matthew Correlation Coefficient (MCC) = 0.68). In the subject classification, accuracy = 0.95 and MCC = 0.93, confirming that it may be a promising tool for MS diagnosis.
In addition to amplitude (axonal loss) and latency (demyelination), it has shown that the singular values of the mfVEP signals provide discriminatory information that may be used to identify subjects with differing degrees of the disease.
本研究旨在开发一种计算机辅助诊断系统，使用多焦视觉诱发电位（mfVEP）识别多发性硬化症（MS）不同发展阶段的患者。使用自动分类器，首先对眼睛进行诊断，然后对受试者进行诊断。
从放射孤立综合征（RIS）患者（n = 30 只眼）、临床孤立综合征（CIS）患者（n = 62 只眼）、明确 MS 患者（n = 56 只眼）和 22 名对照受试者（n = 44 只眼）中获得 mfVEP 信号。CIS 和 MS 组分为视神经炎（ON）受累眼和非 ON 眼亚组。
对于个体眼诊断，使用 mfVEP 信号的强度、潜伏期和奇异值信息形成特征向量。测试了平面多类分类器（FMC）和层次分类器（HC），均使用 k-最近邻（k-NN）算法实现。最佳眼分类器的输出用于对受试者进行分类。在出现分歧的情况下，选择具有最佳 mfVEP 记录的眼睛。
在眼分类器中，HC 的性能优于 FMC（准确性 = 0.74，扩展马修相关系数（MCC）= 0.68）。在受试者分类中，准确性 = 0.95，MCC = 0.93，证实它可能是 MS 诊断的有前途的工具。
除了幅度（轴突损失）和潜伏期（脱髓鞘）外，还表明 mfVEP 信号的奇异值提供了可用于识别不同疾病程度的受试者的鉴别信息。
