PDF(Pubmed)
Abstract：
OBJECTIVE: Multiple sclerosis (MS) is a neuro-inflammatory disease affecting the central nervous system (CNS), where the immune system targets and damages the protective myelin sheath surrounding nerve fibers, inhibiting axonal signal transmission. Demyelinating optic neuritis (ON), a common MS symptom, involves optic nerve damage. We\'ve developed NeuroVEP, a portable, wireless diagnostic system that delivers visual stimuli through a smartphone in a headset and measures evoked potentials at the visual cortex from the scalp using custom electroencephalography electrodes.
METHODS: Subject vision is evaluated using a short 2.5-min full-field visual evoked potentials (ffVEP) test, followed by a 12.5-min multifocal VEP (mfVEP) test. The ffVEP evaluates the integrity of the visual pathway by analyzing the P100 component from each eye, while the mfVEP evaluates 36 individual regions of the visual field for abnormalities. Extensive signal processing, feature extraction methods, and machine learning algorithms were explored for analyzing the mfVEPs. Key metrics from patients\' ffVEP results were statistically evaluated against data collected from a group of subjects with normal vision. Custom visual stimuli with simulated defects were used to validate the mfVEP results which yielded 91% accuracy of classification.
RESULTS: 20 subjects, 10 controls and 10 with MS and/or ON were tested with the NeuroVEP device and a standard-of-care (SOC) VEP testing device which delivers only ffVEP stimuli. In 91% of the cases, the ffVEP results agreed between NeuroVEP and SOC device. Where available, the NeuroVEP mfVEP results were in good agreement with Humphrey Automated Perimetry visual field analysis. The lesion locations deduced from the mfVEP data were consistent with Magnetic Resonance Imaging and Optical Coherence Tomography findings.
CONCLUSIONS: This pilot study indicates that NeuroVEP has the potential to be a reliable, portable, and objective diagnostic device for electrophysiology and visual field analysis for neuro-visual disorders.
METHODS: Subject vision is evaluated using a short 2.5-min full-field visual evoked potentials (ffVEP) test, followed by a 12.5-min multifocal VEP (mfVEP) test. The ffVEP evaluates the integrity of the visual pathway by analyzing the P100 component from each eye, while the mfVEP evaluates 36 individual regions of the visual field for abnormalities. Extensive signal processing, feature extraction methods, and machine learning algorithms were explored for analyzing the mfVEPs. Key metrics from patients\' ffVEP results were statistically evaluated against data collected from a group of subjects with normal vision. Custom visual stimuli with simulated defects were used to validate the mfVEP results which yielded 91% accuracy of classification.
RESULTS: 20 subjects, 10 controls and 10 with MS and/or ON were tested with the NeuroVEP device and a standard-of-care (SOC) VEP testing device which delivers only ffVEP stimuli. In 91% of the cases, the ffVEP results agreed between NeuroVEP and SOC device. Where available, the NeuroVEP mfVEP results were in good agreement with Humphrey Automated Perimetry visual field analysis. The lesion locations deduced from the mfVEP data were consistent with Magnetic Resonance Imaging and Optical Coherence Tomography findings.
CONCLUSIONS: This pilot study indicates that NeuroVEP has the potential to be a reliable, portable, and objective diagnostic device for electrophysiology and visual field analysis for neuro-visual disorders.
摘要：
目的：多发性硬化症（MS）是一种影响中枢神经系统（CNS）的神经炎症性疾病，免疫系统瞄准并破坏围绕神经纤维的保护性髓鞘，抑制轴突信号传输。脱髓鞘性视神经炎(ON),常见的MS症状，涉及视神经损伤.我们开发了NeuroVEP,一个便携式的，无线诊断系统，通过耳机中的智能手机提供视觉刺激，并使用自定义脑电图电极从头皮测量视觉皮层的诱发电位。
方法：使用短2.5分钟全视野视觉诱发电位（ffVEP）测试评估受试者的视力，随后是12.5分钟的多焦点VEP（mfVEP）测试。ffVEP通过分析每只眼睛的P100成分来评估视觉通路的完整性，而mfVEP评估了36个视野区域的异常。广泛的信号处理，特征提取方法，并探索了机器学习算法来分析mfVEP。根据一组视力正常的受试者收集的数据，对患者ffVEP结果的关键指标进行了统计评估。使用具有模拟缺陷的自定义视觉刺激来验证mfVEP结果，该结果产生了91％的分类准确性。
结果：20名受试者，用NeuroVEP装置和仅递送ffVEP刺激的护理标准(SOC)VEP测试装置测试10个对照和10个具有MS和/或ON的对照。在91%的案例中，NeuroVEP和SOC装置的ffVEP结果一致.如有,NeuroVEPmfVEP结果与Humphrey自动视野检查视野分析非常吻合.从mfVEP数据推断的病变位置与磁共振成像和光学相干断层扫描结果一致。
结论：这项初步研究表明，NeuroVEP有可能成为一种可靠的，便携式，用于神经视觉障碍的电生理和视野分析的客观诊断装置。
方法：使用短2.5分钟全视野视觉诱发电位（ffVEP）测试评估受试者的视力，随后是12.5分钟的多焦点VEP（mfVEP）测试。ffVEP通过分析每只眼睛的P100成分来评估视觉通路的完整性，而mfVEP评估了36个视野区域的异常。广泛的信号处理，特征提取方法，并探索了机器学习算法来分析mfVEP。根据一组视力正常的受试者收集的数据，对患者ffVEP结果的关键指标进行了统计评估。使用具有模拟缺陷的自定义视觉刺激来验证mfVEP结果，该结果产生了91％的分类准确性。
结果：20名受试者，用NeuroVEP装置和仅递送ffVEP刺激的护理标准(SOC)VEP测试装置测试10个对照和10个具有MS和/或ON的对照。在91%的案例中，NeuroVEP和SOC装置的ffVEP结果一致.如有,NeuroVEPmfVEP结果与Humphrey自动视野检查视野分析非常吻合.从mfVEP数据推断的病变位置与磁共振成像和光学相干断层扫描结果一致。
结论：这项初步研究表明，NeuroVEP有可能成为一种可靠的，便携式，用于神经视觉障碍的电生理和视野分析的客观诊断装置。
