Dataset Information

Empirical mode decomposition processing to improve multifocal-visual-evoked-potential signal analysis in multiple sclerosis.

ABSTRACT: To study the performance of multifocal-visual-evoked-potential (mfVEP) signals filtered using empirical mode decomposition (EMD) in discriminating, based on amplitude, between control and multiple sclerosis (MS) patient groups, and to reduce variability in interocular latency in control subjects.MfVEP signals were obtained from controls, clinically definitive MS and MS-risk progression patients (radiologically isolated syndrome (RIS) and clinically isolated syndrome (CIS)). The conventional method of processing mfVEPs consists of using a 1-35 Hz bandpass frequency filter (XDFT). The EMD algorithm was used to decompose the XDFT signals into several intrinsic mode functions (IMFs). This signal processing was assessed by computing the amplitudes and latencies of the XDFT and IMF signals (XEMD). The amplitudes from the full visual field and from ring 5 (9.8-15° eccentricity) were studied. The discrimination index was calculated between controls and patients. Interocular latency values were computed from the XDFT and XEMD signals in a control database to study variability.Using the amplitude of the mfVEP signals filtered with EMD (XEMD) obtains higher discrimination index values than the conventional method when control, MS-risk progression (RIS and CIS) and MS subjects are studied. The lowest variability in interocular latency computations from the control patient database was obtained by comparing the XEMD signals with the XDFT signals. Even better results (amplitude discrimination and latency variability) were obtained in ring 5 (9.8-15° eccentricity of the visual field).Filtering mfVEP signals using the EMD algorithm will result in better identification of subjects at risk of developing MS and better accuracy in latency studies. This could be applied to assess visual cortex activity in MS diagnosis and evolution studies.

SUBMITTER: de Santiago L

PROVIDER: S-EPMC5909914 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Empirical mode decomposition processing to improve multifocal-visual-evoked-potential signal analysis in multiple sclerosis.

de Santiago Luis L Sánchez-Morla Eva E Blanco Román R Miguel Juan Manuel JM Amo Carlos C Ortiz Del Castillo Miguel M López Almudena A Boquete Luciano L

PloS one 20180420 4

<h4>Objective</h4>To study the performance of multifocal-visual-evoked-potential (mfVEP) signals filtered using empirical mode decomposition (EMD) in discriminating, based on amplitude, between control and multiple sclerosis (MS) patient groups, and to reduce variability in interocular latency in control subjects.<h4>Methods</h4>MfVEP signals were obtained from controls, clinically definitive MS and MS-risk progression patients (radiologically isolated syndrome (RIS) and clinically isolated synd ...[more]

PMID: 29677200

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Project description:Analysis of long-term multichannel EEG signals for automatic seizure detection is an active area of research that has seen application of methods from different domains of signal processing and machine learning. The majority of approaches developed in this context consist of extraction of hand-crafted features that are used to train a classifier for eventual seizure detection. Approaches that are data-driven, do not use hand-crafted features, and use small amounts of patients' historical EEG data for classifier training are few in number. The approach presented in this paper falls in the latter category, and is based on a signal-derived empirical dictionary approach, which utilizes empirical mode decomposition (EMD) and discrete wavelet transform (DWT) based dictionaries learned using a framework inspired by traditional methods of dictionary learning. Three features associated with traditional dictionary learning approaches, namely projection coefficients, coefficient vector and reconstruction error, are extracted from both EMD and DWT based dictionaries for automated seizure detection. This is the first time these features have been applied for automatic seizure detection using an empirical dictionary approach. Small amounts of patients' historical multi-channel EEG data are used for classifier training, and multiple classifiers are used for seizure detection using newer data. In addition, the seizure detection results are validated using 5-fold cross-validation to rule out any bias in the results. The CHB-MIT benchmark database containing long-term EEG recordings of pediatric patients is used for validation of the approach, and seizure detection performance comparable to the state-of-the-art is obtained. Seizure detection is performed using five classifiers, thereby allowing a comparison of the dictionary approaches, features extracted, and classifiers used. The best seizure detection performance is obtained using EMD based dictionary and reconstruction error feature and support vector machine classifier, with accuracy, sensitivity and specificity values of 88.2, 90.3, and 88.1%, respectively. Comparison is also made with other recent studies using the same database. The methodology presented in this paper is shown to be computationally efficient and robust for patient-specific automatic seizure detection. A data-driven methodology utilizing a small amount of patients' historical data is hence demonstrated as a practical solution for automatic seizure detection.

Project description:BackgroundMultifocal visual evoked potential (MF-VEP) assesses a wider visual field than full-field VEP (FF-VEP) and potentially offers a more precise analysis of optic nerve injury and repair following optic neuritis. MF-VEP may offer advantages over FF-VEP as an endpoint in clinical trials of remyelinating therapies.ObjectiveMF-VEP testing was used to study changes in visual pathways in 48% of RENEW [phase II, opicinumab (anti-LINGO-1; BIIB033) vs. placebo after first acute unilateral optic neuritis] participants.MethodsThis exploratory MF-VEP RENEW substudy compared mean outcomes at weeks 24 and 32 among participants in the intent-to-treat (ITT; n = 39; 72% female; mean age: 32.3 years) and per-protocol (PP; n = 31; 71% female; mean age: 32.2 years) populations in affected and fellow eye latency from fellow eye baseline latency and affected and fellow eye amplitude from their own baselines. Treatment differences were evaluated using analysis of covariance (week 24) and a mixed-effect model of repeated measures (week 32). Last observation carried forward was used to impute missing data at week 24.ResultsA trend for improvement in affected eye MF-VEP latency with opicinumab versus placebo was seen in the ITT and PP populations at weeks 24 and 32. Both treatment groups in the ITT population experienced partial recovery of amplitude in the affected eye at week 32. Notably, the mean change in fellow eye amplitude at weeks 24 and 32 was - 17.57 and - 31.41 nanovolts (nV) in placebo but only - 0.59 and 1.93 nV in the opicinumab group [differences at weeks 24 and 32: 16.98 nV (p = 0.050) and 33.33 nV (p < 0.01), respectively].ConclusionResults from this substudy showed advantages of MF-VEP over FF-VEP in multicenter studies of central nervous system reparative therapies and provide novel evidence that fellow eye visual pathway amplitude loss occurs after optic neuritis but can potentially be prevented by opicinumab treatment.RegistrationClinicalTrials.gov identifier NCT01721161.

Dataset Information

Empirical mode decomposition processing to improve multifocal-visual-evoked-potential signal analysis in multiple sclerosis.

Publications

Empirical mode decomposition processing to improve multifocal-visual-evoked-potential signal analysis in multiple sclerosis.

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