Correction: Empirical mode decomposition processing to improve multifocal-visual-evoked-potential signal analysis in multiple sclerosis.
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ABSTRACT:
SUBMITTER: PLOS ONE Staff
PROVIDER: S-EPMC5929517 | biostudies-literature | 2018
REPOSITORIES: biostudies-literature
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Correction: Empirical mode decomposition processing to improve multifocal-visual-evoked-potential signal analysis in multiple sclerosis.
PloS one 20180501 5
[This corrects the article DOI: 10.1371/journal.pone.0194964.]. ...[more]
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Project description:ObjectiveTo 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.MethodsMfVEP 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.ResultsUsing 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).ConclusionsFiltering 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.
| S-EPMC5909914 | biostudies-literature
Project description:BackgroundTo evaluate the effect of occlusion treatment for anisometropic amblyopia using multifocal visual evoked potentials (mfVEPs).MethodsThe patients for this study comprised 19 patients (mean age 6.05 ± 1.65 years) with anisometropic amblyopia underwent mfVEP analysis using the RETIscan® system before and after occlusion treatment. After dividing the area into six ring areas and four quadrants, we analyzed the amplitudes and latencies of the mfVEPs.ResultsThe amplitudes of ring 1 (central field) in amblyopic eyes after treatment were significantly higher than those in the other rings (p = 0.001). The mfVEP amplitudes in each of the six rings between amblyopic eyes and fellow eyes at diagnosis and after occlusion treatment showed no significant differences. In quadrant 1 the amplitudes of the amblyopic eyes and fellow eyes were significantly different at the time of diagnosis (p = 0.005), whereas after occlusion treatment there was no significant difference (p = 0.888). The amplitudes for each of the six rings at diagnosis and after occlusion treatment in amblyopic eyes versus fellow eyes showed no significant difference. There were also no differences in the amplitudes in each of the four quadrants at the time of diagnosis and after occlusion treatment in amblyopic eyes versus fellow eyes. No significant difference was found in the comparison of latency values in each of the six rings or in each of the four quadrants at diagnosis and after occlusion treatment in amblyopic eyes versus their fellow eyes.ConclusionsThe amplitudes of quadrant 1 in amblyopic eyes compared with those of the fellow eyes at diagnosis were increased after occlusion treatment. Changes of the difference between amblyopic eyes and fellow eyes in quadrant 1 after occlusion treatment could be a useful, objective method for monitoring improvement in visual acuity.
| S-EPMC6090668 | biostudies-literature
Project description:Microcharge induction has recently been applied as a dust detection method. However, in complex environments, the detection device can be seriously polluted by noise. To improve the quality of the measured signal, the characteristics of both the signal and the noise should be analyzed so as to determine an effective noise removal method. Traditional removal methods mostly deal with specific noise signals, and it is difficult to consider the correlation of measured signals between adjacent time periods. To overcome this shortcoming, we describe a method in which wavelet decomposition is applied to the measured signal to obtain sub-band components in different frequency ranges. A time-lapse Pearson method is then used to analyze the correlation of the sub-band components and the noise signal. This allows the sub-band component of the measurement signal that has the strongest correlation with the noise to be determined. Based on multifractal detrended fluctuation analysis combined with empirical mode decomposition, the similarity between the signal sub-band components and the noise sub-band components is analyzed and three indices are employed to determine the multifractal characteristics of the sub-band components. The consistency between noise components and signal components is obtained and the main signal components are verified. Finally, the sub-band components are used to reconstruct the signal, giving the noise-free measured (microcharge induction) signal. The filtered signal presents smoother, multifractal features.
| S-EPMC8367589 | biostudies-literature
Project description:ObjectiveStudies using conventional full-field visual evoked potentials (ffVEP) have reported subtle abnormalities in patients with chronic inflammatory demyelinating polyneuropathy (CIDP). We hypothesize that these abnormalities can be detected in the majority of CIDP patients using enhanced methods.MethodsWe performed a cross-sectional noninterventional study comparing 18 CIDP patients and 18 matched healthy controls using multifocal VEP (mfVEP) as a technique with enhanced sensitivity to detect conduction abnormalities across the spectrum of optic nerve fibers. Patients with confounding diseases (ophthalmologic, diabetes mellitus) were excluded.ResultsThe mean amplitude and latency, as well as the low-contrast visual acuity, did not differ between CIDP patients and controls. Subanalyses revealed latency differences concerning the superior sector of the visual field. Severity markers of CIDP (ODSS, motor nerve conduction velocity) were associated with mfVEP latency delay.InterpretationWe could not adduce evidence for clinically or diagnostically relevant visual pathway involvement in CIDP. The latency differences identified were very subtle and restricted to the superior visual field which cannot be readily explained biologically, anatomically, or pathologically. In summary, we conclude that our study revealed no relevant differences in mfVEP parameters between CIDP patients and controls.
| S-EPMC6093840 | biostudies-literature
Project description:Microtubules (MTs) are essential cytoskeletal polymers of eukaryote cells implicated in various cell functions, including cell division, cargo transfer, and cell signaling. MTs also are highly charged polymers that generate electrical oscillations that may underlie their ability to act as nonlinear transmission lines. However, the oscillatory composition and time-frequency differences of the MT electrical oscillations have not been identified. Here, we applied the Empirical Mode Decomposition (EMD) to bovine brain MT sheet recordings to determine the number and fundamental frequencies of the Intrinsic Modes Functions (IMF) and evaluate their energetic contribution to the electrical signal. As previously reported, raw signals were obtained from cow brain MTs (Cantero et al. Sci Rep 6:27143, 2016), sampled, filtered, and subjected to signal decomposition from representative experiments. Filtered signals (200 Hz) allowed us to identify either six or seven IMFs. The reconstructed tracings faithfully resembled the original signals, with identifiable frequency peaks. To extend the analysis to obtain time-frequency information and the energy implicated in each IMF, we applied the Hilbert-Huang Transform (HHT) and the Continuous Wavelet Transform (CWT) to the same samples. The analyses disclosed the presence of more fundamental frequency peaks than initially reported and evidenced the advantages and disadvantages of each transform. The study indicates that the EMD is a robust approach to quantifying signal decomposition of brain MT oscillations and suggests novel similarities with human brain wave electroencephalogram (EEG) recordings. The evidence points to the potentially fundamental role of MT oscillations in brain electrical activity.
| S-EPMC11412201 | biostudies-literature
Project description:In recent years, the utilization of motor imagery (MI) signals derived from electroencephalography (EEG) has shown promising applications in controlling various devices such as wheelchairs, assistive technologies, and driverless vehicles. However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation. In this work, we propose a novel method that addresses these challenges by employing empirical mode decomposition (EMD) for feature extraction and a parallel convolutional neural network (PCNN) for feature classification. This approach aims to mitigate non-stationary issues, improve performance speed, and enhance classification accuracy. We validate the effectiveness of our proposed method using datasets from the BCI competition IV, specifically datasets 2a and 2b, which contain motor imagery EEG signals. Our method focuses on identifying two- and four-class motor imagery EEG signal classifications. Additionally, we introduce a transfer learning technique to fine-tune the model for individual subjects, leveraging important features extracted from a group dataset. Our results demonstrate that the proposed EMD-PCNN method outperforms existing approaches in terms of classification accuracy. We conduct both qualitative and quantitative analyses to evaluate our method. Qualitatively, we employ confusion matrices and various performance metrics such as specificity, sensitivity, precision, accuracy, recall, and f1-score. Quantitatively, we compare the classification accuracies of our method with those of existing approaches. Our findings highlight the superiority of the proposed EMD-PCNN method in accurately classifying motor imagery EEG signals. The enhanced performance and robustness of our method underscore its potential for broader applicability in real-world scenarios.
| S-EPMC11737786 | biostudies-literature
Project description:The accurate detection of fiducial points in the impedance cardiography signal (ICG) has a decisive impact on the proper estimation of diagnostic parameters such as stroke volume or cardiac output. It is, therefore, necessary to find an algorithm that is able to assess their positions with great precision. The solution to this problem is, however, quite challenging with regard to the high sensitivity of the ICG technique to the noise and varying morphology of the acquired signals. The aim of this study is to propose a novel method that allows us to overcome these limitations. The developed algorithm is based on Empirical Mode Decomposition (EMD)-an effective technique for processing and analyzing various types of non-stationary signals. We find high correlations between the results obtained from the algorithm and annotated by an expert. This, in turn, implies that the difference in estimation of the diagnostic-relevant parameters is small, which suggests that the method can automatically provide precise clinical information.
| S-EPMC9861967 | biostudies-literature
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.
| S-EPMC8710482 | biostudies-literature
Project description:The clinical manifestations of ischemic cardiomyopathy (ICM) bear resemblance to dilated cardiomyopathy (DCM), yet their treatments and prognoses are quite different. Early differentiation between these conditions yields positive outcomes, but the gold standard (coronary angiography) is invasive. The potential use of ECG signals based on variational mode decomposition (VMD) as an alternative remains underexplored. An ECG dataset containing 87 subjects (44 DCM, 43 ICM) is pre-processed for denoising and heartbeat division. Firstly, the ECG signal is processed by empirical mode decomposition (EMD) and VMD. And then, five modes are determined by correlation analysis. Secondly, bispectral analysis is conducted on these modes, extracting corresponding bispectral and nonlinear features. Finally, the features are processed using five machine learning classification models, and a comparative assessment of their classification efficacy is facilitated. The results show that the technique proposed provides a better categorization for DCM and ICM using ECG signals compared to previous approaches, with a highest classification accuracy of 98.30%. Moreover, VMD consistently outperforms EMD under diverse conditions such as different modes, leads, and classifiers. The superiority of VMD on ECG analysis is verified.
| S-EPMC11505311 | biostudies-literature
Project description:ObjectiveTo test the hypothesis that patients with multiple sclerosis (MS) with intereye asymmetry on low contrast letter acuity, and thickness of the retinal nerve fiber layer (RNFL), would exhibit corresponding changes in cortical timing and amplitude responses on pattern reversal multifocal visual evoked potentials (mfVEP), contingent upon variable stimulus contrast.MethodsIn a cross-sectional study, we investigated a cohort of 11 normal subjects and 40 patients with MS, 21 of whom had a history of acute optic neuritis (MS-AON) with an intereye asymmetry with respect to RNFL thickness, and on low contrast letter acuity performance. Pattern reversal mfVEP was performed at high (100%), low (33.3%), and very low (14.2%) Michelson-contrast levels.ResultsCompared to baseline measures at 100% contrast, the mean amplitude of the mfVEP was reduced in MS-AON eyes, upon pattern-reversal stimulation at the 2 lower contrast levels (p < 0.0001). With respect to changes in timing responses, the intereye asymmetry was increased in the MS-AON patients upon lower contrast pattern-reversal stimulation (p < 0.0001 for 33.3% compared to 100%, and p < 0.001 for 14.2% compared to 100%). The fellow eye in 12 (57%; p < 0.001) of the patients with an abnormal eye, and a history of AON, revealed abnormal amplitude and timing responses upon low contrast stimulation (signifying unmasking of occult damage).ConclusionsOur findings support the hypothesis that mfVEP metric abnormalities are contingent upon contrast magnitude during pattern reversal stimulation. Further, this paradigm was capable of unmasking occult abnormalities in a significant number of apparently unaffected eyes.
| S-EPMC4098874 | biostudies-literature