Coding Prony's method in MATLAB and applying it to biomedical signal filtering.

BACKGROUND: The response of many biomedical systems can be modelled using a linear combination of damped exponential functions. The approximation parameters, based on equally spaced samples, can be obtained using Prony's method and its variants (e.g. the matrix pencil method). This paper provides a tutorial on the main polynomial Prony and matrix pencil methods and their implementation in MATLAB and analyses how they perform with synthetic and multifocal visual-evoked potential (mfVEP) signals. This paper briefly describes the theoretical basis of four polynomial Prony approximation methods: classic, least squares (LS), total least squares (TLS) and matrix pencil method (MPM). In each of these cases, implementation uses general MATLAB functions. The features of the various options are tested by approximating a set of synthetic mathematical functions and evaluating filtering performance in the Prony domain when applied to mfVEP signals to improve diagnosis of patients with multiple sclerosis (MS). RESULTS: The code implemented does not achieve 100%-correct signal approximation and, of the methods tested, LS and MPM perform best. When filtering mfVEP records in the Prony domain, the value of the area under the receiver-operating-characteristic (ROC) curve is 0.7055 compared with 0.6538 obtained with the usual filtering method used for this type of signal (discrete Fourier transform low-pass filter with a cut-off frequency of 35 Hz). CONCLUSIONS: This paper reviews Prony's method in relation to signal filtering and approximation, provides the MATLAB code needed to implement the classic, LS, TLS and MPM methods, and tests their performance in biomedical signal filtering and function approximation. It emphasizes the importance of improving the computational methods used to implement the various methods described above.

A signal-to-noise-ratio-based analysis of multifocal visual-evoked potentials in multiple sclerosis risk assessment.

OBJECTIVE: To study the value of using the signal-to-noise ratio (SNR) of multifocal visual-evoked potentials (mfVEPs) in assessment of subjects at risk of developing multiple sclerosis (MS). METHODS: MfVEP signals were obtained from 15 patients with radiologically isolated syndrome (RIS), from 28 patients with clinically isolated syndrome (CIS), from 28 with clinically definite MS and from 24 control subjects. The CIS and MS groups were divided into two subgroups: those with eyes affected by optic neuritis (ON) and those without (non-ON). The mfVEPs' SNR was obtained for both the whole visual field and at various eccentric rings. The area under the curve (AUC) was calculated by comparing the control subjects' mfVEP SNR values with those of the RIS, CIS and MS groups. RESULTS: In whole visual field analysis, risk of developing MS increased as SNR decreased (SNRCONTROL=0.70, SNRRIS=0.62, SNRCIS-nonON=0.54, SNRCIS-ON=0.40, SNRMS-nonON=0.52, SNRMS-ON=0.40). Ring 5 (9.8°-15° eccentricity) was most affected by the SNR decrease, as indicated by its higher AUC values (AUCFULL_EYE=0.81, AUCRING_5=0.89). A significant relationship (Spearman correlation, ρRING_5=0.61) between SNR values and disability severity on the Expanded Disability Status Scale (EDSS) was observed in clinically definite MS patients. CONCLUSION: A new method based on analysis of the SNR of mfVEP signal amplitude improves assessment of patients at risk of developing MS. SIGNIFICANCE: Improved mfVEP assessment of MS-risk patients was achieved by using SNR values at 9.8°-15° eccentricity of the visual field.

Continuous-wavelet-transform analysis of the multifocal ERG waveform in glaucoma diagnosis.

The vast majority of multifocal electroretinogram (mfERG) signal analyses to detect glaucoma study the signals' amplitudes and latencies. The purpose of this paper is to investigate application of wavelet analysis of mfERG signals in diagnosis of glaucoma. This analysis method applies the continuous wavelet transform (CWT) to the signals, using the real Morlet wavelet. CWT coefficients resulting from the scale of maximum correlation are used as inputs to a neural network, which acts as a classifier. mfERG recordings are taken from the eyes of 47 subjects diagnosed with chronic open-angle glaucoma and from those of 24 healthy subjects. The high sensitivity in the classification (0.894) provides reliable detection of glaucomatous sectors, while the specificity achieved (0.844) reflects accurate detection of healthy sectors. The results obtained in this paper improve on the previous findings reported by the authors using the same visual stimuli and database.

Software for analysing multifocal visual evoked potential signal latency progression.

BACKGROUND: This paper describes a new non-commercial software application (mfVEP(2)) developed to process multifocal visual-evoked-potential (mfVEP) signals in latency (monocular and interocular) progression studies. METHOD: The software performs analysis by cross-correlating signals from the same patients. The criteria applied by the software include best channels, signal window, cross-correlation limits and signal-to-noise ratio (SNR). Software features include signal display comparing different tests and groups of sectors (quadrants, rings and hemispheres). RESULTS: The software's performance and capabilities are demonstrated on the results obtained from a patient with acute optic neuritis who underwent 9 follow-up mfVEP tests. Numerical values and graphics are presented and discussed for this case. CONCLUSIONS: The authors present a software application used to study progression in mfVEP signals. It is also useful in research projects designed to improve mfVEP techniques. This software makes it easier for users to manage the signals and allows them to choose various ways of selecting signals and representing results.

Filtering multifocal VEP signals using Prony's method.

BACKGROUND: This paper describes use of Prony's method as a filter applied to multifocal visual-evoked-potential (mfVEP) signals. Prony's method can be viewed as an extension of Fourier analysis that allows a signal to be decomposed into a linear combination of functions with different amplitudes, damping factors, frequencies and phase angles. METHOD: By selecting Prony method parameters, a frequency filter has been developed which improves signal-to-noise ratio (SNR). Three different criteria were applied to data recorded from control subjects to produce three separate datasets: unfiltered raw data, data filtered using the traditional method (fast Fourier transform: FFT), and data filtered using Prony's method. RESULTS: Filtering using Prony's method improved the signals' original SNR by 44.52%, while the FFT filter improved the SNR by 33.56%. The extent to which signal can be separated from noise was analysed using receiver-operating-characteristic (ROC) curves. The area under the curve (AUC) was greater in the signals filtered using Prony's method than in the original signals or in those filtered using the FFT. CONCLUSION: filtering using Prony's method improves the quality of mfVEP signal pre-processing when compared with the original signals, or with those filtered using the FFT.

Improved measurement of intersession latency in mfVEPs.

PURPOSE: The purpose of the study is to present a method (Selfcorr) by which to measure intersession latency differences between multifocal VEP (mfVEP) signals. METHODS: The authors compared the intersession latency difference obtained using a correlation method (Selfcorr) against that obtained using a Template method. While the Template method cross-correlates the subject's signals with a reference database, the Selfcorr method cross-correlates traces across subsequent recordings taken from the same subject. RESULTS: The variation in latency between intersession signals was 0.8 ± 13.6 and 0.5 ± 5.0 ms for the Template and Selfcorr methods, respectively, with a coefficient of variability CV_TEMPLATE = 15.83 and CV_SELFCORR = 5.68 (n = 18, p = 0.0002, Wilcoxon). The number of analyzable sectors with the Template and Selfcorr methods was 36.7 ± 8.5 and 45.3 ± 8.7, respectively (p = 0.0001, paired t test, two tailed). CONCLUSIONS: The Selfcorr method produces smaller intersession mfVEP delays and variability over time than the Template method.

Portable system for temperature monitoring in all phases of wine production.

This paper presents a low-cost and highly versatile temperature-monitoring system applicable to all phases of wine production, from grape cultivation through to delivery of bottled wine to the end customer. Monitoring is performed by a purpose-built electronic system comprising a digital memory that stores temperature data and a ZigBee communication system that transmits it to a Control Centre for processing and display. The system has been tested under laboratory conditions and in real-world operational applications. One of the system's advantages is that it can be applied to every phase of wine production. Moreover, with minimum modification, other variables of interest (pH, humidity, etc.) could also be monitored and the system could be applied to other similar sectors, such as olive-oil production.

Glaucoma detection by wavelet-based analysis of the global flash multifocal electroretinogram.

The current clinical analysis of the multifocal electroretinography (mfERG) recordings for detecting glaucoma is based on standard signal morphology, measuring amplitudes and latencies. However, this analysis is not sensitive enough for detection of small changes in the multifocal electroretinogram signals. Other, more sophisticated, analysis methods should be explored to improve the sensitivity of this diagnostic technique, such as the discrete wavelet transform, proposed in this paper. We present an alternative method for the detection of open angle glaucoma based on the characterization of global flash mfERG signals. The digital signal processing technique is based on wavelets, hitherto unused in this field, for detection of advanced-stage glaucoma. Two markers were obtained from the recorded signals by applying the discrete wavelet transform, which help discriminate healthy from glaucomatous signals.