Cardiac health diagnosis using higher order spectra and support vector machine

The Electrocardiogram (ECG) is an important bio-signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. The HRV signal can be used as a base signal to observe the heart's functioning. These signals are non-linear and non-stationary in nature. So, higher order spectral (HOS) analysis, which is more suitable for non-linear systems and is robust to noise, was used. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, we have extracted seven features from the heart rate signals using HOS and fed them to a support vector machine (SVM) for classification. Our performance evaluation protocol uses 330 subjects consisting of five different kinds of cardiac disease conditions. We demonstrate a sensitivity of 90% for the classifier with a specificity of 87.93%. Our system is ready to run on larger data sets.

Application of higher order spectra to identify epileptic EEG

Epilepsy is characterized by the spontaneous and seemingly unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into this phenomenon. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, we made a comparative study of the performance of Gaussian mixture model (GMM) and Support Vector Machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Results show that the selected HOS based features achieve 93.11% classification accuracy compared to 88.78% with features derived from the power spectrum for a GMM classifier. The SVM classifier achieves an improvement from 86.89% with features based on the power spectrum to 92.56% with features based on the bispectrum.

Pattern recognition using invariants defined from higher order spectra : 2-D image inputs

A new algorithm for extracting features from images for object recognition is described. The algorithm uses higher order spectra to provide desirable invariance properties, to provide noise immunity, and to incorporate nonlinearity into the feature extraction procedure thereby allowing the use of simple classifiers. An image can be reduced to a set of 1D functions via the Radon transform, or alternatively, the Fourier transform of each 1D projection can be obtained from a radial slice of the 2D Fourier transform of the image according to the Fourier slice theorem. A triple product of Fourier coefficients, referred to as the deterministic bispectrum, is computed for each 1D function and is integrated along radial lines in bifrequency space. Phases of the integrated bispectra are shown to be translation- and scale-invariant. Rotation invariance is achieved by a regrouping of these invariants at a constant radius followed by a second stage of invariant extraction. Rotation invariance is thus converted to translation invariance in the second step. Results using synthetic and actual images show that isolated, compact clusters are formed in feature space. These clusters are linearly separable, indicating that the nonlinearity required in the mapping from the input space to the classification space is incorporated well into the feature extraction stage. The use of higher order spectra results in good noise immunity, as verified with synthetic and real images. Classification of images using the higher order spectra-based algorithm compares favorably to classification using the method of moment invariants

Shape discrimination using invariants defined from higher-order spectra

An approach to pattern recognition using invariant parameters based on higher-order spectra is presented. In particular, bispectral invariants are used to classify one-dimensional shapes. The bispectrum, which is translation invariant, is integrated along straight lines passing through the origin in bifrequency space. The phase of the integrated bispectrum is shown to be scale- and amplification-invariant. A minimal set of these invariants is selected as the feature vector for pattern classification. Pattern recognition using higher-order spectral invariants is fast, suited for parallel implementation, and works for signals corrupted by Gaussian noise. The classification technique is shown to distinguish two similar but different bolts given their one-dimensional profiles

A general procedure for the derivation of principal domains of higher-order spectra

A general procedure to determine the principal domain (i.e., nonredundant region of computation) of any higher-order spectrum is presented, using the bispectrum as an example. The procedure is then applied to derive the principal domain of the trispectrum of a real-valued, stationary time series. These results are easily extended to compute the principal domains of other higher-order spectra

Position, rotation, and scale invariant recognition of images using higher-order spectra

A new approach to recognition of images using invariant features based on higher-order spectra is presented. Higher-order spectra are translation invariant because translation produces linear phase shifts which cancel. Scale and amplification invariance are satisfied by the phase of the integral of a higher-order spectrum along a radial line in higher-order frequency space because the contour of integration maps onto itself and both the real and imaginary parts are affected equally by the transformation. Rotation invariance is introduced by deriving invariants from the Radon transform of the image and using the cyclic-shift invariance property of the discrete Fourier transform magnitude. Results on synthetic and actual images show isolated, compact clusters in feature space and high classification accuracies

Single crystal Raman spectroscopy of selected arsenite, antimonite and hydroxyantimonate minerals

This thesis concentrates on the characterisation of selected arsenite, antimonite, and hydroxyantimonate minerals based on their vibrational spectra. A number of natural arsenite and antimonite minerals were studied by single crystal Raman spectroscopy in order to determine the contribution of bridging and terminal oxygen atoms to the vibrational spectra. A series of natural hydrated antimonate minerals was also compared and contrasted using single crystal Raman spectroscopy to determine the contribution of the isolated antimonate ion. The single crystal data allows each band in the spectrum to be assigned to a symmetry species. The contribution of bridging and terminal oxygen atoms in the case of the arsenite and antimonite minerals was determined by factor group analysis, the results of which are correlated with the observed symmetry species. In certain cases, synthetic analogues of a mineral and/or synthetic compounds isostructural or related to the mineral of interest were also prepared. These synthetic compounds are studied by non-oriented Raman spectroscopy to further aid band assignments of the minerals of interest. Other characterisation techniques include IR spectroscopy, SEM and XRD. From the single crystal data, it was found that good separation between different symmetry species is observed for the minerals studied.

Pattern recognition using invariants defined from higher order spectra - one-dimensional inputs

A new approach to pattern recognition using invariant parameters based on higher order spectra is presented. In particular, invariant parameters derived from the bispectrum are used to classify one-dimensional shapes. The bispectrum, which is translation invariant, is integrated along straight lines passing through the origin in bifrequency space. The phase of the integrated bispectrum is shown to be scale and amplification invariant, as well. A minimal set of these invariants is selected as the feature vector for pattern classification, and a minimum distance classifier using a statistical distance measure is used to classify test patterns. The classification technique is shown to distinguish two similar, but different bolts given their one-dimensional profiles. Pattern recognition using higher order spectral invariants is fast, suited for parallel implementation, and has high immunity to additive Gaussian noise. Simulation results show very high classification accuracy, even for low signal-to-noise ratios.

Higher-Order spectra of nonlinear polynomial models for Chua's circuit

Polynomial models are shown to simulate accurately the quadratic and cubic nonlinear interactions (e.g. higher-order spectra) of time series of voltages measured in Chua's circuit. For circuit parameters resulting in a spiral attractor, bispectra and trispectra of the polynomial model are similar to those from the measured time series, suggesting that the individual interactions between triads and quartets of Fourier components that govern the process dynamics are modeled accurately. For parameters that produce the double-scroll attractor, both measured and modeled time series have small bispectra, but nonzero trispectra, consistent with higher-than-second order nonlinearities dominating the chaos.

N-Aryl stilbazolium dyes as sensitizers for solar cells

Eight new N-arylstilbazolium chromophores with electron donating –NR2 (R = Me or Ph) substituents have been synthesized via Knoevenagel condensations and isolated as their PF6− salts. These compounds have been characterized by using various techniques including 1H NMR and IR spectroscopies and electrospray mass spectrometry. UV–vis absorption spectra recorded in acetonitrile are dominated by intense, low energy π → π* intramolecular charge-transfer (ICT) bands, and replacing Me with Ph increases the ICT energies. Cyclic voltammetric studies show irreversible reduction processes, together with oxidation waves that are irreversible for R = Me, but reversible for R = Ph. Single crystal X-ray structures have been determined for three of the methyl ester-substituted stilbazolium salts and for the Cl− salts of their picolinium precursors. Time-dependent density functional theory calculations afford reasonable predictions of ICT energies, but greater rigour is necessary for –NPh2 derivatives. The four new acid-functionalized dyes give moderate sensitization efficiencies (ca. 0.2%) when using TiO2-based photoanodes, with relatively higher values for R = Ph vs Me, while larger efficiencies (up to 0.8%) are achieved with ZnO substrates.

The effect of various pH, ionic strength and temperature on papain hydrolysis of salivary film

Stimulated human whole saliva (WS) was used to study the dynamics of papain hydrolysis at defined pH, ionic strength and temperature with the view of reducing an acquired pellicle. A quartz crystal microbalance with dissipation (QCM-D) was used to monitor the changes in frequency due to enzyme hydrolysis of WS films and the hydrolytic parameters were calculated using an empirical model. The morphological and conformational changes of the salivary films before and after enzymatic hydrolysis were characterized by atomic force microscopy (AFM) imaging and grazing angle infrared spectroscopy (GA-FTIR) spectra, respectively. The characteristics of papain hydrolysis of WS films were pH-, ionic strength- and temperature-dependent. The WS films were partially removed by the action of enzyme, resulting thinner and smoother surfaces. The IR data suggested that hydrolysis-induced deformation did not occur onto the remnants salivary films. The processes of papain hydrolysis of WS films can be controlled by properly regulating pH, ionic strength and temperature.

Homo- and heteronuclear meso,meso-(E)-Ethene-1,2-diyl-Linked Diporphyrins : preparation, X-ray crystal structure, electronic absorption and emission spectra and density functional theory calculations

Homo-and heteronuclear meso,meso-(E)-ethene-1,2-diyl-linked diporphyrins have been prepared by the Suzuki coupling of porphyrinylboronates and iodovinylporphyrins. Combinations comprising 5,10,15-triphenylporphyrin (TriPP) on both ends of the ethene-1,2-diyl bridge M 210 (M 2=H 2/Ni, Ni 2, Ni/Zn, H 4, H 2Zn, Zn 2) and 5,15-bis(3,5-di-tert-butylphenyl)porphyrinato-nickel(II) on one end and H 2, Ni, and ZnTriPP on the other (M 211), enable the first studies of this class of compounds possessing intrinsic polarity. The compounds were characterized by electronic absorption and steady state emission spectra, 1H NMR spectra, and for the Ni 2 bis(TriPP) complex Ni 210, single crystal X-ray structure determination. The crystal structure shows ruffled distortions of the porphyrin rings, typical of Ni II porphyrins, and the (E)-C 2H 2 bridge makes a dihedral angle of 50° with the mean planes of the macrocycles. The result is a stepped parallel arrangement of the porphyrin rings. The dihedral angles in the solid state reflect the interplay of steric and electronic effects of the bridge on interporphyrin communication. The emission spectra in particular, suggest energy transfer across the bridge is fast in conformations in which the bridge is nearly coplanar with the rings. Comparisons of the fluorescence behaviour of H 410 and H 2Ni10 show strong quenching of the free base fluorescence when the complex is excited at the lower energy component of the Soret band, a feature associated in the literature with more planar conformations. TDDFT calculations on the gas-phase optimized geometry of Ni 210 reproduce the features of the experimental electronic absorption spectrum within 0.1 eV. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Inactivation and structural changes of E. Cloacae and B. Subtilis Endospores during IR laser water treatment

IR radiation has been studied for micro-organism inactivation of bacterial spores on metal substrates [1] and on metal and paper substrates [2]. A near-point near infrared laser water treatment apparatus for use in dental hand-pieces was also developed [3]. To date water sterilisation research using a mid-IR laser technique is very rare. According to the World Health Organisation [4], examinations for faecal indicator bacteria remain the most sensitive and specific way of assessing the hygienic quality of water. Bacteria that fall into this group are E. coli, other coliform bacteria (including E. cloacae) and to a lesser extent, faecal streptococci [5]. Protozoan cysts from organisms which cause giardiasis are the most frequently identified cause of waterborne diseases in developed countries [6,7]. The use of aerobic bacterial endospores to monitor the efficiency of various water treatments has been shown to provide a reliable and simple indicator of overall performance of water treatment[8,9].The efficacy of IR radiation for water disinfection compared to UV treatment has been further investigated in the present study. In addition FTIR spectroscopy in conjunction with Principle Component Analysis was used to characterise structural changes within the bacterial cells and endospores following IR laser treatment. Changes in carbohydrate content of E. cloacae following IR laser treatment were observed.

Near infrared (NIR) absorption spectra correlates with subchondral bone micro-CT parameters in osteoarthritic rat models

Determining the properties and integrity of subchondral bone in the developmental stages of osteoarthritis, especially in a form that can facilitate real-time characterization for diagnostic and decision-making purposes, is still a matter for research and development. This paper presents relationships between near infrared absorption spectra and properties of subchondral bone obtained from 3 models of osteoarthritic degeneration induced in laboratory rats via: (i) menisectomy (MSX); (ii) anterior cruciate ligament transaction (ACL); and (iii) intra-articular injection of mono-ido-acetate (1 mg) (MIA), in the right knee joint, with 12 rats per model group (N = 36). After 8 weeks, the animals were sacrificed and knee joints were collected. A custom-made diffuse reflectance NIR probe of diameter 5 mm was placed on the tibial surface and spectral data were acquired from each specimen in the wavenumber range 4000–12 500 cm− 1. After spectral acquisition, micro computed tomography (micro-CT) was performed on the samples and subchondral bone parameters namely: bone volume (BV) and bone mineral density (BMD) were extracted from the micro-CT data. Statistical correlation was then conducted between these parameters and regions of the near infrared spectra using multivariate techniques including principal component analysis (PCA), discriminant analysis (DA), and partial least squares (PLS) regression. Statistically significant linear correlations were found between the near infrared absorption spectra and subchondral bone BMD (R2 = 98.84%) and BV (R2 = 97.87%). In conclusion, near infrared spectroscopic probing can be used to detect, qualify and quantify changes in the composition of the subchondral bone, and could potentially assist in distinguishing healthy from OA bone as demonstrated with our laboratory rat models.