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.

Near infrared optical materials from polymeric amorphous carbon synthesized by collisional plasma process

The synthesis of polymerlike amorphous carbon(a-C:H) thin-films by microwave excited collisional hydrocarbon plasma process is reported. Stable and highly aromatic a-C:H were obtained containing significant inclusions of poly(p-phenylene vinylene) (PPV). PPV confers universal optoelectronic properties to the synthesized material. That is a-C:H with tailor-made refractive index are capable of becoming absorption-free in visible (red)-near infrared wavelength range. Production of large aromatic hydrocarbon including phenyl clusters and/or particles is attributed to enhanced coagulation of elemental plasma species under collisional plasma conditions. Detailed structural and morphological changes that occur in a-C:H during the plasma synthesis are also described.

A vibrational spectroscopic study of the mineral hinsdalite (Pb,Sr)Al3(PO4)(SO4)(OH)6

The objective of this research is to determine the molecular structure of the mineral hinsdalite using vibrational spectroscopy. The mineral hinsdalite (Pb,Sr)Al3(PO4,SO4)2(OH)6 is a hydroxy phosphate-sulphate mineral belonging to the beudantite subgroup of alunites. The mineral is interesting because it contains two oxyanions, phosphate and sulphate, which is unusual. The formation of hinsdalite offers a mechanism for the removal of phosphate from the environment. The mineral has been characterised by Raman spectroscopy and infrared spectroscopy. The spectra are then related to the molecular structure of the mineral. Bands at various wavenumbers are assigned to the different vibrational modes of hinsdalite, which were then associated to the molecular structure of the mineral. Bands were primarily assigned to phosphate and sulphate stretching and bending modes. The Raman spectrum is characterised by an intense sharp band at 982 cm-1 with a component band at 997 cm-1 assigned to the ν1 (PO4)3- symmetric stretching modes. Two symmetric stretching modes for both phosphate and sulphate supported the concept of non-equivalent phosphate and sulphate units in the mineral structure. Bands in the OH stretching region enabled hydrogen bond distances to be calculated. Hinsdalite is characterised by disordered phosphate/sulphate tetrahedra and non-equivalent phosphate units are observed in the vibrational spectrum of hinsdalite.

A Raman spectroscopic study of the different vanadate-groups in solid-state compounds, model case : mineral phases vésigniéite, BaCu3(VO4)2(OH)2, and volborthite, Cu3V2O7(OH)2•2H2O

Raman spectroscopy has been used to study vanadates in the solid state. The molecular structure of the vanadate minerals vésigniéite [BaCu3(VO4)2(OH)2] and volborthite [Cu3V2O7(OH)2·2H2O] have been studied by Raman spectroscopy and infrared spectroscopy. The spectra are related to the structure of the two minerals. The Raman spectrum of vésigniéite is characterized by two intense bands at 821 and 856 cm−1 assigned to ν1 (VO4)3− symmetric stretching modes. A series of infrared bands at 755, 787 and 899 cm−1 are assigned to the ν3 (VO4)3− antisymmetric stretching vibrational mode. Raman bands at 307 and 332 cm−1 and at 466 and 511 cm−1 are assigned to the ν2 and ν4 (VO4)3− bending modes. The Raman spectrum of volborthite is characterized by the strong band at 888 cm−1, assigned to the ν1 (VO3) symmetric stretching vibrations. Raman bands at 858 and 749 cm−1 are assigned to the ν3 (VO3) antisymmetric stretching vibrations; those at 814 cm−1 to the ν3 (VOV) antisymmetric vibrations; that at 508 cm−1 to the ν1 (VOV) symmetric stretching vibration and those at 442 and 476 cm−1 and 347 and 308 cm−1 to the ν4 (VO3) and ν2 (VO3) bending vibrations, respectively. The spectra of vésigniéite and volborthite are similar, especially in the region of skeletal vibrations, even though their crystal structures differ.

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