Glaucoma Detection from Color Fundus Images

Glaucoma is a pathological condition, progressive neurodegeneration of the optic nerve, which causes vision loss. The damage to the optic nerve occurs due to the increase in pressure within the eye. Glaucoma is evaluated by monitoring intra ocular pressure (IOP), visual field and the optic disc appearance (cup-to-disc ratio). Cup-to disc ratio (CDR) is normally a time invariant feature. Therefore, it is one of the most accepted indicator of this disease and the disease progression. In this paper, active contour method is used to find the CDR from the color fundus images to determine pathological process of glaucoma. The method is applied on 25 nos of color fundus images obtained from optic disc organization UK having normal and pathological images. The proposed technique able to categorize all the glaucoma disease images

Retinal Image Analysis: A Review

Images of the eye ground or retina not only provide an insight to important parts of the visual system but also reflect the general state of health of the entire human body. Automated retina image analysis is becoming an important screening tool for early detection of certain risks and diseases like diabetic retinopathy, hypertensive retinopathy, age related macular degeneration, glaucoma etc. This can in turn be used to reduce human errors or to provide services to remote areas. In this review paper, we discuss some of the current techniques used to automatically detect the important clinical features of retinal image, such as the blood vessels, optic disc and macula. The quantitative analysis and measurements of these features can be used to better understand the relationship between various diseases and the retinal features

Automated detection of heart ailments from 12-lead ECG using complex wavelet sub-band bi-spectrum features

The complex wavelet sub-band bi-spectrum (CWSB) features are proposed for detection and classification of myocardial infarction (MI), heart muscle disease (HMD) and bundle branch block (BBB) from 12-lead ECG. The dual tree CW transform of 12-lead ECG produces CW coefficients at different sub-bands. The higher-order CW analysis is used for evaluation of CWSB. The mean of the absolute value of CWSB, and the number of negative phase angle and the number of positive phase angle features from the phase of CWSB of 12-lead ECG are evaluated. Extreme learning machine and support vector machine (SVM) classifiers are used to evaluate the performance of CWSB features. Experimental results show that the proposed CWSB features of 12-lead ECG and the SVM classifier are successful for classification of various heart pathologies. The individual accuracy values for MI, HMD and BBB classes are obtained as 98.37, 97.39 and 96.40%, respectively, using SVM classifier and radial basis function kernel function. A comparison has also been made with existing 12-lead ECG-based cardiac disease detection techniques

Analysis of physiological signals using state space correlation entropy

In this letter, the authors propose a new entropy measure for analysis of time series. This measure is termed as the state space correlation entropy (SSCE). The state space reconstruction is used to evaluate the embedding vectors of a time series. The SSCE is computed from the probability of the correlations of the embedding vectors. The performance of SSCE measure is evaluated using both synthetic and real valued signals. The experimental results reveal that, the proposed SSCE measure along with SVM classifier have sensitivity value of 91.60%, which is higher than the performance of both sample entropy and permutation entropy features for detection of shockable ventricular arrhythmia

Block sparsity-based joint compressed sensing recovery of multi-channel ECG signals

In recent years, compressed sensing (CS) has emerged as an effective alternative to conventional wavelet based data compression techniques. This is due to its simple and energy-efficient data reduction procedure, which makes it suitable for resource-constrained wireless body area network (WBAN)-enabled electrocardiogram (ECG) telemonitoring applications. Both spatial and temporal correlations exist simultaneously in multi-channel ECG (MECG) signals. Exploitation of both types of correlations is very important in CS-based ECG telemonitoring systems for better performance. However, most of the existing CS-based works exploit either of the correlations, which results in a suboptimal performance. In this work, within a CS framework, the authors propose to exploit both types of correlations simultaneously using a sparse Bayesian learning-based approach. A spatiotemporal sparse model is employed for joint compression/reconstruction of MECG signals. Discrete wavelets transform domain block sparsity of MECG signals is exploited for simultaneous reconstruction of all the channels. Performance evaluations using Physikalisch-Technische Bundesanstalt MECG diagnostic database show a significant gain in the diagnostic reconstruction quality of the MECG signals compared with the state-of-the art techniques at reduced number of measurements. Low measurement requirement may lead to significant savings in the energy-cost of the existing CS-based WBAN systems

Discrimination of Types of Seizure Using Brain Rhythms Based on Markov Transition Field and Deep Learning

Discrimination of types of seizure using the Electroencephalogram (EEG) signal has always been a challenging task due to minuscule differences among different types of seizures. In this regard, deep learning (DL) which has already evidenced notable performance in image recognition could be suitable. However, a few attempts have been made so far in this regard mainly by constructing 2D input images for DL from 1D EEG signals directly using various techniques. Besides, the quality of the generated images has not been verified. Therefore, in this work, 2D images for the DL pipeline have been generated from brain rhythms, which already displayed remarkable performance in analyzing various brain activities. For this purpose, the Markov transition field transformation technique has been employed for 2D image construction by preserving statistical dynamics characteristics of EEG signals, which are very important during the discrimination of different types of seizures. And, a convolution neural network (CNN) has been used for classification. Further, the quality of the 2D image along with appropriate brain rhythms have also been investigated. For experimental validation, EEG recordings of six different types of seizure that are provided by the Temple University EEG dataset (TUH v1.5.2) has been taken into account. The proposed method has achieved the highest classification accuracy and weighted ${F}1$ -score up to 91.1% and 91.0% respectively. Further analysis shows that higher image resolution can provide the best classification accuracy. In addition, the $\delta$ rhythm has been found the most suitable in seizure type classification. In a comparative study, the proposed idea demonstrated its superiority by displaying the uppermost classification performance

Feature optimisation for stress recognition in speech

Mel-frequency cepstral coefficients introduced biologically-inspired features into speech technology, becoming the most commonly used representation for speech, speaker and emotion recognition, and even for applications in music. While this representation is quite popular, it is ambitious to assume that it would provide the best results for every application, as it is not designed for each specific objective.This work proposes a methodology to learn a speech representation from data by optimising a filter bank, in order to improve results in the classification of stressed speech. Since population-based metaheuristics have proved successful in related applications, an evolutionary algorithm is designed to search for a filter bank that maximises the classification accuracy. For the codification, spline functions are used to shape the filter banks, which allows reducing the number of parameters to optimise. The filter banks obtained with the proposed methodology improve the results in stressed and emotional speech classification.Fil: Vignolo, Leandro Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Prasanna, S.R. Mahadeva. Indian Institute of Technology Guwahati; IndiaFil: Dandapat, Samarendra. Indian Institute of Technology Guwahati; IndiaFil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin