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Automated detection of preterm condition using uterine electromyography based topological features

Vinothini S., Punitha N., Karthick P.A., Ramakrishnan S.

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

293--305

EN

Accurate prediction of preterm birth is a global, public health priority. This necessitates the need for an efficient technique that aids in early diagnosis. The objective of this study is to develop an automated system for an effective detection of preterm (weeks of gestation < 37) condition using Electrohysterography (EHG) and topological features associated with the frequency components of signals. The EHG signals recorded prior to gestational age of 26 weeks are considered. The pre-processed signals are subjected to discrete Fourier transform to obtain the Fourier coefficients. The envelope is computed from the boundary of the complex Fourier coefficients identified using the a-shape method. Topological features namely, area, perimeter, circularity, convexity, ellipse variance and bending energy are extracted from the envelope. Classifications based on threshold-determination method and machine learning algorithms namely, naïve Bayes, decision tree and random forest are employed to differentiate the term and preterm conditions. The results show that the Fourier coefficients of EHG signals exhibit different shapes in the term and preterm conditions. The regularity of signals is found to increase in preterm condition. All the features are found to have significant differences between these two conditions. Bending energy as a single biomarker achieves a maximum accuracy of 80.7%. The random forest model based on the topological features detects the conditions with the maximum accuracy and positive predictive value of about 98.6%. Therefore, the proposed automated system seems to be effective and could be used for the accurate detection of term and preterm conditions.

2

Thermal and mechanical properties of sustainable lightweight strain hardening geopolymer composites

Nematollahi B., Ranade R., Sanjayan J., Ramakrishnan S.

Archives of Civil and Mechanical Engineering

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2017

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Vol. 17, no. 1

55--64

EN

The thermal and mechanical properties of sustainable lightweight engineered geopolymer composites (EGCs), exhibiting strain-hardening behavior under uniaxial tension, are reported in this study. Fly ash-based geopolymer was used as complete replacement of cement binder to significantly increase the environmental sustainability of the composite compared to the engineered cementitious composite (ECC). Additionally, three types of lightweight aggregates including expanded perlite, microscopic hollow ceramic spheres and expanded recycled glass were used as complete replacement of micro-silica sand to reduce density and thermal conductivity of the composite. The influences of the type of aggregates on the fresh and hardened properties of the composite including matrix workability, density, compressive strength, thermal conductivity and uniaxial tensile performance were experimentally evaluated. The results indicated that the density and compressive strength of all EGCs developed in this study, even the EGC containing normal weight micro-silica sand, were less than 1833 kg/m3 and more than 43.4 MPa, respectively, meeting the density and compressive strength requirements for structural lightweight concrete. Replacing normal weight micro-silica sand with lightweight aggregates reduced the compressive and tensile strengths of the EGCs by a maximum of 24% and 32%, respectively. However, the tensile ductility of the EGCs containing lightweight aggregates was comparable to that of the EGC containing micro-silica sand. In addition, the thermal conductivity of the EGCs containing lightweight aggregates were significantly (38–49%) lower than that of the EGC containing normal weight micro-silica sand, resulting in an end-product that is greener, lighter, and provides better thermal insulation than ECC.

3

Comparison of two interpolation methods for empirical mode decomposition based evaluation of radiographic femur bone images

Udhayakumar G., Sujatha C. M., Ramakrishnan S.

Acta of Bioengineering and Biomechanics

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2013

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Vol. 15, no. 2

73--80

EN

Analysis of bone strength in radiographic images is an important component of estimation of bone quality in diseases such as osteoporosis. Conventional radiographic femur bone images are used to analyze its architecture using bi-dimensional empirical mode decomposition method. Surface interpolation of local maxima and minima points of an image is a crucial part of bi-dimensional empirical mode decomposition method and the choice of appropriate interpolation depends on specific structure of the problem. In this work, two interpolation methods of bi-dimensional empirical mode decomposition are analyzed to characterize the trabecular femur bone architecture of radiographic images. The trabecular bone regions of normal and osteoporotic femur bone images (N = 40) recorded under standard condition are used for this study. The compressive and tensile strength regions of the images are delineated using pre-processing procedures. The delineated images are decomposed into their corresponding intrinsic mode functions using interpolation methods such as Radial basis function multiquadratic and hierarchical b-spline techniques. Results show that bi-dimensional empirical mode decomposition analyses using both interpolations are able to represent architectural variations of femur bone radiographic images. As the strength of the bone depends on architectural variation in addition to bone mass, this study seems to be clinically useful.

4

Wavelet-based modeling of singular values for image texture classification

Ramakrishnan S., Selvan S.

Machine Graphics and Vision

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2006

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Vol. 15, No. 2

211-225

EN

A new algorithm based on the wavelet packet transform is proposed for the classification of image textures. Energy matrices are formed from subband coefficients of the wavelet packet transform. Singular value decomposition is then employed on the energy matrices. The probability density function of singular values is modeled as exponential distribution, and the model parameter is estimated using the maximum likelihood estimation technique. The model parameter, one for each subband, is used to form the feature vector. Classification is carried out using the Kullback-Leibler Distance (KLD). Performance of the algorithm is compared with model-based and feature-based methods in terms of the signal-to-noise ratio and the classification rate. Experimental results prove that the proposed algorithm achieves better classification rate under noisy environment.