Informatics Publishing Limited

Tushar H. Jaware ¹, K. B. Khanchandani ¹, Anita Zurani ²

Affiliations
1 SSGMCE, Near Krishna Cottage, District Buldana, Shegaon - 444203, Maharashtra, IN
2 SGBAU, Near Tapovan Gate, Amravati - 444602, Maharashtra, IN

Abstract

Objectives: Every year millions of babies born preterm and it is a serious issue in developed countries. Preterm birth is worldwide problem of young children. Methods/Statistical analysis: Advances in Magnetic Resonance Imaging (MRI), comprehensive images of the newborn brain is visualized non-invasively. Here we are focusing on the early assessment of brain development in neonates and premature infants using multi stage segmentation and classification approach with quantitative analysis. To achieve higher segmentation and classification accuracy neural network classifier are used. It is also helpful in detection and classification of more number of brain tissues. Findings: The accurate segmentation and classification of brain tissues is very much useful in evaluating the brain development of newborn and premature infants. Therefore, newborn brain MRI forms a crucial part in analytical neuro-imaging; principally in the neonatal stage.Our method gives higher dice similarity index values and higher computational speed as compared to existing methods. Application: Resulting segmentations are used for volumetric analysis and quantification of cortical descriptions. This analysis plays vital role for neonatologists in early detection and diagnosis of neural impairments.

Keywords

Classification, MRI, Multi-kernel Support Vector Machine, Newborn, Preterm, Segmentation.

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Mahesh B. Dembrani ¹, K. B. Khanchandani ², Anita Zurani ¹

Affiliations
1 SGBAU, Amravati - 444602, Maharashtra, IN
2 SSGMCE, Shegaon - 444203, Maharashtra, IN

Abstract

Objectives: Digital signal processing applications are becoming more prevalent in everyday use. Because of this widespread usage and advances in technology, DSP algorithms themselves are being subjected to more demanding specifications. Methods/Statistical Analysis: There is a constant need for designing systems with higher accuracy, sensitivity and specificity. Electrocardiogram signal comprises of several mixed signals along with desired signal. Various techniques are used to separate this mixed signal. In this paper a mixed signal filter and have been proposed and developed, in order to obtain high performance through simulation. Findings: Distributed Arithmetic mixed signal filter is proposed in this paper through which various moving arithmetic operations are used to improve the response for real time QRS detection. This complete technique gives accurate detection of QRS wave with high memory efficiency and high speed. Application/ Improvements: Performance analysis is validated by using Cardiac signal database. This analysis is based on accuracy, specificity and sensitivity which show maximum response to obtain the true conditions for ECG Signal.

Keywords

DBE. Distributed Arithmetic, ECG Signal, Mixed Signal Filter, QRS detection.

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