i-manager's Journal on Digital Signal Processing (JDP)


Volume 2 Issue 4 October - December 2014

Research Paper

Analysis of Heart Sounds Segmentation using discrete Wavelet transforms

Lekram Premlal Bahekar* , Abhishek Misal**, Jitendra Bhonde***, Chandrakumar Bahekar****
* Assistant Professor and Research Scholar, Department of Electronics & Tele., Madhukarrao Pandav College of Engineering, Bhandara, India.
** Sr.Assistant Professor, Department of Electronics & Telecommunication Engineering, Chhatrapati Shivaji Institute of Technology, Durg, India.
***Assistant Professor and Research Scholar, Department of Computer Engineering, Madhukarrao Pandav College of Engineering, Bhandara, India.
**** Print mail reporter in Gondia District and research scholar Madhukarrao Pandav College of Engineering Bhandara, India.
Bahekar,L,P.,Misal,A.,Bhonde,J.,Bahekar,C.(2014). Analysis of Heart Sounds Segmentation Using Discrete Wavelet Transforms. i-manager’s Journal on Digital Signal Processing, 2(4), 1-7. https://doi.org/10.26634/jdp.2.4.3141

Abstract

PCG signal recordings are so complex and non stationary signal, they are also affected by different kinds of noise the segmentation method followed by the time and frequency domain analysis characterization of some phonocardiogram (PCG) signals. The paper using the Discrete Wavelet Transform (DWT) in decomposition signal. In the segmentation technique, we calculate the signal to noise ratio and peak signal to noise ratio and energy of the details coefficients at each level and threshold it in order to detect the murmur of heart sound signals. The results of the method illustrate clearly the detection of the main components S1, S2, S3, S4 Pathological murmurs and the identification of the valves disease.

Research Paper

Application of Polynomial Approximation Techniques for Smoothing ECG Signals

Ashish Rohila* , V. K. Giri**
* M.Tech Student, Department of Electrical Engineering, MMM University of Technology, Gorakhpur, UP, India.
** Professor and Head, Department of Electrical Engineering, MMM University of Technology, Gorakhpur, UP, India.
Rohila,A., and Giri.V.K. (2014). Application Of Polynomial Approximation Techniques For Smoothing ECG Signals. i-manager’s Journal on Digital Signal Processing, 2(4), 8-13. https://doi.org/10.26634/jdp.2.4.3142

Abstract

Electrocardiogram (ECG) is the recording of the electrical activities of the heart. ECG signals are recorded on the body surface with the help of surface electrodes. While recording, different artifacts get introduced in the signal like; electrode contact noise, motion artifacts, base line drift, base line wander, electrosurgical noise, and power line interferences. Some kind of signal processing is required to get meaningful information from ECG. Now a days digital signal processing is preferred over analog signal processing. Various digital signal processing techniques have been developed over a period of last four decades for removing noise from ECG signal. The curve fitting is a simple and widely used technique for smoothing ECG signal (removing high frequency noise). In this paper we have presented a comparison study of various smoothing filters to filter high frequency noise. To compare performance of various smoothing filters Power Spectral Density (PSD), Average Power and Percent Root mean square Difference (PRD) have been calculated.

Research Paper

Centre Frequency Selection for Active Sonar

M. Rajeswari* , S. Koteswara Rao**, B.L.Prakash***
* M.Tech Scholar, Department of ECE, Vignan's Institute of Information Technology, Visakhapatnam, A.P., India
** Sr. Professor, Department of ECE, Vignan's Institute of Engineering for Women, Visakhapatnam, A.P., India.
*** Professor, Department of ECE, Vignan's Institute of Information Technology, Visakhapatnam, A.P., India
Rajeswari.M.,Rao,K.S.,Prakash.B.L.,(2014). Centre Frequency Selection for Active SONAR. i-manager’s Journal on Digital Signal Processing, 2(4), 14-18. https://doi.org/10.26634/jdp.2.4.3143

Abstract

Attenuation of sound is the process of decay in the amplitude due to combination of absorption and scattering. Transmitted pulse is influenced by many factors for selecting the center frequency of active sonar. For example, for a fixed size transmitter array with constant radiated power, ambient noise decreases with frequency and the source level increases with frequency. These considerations would suggest the use of high center frequency. On other hand the absorption of the medium increases with frequency Here, in this paper the relationship between the range and optimum frequency is derived which helps in selecting the center frequency.

Research Paper

Direct Data Compression Techniques of ECG- An Overview

Himani Tiwari* , Vikas Patel**, V. K. Giri***
* PG. Scholar, Department of Electrical Engineering, M.M.M. University of Technology, Gorakhpur, India.
** Faculty, Department of Electrical Engineering, M.M.M. University of Technology, Gorakhpur, India.
*** Professor, Department of Electrical Engineering, M.M.M. University of Technology, Gorakhpur, India.
Tiwari,H.,Patel,V.,Giri.V.K.,(2014). Direct Data Compression Techniques of ECG- An Overview. i-manager’s Journal on Digital Signal Processing, 2(4),19-29. https://doi.org/10.26634/jdp.2.4.3144

Abstract

A wide variety of algorithms have been devised for the compression of ECG signals during last five decades. These techniques have not only brought about a considerable reduction in ECG data volume for storage but also enabled economic and efficient transmission of data for distant analysis. The main purpose of this paper is to present an overview of ECG compression methods especially the direct data compression methods as well as the various performance measures governing the effectiveness of these methods. Broadly ECG compression methods have been classified as direct compression method, transformation method and parameter extraction method. However, this paper addresses the various direct data compression techniques such as AZTEC, Modified AZTEC, Turning Point Technique, CORTES, Fan, SAPA, Entropy Coding, Peak-Picking, Cycle to Cycle compression and ECG data compression by DPCM.

Research Paper

Review on Acoustic Modeling for Continuous Speech Recognition

R.Mohan* , M.Kalamani**
* M.E Scholar, Applied Electronics, Bannari Amman Institute of Technology, Sathyamangalam, India.
** Assistant Professor (Sr.G) Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam, India.
Mohan.R., and Kalamani.M. (2014). Review On Acoustic Modeling For Continuous Speech Recognition. i-manager’s Journal on Digital Signal Processing, 2(4), 30-33. https://doi.org/10.26634/jdp.2.4.3145

Abstract

The speech recognition is the most important research area to recognize the speech signal by the computer. To develop the recognition rate of the continuous speech signal, we preferred frontend process such as speech segmentation, feature extraction (MFCC) and clustering techniques i.e., Fuzzy c means clustering is the formation of clusters from the extracted features based on similar sense and form the optimum number of clusters. In speech recognition the acoustic models are the major role to testing the trained data. Here the acoustic models for continuous speech recognition was discussed i.e., The Hidden morkov model (HMM),Gaussian mixture model(GMM) and GMM-UBM(Universal Background Model) are the most suitable acoustic models which are used for train the speech signal and recognize the corresponding text data.