Bioscience & Engineering: An International Journal (BIOEJ)

ISSN : 2349 - 848X

http://airccse.com/bioej/index.html

paper submission Link:

http://allcfps.com/noncs/submission/index.php

Article Title : LOW POWER CLASS AB SI POWER AMPLIFIER FOR WIRELESS MEDICAL SENSOR NETWORK

For More Details : https://airccse.com/bioej/papers/3316bioej01.pdf

Bioscience & Engineering: An International Journal (BIOEJ)

ISSN : 2349- 848X

http://airccse.com/bioej/index.html

paper submission :-

http://allcfps.com/noncs/submission/index.php

Submission Deadline : December 01, 2018

Bioscience & Engineering: An International Journal (BIOEJ)

ISSN : 2349- 848X

http://airccse.com/bioej/index.html

Scope & Topics

Bioscience & Engineering: An International Journal (BIOEJ) is a peer-reviewed, open access journal that addresses the impacts and challenges of Bioscience, Bioengineering and Applications. The journal documents practical and theoretical results which make a fundamental contribution for the development of Bioscience & Engineering.

This journal aims to bring together researchers and practitioners in all Bioscience & Engineering aspects, including (but not limited to)

Topic of Interest :

• Biochemical Engineering
• Biochemistry
• Bioinformatics
• Health Informatics
• Biomedicine
• Bioscience Engineering
• Biotechnology
• Bio-fermentation Technology
• Food Science & Technology
• Genetics
• Geomicrobiology
• Microbiology
• Molecular Biology of Plants
• Zoo physiology

Paper Submission

Authors are invited to submit papers for this journal through Submission System.  Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.

Important Dates

• Submission Deadline : December 01, 2018
• Notification                   : January 01, 2019
• Final Manuscript Due : January 09, 2019
• Publication Date          : Determined by the Editor-in-Chief

For other details please visit : http://airccse.com/bioej/index.html

Bioscience & Engineering: An International Journal (BIOEJ)

ISSN : 2349 - 848X

http://airccse.com/bioej/index.html

paper submission :-

http://allcfps.com/noncs/submission/index.php

PHONOCARDIOGRAM-BASED DIAGNOSIS USING MACHINE LEARNING: PARAMETRIC ESTIMATION WITH MULTIVARIANT CLASSIFICATION

Shaima Abdelmageed1 , Mohammed Elmusrati2

School of Technology and Innovation, University of Vasa, Vasa, Finland

ABSTRACT

The heart sound signal, Phonocardiogram (PCG) is difficult to interpret even for experienced cardiologists. Interpretation are very subjective depending on the hearing ability of the physician. mHealth has been the adopted approach towards quick diagnosis using mobile devices. However, it has been

challenging due to the required high quality of data, high computation load, and high-power consumption. The aim of this paper is to diagnose the heart condition based on Phonocardiogram analysis using Machine Learning techniques assuming limited processing power to be encapsulated later in a mobile device. The cardiovascular system is modelled in a transfer function to provide PCG signal recording as it would be recorded at the wrist. The signal is, then, decomposed using filter bank and the analysed using discriminant function. The results showed that PCG with a 19 dB Signal-to-Noise-Ratio can lead to 97.33%

successful diagnosis.

KEYWORDS

Analysis, Classification, data quality, diagnosis, filter banks, mHealth, PCG, SNR, transfer function, Wavelet Transform

https://airccse.com/bioej/papers/5418bioej01.pdf