Bioscience & Engineering: An International Journal (BIOEJ)
ISSN : 2349- 848X
Call for Papers:
Contact us: bioej@airccse.com
Wednesday, April 29, 2020
Wednesday, April 22, 2020
Call for Papers - 6th International Conference on Bioscience & Engineering (BIOE-2020)
6th International Conference on Bioscience & Engineering (BIOE-2020)
June 27-28, 2020, Copenhagen, Denmark
Submission Deadline : April 25, 2020
Contact Us : bioeconf@yahoo.com or bioe@acsit2020.org
Monday, April 20, 2020
Call for Papers - Bioscience & Engineering: An International Journal (BIOEJ)
Bioscience & Engineering: An International Journal (BIOEJ)
ISSN : 2349- 848X
Call for Papers:
Contact us: bioej@airccse.com
Submission System: http://airccse.com/submission/home.html
Wednesday, April 15, 2020
Phonocardiogram Based Diagnosis Using Machine Learning : Parametric Estimation with Multivariant Classification
Phonocardiogram Based Diagnosis Using Machine Learning : Parametric Estimation with Multivariant Classification
Shaima Abdelmageed and Mohammed Elmusrati, University of 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
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