Preliminary Detection of Lung Diseases in Pediatric Population using Soft Computing
Sibghatullah Khan1, Syed Jahangir Badashah2, Mallikarjun Mudda3
1Sibghatullah Khan, Associate Professor, Department of ECE Sreenidhi Institute of Science and Technology Hyderabad India.
2Syed Jahangir Badashah, Associate Professor, Department of ECE Sreenidhi Institute of Science and Technology Hyderabad India.
3Mallikarjun Mudda, Associate Professor, Department of ECE Sreenidhi Institute of Science and Technology Hyderabad India.
Manuscript received on 5 August 2019. | Revised Manuscript received on 11 August 2019. | Manuscript published on 30 September 2019. | PP: 2731-2735 | Volume-8 Issue-3 September 2019 | Retrieval Number: B2005078219/2019©BEIESP | DOI: 10.35940/ijrte.B2005.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The investigations from recent studies clearly show the potential of lung sounds in detection of lung abnormalities in human subjects. This paper aims to analyze lung sounds acquired using special electronic stethoscope for detection adventitious sounds arising out of pathological lungs due to various disease like brochities especially in pediatric population. For acquisition and recording of lung sounds, 3M Littmann 3200 model is utilized. After verifying fidelity of electronic stethoscope, the analysis of lung sounds was carried out by various spectral and temporal features. The features extracted were fed to artificial neural network for classification. Various combinations of ANN with different topologies were experimented. The overall accuracy of obtained with one hidden layer GFF is 94.95%.
Keyword: Lung Diseases; Electronic Stethoscope; Spectral feature; Artificial Neural Network
Scope of the Article: Artificial Intelligence