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Sensors 2015, 15(6), 13132-13158;

Using K-Nearest Neighbor Classification to Diagnose Abnormal Lung Sounds

Department of Management Information Systems, Central Taiwan University of Science and Technology, Taichung 40601, Taiwan, China
Department of Computer Science and Information Engineering, Minghsin University of Science and Technology, Hsinchu 30401, Taiwan, China
Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan, China
Institute of Biomedical Engineering and Material Science, Central Taiwan University of Science and Technology, Taichung 40601, Taiwan, China
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 12 February 2015 / Revised: 28 May 2015 / Accepted: 28 May 2015 / Published: 4 June 2015
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [1695 KB, uploaded 5 June 2015]   |  


A reported 30% of people worldwide have abnormal lung sounds, including crackles, rhonchi, and wheezes. To date, the traditional stethoscope remains the most popular tool used by physicians to diagnose such abnormal lung sounds, however, many problems arise with the use of a stethoscope, including the effects of environmental noise, the inability to record and store lung sounds for follow-up or tracking, and the physician’s subjective diagnostic experience. This study has developed a digital stethoscope to help physicians overcome these problems when diagnosing abnormal lung sounds. In this digital system, mel-frequency cepstral coefficients (MFCCs) were used to extract the features of lung sounds, and then the K-means algorithm was used for feature clustering, to reduce the amount of data for computation. Finally, the K-nearest neighbor method was used to classify the lung sounds. The proposed system can also be used for home care: if the percentage of abnormal lung sound frames is > 30% of the whole test signal, the system can automatically warn the user to visit a physician for diagnosis. We also used bend sensors together with an amplification circuit, Bluetooth, and a microcontroller to implement a respiration detector. The respiratory signal extracted by the bend sensors can be transmitted to the computer via Bluetooth to calculate the respiratory cycle, for real-time assessment. If an abnormal status is detected, the device will warn the user automatically. Experimental results indicated that the error in respiratory cycles between measured and actual values was only 6.8%, illustrating the potential of our detector for home care applications. View Full-Text
Keywords: K-means algorithm; K-nearest neighbor; lung sound; MFCC; stethoscope K-means algorithm; K-nearest neighbor; lung sound; MFCC; stethoscope

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Chen, C.-H.; Huang, W.-T.; Tan, T.-H.; Chang, C.-C.; Chang, Y.-J. Using K-Nearest Neighbor Classification to Diagnose Abnormal Lung Sounds. Sensors 2015, 15, 13132-13158.

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