![]() ![]() Finally, the K-nearest neighbor method was used to classify the 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. This study has developed a digital stethoscope to help physicians overcome these problems when diagnosing abnormal lung sounds. 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. PMC articleĪ reported 30% of people worldwide have abnormal lung sounds, including crackles, rhonchi, and wheezes. 6 Institute of Biomedical Engineering and Material Science, Central Taiwan University of Science and Technology, Taichung 40601, Taiwan, China. 5 Department of Management Information Systems, Central Taiwan University of Science and Technology, Taichung 40601, Taiwan, China. 4 Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan, China. 3 Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan, China. 2 Department of Computer Science and Information Engineering, Minghsin University of Science and Technology, Hsinchu 30401, Taiwan, China. 1 Department of Management Information Systems, Central Taiwan University of Science and Technology, Taichung 40601, Taiwan, China.
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