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.