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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.

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Abstract

Partial transmit sequence (PTS) technique requires an exhaustive search over all combinations of allowed phase weighting factors, the search complexity increases exponentially with the number of sub-blocks. In this paper, a novel combing strategy that employs sub-block partition scheme and phase factors for PTS in orthogonal frequency division multiplexing (OFDM) system is proposed. We present an OFDM system, which through the use of sub-optimal PTS based on particle swarm optimization (PSO) algorithm, achieves low computation complexity to find optimum phase weighting factors and reduce the peak-to-average power ratio (PAPR). Furthermore, to work around potentially computational intractability, the proposed scheme exploits heuristics in consideration of both global and local exploration partial transmit sequence. Simulation results show that the proposed PSO-based PTS can efficiently find the optimum phase weighting factor, and can achieve the lower PAPR with moderate complexity.