The number of deaths due to cardiovascular and respiratory diseases is increasing annually. Cardiovascular diseases with high mortality rates, such as strokes, are frequently caused by atrial fibrillation without subjective symptoms. Chronic obstructive pulmonary disease is another condition in which early detection is difficult owing to the slow progression of the disease. Hence, a device that enables the early diagnosis of both diseases is necessary. In our previous study, a sensor for monitoring biological sounds such as vascular and respiratory sounds was developed and a noise reduction method based on semi-supervised convolutive non-negative matrix factorization (SCNMF) was proposed for the noisy environments of users. However, SCNMF attenuated part of the biological sound in addition to the noise. Therefore, this paper proposes a novel noise reduction method that achieves less distortion by imposing orthogonality constraints on the SCNMF. The effectiveness of the proposed method was verified experimentally using the biological sounds of 21 subjects. The experimental results showed an average improvement of 1.4 dB in the signal-to-noise ratio and 2.1 dB in the signal-to-distortion ratio over the conventional method. These results demonstrate the capability of the proposed approach to measure biological sounds even in noisy environments.
COPD is one of the most prevalent and deadliest diseases in the world today, and in 2019 it was the third leading cause of death in the world. Primary-care provider recommendations hold promise to expand the recognition of COPD in its incipient stages. For this reason, assessing the status of tracheal ventilation is crucial. To this end, for early detection and early treatment, a biological sound measurement system had been developed that measures vascular and respiratory sounds simultaneously. The system obtains the biological sound by attaching the biological sound sensor on the head. That quantitative assessment of the patient's degree of illness through changes in their tracheal sounds before and after exercise. In this study, the aim is to use wavelet analysis to isolate the noise generated during daily action for extracting the respiratory sound, including stepping conduction noise, short-time friction noise, and the measured vascular sound. Since the original signal-to-noise ratio is too small, the result evaluates the system's effectiveness by comparing the noise-free signal with the absolute noise. As a result, the amplitude and duration of noise are effectively reduced without artifacts. The signal is improved by about 30 dB on average. So, the proposed system is expected to be applied to telemedicine.
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