Digitalized detecting analysis method for pulmonary interstitial fibrosis based on lung sound

Abstract

The invention relates to a digitalized detecting analysis method for pulmonary interstitial fibrosis based on a lung sound. According to the method, first, normalization processing is conducted on lung sound data acquired in a certain time period, then wavelet decomposition is conducted, a wavelet decomposition detail component in a main distribution frequency band of a pulmonary interstitial fibrosis crackle signal is selected, and standard deviation of a related wavelet component decomposition signal is calculated. Quantitative analysis is conducted on the lung sound of a patient with the pulmonary interstitial fibrosis by the adoption of the detecting method to evaluate the severe degree of the pulmonary interstitial fibrosis of the patient, health of the patient is not influenced, detection is safer, detection and analysis can be conducted for many times, analysis is more objective, accuracy is high, cost is low, operation is simple, and the digitalized detecting analysis method is suitable for monitoring the illness degree of a patient with interstitial pneumonia.

Description

technical field [0001] The invention relates to the field of medical signal analysis, in particular to a digital detection and analysis method for pulmonary interstitial fibrosis based on lung sounds. Background technique [0002] Pulmonary interstitial fibrosis is a common lung disease. At present, doctors usually evaluate the severity of pulmonary fibrosis by observing chest X-rays or chest CT films. Because X-rays are radioactive and have a great impact on the health of patients, it is not easy to perform frequent chest X-rays and chest CT examinations. And the cost is also higher. [0003] By auscultating the lung sounds of patients with pulmonary interstitial fibrosis, doctors can hear crackles related to the disease and understand the patient's condition to a certain extent. Auscultation is simple and easy, and can be operated anytime and anywhere without affecting the health of patients. It can be tested and analyzed multiple times, which is convenient for doctors t...

AI Technical Summary

Problems solved by technology

However, its accuracy depends on the doctor's personal experience, and the objectivity is poor.

Moreover, the disadvantage of auscultation by human ears is that it is non-quantitative and subjectivity. It can only be auscultated by a single person. The comparison before and after can only be the individual behavior of the doctor, and it is difficult to conduct quantitative analysis.

The limitation of the performance of the stethoscope will also prevent the lung sound signal from being transmitted well, and the sensitivity of the human ear to sound varies from person to person. Even specially trained specialists have certain limitations in their subjective perception and experience, resulting in The diagnostic results are greatly affected by human factors, resulting in low diagnostic accuracy

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