Wheeze sound analysis using computer-based techniques: a systematic review

Contact and Remote Breathing Rate Monitoring Techniques: A Review

Breathing rate monitoring is a must for hospitalized patients with the current coronavirus disease 2019 (COVID-19). We review in this paper recent implementations of breathing monitoring techniques, where both contact and remote approaches are presented. It is known that with non-contact monitoring, the patient is not tied to an instrument, which improves patients' comfort and enhances the accuracy of extracted breathing activity, since the distress generated by a contact device is avoided. Remote breathing monitoring allows screening people infected with COVID-19 by detecting abnormal respiratory patterns. However, non-contact methods show some disadvantages such as the higher set-up complexity compared to contact ones. On the other hand, many reported contact methods are mainly implemented using discrete components. While, numerous integrated solutions have been reported for non-contact techniques, such as continuous wave (CW) Doppler radar and ultrawideband (UWB) pulsed radar. These radar chips are discussed and their measured performances are summarized and compared.

[Lung sounds can be used as an indicator for assessing severity of chronic obstructive pulmonary disease at the initial diagnosis]

OBJECTIVE

To assess the value of pulmonary auscultation for evaluating the severity of chronic obstructive pulmonary disease (COPD) at the initial diagnosis.

METHODS

The patients with newly diagnosed COPD in our hospital between May, 2016 and May, 2019 were enrolled in this study. According to the findings of pulmonary auscultation, the lung sounds were classified into 5 groups: normal breathing sounds, weakened breathing sounds, weakened breathing sounds with wheezing, obviously weakened breathing sounds, and obviously weakened breathing sounds with wheezing. The pulmonary function of the patients was graded according to GOLD guidelines, and the differential diagnosis of COPD from asthmatic asthma COPD overlap (ACO) was made based on the GOLD guidelines and the European Respiratory Criteria.

RESULTS

A total of 1046 newly diagnosed COPD patients were enrolled, including 949 male and 97 female patients with a mean age of 62.6± 8.71. According to the GOLD criteria, 88.1% of the patients were identified to have moderate or above COPD, 50.0% to have severe or above COPD; a further diagnosis of ACO was made in 347 (33.2%) of the patients. ANOVA analysis showed significant differences in disease course, FEV1, FEV1%, FEV1/FVC, FVC, FVC% and mMRC among the 5 auscultation groups (P < 0.001), but FENO did not differ significantly among them (P=0.097). The percentage of patients with wheezing in auscultation was significantly greater in ACO group than in COPD group (P < 0.001). Spearman correlation analysis showed that lung sounds was significantly correlated with disease severity, FEV1, FEV1%, FVC and FVC% of the patients (P < 0.001); Multiple linear regression analysis showed that a longer disease course, a history of smoking and lung sounds were all associated with poorer lung functions and a greater disease severity.

CONCLUSIONS

Lung sounds can be used as an indicator for assessing the severity of COPD at the initial diagnosis.

Analysis of wheeze sounds during tidal breathing according to severity levels in asthma patients

Objective: This study aimed to statistically analyze the behavior of time-frequency features in digital recordings of wheeze sounds obtained from patients with various levels of asthma severity (mild, moderate, and severe), and this analysis was based on the auscultation location and/or breath phase. Method: Segmented and validated wheeze sounds were collected from the trachea and lower lung base (LLB) of 55 asthmatic patients during tidal breathing maneuvers and grouped into nine different datasets. The quartile frequencies F₂₅, F₅₀, F₇₅, F₉₀ and F₉₉, mean frequency (MF) and average power (AP) were computed as features, and a univariate statistical analysis was then performed to analyze the behavior of the time-frequency features. Results: All features generally showed statistical significance in most of the datasets for all severity levels [χ² = 6.021-71.65, p < 0.05, η² = 0.01-0.52]. Of the seven investigated features, only AP showed statistical significance in all the datasets. F₂₅, F₇₅, F₉₀ and F₉₉ exhibited statistical significance in at least six datasets [χ² = 4.852-65.63, p < 0.05, η² = 0.01-0.52], and F₂₅, F₅₀ and MF showed statistical significance with a large η² in all trachea-related datasets [χ² = 13.54-55.32, p < 0.05, η² = 0.13-0.33]. Conclusion: The results obtained for the time-frequency features revealed that (1) the asthma severity levels of patients can be identified through a set of selected features with tidal breathing, (2) tracheal wheeze sounds are more sensitive and specific predictors of severity levels and (3) inspiratory and expiratory wheeze sounds are almost equally informative.