Computerized respiratory sound analysis in people with dementia: a first-step towards diagnosis and monitoring of respiratory conditions

Computerized respiratory sound analysis has been shown to be an objective and reliable way to assess respiratory diseases. However, its application in non-collaborative populations, such as people with dementia, is still unknown. Therefore this study aimed to characterize normal and adventitious respiratory sounds (NRS; ARS) in older people with and without dementia. A cross-sectional study including two groups of 30 subjects with dementia and 30 subjects without dementia was performed. Digital auscultation was used to record NRS and ARS per breathing-phase (inspiration/expiration) at trachea and thorax. Frequency at percentiles 25, 50 and 75, frequency at maximum-intensity, maximum-intensity (I max) and mean-intensity (I mean) characterized NRS. Crackle number, frequency, initial-deflection-width, 2cycle-duration, and largest-deflection-width and wheeze number, frequency and occupation-rate characterized ARS. Groups were similar in socio-demographics, except for anthropometrics. No significant differences were found between groups in NRS frequency or ARS at trachea or thorax. Significant lower I max (inspiration: 36.88(29.42;39.92) versus 39.84(36.50;44.17) p  =  0.007; expiration: 34.51(32.06;38.87) versus 42.33(36.92;44.98) p  <  0.001) and I mean (inspiration: 15.23(12.08;18.60) versus 18.93(15.64;21.82) p  =  0.003 and expiration: 14.57(12.08;18.30) versus 18.87(15.64;21.44) p  =  0.001) at trachea and higher I mean (inspiration: 17.29(16.04;19.31) versus 16.45(15.05; 18.79) p  =  0.005 and expiration: 16.71(15.31;18.56) versus 16.38(14.40;17.85) p  =  0.011) at thorax were found in subjects with dementia when compared with subjects without dementia. To conclude, people with and without dementia had similar NRS and ARS characteristics, except for NRS intensity. Computerized respiratory sound analysis was feasible in a non-collaborative population. Further research is needed to enhance the use of respiratory acoustics in non-collaborative populations, with strong potential to be applied in different settings for diagnosis and monitoring purposes

Computerized respiratory sounds can differentiate smokers and non-smokers

Cigarette smoking is often associated with the development of several respiratory diseases however, if diagnosed early, the changes in the lung tissue caused by smoking may be reversible. Computerised respiratory sounds have shown to be sensitive to detect changes within the lung tissue before any other measure, however it is unknown if it is able to detect changes in the lungs of healthy smokers. This study investigated the differences between computerised respiratory sounds of healthy smokers and non-smokers. Healthy smokers and non-smokers were recruited from a university campus. Respiratory sounds were recorded simultaneously at 6 chest locations (right and left anterior, lateral and posterior) using air-coupled electret microphones. Airflow (1.0–1.5 l/s) was recorded with a pneumotachograph. Breathing phases were detected using airflow signals and respiratory sounds with validated algorithms. Forty-four participants were enrolled: 18 smokers (mean age 26.2, SD = 7 years; mean FEV1 % predicted 104.7, SD = 9) and 26 non-smokers (mean age 25.9, SD = 3.7 years; mean FEV1 % predicted 96.8, SD = 20.2). Smokers presented significantly higher frequency at maximum sound intensity during inspiration [(M = 117, SD = 16.2 Hz vs. M = 106.4, SD = 21.6 Hz; t(43) = −2.62, p = 0.0081, d z = 0.55)], lower expiratory sound intensities (maximum intensity: [(M = 48.2, SD = 3.8 dB vs. M = 50.9, SD = 3.2 dB; t(43) = 2.68, p = 0.001, d z = −0.78)]; mean intensity: [(M = 31.2, SD = 3.6 dB vs. M = 33.7,SD = 3 dB; t(43) = 2.42, p = 0.001, d z = 0.75)] and higher number of inspiratory crackles (median [interquartile range] 2.2 [1.7–3.7] vs. 1.5 [1.2–2.2], p = 0.081, U = 110, r = −0.41) than non-smokers. Significant differences between computerised respiratory sounds of smokers and non-smokers have been found. Changes in respiratory sounds are often the earliest sign of disease. Thus, computerised respiratory sounds might be a promising measure to early detect smoking related respiratory diseases

Detecting unilateral phrenic paralysis by acoustic respiratory analysis

The consequences of phrenic nerve paralysis vary from a considerable reduction in respiratory function to an apparently normal state. Acoustic analysis of lung sound intensity (LSI) could be an indirect non-invasive measurement of respiratory muscle function, comparing activity on the two sides of the thoracic cage. Lung sounds and airflow were recorded in ten males with unilateral phrenic paralysis and ten healthy subjects (5 men/5 women), during progressive increasing airflow maneuvers. Subjects were in sitting position and two acoustic sensors were placed on their back, on the left and right sides. LSI was determined from 1.2 to 2.4 L/s between 70 and 2000 Hz. LSI was significantly greater on the normal (19.3±4.0 dB) than the affected (5.7±3.5 dB) side in all patients (p = 0.0002), differences ranging from 9.9 to 21.3 dB (13.5±3.5 dB). In the healthy subjects, the LSI was similar on both left (15.1±6.3 dB) and right (17.4±5.7 dB) sides (p = 0.2730), differences ranging from 0.4 to 4.6 dB (2.3±1.6 dB). There was a positive linear relationship between the LSI and the airflow, with clear differences between the slope of patients (about 5 dB/L/s) and healthy subjects (about 10 dB/L/s). Furthermore, the LSI from the affected side of patients was close to the background noise level, at low airflows. As the airflow increases, the LSI from the affected side did also increase, but never reached the levels seen in healthy subjects. Moreover, the difference in LSI between healthy and paralyzed sides was higher in patients with lower FEV1 (%). The acoustic analysis of LSI is a relevant non-invasive technique to assess respiratory function. This method could reinforce the reliability of the diagnosis of unilateral phrenic paralysis, as well as the monitoring of these patients.Peer ReviewedPostprint (published version