Detection of bronchial breathing caused by pneumonia

Design of Wearable Breathing Sound Monitoring System for Real-Time Wheeze Detection

In the clinic, the wheezing sound is usually considered as an indicator symptom to reflect the degree of airway obstruction. The auscultation approach is the most common way to diagnose wheezing sounds, but it subjectively depends on the experience of the physician. Several previous studies attempted to extract the features of breathing sounds to detect wheezing sounds automatically. However, there is still a lack of suitable monitoring systems for real-time wheeze detection in daily life. In this study, a wearable and wireless breathing sound monitoring system for real-time wheeze detection was proposed. Moreover, a breathing sounds analysis algorithm was designed to continuously extract and analyze the features of breathing sounds to provide the objectively quantitative information of breathing sounds to professional physicians. Here, normalized spectral integration (NSI) was also designed and applied in wheeze detection. The proposed algorithm required only short-term data of breathing sounds and lower computational complexity to perform real-time wheeze detection, and is suitable to be implemented in a commercial portable device, which contains relatively low computing power and memory. From the experimental results, the proposed system could provide good performance on wheeze detection exactly and might be a useful assisting tool for analysis of breathing sounds in clinical diagnosis.

Thoracic auscultation in captive bottlenose dolphins (Tursiops truncatus), California sea lions (Zalophus californianus), and South African fur seals (Arctocephalus pusillus) with an electronic stethoscope

Thoracic auscultation is an important diagnostic method used in cases of suspected pulmonary disease in many species, as respiratory sounds contain significant information on the physiology and pathology of the lungs and upper airways. Respiratory diseases are frequent in marine mammals and are often listed as one of their main causes of death. The aim of this study was to investigate and report baseline parameters for the electronic-mediated thoracic auscultation of one cetacean species and two pinniped species in captivity. Respiratory sounds from 20 captive bottlenose dolphins (Tursiops truncatus), 6 California sea lions (Zalophus californianus), and 5 South African fur seals (Arctocephalus pusillus) were recorded with an electronic stethoscope. The sounds were analyzed for duration of the respiratory cycle, adventitious sounds, and peak frequencies of recorded sounds during expiration and inspiration as well as for sound intensity as reflected by waveform amplitude during the respiratory cycle. In respiratory cycles of the bottlenose dolphins' expiring "on command," the duration of the expiration was significantly shorter than the duration of the inspiration. In the examined pinnipeds of this study, there was no clear pattern concerning the duration of one breathing phase: Adventitious sounds were detected most often in bottlenose dolphins that were expiring on command and could be compared with "forced expiratory wheezes" in humans. This is the first report of forced expiratory wheezes in bottlenose dolphins; they can easily be misinterpreted as pathologic respiratory sounds. The peak frequencies of the respiratory sounds reached over 2,000 Hz in bottlenose dolphins and over 1,000 Hz in California sea lions and South African fur seals, but the variation of the frequency spectra was very high in all animals. To the authors' knowledge, this is the first systematic analysis of respiratory sounds of bottlenose dolphins and two species of pinnipeds.

On applying continuous wavelet transform in wheeze analysis

The identification of continuous abnormal lung sounds, like wheezes, in the total breathing cycle is of great importance in the diagnosis of obstructive airways pathologies. To this vein, the current work introduces an efficient method for the detection of wheezes, based on the time-scale representation of breath sound recordings. The employed Continuous Wavelet Transform is proven to be a valuable tool at this direction, when combined with scale-dependent thresholding. Analysis of lung sound recordings from 'wheezing' patients shows promising performance in the detection and extraction of wheezes from the background noise and reveals its potentiality for data-volume reduction in long-term wheezing screening, such as in sleep-laboratories.

Mobile nocturnal long-term monitoring of wheezing and cough

Changes in normal lung sounds are an important sign of pathophysiological processes in the bronchial system and lung tissue. For the diagnosis of bronchial asthma, coughing and wheezing are important symptoms that indicate the existence of obstruction. In particular, nocturnal long-term acoustic monitoring and assessment make sense for qualitative and quantitative detection and documentation. Previous methods used for lung function diagnosis require active patient cooperation that is not possible during sleep. We developed a mobile device based on the CORSA standard that allows the recording of respiratory sounds throughout the night. To date, we have recorded 133 patients with different diagnoses (80 male, 53 female), of whom 38 were children. In 68 of the patients we could detect cough events and in 87 we detected wheezing. The recording method was tolerated by all participating adults and children. Our mobile system allows non-invasive and cooperation-independent nocturnal monitoring of acoustic symptoms in the domestic environment, especially at night, when most ailments occur.

Wheeze detection based on time-frequency analysis of breath sounds

Abnormal breath sounds like wheezes are observed in patients with obstructive pulmonary diseases. The aim of this study was to construct an automatic technique for wheeze detection and monitoring using spectral analysis. Wheezes from 13 patients with diagnosed asthma, chronic obstructive pulmonary disease and pneumonia were recorded and a time-frequency wheeze detector (TF-WD) based on TF wheeze characteristics was constructed. The TF-WD was evaluated using 337 wheezes by comparing its findings with those from clinical auscultation performed by two experts. In addition, the TF-WD was tested against artificial noise. The experimental and testing results justified the efficient performance and high noise robustness of the TF-WD.