Detection and classification of adventitious acoustic lung sounds plays an important role in diagnosing, monitoring, controlling and, caring the patients with lung diseases. Such systems can be presented as different platforms like medical devices, standalone software or smartphone application. Ubiquity of smartphones and widespread use of the corresponding applications make such a device an attractive platform for hosting the detection and classification systems for adventitious lung sounds. In this paper, the smartphone-based systems for automatic detection and classification of the adventitious lung sounds are surveyed. Such adventitious sounds include cough, wheeze, crackle and, snore. Relevant sounds related to abnormal respiratory activities are considered as well. The methods are shortly described and the analyzing algorithms are explained. The analysis includes detection and/or classification of the sound events. A summary of the main surveyed methods together with the classification parameters and used features for the sake of comparison is given. Existing challenges, open issues and future trends will be discussed as well.

Introduction: Night-time respiratory symptoms have a considerable impact on sleep and life quality in patients with chronic obstructive pulmonary disease (COPD). Lack of awareness of night-time symptoms can lead to worsened COPD control. Automated long-term monitoring of respiratory symptoms with LEOSound enables assessment of nocturnal wheezing and cough. Methods: In this observational study we investigated the prevalence and severity of cough and wheezing in patients with stable COPD [Global Initiative for Chronic Obstructive Lung Disease (GOLD) II–IV] disease for two consecutive nights with the LEOSound system. 48 patients (30 males, 63%) were eligible for inclusion, median age was 67 years, and body mass index (BMI) was 25.3 kg/m². Results: In 15 out of 48 patients (31%), we found wheezing periods for at least 10-minute duration. Wheezing periods >30 minutes were monitored in seven patients and wheezing periods >60 minutes were monitored in three patients. The maximum duration of wheezing was 470 minutes in one patient with COPD II. The median wheezing rate differed between the COPD stages and between active and non-active smokers. Cough was found in 42 patients (87.5%) with a range of 1–326 events. The cough-period-index in night one was 0.83 n/hour (P25:0.33||P75: 2.04) and night two 0.97 n/hour (P25:0.25||P75: 1.9). Most of the cough events were non-productive with a median of 0.86. Conclusions: Night-time symptoms are common in COPD patients. LEOSound offers an opportunity to evaluate objectively night-time symptoms like wheezing and cough in patients with COPD which remain otherwise unnoticed. We found a high incidence of night-time wheezing in these patients, which was related to persistant smoking.

Data integration is an important task in medical informatics and highly impacts the gain out of existing health information data. These tasks are using implemented as extract transform and load processes. By introducing HL7 FHIR as an intermediate format, our aim was to integrate heterogeneous data from a German pulmonary hypertension registry into an OMOP Common Data Model. First, domain knowledge experts defined a common parameter set, which was subsequently mapped to standardized terminologies like LOINC or SNOMED-CT. Data was extracted as HL7 FHIR Bundle to be transformed to OMOP CDM by using XSLT. We successfully transformed the majority of data elements to the OMOP CDM in a feasible time.

Purpose: Chronic cough is one of the main symptoms of COPD. Ambulatory objective monitoring provides novel insights into the determinants and characteristics of nighttime cough in COPD. Materials and methods: Nighttime cough was monitored objectively by LEOSound lung sound monitor in patients with stable COPD II–IV. In 30 patients, with 10 patients in each stage group, nighttime cough was analyzed for epoch frequency, epoch severity (epoch length and coughs per epoch), and pattern (productive or nonproductive). Results: Cough was found in all patients ranging from 1 to 294 events over the recording period. In 29 patients, cough epochs were monitored, ranging from 1 to 75 epochs. The highest amount of cough epochs was found in patients with COPD stage III. Active smokers had significantly more productive cough epochs (61%) than nonsmokers (24%). Conclusion: We found a high rate of nighttime cough epochs in patients with COPD, especially in those in stage III. Productive cough was predominantly found in patients with persistent smoking. LEOSound lung sound monitor offers a practical and valuable opportunity to evaluate cough objectively.