Abstract: The objective monitoring of cough for extended periods of time has long been recognized as an important step towards a better understanding of this symptom, and a better management of chronic cough patients. In this paper, we present a system for the automatic analysis of 24-h, continuous, ambulatory recordings of cough. The system uses audio recordings from a miniature microphone and the detection algorithm is based on statistical models of the time-spectral characteristics of cough sounds. We validated the system against manual counts obtained by a trained observer on 40 ambulatory recordings and our results show a median sensitivity value of 85.7%, median positive predictive value of 94.7% and median false positive rate of 0.8 events/h. An analysis application was developed, with a graphical user interface, allowing the use of the system in clinical settings by technical or medical staff. The result of the analysis of a recording session is presented as a concise, graphical-based report. The modular nature of the system interface facilitates its enhancement with the integration of further modules.

Abstract: Chronic cough is a common reason for presentation to both general practice and respiratory clinics. In up to 25% of cases, the cause remains unclear after extensive investigations. We report 4 patients presenting with an isolated chronic cough who were subsequently found to have obstructive sleep apnoea. The cough improved rapidly with nocturnal continuous positive airway pressure therapy. Further studies are required to investigate the prevalence of coexistence of these common conditions.

Abstract: BACKGROUND: Little is known about the frequency of cough in health and in patients with chronic cough. METHODS: We measured cough frequency and its relationship with other markers of cough severity in 20 patients with chronic cough and 9 healthy subjects using the Leicester Cough Monitor (LCM), which is an automated ambulatory digital cough monitor that records sound only. All subjects had a 6-h recording and recordings were manually counted. A subgroup of 6 normals and 6 patients with a stable chronic cough had repeat measurements up to 6 months apart. RESULTS: Mean (sem) cough counts/hour were 43(8) in patients with chronic cough and 2(1) in normals (mean difference 41; 95% confidence interval 24-59; P<0.001). The cough counts were repeatable (within subject standard deviation: 23 coughs/hour; intraclass correlation coefficient 0.8). Cough counts correlated significantly with physical (r=-0.6, P=0.03), social (r=-0.7, P=0.01) and total Leicester Cough Questionnaire (LCQ) health status scores (r=-0.6, P=0.03) and cough sensitivity (concentration of capsaicin causing 5 coughs: r=0.9, P=0.008). CONCLUSION: We have shown that there are marked differences in cough frequency between patients with chronic cough and healthy subjects, that these measurements are repeatable, and that they correlate with cough-specific health status.

Abstract: Cough is a common symptom of many respiratory diseases. The evaluation of its intensity and frequency of occurrence could provide valuable clinical information in the assessment of patients with chronic cough. In this paper we propose the use of hidden Markov models (HMMs) to automatically detect cough sounds from continuous ambulatory recordings. The recording system consists of a digital sound recorder and a microphone attached to the patient's chest. The recognition algorithm follows a keyword-spotting approach, with cough sounds representing the keywords. It was trained on 821 min selected from 10 ambulatory recordings, including 2473 manually labeled cough events, and tested on a database of nine recordings from separate patients with a total recording time of 3060 min and comprising 2155 cough events. The average detection rate was 82% at a false alarm rate of seven events/h, when considering only events above an energy threshold relative to each recording's average energy. These results suggest that HMMs can be applied to the detection of cough sounds from ambulatory patients. A postprocessing stage to perform a more detailed analysis on the detected events is under development, and could allow the rejection of some of the incorrectly detected events.