Chronic cough can be the sole presenting symptom for patients with obstructive sleep apnoea. We investigated the prevalence, severity and factors associated with chronic cough in patients with sleep-disordered breathing (SDB).We invited 108 consecutive patients who had been referred for evaluation of SDB to complete a comprehensive questionnaire on respiratory and sleep health, which included the Leicester Cough Questionnaire (cough specific quality of life; LCQ), Epworth Sleepiness Scale (ESS) and the Mayo Clinic gastro-oesophageal questionnaire. Chronic cough was defined as cough for a duration of .2 months.33% of patients with SDB reported a chronic cough. Patients with a chronic cough had impaired cough related-quality of life affecting all health domains (mean¡SEM LCQ score 17.7¡0.7; normal521). Patients with SDB and chronic cough were predominantly females (61% versus 17%; p,0.001) and reported more nocturnal heartburn (28% versus 5%; p50.03) and rhinitis (44% versus 14%; p50.02) compared to those without SDB. There were no significant differences in ESS, respiratory disturbance index, body mass index, or symptoms of breathlessness, wheeze, snoring, dry mouth and choking between those with cough and those without.Chronic cough is prevalent in patients with SDB and is associated with female sex, symptoms of nocturnal heartburn and rhinitis. Further studies are required to investigate the impact of continuous positive airway pressure therapy on cough associated with SDB to explore the mechanism of this association.

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.

Quality of sleep greatly affects a person's physiological well-being. Traditional sleep monitoring systems are expensive in cost and intrusive enough that they disturb the natural sleep of clinical patients. In our previous work, we proposed a non-intrusive sleep monitoring system to first record depth video in real-time, then offline analyze recorded depth data to track a patient's chest and abdomen movements over time. Detection of abnormal breathing is then interpreted as episodes of apnoea or hypopnoea. Leveraging on recent advances in graph signal processing (GSP), in this paper we propose two new additions to further improve our sleep monitoring system. First, temporal denoising is performed using a block motion vector smoothness prior expressed in the graph-signal domain, so that unwanted temporal flickering can be removed. Second, a graph-based event classification scheme is proposed, so that detection of apnoea / hypopnoea can be performed accurately and robustly. Experimental results show first that graph-based temporal denoising scheme outperforms an implementation of temporal median filter in terms of flicker removal. Second, we show that our graph-based event classification scheme is noticeably more robust to errors in training data than two conventional implementations of support vector machine (SVM)

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