Development and validation of a novel non-contact monitor of nocturnal respiration for identifying sleep-disordered breathing in patients with heart failure.

AIMS: At least 50% of patients with heart failure (HF) may have sleep-disordered breathing (SDB). Overnight in-hospital polysomnography (PSG) is considered the gold standard for diagnosis, but a lack of access to such testing contributes to under-diagnosis of SDB. Therefore, there is a need for simple and reliable validated methods to aid diagnosis in patients with HF. The aim of this study was to investigate the accuracy of a non-contact type IV screening device, SleepMinderTM (SM), compared with in-hospital PSG for detecting SDB in patients with HF. METHODS AND RESULTS: The study included 75 adult patients with systolic HF and suspected SDB who underwent simultaneous PSG and SM recordings. An algorithm was developed from the SM signals, using digital signal processing and pattern recognition techniques to calculate the SM apnoea-hypopnoea index (AHI). This was then compared with expert-scored PSGAHI . The SM algorithm had 70% sensitivity and 89% specificity for identifying patients with clinically significant SDB (AHI ≥ 15/h). At this threshold, it had a positive likelihood ratio of 6.3 and a negative likelihood ratio of 0.16. The overall accuracy of the SMAHI algorithm was 85.8% as shown by the area under a receiver operator characteristic curve. The mean AHI with SM was 3.8/h (95% confidence interval 0.5-7.1) lower than that with PSG. CONCLUSIONS: The accuracy of the non-contact type IV screening device SM is good for clinically significant SDB in patients with systolic HF and could be considered as a simple first step in the diagnostic pathway.

Early Warning of Acute Decompensation in Heart Failure Patients Using a Noncontact Measure of Stability Index.

OBJECTIVE: Hospitalization due to heart failure (HF) continues to be a major clinical and economic challenge. To reduce hospitalization, this paper proposes a novel home telemonitoring system for an early warning of acute decompensation in patients with chronic stable New York Heart Association class II-IV HF. METHODS: It is based on deriving nocturnal respiratory related time series using a noncontact radio-wave bio-motion sensor. The system generates an alert when there is a change in the underlying probability distribution of the time series which can be regarded as a surrogate marker of patient stability. RESULTS: The system's performance is evaluated using dual-site longitudinal data collected from 104 HF patients over 12-24 months. The system reported an average sensitivity of 0.62 to detect a change during an episode of acute decompensation and an average specificity of 0.67 on the blind validation set, when the frequency of alert was four weeks. The system also performed well to predict acute decompensation with an average sensitivity of 0.55 and specificity of 0.73 on the validation set, where an event window was defined as three weeks preceding an event. CONCLUSION: These results demonstrate that the design and implementation of such a system is a positive step toward developing noncontact systems capable of preventing acute decompensation, reducing readmissions to hospital and ensuring better quality of life for HF patients.