Validation of a New System Using Tracheal Body Sound and Movement Data for Automated Apnea-Hypopnea Index Estimation

Study Objectives:

The current gold standard for assessment of obstructive sleep apnea is the in-laboratory polysomnography. This approach has high costs and inconveniences the patient, whereas alternative ambulatory systems are limited by reduced diagnostic abilities (type 4 monitors, 1 or 2 channels) or extensive setup (type 3 monitors, at least 4 channels). The current study therefore aims to validate a simplified automated type 4 monitoring system using tracheal body sound and movement data.

Methods:

Data from 60 subjects were recorded at the University Hospital Ulm. All subjects have been regular patients referred to the sleep center with suspicion of sleep-related breathing disorders. Four recordings were excluded because of faulty data. The study was of prospective design. Subjects underwent a full-night screening using diagnostic in-laboratory polysomnography and the new monitoring system concurrently. The apnea-hypopnea index (AHI) was scored blindly by a medical technician using in-laboratory polysomnography (AHI_PSG). A unique algorithm was developed to estimate the apneahypopnea index (AHI_est) using the new sleep monitor.

Results:

AHI_est strongly correlates with AHI_PSG (r² = .9871). A mean ± 1.96 standard deviation difference between AHI_est and AHI_PSG of 1.2 ± 5.14 was achieved. In terms of classifying subjects into groups of mild, moderate, and severe sleep apnea, the evaluated new sleep monitor shows a strong correlation with the results obtained by polysomnography (Cohen kappa > 0.81). These results outperform previously introduced similar approaches.

Conclusions:

The proposed sleep monitor accurately estimates AHI and diagnoses sleep apnea and its severity. This minimalistic approach may address the need for a simple yet reliable diagnosis of sleep apnea in an ambulatory setting.

Clinical Trial Registration:

Trial name: Validation of a new method for ambulant diagnosis of sleep related breathing disorders using body sound; URL: http://ift.tt/2lC7T2H; Identifier: DRKS00011195

Citation:

Kalkbrenner C, Eichenlaub M, Rüdiger S, Kropf-Sanchen C, Brucher R, Rottbauer W. Validation of a new system using tracheal body sound and movement data for automated apnea-hypopnea index estimation. J Clin Sleep Med. 2017;13(10):1123–1130.

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