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

Christoph Kalkbrenner; Manuel Eichenlaub; Stefan Rüdiger; Cornelia Kropf-Sanchen; Rainer Brucher; Wolfgang Rottbauer

doi:10.5664/jcsm.6752

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

J Clin Sleep Med. 2017 Oct 15;13(10):1123-1130. doi: 10.5664/jcsm.6752.

Authors

Christoph Kalkbrenner¹, Manuel Eichenlaub², Stefan Rüdiger³, Cornelia Kropf-Sanchen³, Rainer Brucher¹, Wolfgang Rottbauer³

Affiliations

¹ Faculty of Medical Engineering, University of Applied Science Ulm, Ulm, Germany.
² School of Engineering, University of Warwick, Coventry, United Kingdom.
³ Department of Internal Medicine II, University Hospital Ulm, Ulm, Germany.

Abstract

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: https://drks-neu.uniklinik-freiburg.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00011195; Identifier: DRKS00011195.

Keywords: monitoring; movement analysis; respiratory sounds; sleep apnea.

Publication types

Validation Study

MeSH terms

Algorithms
Female
Humans
Male
Middle Aged
Monitoring, Physiologic / instrumentation*
Monitoring, Physiologic / methods*
Movement
Prospective Studies
Reproducibility of Results
Respiratory Sounds / diagnosis*
Respiratory Sounds / physiology
Sensitivity and Specificity
Sleep Apnea, Obstructive / diagnosis*
Trachea / physiology*