Acoustical flow estimation in patients with obstructive sleep apnea during Sleep

Yadollahi, Azadeh; Azarbarzin, Ali; Montazeri, Aman; Moussavi, Zahra

doi:10.1109/embc.2012.6346755

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…The importance of this step is emphasized by the fact that during the night the overall relationship between breathing sound amplitude and airflow can vary significantly, mainly dependent on sleeping position. Based on the findings of previous studies [16][17][18] it is possible to continuously relate and revise certain features of every breathing cycle to the respective amount of airflow. Finally, this technique is utilized for the classification of the previously detected segments during the first step into apnea, hypopnea, or normal breathing.…”

Section: Analysis Of Sound and Movement Datamentioning

confidence: 99%

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

Kalkbrenner

Eichenlaub

Rüdiger

et al. 2017

Journal of Clinical Sleep Medicine

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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 (AHIPSG). A unique algorithm was developed to estimate the apneahypopnea index (AHIest) using the new sleep monitor. Results: AHIest strongly correlates with AHIPSG (r 2 = .9871). A mean ± 1.96 standard deviation difference between AHIest and AHIPSG 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 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. I NTRO DUCTI O NWith a prevalence of 4% in adult men and 2% in women, obstructive sleep apnea (OSA) is one of the most common sleeprelated breathing disorders.1 Additionally, more than 75% of people suffering from moderate OSA are either undiagnosed or untreated.2 OSA is characterized by multiple breathing cessations during the night due to different possible causes. If untreated, this disorder can lead to extensive daytime sleepiness 3 and an elevated risk for cardiovascular disease. [4][5][6] The main criteria used to indicate the severity of OSA is the apneahypopnea index (AHI), which describes the mean number of breathing pa...

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Section: Analysis Of Sound and Movement Datamentioning

confidence: 99%

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

Kalkbrenner

Eichenlaub

Rüdiger

et al. 2017

Journal of Clinical Sleep Medicine

View full text Add to dashboard Cite

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Apnea and heart rate detection from tracheal body sounds for the diagnosis of sleep-related breathing disorders

Kalkbrenner

Eichenlaub

Rüdiger

et al. 2017

Med Biol Eng Comput

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Sleep apnea is one of the most common sleep disorders. Here, patients suffer from multiple breathing pauses longer than 10 s during the night which are referred to as apneas. The standard method for the diagnosis of sleep apnea is the attended cardiorespiratory polysomnography (PSG). However, this method is expensive and the extensive recording equipment can have a significant impact on sleep quality falsifying the results. To overcome these problems, a comfortable and novel system for sleep monitoring based on the recording of tracheal sounds and movement data is developed. For apnea detection, a unique signal processing method utilizing both signals is introduced. Additionally, an algorithm for extracting the heart rate from body sounds is developed. For validation, ten subjects underwent a full-night PSG testing, using the developed sleep monitor in concurrence. Considering polysomnography as gold standard the developed instrumentation reached a sensitivity of 92.8% and a specificity of 99.7% for apnea detection. Heart rate measured with the proposed method was strongly correlated with heart rate derived from conventional ECG (r = 0.8164). No significant signal losses are reported during the study. In conclusion, we demonstrate a novel approach to reliably and noninvasively detect both apneas and heart rate during sleep.

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Towards a Truly Wearable, Non-Invasive Respiration Monitoring System

Chen¹

2015

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Acoustical flow estimation in patients with obstructive sleep apnea during Sleep

Cited by 5 publications

References 24 publications

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

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

Apnea and heart rate detection from tracheal body sounds for the diagnosis of sleep-related breathing disorders

Towards a Truly Wearable, Non-Invasive Respiration Monitoring System

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