Characterization and comparison of a 2-, 4- and 8-MHz central venous catheter ultrasound probe for venous air emboli detection.

This paper presents a concept for detection of venous air emboli inside the superior vena cava using a central venous catheter with integrated Doppler ultrasound transducer installed on the tip. Several Doppler probes each with a single insonation frequencies of 2 MHz, 4 MHz or 8 MHz are characterized and compared for usefulness in this scenario. During in vitro experiments using an artificial blood circulatory with blood mimicking fluid bubbles with defined volumes were injected and recorded as gaseous embolic events. The in vitro results of measured embolus-blood-ratio values (EBR) in respect to the air bubbles volumes and its echogenicity showed a good correlation with the simulation model of spherical cross section scattering of such air bubbles. It is shown that the probe design still needs some improvements using a 4 MHz insonation frequency to get a useable detection sensitivity in such scenario within vena cava superior. The results suggest that it is possible to estimate the air bubble volume corresponding to the EBR using such a catheter probe.

Automated sleep stage classification based on tracheal body sound and actigraphy.

The current gold standard for assessment of most sleep disorders is the in-laboratory polysomnography (PSG). This approach produces high costs and inconveniences for the patients. An accessible and simple preliminary screening method to diagnose the most common sleep disorders and to decide whether a PSG is necessary or not is therefore desirable. A minimalistic type-4 monitoring system which utilized tracheal body sound and actigraphy to accurately diagnose the obstructive sleep apnea syndrome was previously developed. To further improve the diagnostic ability of said system, this study aims to examine if it is possible to perform automated sleep staging utilizing body sound to extract cardiorespiratory features and actigraphy to extract movement features. A linear discriminant classifier based on those features was used for automated sleep staging using the type-4 sleep monitor. For validation 53 subjects underwent a full-night screening at Ulm University Hospital using the developed sleep monitor in addition to polysomnography. To assess sleep stages from PSG, a trained technician manually evaluated EEG, EOG, and EMG recordings. The classifier reached 86.9% accuracy and a Kappa of 0.69 for sleep/wake classification, 76.3% accuracy and a Kappa of 0.42 for Wake/REM/NREM classification, and 56.5% accuracy and a Kappa of 0.36 for Wake/REM/light sleep/deep sleep classification. For the calculation of sleep efficiency (SE), a coefficient of determination r2 of 0.78 is reached. Additionally, subjects were classified into groups of SEs (SE≥40%, SE≥60% and SE≥80%). A Cohen's Kappa >0.61 was reached for all groups, which is considered as substantial agreement. The presented method provides satisfactory performance in sleep/wake and wake/REM/NREM sleep staging while maintaining a simple setup and offering high comfort. This minimalistic approach may address the need for a simple yet reliable preliminary sleep screening in an ambulatory setting.

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

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 2 = 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.

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 (AHIPSG). A unique algorithm was developed to estimate the apneahypopnea index (AHIest) using the new sleep monitor. RESULTS: AHIest strongly correlates with AHIPSG (r2 = .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.

Sleep monitoring using body sounds and motion tracking.

This paper presents a system for sleep monitoring that can continuously analyze snoring, breathing, sleep phases and the activity of the patient during the night and the beginning of the day. Early results show that the system can be used to detect the occurrence of obstructive sleep apnea syndrome (OSAS). OSAS is traditionally diagnosed using polysomnography, which requires a whole night stay at the sleep laboratory of a hospital, where the patient is attached to multiple electrodes and sensors. Our system detects heartbeats, breathing, snoring, sleeping positions and movements using a special electret microphone and an inertial measurement unit (IMU). The system first analyses the sleep using the acoustic information provided by the electret microphone. From the acoustic information breathing events and heartbeats are identified. The system also analyses the patient's activity and positions from data delivered by the IMU. The information from both sensors is fused to detect sleep events. First experiments show that the system is capable of detecting and interpreting relevant data to improve sleep monitoring.