Assessment of respiratory effort during sleep: Esophageal pressure versus noninvasive monitoring techniques

Vandenbussche, Nle Nele; Overeem, Sebastiaan; Dijk, Hans van; Simons, Pjm Pieter; Pevernagie, Dirk

doi:10.1016/j.smrv.2014.12.006

Cited by 56 publications

(52 citation statements)

References 54 publications

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“…Increased respiratory effort is a sign of obstructive sleep-disordered breathing and may be recorded from mechanical, electrical, and electromechanical signals. 2 Monitoring of esophageal pressure is the gold standard, as the pressure signal directly reflects the respiratory muscle force, 2 but this method is invasive and usually poorly tolerated and may cause sleep disturbances. 3 Characterization of respiratory efforts by means of esophageal pressure is thus not used in routine care.…”

Section: Introductionmentioning

confidence: 99%

Validation of a Suprasternal Pressure Sensor for Sleep Apnea Classification in Children

Amaddeo

Fernandez‐Bolaños

et al. 2016

Journal of Clinical Sleep Medicine

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Study Objectives: The recognition and characterization of respiratory events is crucial when interpreting sleep studies. The aim of the study was to validate the PneaVoX sensor, which integrates the recording of respiratory effort by means of suprasternal pressure (SSP), respiratory flow, and snoring for the classification of sleep apneas in children. Methods: Sleep recordings of 20 children with a median age of 7.5 (0.5-16.5) years were analyzed. Scoring of apneas according to the American Academy of Sleep Medicine (AASM) guidelines using nasal pressure, oronasal thermal sensor and respiratory efforts by means of respiratory inductance plethysmography (RIP), was compared to a scoring using the PneaVoX sensor and nasal pressure, without the oronasal thermal sensor nor RIP, during a dual blind study. Results: The percentage of sleep time recording without artifacts was 97%, 97%, 87%, 65%, and 98% for the respiratory flow and SSP from the PneaVoX sensor, oronasal thermal sensor, nasal pressure, and RIP, respectively. As compared to the AASM scoring with RIP, sensitivity and specificity of the SSP for the scoring of central apneas were 75% and 99% for the first reader, and 70% and 100% for the second reader, respectively. Sensitivity and specificity for the scoring of obstructive apneas were 98% and 75%, and 100% and 70%, respectively. A significant number of apneas scored as central by RIP were scored as obstructive by the SSP. Conclusions:The PneaVoX sensor has a high degree of scorability in children. The PneaVoX sensor is a useful adjunct for characterizing apneas.

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Section: Introductionmentioning

confidence: 99%

Validation of a Suprasternal Pressure Sensor for Sleep Apnea Classification in Children

Amaddeo

Fernandez‐Bolaños

et al. 2016

Journal of Clinical Sleep Medicine

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“…A continuous and automatic monitoring system can be a needful instrument for medical staff, especially for monitoring patients affected by respiratory diseases. In addition, simultaneous monitoring of respiratory function and activity level may be beneficial in the monitoring of chronic conditions such as chronic obstructive pulmonary disorder [1]- [2]. Hence, the development of monitoring systems has become a topical issue [1]- [7].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, simultaneous monitoring of respiratory function and activity level may be beneficial in the monitoring of chronic conditions such as chronic obstructive pulmonary disorder [1]- [2]. Hence, the development of monitoring systems has become a topical issue [1]- [7]. In the literature, many studies have explored respiration monitoring.…”

Section: Introductionmentioning

confidence: 99%

Field Programmable Gate Array (FPGA) Respiratory Monitoring System Using a Flow Microsensor and an Accelerometer

Mellal

Laghrouche

Bui

2017

Measurement Science Review

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This paper describes a non-invasive system for respiratory monitoring using a Micro Electro Mechanical Systems (MEMS) flow sensor and an IMU (Inertial Measurement Unit) accelerometer. The designed system is intended to be wearable and used in a hospital or at home to assist people with respiratory disorders. To ensure the accuracy of our system, we proposed a calibration method based on ANN (Artificial Neural Network) to compensate the temperature drift of the silicon flow sensor. The sigmoid activation functions used in the ANN model were computed with the CORDIC (COordinate Rotation DIgital Computer) algorithm. This algorithm was also used to estimate the tilt angle in body position. The design was implemented on reconfigurable platform FPGA.

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“…[1][2][3][4] Central apneas are identified by absence of deflections in these signals during the apnea. However, in some individuals, deflections in the RIP signals during obstructive apneas are small and can result in misclassification of an obstructive apnea as a central apnea or of a mixed apnea as a central apnea.…”

Section: Introductionmentioning

confidence: 99%

“…5,6 The gold standard for detection of respiratory effort is esophageal manometry. [1][2][3][4] Deflections in esophageal pressure not only detect respiratory effort, but unlike effort belt signals, reflect the level of inspiratory effort. However, esophageal manometry is rarely used in the clinical setting because it is more invasive and requires special training and equipment.…”

Section: Introductionmentioning

confidence: 99%

Use of Chest Wall Electromyography to Detect Respiratory Effort during Polysomnography

Berry

Ryals

Girdhar

et al. 2016

Journal of Clinical Sleep Medicine

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Study Objectives: To evaluate the ability of chest wall EMG (CW-EMG) using surface electrodes to classify apneas as obstructive, mixed, or central compared to classification using dual channel uncalibrated respiratory inductance plethysmography (RIP). Methods: CW-EMG was recorded from electrodes in the eighth intercostal space at the right mid-axillary line. Consecutive adult clinical sleep studies were retrospectively reviewed, and the first 60 studies with at least 10 obstructive and 10 mixed or central apneas and technically adequate tracings were selected. Four obstructive and six central or mixed apneas (as classified by previous clinical scoring) were randomly selected. A blinded experienced scorer classified the apneas on the basis of tracings showing either RIP channels or the CW-EMG channel. The agreement using the two classification methods was determined by kappa analysis and intraclass correlation. Results: The percentage agreement was 89.5%, the kappa statistic was 0.83 (95% confidence interval 0.79 to 0.87), and the intraclass correlation was 0.83, showing good agreement. Of the 249 apneas classified as central by RIP, 26 were classified as obstructive (10.4%) and 7 as mixed (2.8%) by CW-EMG. Of the 229 events classified as central by CW-EMG, 7 (3.1%) were classified as obstructive and 6 (2.6%) as mixed by RIP. Conclusions: Monitoring CW-EMG may provide a clinically useful method of detection of respiratory effort when used with RIP and can prevent false classification of apneas as central. RIP can rarely detect respiratory effort not easily discernible by CW-EMG and the combination of the two methods is more likely to avoid apnea misclassification. Keywords: apnea, diaphragmatic EMG, polysomnography, respiratory effort Citation: Berry RB, Ryals S, Girdhar A, Wagner MH. Use of chest wall electromyography to detect respiratory effort during polysomnography.

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Assessment of respiratory effort during sleep: Esophageal pressure versus noninvasive monitoring techniques

Cited by 56 publications

References 54 publications

Validation of a Suprasternal Pressure Sensor for Sleep Apnea Classification in Children

Validation of a Suprasternal Pressure Sensor for Sleep Apnea Classification in Children

Field Programmable Gate Array (FPGA) Respiratory Monitoring System Using a Flow Microsensor and an Accelerometer

Use of Chest Wall Electromyography to Detect Respiratory Effort during Polysomnography

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