Treatment of obstructive sleep apnea syndrome by monitoring patients airflow signals

Kermit, Martin; Eide, Åge; Linblad, Thomas; Waldemark, Karina E.

doi:10.1016/s0167-8655(99)00157-9

Cited by 14 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our work, Haar and Daubechies-5 wavelets have been evaluated due to their previous suitability to the AF signal [ 33 , 34 ]. Figure 3 shows the mother wavelets Haar and Daubechies-5, as well as a 10-min segment of AF signal.…”

Section: Methodsmentioning

confidence: 99%

“…The AF signal was segmented into epochs of length 2 16 samples (≈10-min), as conducted in previous studies [ 12 , 39 ]. Thus, DWT decomposition for each segment was carried out at 16 levels ( N = log 2 (2 16 )), using Haar and Daubechies-5 as mother wavelets [ 33 , 34 ]. An example of decomposition of AF segment by means of Daubechies-5 can be observed in Figure 2 b.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Wavelet Analysis of Overnight Airflow to Detect Obstructive Sleep Apnea in Children

Barroso-García

Gutiérrez‐Tobal

Gozal

et al. 2021

Sensors

View full text Add to dashboard Cite

This study focused on the automatic analysis of the airflow signal (AF) to aid in the diagnosis of pediatric obstructive sleep apnea (OSA). Thus, our aims were: (i) to characterize the overnight AF characteristics using discrete wavelet transform (DWT) approach, (ii) to evaluate its diagnostic utility, and (iii) to assess its complementarity with the 3% oxygen desaturation index (ODI3). In order to reach these goals, we analyzed 946 overnight pediatric AF recordings in three stages: (i) DWT-derived feature extraction, (ii) feature selection, and (iii) pattern recognition. AF recordings from OSA patients showed both lower detail coefficients and decreased activity associated with the normal breathing band. Wavelet analysis also revealed that OSA disturbed the frequency and energy distribution of the AF signal, increasing its irregularity. Moreover, the information obtained from the wavelet analysis was complementary to ODI3. In this regard, the combination of both wavelet information and ODI3 achieved high diagnostic accuracy using the common OSA-positive cutoffs: 77.97%, 81.91%, and 90.99% (AdaBoost.M2), and 81.96%, 82.14%, and 90.69% (Bayesian multi-layer perceptron) for 1, 5, and 10 apneic events/hour, respectively. Hence, these findings suggest that DWT properly characterizes OSA-related severity as embedded in nocturnal AF, and could simplify the diagnosis of pediatric OSA.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Wavelet Analysis of Overnight Airflow to Detect Obstructive Sleep Apnea in Children

Barroso-García

Gutiérrez‐Tobal

Gozal

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…There are numerous methods existing for the off-line analysis of respiration signals stored in PSG records [8]- [14]. Usually, the vendor of the PSG device provides its own signal analyzing software package.…”

Section: B State-of-the-art Apnea Diagnosismentioning

confidence: 99%

A novel method for the detection of apnea and hypopnea events in respiration signals

Varady

Micsik

Benedek³

et al. 2002

IEEE Trans. Biomed. Eng.

173

View full text Add to dashboard Cite

Abstract-The monitoring of breathing dynamics is an essential diagnostic tool in various clinical environments, such as sleep diagnostics, intensive care and neonatal monitoring. This paper introduces an innovative signal classification method that is capable of on-line detection of the presence or absence of normal breathing. Four different artificial neural networks are presented for the recognition of three different patterns in the respiration signals (normal breathing, hypopnea, and apnea). Two networks process the normalized respiration signals directly, while another two use sophisticatedly preprocessed signals. The development of the networks was based on training sets from the polysomnographic records of nine different patients. The detection performance of the networks was tested and compared by using up to 8000 untrained breathing patterns from 16 different patients. The networks which classified the preprocessed respiration signals produced an average detection performance of over 90%. In the light of the moderate computational power used, the presented method is not only viable in clinical polysomnographs and respiration monitors, but also in portable devices.

show abstract

“…To date, various techniques and instruments have been developed for continuous RR and TV monitoring, including contact and non-contact methods [9][10][11][12][13][14]. For the contact monitoring, sensors are usually placed over the human body to measure respiratory activities, such as airflow [9], blood oxygen saturation (SpO 2 ) [10], electrocardiogram (ECG) morphology [11] and abdominal movement [12]. The non-contact methods usually involve image sensors or radiological technologies without direct contact with the human body, but are often immovable and complicated [13,14].…”

Section: Introductionmentioning

confidence: 99%

Wearable respiration monitoring using an in-line few-mode fiber Mach-Zehnder interferometric sensor

Wang

Zhao

Sun

et al. 2019

Biomed. Opt. Express

View full text Add to dashboard Cite

Continuous respiratory monitoring is extensively important in clinical applications. To effectively assess respiration rate (RR), tidal volume (TV), and minute ventilation (MV), we propose and experimentally demonstrate a respiration monitoring system using an in-line few-mode fiber Mach-Zehnder interferometer (FMF-MZI), which is the first to introduce in-line MZI into an optimal wearable design for respiration rate and volume monitoring. The optimal linear region of the proposed sensor is analyzed and positioned by a flexible arch structure with curvature sensitivity up to 8.53 dB/m −1 . Respiration monitoring results are in good agreement with a standard spirometer among different individuals. The difference in TV estimation is ± 0.2 L, and the overall error of MV estimation is less than 5%.

show abstract

Treatment of obstructive sleep apnea syndrome by monitoring patients airflow signals

Cited by 14 publications

References 7 publications

Wavelet Analysis of Overnight Airflow to Detect Obstructive Sleep Apnea in Children

Wavelet Analysis of Overnight Airflow to Detect Obstructive Sleep Apnea in Children

A novel method for the detection of apnea and hypopnea events in respiration signals

Wearable respiration monitoring using an in-line few-mode fiber Mach-Zehnder interferometric sensor

Contact Info

Product

Resources

About