Screening of obstructive sleep apnea using Hilbert–Huang decomposition of oronasal airway pressure recordings

Caseiro, Paulo; Fonseca-Pinto, Rui; Andrade, Alexandre

doi:10.1016/j.medengphy.2010.01.008

Cited by 37 publications

(23 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Up to nine features were extracted and analysed from the Hilbert transform of 41 oronasal airflow recordings [53]. The highest diagnostic ability was showed by the 25th frequency percentile of the Hilbert spectrum histogram (87.8% Acc and 0.877 AROC).…”

Section: Discussionmentioning

confidence: 99%

Assessment of Time and Frequency Domain Entropies to Detect Sleep Apnoea in Heart Rate Variability Recordings from Men and Women

Gutiérrez‐Tobal

Álvarez

Gómez‐Pilar

et al. 2015

Entropy

View full text Add to dashboard Cite

Heart rate variability (HRV) provides useful information about heart dynamics both under healthy and pathological conditions. Entropy measures have shown their utility to characterize these dynamics. In this paper, we assess the ability of spectral entropy (SE) and multiscale entropy (MsE) to characterize the sleep apnoea-hypopnea syndrome (SAHS) in HRV recordings from 188 subjects. Additionally, we evaluate eventual differences in these analyses depending on the gender. We found that the SE computed from the very low frequency band and the low frequency band showed ability to characterize SAHS regardless the gender; and that MsE features may be able to distinguish gender specificities. SE and MsE showed complementarity to detect SAHS, since several features from both analyses were automatically selected by the forward-selection backward-elimination algorithm. Finally, SAHS was modelled through logistic regression (LR) by using optimum sets of selected features. Modelling SAHS by genders reached significant higher performance than doing it in a jointly way. The highest diagnostic ability was reached by modelling SAHS in women. The LR classifier achieved 85.2% accuracy (Acc) and 0.951 area under the ROC curve (AROC). LR for men reached 77.6% Acc and 0.895 AROC, whereas LR for the whole OPEN ACCESSEntropy 2015, 17 124 set reached 72.3% Acc and 0.885 AROC. Our results show the usefulness of the SE and MsE analyses of HRV to detect SAHS, as well as suggest that, when using HRV, SAHS may be more accurately modelled if data are separated by gender.

show abstract

Section: Discussionmentioning

confidence: 99%

Assessment of Time and Frequency Domain Entropies to Detect Sleep Apnoea in Heart Rate Variability Recordings from Men and Women

Gutiérrez‐Tobal

Álvarez

Gómez‐Pilar

et al. 2015

Entropy

View full text Add to dashboard Cite

show abstract

“…Various features extracted from respiration signals suitable for detection of sleep apnea by using non-linear [14], time-frequency [15], time [16] and frequency domain [17] algorithms have already been studied. These algorithms can detect episodes of apnea at 80-95% precision.…”

Section: Introductionmentioning

confidence: 99%

Sleep apnea classification based on respiration signals by using ensemble methods

Avci

Akbaş

2015

BME

View full text Add to dashboard Cite

Abstract. In this study, an efficient and robust method classifying the minute based occurrence of sleep apnea is aimed. Three respiration signals obtained from abdominal, chest and nasal way extracted from polysomnography recordings. Wavelet transform based on feature extraction methods are applied on the 1 minute length respiration signals. Dimension reduction process is facilitated by using principal component analysis. The features obtained from 8 recordings are used for the classification sleep apnea by using three ensemble classifiers. According to the results, the classification accuracies have been obtained between 92.07-98.43%, 92.75-98.68% and 92.42-98.61% by using three different ensemble classifier based on abdominal, chest and nasal based analysis, respectively for AdaBoost, Random Forest and Random Subspace. However the best result is obtained analyzing nasal based respiratory signal by using Random Forest method. In this case accuracy is 98.68%.

show abstract

“…In addition, automated signal processing techniques have been widely applied during the last years to improve the diagnostic ability of single-channel cardiorespiratory recordings. In this regard, single-lead ECG [27,28], single-channel AF [29,30,31] and blood oxygen saturation (SpO2) from nocturnal pulse oximetry [25,32,33] have been predominantly studied. In order to standardize portable monitoring assessment and help 7 physicians to uniformly classify and properly evaluate sleep testing devices, different studies have been carried out during the last years [24,26,34].…”

Section: Osas Diagnosismentioning

confidence: 99%

Positive airway pressure and electrical stimulation methods for obstructive sleep apnea treatment: a patent review (2005 – 2014)

Álvarez

Gutiérrez‐Tobal

Campo

et al. 2015

Expert Opinion on Therapeutic Patents

View full text Add to dashboard Cite

Introduction. Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a major health problem with significant negative effects on the health and quality of life. Continuous positive airway pressure (CPAP) is currently the primary treatment option and it is considered the most effective therapy for OSAHS. Nevertheless, comfort issues due to improper fit to patient's changing needs and breathing gas leakage limit the patient's adherence to treatment.

show abstract

Screening of obstructive sleep apnea using Hilbert–Huang decomposition of oronasal airway pressure recordings

Cited by 37 publications

References 19 publications

Assessment of Time and Frequency Domain Entropies to Detect Sleep Apnoea in Heart Rate Variability Recordings from Men and Women

Assessment of Time and Frequency Domain Entropies to Detect Sleep Apnoea in Heart Rate Variability Recordings from Men and Women

Sleep apnea classification based on respiration signals by using ensemble methods

Positive airway pressure and electrical stimulation methods for obstructive sleep apnea treatment: a patent review (2005 – 2014)

Contact Info

Product

Resources

About