A new method for sleep apnea classification using wavelets and feedforward neural networks

Fontenla-Romero, Óscar; Guijarro-Berdiñas, Bertha; Alonso‐Betanzos, Amparo; Moret‐Bonillo, Vicente

doi:10.1016/j.artmed.2004.07.014

Cited by 81 publications

(63 citation statements)

References 18 publications

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“…An overall of 715 manual, gold-standard hypopnea scorings of 28 patients were automatically classified, thus representing a significant increase in the number of patients with systematic Pes measurement and in the number of manual, gold-standard annotations than in comparable studies [5], [7], [10].…”

Section: Discussionmentioning

confidence: 99%

“…Researchers have been recently trying to develop methods for the noninvasive differentiation of central and obstructive apneas/hypopneas with different approaches, such as wavelet analysis of the airflow signal [7], forced-oscillation technique (FOT) [8], [9], and pulse-transit-time (PTT) [10]. However, the clinical adoption of these techniques has been limited due to the difficultness to achieve validation in clinical routine.…”

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confidence: 99%

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An Invasive and a Noninvasive Approach for the Automatic Differentiation of Obstructive and Central Hypopneas

Morgenstern

Schwaibold²,

Randerath

et al. 2010

IEEE Trans. Biomed. Eng.

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Abstract-The automatic differentiation of obstructive and central respiratory events is a major challenge in the diagnosis of sleep-disordered breathing. Esophageal pressure (Pes) measurement is the gold-standard method to identify these events. This study presents a new classifier that automatically differentiates obstructive and central hypopneas with the Pes signal and a new approach for an automatic noninvasive classifier with nasal airflow. An overall of 28 patients underwent night polysomnography with Pes recording, and a total of 769 hypopneas were manually scored by human experts to create a gold-standard annotation set. Features were automatically extracted from the Pes signal to train and test the classifiers (discriminant analysis, support vector machines, and adaboost). After a significantly (p < 0.01) higher incidence of inspiratory flow limitation episodes in obstructive hypopneas was objectively, invasively assessed compared to central hypopneas, the feasibility of an automatic noninvasive classifier with features extracted from the airflow signal was demonstrated. The automatic invasive classifier achieved a mean sensitivity, specificity, and accuracy of 0.90 after a 100-fold cross validation. The automatic noninvasive feasibility study obtained similar hypopnea differentiation results as a manual noninvasive classification algorithm. Hence, both systems seem promising for the automatic differentiation of obstructive and central hypopneas.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

An Invasive and a Noninvasive Approach for the Automatic Differentiation of Obstructive and Central Hypopneas

Morgenstern

Schwaibold²,

Randerath

et al. 2010

IEEE Trans. Biomed. Eng.

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“…The time markers of the manually scored hyopapneas were imported for our automatic processing, as the automatic detection of apneas/hypopneas in the flow signal has already been proficiently solved by others [7]. The specific purpose of this study is to propose a new method for the differentiation of obstructive/central hypopneas.…”

Section: B Pre-processing and Detection Of Inspirationsmentioning

confidence: 99%

“…An overall of 351 manual, gold-standard hypopnea scorings were used in this study, which represents twice the size for a gold-standard annotation set than in other studies [5,7]. As our classifiers were trained and tested with these gold-standard scorings, the results of our automatic classification should also be acceptable from a human scorer's perspective.…”

Section: Disscusion and Conclusionmentioning

confidence: 99%

“…Currently, esophageal pressure (Pes) measurement is considered the gold-standard technique for measurement of respiratory effort and the identification of obstructive and central events [2,3]. Still, the complexity and invasiveness of esophageal pressure manometry and its impact on sleep [4] [5][6][7]. However, the clinical adoption of these techniques has been slow, mostly due to their limited clinical validation.…”

Section: Introduction Ne Of the Most Important Aspects Of The Diagmentioning

confidence: 99%

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Automatic differentiation of obstructive and central hypopneas with esophageal pressure measurement during sleep

Morgenstern

Schwaibold²,

Randerath

et al. 2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-The differentiation between obstructive and central respiratory events is one of the most recurrent tasks in the diagnosis of sleep disordered breathing. Esophageal pressure measurement is the gold-standard method to assess respiratory effort and identify these events. But as its invasiveness discourages its use in clinical routine, noninvasisve systems have been proposed for differentiation. However, their adoption has been slow due to their limited clinical validation, as the creation of manual, gold-standard validation sets by human experts is a cumbersome procedure. In this study, a new system is proposed for an objective automatic, gold-standard differentiation between obstructive and central hypopneas with the esophageal pressure signal. First, an overall of 356 hypopneas of 16 patients were manually scored by a human expert to create a gold-standard validation set. Then, features were extracted from each hypopnea to train and test classifiers (Discriminant Analysis, Support Vector Machines and adaboost classifiers) to differentiate between central and obstructive hypopneas with the gold-standard esophageal pressure signal. The automatic differentiation system achieved promising results, with a sensitivity of 0.88, a specificity of 0.93 and an accuracy of 0.90. Hence, this system seems promising for an automatic, gold-standard differentiation between obstructive and central hypopneas.

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Diagnosis of Obstructive Sleep Apnea Using Logistic Regression and Artificial Neural Networks Models

Sheta

Turabieh

Braik

et al. 2019

Advances in Intelligent Systems and Computing

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A new method for sleep apnea classification using wavelets and feedforward neural networks

Cited by 81 publications

References 18 publications

An Invasive and a Noninvasive Approach for the Automatic Differentiation of Obstructive and Central Hypopneas

An Invasive and a Noninvasive Approach for the Automatic Differentiation of Obstructive and Central Hypopneas

Automatic differentiation of obstructive and central hypopneas with esophageal pressure measurement during sleep

Diagnosis of Obstructive Sleep Apnea Using Logistic Regression and Artificial Neural Networks Models

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