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

Varady, P.; Micsik, Tamás; Benedek, S.; Benyo, Z.

doi:10.1109/tbme.2002.802009

Cited by 172 publications

(104 citation statements)

References 14 publications

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“…The time markers of the 769 manually identified hypopneas, indicating the starting time and the duration in seconds of each hypopnea, were imported for the automatic processing, as the automatic detection of hypopneas with classical PSG criteria [3] has already been solved proficiently [20]. The specific purpose of this study is to find new methods for the automatic differentiation of obstructive and central hypopneas.…”

Section: Preprocessing and Detection Of Respiratory Cyclesmentioning

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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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: Preprocessing and Detection Of Respiratory Cyclesmentioning

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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“…One class is based on breath signals while the other is based on electromyogram (EMG) signals. Several research papers [13], [14], [38], [42], [45] have examined methods belonging to the first class. Assessment of respiratory signal is a key step to diagnose OSA.…”

mentioning

confidence: 99%

A Neural Network Method for Detection of Obstructive Sleep Apnea and Narcolepsy Based on Pupil Size and EEG

Liu

Pang

Lloyd

2008

IEEE Trans. Neural Netw.

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Abstract-Electroencephalogram (EEG) is able to indicate states of mental activity ranging from concentrated cognitive efforts to sleepiness. Such mental activity can be reflected by EEG energy. In particular, intrusion of EEG theta wave activity into the beta activity of active wakefulness has been interpreted as ensuing sleepiness. Pupil behavior can also provide information regarding alertness. This paper develops an innovative signal classification method that is capable of differentiating subjects with sleep disorders which cause excessive daytime sleepiness (EDS) from normal control subjects who do not have a sleep disorder based on EEG and pupil size. Subjects with sleep disorders include persons with untreated obstructive sleep apnea (OSA) and narcolepsy. The Yoss pupil staging rule is used to scale levels of wakefulness and at the same time theta energy ratios are calculated from the same 2-s sliding windows by Fourier or wavelet transforms. Then, an artificial neural network (NN) of modified adaptive resonance theory (ART2) is utilized to identify the two groups within a combined group of subjects including those with OSA and healthy controls. This grouping from the NN is then compared with the actual diagnostic classification of subjects as OSA or controls and is found to be 91% accurate in differentiating between the two groups. The same algorithm results in 90% correct differentiation between narcoleptic and control subjects.Index Terms-Adaptive resonance theory (ART2), electroencephalogram (EEG), healthy controls, narcolepsy, neural network (NN), obstructive sleep apnea (OSA), pupil stage.

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“…Hyperventilation, i.e., an increased rate of pulmonary ventilation and considerably decreased depth of breathing, often leading to syncope, 40,41 is closely related to anxiety and panic disorders. 42,43 Breathing patterns in persons experiencing panic attacks are distinguished by high variability and irregularity.…”

Section: Basic Physiological Parameters and Their Link To Claustrophobiamentioning

confidence: 99%

Fiber-optic sensors for monitoring patient physiological parameters: a review of applicable technologies and relevance to use during magnetic resonance imaging procedures

Dziuda

2015

J. Biomed. Opt.

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Abstract. The issues involved with recording vital functions in the magnetic resonance imaging (MRI) environment using fiber-optic sensors are considered in this paper. Basic physiological parameters, such as respiration and heart rate, are fundamental for predicting the risk of anxiety, panic, and claustrophobic episodes in patients undergoing MRI examinations. Electronic transducers are generally hazardous to the patient and are prone to erroneous operation in heavily electromagnetically penetrated MRI environments; however, nonmetallic fiberoptic sensors are inherently immune to electromagnetic effects and will be crucial for acquiring the abovementioned physiological parameters. Forty-seven MRI-tested or potentially MRI-compatible sensors have appeared in the literature over the last 20 years. The author classifies these sensors into several categories and subcategories, depending on the sensing element placement, method of application, and measurand type. The author includes five in-house-designed fiber Bragg grating based sensors and shares experience in acquiring physiological measurements during MRI scans. This paper aims to systematize the knowledge of fiber-optic techniques for recording life functions and to indicate the current directions of development in this area.

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

Cited by 172 publications

References 14 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

A Neural Network Method for Detection of Obstructive Sleep Apnea and Narcolepsy Based on Pupil Size and EEG

Fiber-optic sensors for monitoring patient physiological parameters: a review of applicable technologies and relevance to use during magnetic resonance imaging procedures

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