ApEn for assessing hypoxemia severity in obstructive sleep apnea hypopnea syndrome patients

Liu, Jie; Huang, Rong; Xiao, Yi; Lin, Shen

doi:10.1007/s11325-019-02004-0

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…4. It consists of two loops for shifting matrices (9). The matrix (11) is ineffective for large dataset.…”

Section: Results Of Numerical Experimentsmentioning

confidence: 99%

“…It is widely used in medical field and statistic researches. For example, it has been applied to classify EEG in psychiatric diseases [3,4,5], to analyze HRV [6,7,8], to assess hypoxemia severity [9], to measure of analgesia depth during propofol-remifentanil anesthesia [10], to predict of treatment resistance in obsessive-compulsive disorder patients [11], to analyze a complexity of cardiotocographic examinations [12], to detect coronary heart disease [13], to detect early fault of ball bearing [14], etc. Also entropy measurements have been explored in activities of daily living [15].…”

Section: Introductionmentioning

confidence: 99%

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Approximate Entropy Technique of Calculation Based on Parallel Computation with Usage of GPU

Mullayanov¹,

Krushkov²,

Nasyrov³

2020

Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)

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A time of computing has a significant importance in some domains. Especially, it comes a medicine. Such method as an approximate entropy is widely used to analyze a biomedical data, but it has non-linear algorithmic complexity. Therefore, there is a requirement to decrease a time of calculating of an approximate entropy. In this paper new approach of calculating by using matrices and graphic processor unit is proposed. In addition, some results of this approach are shown. In order to propose a solution to this problem, it was necessary to evaluate the complexity of the algorithm for calculating approximate entropy. In this regard, it is also necessary to show its asymptotic complexity using the notation big-O. Based on the obtained complexity estimates, a new approach based on matrix calculations was developed and proposed. In this case, the matrix calculations themselves are implemented in the form of parallel computing using a graphics processor (GPU). The graphics processor has been widely used for machine learning and data mining, where parallel computing is of great importance in solving the problem of improving performance. Since matrix operations are well parallelized, this makes it possible to speed up the execution of matrix calculations. For calculations, one of the most promising tools is currently the TensorFlow platform. The comparative effectiveness of the proposed approach was evaluated.

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“…4. It consists of two loops for shifting matrices (9). The matrix (11) is ineffective for large dataset.…”

Section: Results Of Numerical Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Approximate Entropy Technique of Calculation Based on Parallel Computation with Usage of GPU

Mullayanov¹,

Krushkov²,

Nasyrov³

2020

Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)

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show abstract

“…Kaimakamis et al developed and tested a predictive model for the presence and severity of OSA using a simple linear equation, utilizing nonlinear features extracted from respiratory recordings; nasal cannula flow and thoracic belt movement [21]. Similarly, Liu et al used the full night blood oxygen values from PSG recordings to compute approximate entropy to differentiate obstructive sleep apnea-hypopnea syndrome patients from normal controls (snorers in this case) and show the association to the apnea-hypopnea index (AHI) [43]. Moreover, they evidenced that it was possible to identify different degrees of severity of OSA on the basis of the degree of nocturnal oxygen deficiency.…”

Section: Discussionmentioning

confidence: 99%

Sensorimotor Cortical Activity during Respiratory Arousals in Obstructive Sleep Apnea

Bahr

Koirala

Hanif

et al. 2022

IJMS

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Intensity of respiratory cortical arousals (RCA) is a pathophysiologic trait in obstructive sleep apnea (OSA) patients. We investigated the brain oscillatory features related to respiratory arousals in moderate and severe OSA. Raw electroencephalography (EEG) data recorded during polysomnography (PSG) of 102 OSA patients (32 females, mean age 51.6 ± 12 years) were retrospectively analyzed. Among all patients, 47 had moderate (respiratory distress index, RDI = 15–30/h) and 55 had severe (RDI > 30/h) OSA. Twenty RCA per sleep stage in each patient were randomly selected and a total of 10131 RCAs were analyzed. EEG signals obtained during, five seconds before and after the occurrence of each arousal were analyzed. The entropy (approximate (ApEn) and spectral (SpEn)) during each sleep stage (N1, N2 and REM) and area under the curve (AUC) of the EEG signal during the RCA was computed. Severe OSA compared to moderate OSA patients showed a significant decrease (p < 0.0001) in the AUC of the EEG signal during the RCA. Similarly, a significant decrease in spectral entropy, both before and after the RCA was observed, was observed in severe OSA patients when compared to moderate OSA patients. Contrarily, the approximate entropy showed an inverse pattern. The highest increase in approximate entropy was found in sleep stage N1. In conclusion, the dynamic range of sensorimotor cortical activity during respiratory arousals is sleep-stage specific, dependent on the frequency of respiratory events and uncoupled from autonomic activation. These findings could be useful for differential diagnosis of severe OSA from moderate OSA.

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“…In another study, the ApEn of oxygen saturation was demonstrated to be related to the AHI, ODI, and T90. 51 In addition, a study on the correlation between the ApEn of oxygen saturation and the AHI based on analysis of oximetric data concluded that ApEn may be a useful approach in diagnosing OSA. 52 Moreover, studies on ventilatory instability in OSA 53,54 revealed that breathing irregularity may be an outcome of sleep-disordered breathing, because breathing instability improved after treating patients with OSA with CPAP.…”

Section: Emerging Potential Markers Without Specific Quantification Amentioning

confidence: 99%

<p>A Review of Current Tools Used for Evaluating the Severity of Obstructive Sleep Apnea</p>

Cao

Luo

Xiao

2020

NSS

Self Cite

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ApEn for assessing hypoxemia severity in obstructive sleep apnea hypopnea syndrome patients

Cited by 4 publications

References 22 publications

Approximate Entropy Technique of Calculation Based on Parallel Computation with Usage of GPU

Approximate Entropy Technique of Calculation Based on Parallel Computation with Usage of GPU

Sensorimotor Cortical Activity during Respiratory Arousals in Obstructive Sleep Apnea

<p>A Review of Current Tools Used for Evaluating the Severity of Obstructive Sleep Apnea</p>

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