Improving the understanding of sleep apnea characterization using Recurrence Quantification Analysis by defining overall acceptable values for the dimensionality of the system, the delay, and the distance threshold

Autor: Gabriel Juliá-Serdá, Antonio G. Ravelo-García, S. Martin-Gonzalez, Juan L. Navarro-Mesa, G Marcelo Ramírez-Ávila
Rok vydání: 2017
Předmět:
Databases
Factual
Pulmonology
Computer science
Apnea
Entropy
lcsh:Medicine
01 natural sciences
Systems Science
Electrocardiography
Database and Informatics Methods
0302 clinical medicine
Recurrence
Heart Rate
Medicine and Health Sciences
Heart rate variability
lcsh:Science
Sleep Apnea
Obstructive
Multidisciplinary
Physics
Sleep apnea
Dynamical Systems
Bioassays and Physiological Analysis
Neurology
Area Under Curve
Physical Sciences
Thermodynamics
medicine.symptom
Algorithms
Research Article
Computer and Information Sciences
Sleep Apnea
Cardiac rate
Cardiology
Research and Analysis Methods
03 medical and health sciences
0103 physical sciences
medicine
Humans
010306 general physics
business.industry
lcsh:R
Electrophysiological Techniques
Pattern recognition
Models
Theoretical
medicine.disease
ROC Curve
Nonlinear Dynamics
Recurrence quantification analysis
lcsh:Q
Artificial intelligence
Cardiac Electrophysiology
business
Sleep Disorders
030217 neurology & neurosurgery
Biomarkers
Mathematics
Zdroj: PLoS ONE
PLoS ONE, Vol 13, Iss 4, p e0194462 (2018)
Repositorio Institucional de la Consejería de Sanidad de la Comunidad de Madrid
Consejería de Sanidad de la Comunidad de Madrid
ISSN: 1932-6203
Popis: Our contribution focuses on the characterization of sleep apnea from a cardiac rate point of view, using Recurrence Quantification Analysis (RQA), based on a Heart Rate Variability (HRV) feature selection process. Three parameters are crucial in RQA: those related to the embedding process (dimension and delay) and the threshold distance. There are no overall accepted parameters for the study of HRV using RQA in sleep apnea. We focus on finding an overall acceptable combination, sweeping a range of values for each of them simultaneously. Together with the commonly used RQA measures, we include features related to recurrence times, and features originating in the complex network theory. To the best of our knowledge, no author has used them all for sleep apnea previously. The best performing feature subset is entered into a Linear Discriminant classifier. The best results in the "Apnea-ECG Physionet database" and the "HuGCDN2014 database" are, according to the area under the receiver operating characteristic curve, 0.93 (Accuracy: 86.33%) and 0.86 (Accuracy: 84.18%), respectively. Our system outperforms, using a relatively small set of features, previously existing studies in the context of sleep apnea. We conclude that working with dimensions around 7-8 and delays about 4-5, and using for the threshold distance the Fixed Amount of Nearest Neighbours (FAN) method with 5% of neighbours, yield the best results. Therefore, we would recommend these reference values for future work when applying RQA to the analysis of HRV in sleep apnea. We also conclude that, together with the commonly used vertical and diagonal RQA measures, there are newly used features that contribute valuable information for apnea minutes discrimination. Therefore, they are especially interesting for characterization purposes. Using two different databases supports that the conclusions reached are potentially generalizable, and are not limited by database variability.
Databáze: OpenAIRE
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