Please use this identifier to cite or link to this item:
http://hdl.handle.net/10553/122161
Title: | Combining Heart Rate Variability and Oximetry to Improve Apneic Event Screening in Non-Desaturating Patients | Authors: | Martín González, Sofía Isabel Ravelo García, Antonio Gabriel Navarro Mesa, Juan Luis Hernández Pérez, Eduardo |
UNESCO Clasification: | 3314 Tecnología médica | Keywords: | Apnea detection Cepstrum coefficients Detrended fluctuation analysis Heart rate variability |
Issue Date: | 2023 | Journal: | Sensors (Switzerland) | Abstract: | In this paper, we thoroughly analyze the detection of sleep apnea events in the context of Obstructive Sleep Apnea (OSA), which is considered a public health problem because of its high prevalence and serious health implications. We especially evaluate patients who do not always show desaturations during apneic episodes (non-desaturating patients). For this purpose, we use a database (HuGCDN2014-OXI) that includes desaturating and non-desaturating patients, and we use the widely used Physionet Apnea Dataset for a meaningful comparison with prior work. Our system combines features extracted from the Heart-Rate Variability (HRV) and SpO2, and it explores their potential to characterize desaturating and non-desaturating events. The HRV-based features include spectral, cepstral, and nonlinear information (Detrended Fluctuation Analysis (DFA) and Recurrence Quantification Analysis (RQA)). SpO2-based features include temporal (variance) and spectral information. The features feed a Linear Discriminant Analysis (LDA) classifier. The goal is to evaluate the effect of using these features either individually or in combination, especially in non-desaturating patients. The main results for the detection of apneic events are: (a) Physionet success rate of 96.19%, sensitivity of 95.74% and specificity of 95.25% (Area Under Curve (AUC): 0.99); (b) HuGCDN2014-OXI of 87.32%, 83.81% and 88.55% (AUC: 0.934), respectively. The best results for the global diagnosis of OSA patients (HuGCDN2014-OXI) are: success rate of 95.74%, sensitivity of 100%, and specificity of 89.47%. We conclude that combining both features is the most accurate option, especially when there are non-desaturating patterns among the recordings under study. | URI: | http://hdl.handle.net/10553/122161 | ISSN: | 1424-8220 | DOI: | 10.3390/s23094267 | Source: | Sensors (Switzerland) [ISSN 1424-8220], v. 23 (9), 4267, (2023) |
Appears in Collections: | Artículos |
WEB OF SCIENCETM
Citations
1
checked on Nov 24, 2024
Page view(s)
75
checked on Jun 29, 2024
Download(s)
30
checked on Jun 29, 2024
Google ScholarTM
Check
Altmetric
Share
Export metadata
Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.