Verónica Barroso-García1, Gonzalo C Gutiérrez-Tobal2, Leila Kheirandish-Gozal3, Daniel Álvarez4, Fernando Vaquerizo-Villar2, Pablo Núñez2, Félix Del Campo4, David Gozal3, Roberto Hornero5. 1. Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain. Electronic address: veronica.barroso@gib.tel.uva.es. 2. Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain. 3. Department of Child Health, The University of Missouri School of Medicine, Columbia, MO, USA. 4. Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain; Sleep-Ventilation Unit, Pneumology Service, Río Hortega University Hospital, Valladolid, Spain. 5. Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain; IMUVA, Instituto de Investigación en Matemáticas, Universidad de Valladolid, Valladolid, Spain. Electronic address: http://www.gib.tel.uva.es.
Abstract
BACKGROUND AND OBJECTIVE: In-laboratory overnight polysomnography (PSG) is the gold standard method to diagnose the Sleep Apnoea-Hypopnoea Syndrome (SAHS). PSG is a complex, expensive, labour-intensive and time-consuming test. Consequently, simplified diagnostic methods are desirable. We propose the analysis of the airflow (AF) signal by means of recurrence plots (RP) features. The main goal of our study was to evaluate the utility of the information from RPs of the AF signals to detect paediatric SAHS at different levels of severity. In addition, we also evaluated the complementarity with the 3% oxygen desaturation index (ODI3). METHODS: 946 AF and blood oxygen saturation (SpO2) recordings from children ages 0-13 years were used. The population under study was randomly split into training (60%) and test (40%) sets. RP was computed and 9 RP features were extracted from each AF recording. ODI3 was also calculated from each SpO2 recording. A feature selection stage was conducted in the training group by means of the fast correlation-based filter (FCBF) methodology to obtain a relevant and non-redundant optimum feature subset. A multi-layer perceptron neural network with Bayesian approach (BY-MLP), trained with these optimum features, was used to estimate the apnoea-hypopnoea index (AHI). RESULTS: 8 of the RP features showed statistically significant differences (p-value <0.01) among the SAHS severity groups. FCBF selected the maximum length of the diagonal lines from RP, as well as the ODI3. Using these optimum features, the BY-MLP model achieved 83.2%, 78.5%, and 91.0% accuracy in the test group for the AHI thresholds 1, 5, and 10 events/h, respectively. Moreover, this model reached a negative likelihood ratio of 0.1 for 1 event/h and a positive likelihood ratio of 13.7 for 10 events/h. CONCLUSIONS: RP analysis enables extraction of useful SAHS-related information from overnight AF paediatric recordings. Moreover, it provides complementary information to the widely-used clinical variable ODI3. Thus, RP applied to AF signals can be used along with ODI3 to help in paediatric SAHS diagnosis, particularly to either confirm the absence of SAHS or the presence of severe SAHS.
BACKGROUND AND OBJECTIVE: In-laboratory overnight polysomnography (PSG) is the gold standard method to diagnose the Sleep Apnoea-Hypopnoea Syndrome (SAHS). PSG is a complex, expensive, labour-intensive and time-consuming test. Consequently, simplified diagnostic methods are desirable. We propose the analysis of the airflow (AF) signal by means of recurrence plots (RP) features. The main goal of our study was to evaluate the utility of the information from RPs of the AF signals to detect paediatric SAHS at different levels of severity. In addition, we also evaluated the complementarity with the 3% oxygen desaturation index (ODI3). METHODS: 946 AF and blood oxygen saturation (SpO2) recordings from children ages 0-13 years were used. The population under study was randomly split into training (60%) and test (40%) sets. RP was computed and 9 RP features were extracted from each AF recording. ODI3 was also calculated from each SpO2 recording. A feature selection stage was conducted in the training group by means of the fast correlation-based filter (FCBF) methodology to obtain a relevant and non-redundant optimum feature subset. A multi-layer perceptron neural network with Bayesian approach (BY-MLP), trained with these optimum features, was used to estimate the apnoea-hypopnoea index (AHI). RESULTS: 8 of the RP features showed statistically significant differences (p-value <0.01) among the SAHS severity groups. FCBF selected the maximum length of the diagonal lines from RP, as well as the ODI3. Using these optimum features, the BY-MLP model achieved 83.2%, 78.5%, and 91.0% accuracy in the test group for the AHI thresholds 1, 5, and 10 events/h, respectively. Moreover, this model reached a negative likelihood ratio of 0.1 for 1 event/h and a positive likelihood ratio of 13.7 for 10 events/h. CONCLUSIONS: RP analysis enables extraction of useful SAHS-related information from overnight AF paediatric recordings. Moreover, it provides complementary information to the widely-used clinical variable ODI3. Thus, RP applied to AF signals can be used along with ODI3 to help in paediatric SAHS diagnosis, particularly to either confirm the absence of SAHS or the presence of severe SAHS.
Authors: Daniel Álvarez; Gonzalo C Gutiérrez-Tobal; Fernando Vaquerizo-Villar; Fernando Moreno; Félix Del Campo; Roberto Hornero Journal: Adv Exp Med Biol Date: 2022 Impact factor: 3.650
Authors: Esther Oceja; Paula Rodríguez; María José Jurado; Maria Luz Alonso; Genoveva Del Río; María Ángeles Villar; Olga Mediano; Marian Martínez; Santiago Juarros; Milagros Merino; Jaime Corral; Carmen Luna; Leila Kheirandish-Gozal; David Gozal; Joaquín Durán-Cantolla Journal: Methods Protoc Date: 2021-01-19