Symbolic dynamics to enhance diagnostic ability of portable oximetry from the Phone Oximeter in the detection of paediatric sleep apnoea

Objective: This study is aimed at assessing symbolic dynamics as a reliable technique to characterize complex fluctuations of portable oximetry in the context of automated detection of childhood obstructive sleep apnoea-hypopnoea syndrome (OSAHS). Approach: Nocturnal oximetry signals from 142 children with suspected OSAHS were acquired using the Phone Oximeter: a portable device that integrates a pulse oximeter with a smartphone. An apnoea-hypopnoea index (AHI) ⩾ 5 events h−1 from simultaneous in-lab polysomnography was used to confirm moderate-to-severe childhood OSAHS. Symbolic dynamics was used to parameterise non-linear changes in the overnight oximetry profile. Conventional indices, anthropometric measures, and time-domain linear statistics were also considered. Forward stepwise logistic regression was used to obtain an optimum feature subset. Logistic regression (LR) was used to identify children with moderate-to-severe OSAHS. Main results: The histogram of 3-symbol words from symbolic dynamics showed significant differences (p < 0.01) between children with AHI < 5 events h−1 and moderate-to-severe patients (AHI ⩾ 5 events h−1). Words representing increasing oximetry values after apnoeic events (re-saturations) showed relevant diagnostic information. Regarding the performance of individual characterization approaches, the LR model composed of features from symbolic dynamics alone reached a maximum performance of 78.4% accuracy (65.2% sensitivity; 86.8% specificity) and 0.83 area under the ROC curve (AUC). The classification performance improved combining all features. The optimum model from feature selection achieved 83.3% accuracy (73.5% sensitivity; 89.5% specificity) and 0.89 AUC, significantly (p <0.01) outperforming the other models. Significance: Symbolic dynamics provides complementary information to conventional oximetry analysis enabling reliable detection of moderate-to-severe paediatric OSAHS from portable oximetry

Reliability of Machine Learning to Diagnose Pediatric Obstructive Sleep Apnea: Systematic Review and Meta-Analysis

Background Machine-learning approaches have enabled promising results in efforts to simplify the diagnosis of pediatric obstructive sleep apnea (OSA). A comprehensive review and analysis of such studies increase the confidence level of practitioners and healthcare providers in the implementation of these methodologies in clinical practice. Objective To assess the reliability of machine-learning-based methods to detect pediatric OSA. Data Sources Two researchers conducted an electronic search on the Web of Science and Scopus using term, and studies were reviewed along with their bibliographic references. Eligibility Criteria Articles or reviews (Year 2000 onwards) that applied machine learning to detect pediatric OSA; reported data included information enabling derivation of true positive, false negative, true negative, and false positive cases; polysomnography served as diagnostic standard. Appraisal and Synthesis Methods Pooled sensitivities and specificities were computed for three apnea-hypopnea index (AHI) thresholds: 1 event/hour (e/h), 5 e/h, and 10 e/h. Random-effect models were assumed. Summary receiver-operating characteristics (SROC) analyses were also conducted. Heterogeneity (I 2) was evaluated, and publication bias was corrected (trim and fill). Results Nineteen studies were finally retained, involving 4767 different pediatric sleep studies. Machine learning improved diagnostic performance as OSA severity criteria increased reaching optimal values for AHI = 10 e/h (0.652 sensitivity; 0.931 specificity; and 0.940 area under the SROC curve). Publication bias correction had minor effect on summary statistics, but high heterogeneity was observed among the studies.Sociedad Española de Neumología y Cirugía Torácica. Grant Number: 649/2018 Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina Ministerio de Ciencia, Innovación y Universidades. Grant Numbers: D. Álvarez is supported by a "Ramón y Cajal" gra, DPI2017-84280-R, RTC-2017-6516-1 Sociedad Española de Sueño. Grant Number: Beca de Investigación SES 2019 University of Missouri. Grant Number: Tier 2 Children's Miracle Network Endowed Professorship Leda J. Sears Foundation European Regional Development Fund. Grant Numbers: Cooperation Programme Interreg V-A Spain-Portugal, DPI2017-84280-R, RTC-2017-6516-1 National Institutes of Health. Grant Numbers: AG061824, HL130984, HL14054

Ensemble-learning regression to estimate sleep apnea severity using at-home oximetry in adults

Producción CientíficaOvernight pulse oximetry has shown usefulness to simplify obstructive sleep apnea (OSA) diagnosis when combined with machine-learning approaches. However, the development and evaluation of a single model with ability to reach high diagnostic performance in both community-based non-referral and clinical referral cohorts are still pending. Since ensemble-learning algorithms are known for their generalization ability, we propose a least-squares boosting (LSBoost) model aimed at estimating the apnea–hypopneaindex (AHI), as the correlate clinical measure of disease severity. A thorough characterization of 8,762 nocturnal blood-oxygen saturation signals (SpO 2) obtained at home was conducted to extract the oximetric information subsequently used in the training, validation, and test stages. The estimated AHI derived from our model achieved high diagnostic ability in both referral and non-referral cohorts reaching intra-class correlation coefficients within 0.889–0.924, and Cohen’s within 0.478–0.663 when considering the four OSA severity categories. These resulted in accuracies ranging 87.2%–96.6%, 81.1%–87.6%, and 91.6%–94.6% when assessing the three typical AHI severity thresholds, 5 events/hour (e/h), 15 e/h, and 30 e/h, respectively. Our model also revealed the importance of the SpO 2 predictors, thereby minimizing the ‘black box’ perception traditionally attributed to the machine-learning approaches. Furthermore, a decision curve analysis emphasized the clinical usefulness of our proposal. Therefore, we conclude that the LSBoost-based model can foster development of clinically applicable and cost saving protocols for detection of patients attending primary care services, or to avoid full polysomnography in specialized sleep facilities, thus demonstrating the diagnostic usefulness of SpO 2 signals obtained at home.This work was supported by ‘Ministerio de Ciencia, Innovación y Universidades’ and ‘European Regional Development Fund (FEDER)’ under projects DPI2017-84280-R and RTC-2017- 6516-1, by Sociedad Española de Neumología y Cirugía Torácica (SEPAR) under project 649/2018, Sociedad Española de Sueño (SES) under project ‘‘Beca de Investigación SES 2019’’, by ‘European Commission’ and ‘FEDER’ under projects ‘Análisis y correlación entre el genoma completo y la actividad cerebral para la ayuda en el diagnóstico de la enfermedad de Alzheimer’ and ‘Análisis y correlación entre la epigenética y la actividad cerebral para evaluar el riesgo de migraña crónica y episódica en mujeres’ (‘Cooperation Programme Interreg V-A Spain-Portugal POCTEP 2014–2020’), and by ‘CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN)’ through ‘Instituto de Salud Carlos III’ co-funded with FEDER funds. D. Álvarez is supported by a ‘‘Ramón y Cajal’’ grant (RYC2019-028566-I) from the ‘Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación’ co-funded by the European Social Fund. Vaquerizo-Villar was in receipt of a ‘‘Ayuda para contratos predoctorales para la Formación de Profesorado Universitario (FPU)’’ grant from the ‘‘Ministerio de Educación, Cultura y Deporte’’ (FPU16/02938). Funders had no role in the design of the study, nor in the collection, analysis, or interpretation of data, nor in manuscript preparation. LKG and DG are supported in part by National Institutes of Health grants HL130984 and HL140548 and a University of Missouri Tier 2 grant