Wavelet analysis of oximetry recordings to assist in the automated detection of moderate-to-severe pediatric sleep apnea-hypopnea syndrome

Background The gold standard for pediatric sleep apnea hypopnea syndrome (SAHS) is overnight polysomnography, which has several limitations. Thus, simplified diagnosis techniques become necessary. Objective The aim of this study is twofold: (i) to analyze the blood oxygen saturation (SpO2) signal from nocturnal oximetry by means of features from the wavelet transform in order to characterize pediatric SAHS; (ii) to evaluate the usefulness of the extracted features to assist in the detection of pediatric SAHS. Methods 981 SpO2 signals from children ranging 2–13 years of age were used. Discrete wavelet transform (DWT) was employed due to its suitability to deal with non-stationary signals as well as the ability to analyze the SAHS-related low frequency components of the SpO2 signal with high resolution. In addition, 3% oxygen desaturation index (ODI3), statistical moments and power spectral density (PSD) features were computed. Fast correlation-based filter was applied to select a feature subset. This subset fed three classifiers (logistic regression, support vector machines (SVM), and multilayer perceptron) trained to determine the presence of moderate-to-severe pediatric SAHS (apnea-hypopnea index cutoff ≥ 5 events per hour). Results The wavelet entropy and features computed in the D9 detail level of the DWT reached significant differences associated with the presence of SAHS. All the proposed classifiers fed with a selected feature subset composed of ODI3, statistical moments, PSD, and DWT features outperformed every single feature. SVM reached the highest performance. It achieved 84.0% accuracy (71.9% sensitivity, 91.1% specificity), outperforming state-of-the-art studies in the detection of moderate-to-severe SAHS using the SpO2 signal alone. Conclusion Wavelet analysis could be a reliable tool to analyze the oximetry signal in order to assist in the automated detection of moderate-to-severe pediatric SAHS. Hence, pediatric subjects suffering from moderate-to-severe SAHS could benefit from an accurate simplified screening test only using the SpO2 signal.

[1]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[2]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[3]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[4]  O. Rioul,et al.  Wavelets and signal processing , 1991, IEEE Signal Processing Magazine.

[5]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[6]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[7]  T. Young,et al.  Automated detection and classification of sleep-disordered breathing from conventional polysomnography data. , 1997, Sleep.

[8]  David Watts.  Apnea , 1997, The Lancet.

[9]  E. Basar,et al.  Wavelet entropy: a new tool for analysis of short duration brain electrical signals , 2001, Journal of Neuroscience Methods.

[10]  B. Grant,et al.  Prediction of the apnea-hypopnea index from overnight pulse oximetry. , 2003, Chest.

[11]  Karl Rihaczek,et al.  1. WHAT IS DATA MINING? , 2019, Data Mining for the Social Sciences.

[12]  W. Flemons,et al.  Comparison of home oximetry monitoring with laboratory polysomnography in children. , 2003, Chest.

[13]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[14]  D. Altman,et al.  Diagnostic tests 4: likelihood ratios , 2004, BMJ : British Medical Journal.

[15]  PROCEssIng magazInE.  IEEE Signal Processing Magazine , 2004 .

[16]  K. Brown,et al.  Planning adenotonsillectomy in children with obstructive sleep apnea: the role of overnight oximetry. , 2004, Pediatrics.

[17]  Huan Liu,et al.  Efficient Feature Selection via Analysis of Relevance and Redundancy , 2004, J. Mach. Learn. Res..

[18]  M. Bister,et al.  Automated detection of obstructive apnea and hypopnea events from oxygen saturation signal , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  Amparo Alonso-Betanzos,et al.  A new method for sleep apnea classification using wavelets and feedforward neural networks , 2005, Artif. Intell. Medicine.

[20]  Chwan-Lu Tseng,et al.  A NEW APPROACH FOR IDENTIFYING SLEEP APNEA SYNDROME USING WAVELET TRANSFORM AND NEURAL NETWORKS , 2006 .

[21]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[22]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[23]  Elif Derya Übeyli,et al.  Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: A pilot study , 2008, Digit. Signal Process..

[24]  Marimuthu Palaniswami,et al.  Support Vector Machines for Automated Recognition of Obstructive Sleep Apnoea Syndrome from Electrocardiogram Recordings , 2009 .

[25]  Richard G. Shiavi,et al.  Determining the Presence of Bias Error Using Statistical Methods , 2009, IEEE Transactions on Information Technology in Biomedicine.

[26]  Necmettin Sezgin,et al.  Classification of sleep apnea by using wavelet transform and artificial neural networks , 2010, Expert Syst. Appl..

[27]  M Matteucci,et al.  Automatic screening of obstructive sleep apnea from the ECG based on empirical mode decomposition and wavelet analysis , 2010, Physiological measurement.

[28]  J. Terán-Santos,et al.  Prólogo [Documento de consenso del síndrome de apneas-hipopneas durante el sueño en niños] , 2011 .

[29]  David Gozal,et al.  [Consensus document on sleep apnea-hypopnea syndrome in children (full version). Sociedad Española de Sueño. El Área de Sueño de la Sociedad Española de Neumología y Cirugía Torácica(SEPAR)]. , 2011, Archivos de bronconeumologia.

[30]  G. Church,et al.  The role of polysomnography in diagnosing and treating obstructive sleep apnea in pediatric patients. , 2012, Current problems in pediatric and adolescent health care.

[31]  S. Quan,et al.  Rules for scoring respiratory events in sleep: update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events. Deliberations of the Sleep Apnea Definitions Task Force of the American Academy of Sleep Medicine. , 2012, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[32]  A. Halbower,et al.  Diagnosis and Management of Childhood Obstructive Sleep Apnea Syndrome , 2012, Pediatrics.

[33]  E. Katz,et al.  Obstructive sleep apnea in infants. , 2012, American journal of respiratory and critical care medicine.

[34]  Christian F Poets,et al.  Diagnosis of obstructive sleep apnea in children: a systematic review. , 2013, Sleep medicine reviews.

[35]  Niels Wessel,et al.  Assessment of Feature Selection and Classification Approaches to Enhance Information from overnight oximetry in the Context of Apnea Diagnosis , 2013, Int. J. Neural Syst..

[36]  M. Su,et al.  Usefulness of desaturation index for the assessment of obstructive sleep apnea syndrome in children. , 2013, International journal of pediatric otorhinolaryngology.

[37]  Li Chang,et al.  Combination of symptoms and oxygen desaturation index in predicting childhood obstructive sleep apnea. , 2013, International journal of pediatric otorhinolaryngology.

[38]  Leila Kheirandish-Gozal,et al.  Overnight polysomnography versus respiratory polygraphy in the diagnosis of pediatric obstructive sleep apnea. , 2014, Sleep.

[39]  W. Karlen,et al.  Development of a Screening Tool for Sleep Disordered Breathing in Children Using the Phone Oximeter™ , 2014, PloS one.

[40]  Walter Karlen,et al.  Oxygen saturation resolution influences regularity measurements , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[41]  Yvonne Vergouwe,et al.  Towards better clinical prediction models: seven steps for development and an ABCD for validation. , 2014, European heart journal.

[42]  M. Villa,et al.  Diagnosis of Pediatric Obstructive Sleep Apnea Syndrome in Settings With Limited Resources. , 2015, JAMA otolaryngology-- head & neck surgery.

[43]  M. Villa,et al.  Obstructive sleep disordered breathing in 2- to 18-year-old children: diagnosis and management , 2015, European Respiratory Journal.

[44]  L. Kheirandish-Gozal,et al.  Pediatric OSAS: Oximetry can provide answers when polysomnography is not available. , 2016, Sleep medicine reviews.

[45]  Leila Kheirandish-Gozal,et al.  Effect of Sleep-disordered Breathing Severity on Cognitive Performance Measures in a Large Community Cohort of Young School-aged Children. , 2016, American journal of respiratory and critical care medicine.

[46]  G. C. Gutiérrez-Tobal,et al.  Automated Screening of Children With Obstructive Sleep Apnea Using Nocturnal Oximetry: An Alternative to Respiratory Polygraphy in Unattended Settings. , 2017, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[47]  G. C. Gutiérrez-Tobal,et al.  Nocturnal Oximetry‐based Evaluation of Habitually Snoring Children , 2017, American journal of respiratory and critical care medicine.

[48]  G. C. Gutiérrez-Tobal,et al.  Automated detection of childhood sleep apnea using discrete wavelet transform of nocturnal oximetry and anthropometric variables , 2017 .

[49]  Daniel Álvarez,et al.  Usefulness of discrete wavelet transform in the analysis of oximetry signals to assist in childhood sleep apnea-hypopnea syndrome diagnosis , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[50]  J. Rowley,et al.  Who Needs Oxygen with Positive Airway Pressure Therapy? , 2017, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[51]  Daniel Álvarez,et al.  Utility of bispectrum in the screening of pediatric sleep apnea-hypopnea syndrome using oximetry recordings , 2018, Comput. Methods Programs Biomed..