Pattern recognition in airflow recordings to assist in the sleep apnoea–hypopnoea syndrome diagnosis

Abstract This paper aims at detecting sleep apnoea–hypopnoea syndrome (SAHS) from single-channel airflow (AF) recordings. The study involves 148 subjects. Our proposal is based on estimating the apnoea–hypopnoea index (AHI) after global analysis of AF, including the investigation of respiratory rate variability (RRV). We exhaustively characterize both AF and RRV by extracting spectral, nonlinear, and statistical features. Then, the fast correlation-based filter is used to select those relevant and non-redundant. Multiple linear regression, multi-layer perceptron (MLP), and radial basis functions are fed with the features to estimate AHI. A conventional approach, based on scoring apnoeas and hypopnoeas, is also assessed for comparison purposes. An MLP model trained with AF and RRV selected features achieved the highest agreement with the true AHI (intra-class correlation coefficient = 0.849). It also showed the highest diagnostic ability, reaching 92.5 % sensitivity, 89.5 % specificity and 91.5 % accuracy. This suggests that AF and RRV can complement each other to estimate AHI and help in SAHS diagnosis.

[1]  Erik W. Jensen,et al.  EEG complexity as a measure of depth of anesthesia for patients , 2001, IEEE Trans. Biomed. Eng..

[2]  Abraham Lempel,et al.  Compression of individual sequences via variable-rate coding , 1978, IEEE Trans. Inf. Theory.

[3]  Najib T. Ayas,et al.  Nasal pressure recordings to detect obstructive sleep apnea , 2006, Sleep and Breathing.

[4]  F. Kianifard.  Applied Multivariate Data Analysis, Volume I: Regression and Experimental Design , 1992 .

[5]  D. Cysarz,et al.  Comparison of Respiratory Rates Derived from Heart Rate Variability, ECG Amplitude, and Nasal/Oral Airflow , 2008, Annals of Biomedical Engineering.

[6]  T. Penzel,et al.  The SleepStripTM: an apnoea screener for the early detection of sleep apnoea syndrome , 2002, European Respiratory Journal.

[7]  W. Flemons,et al.  Home diagnosis of sleep apnea: a systematic review of the literature. An evidence review cosponsored by the American Academy of Sleep Medicine, the American College of Chest Physicians, and the American Thoracic Society. , 2003, Chest.

[8]  A. Schwartz,et al.  Adult obstructive sleep apnea: pathophysiology and diagnosis. , 2007, Chest.

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

[10]  T. Gislason,et al.  Role of snoring and daytime sleepiness in occupational accidents. , 2001, American journal of respiratory and critical care medicine.

[11]  J. Jobson,et al.  Applied Multivariate Data Analysis: Regression and Experimental Design , 1999 .

[12]  César Hervás-Martínez,et al.  Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection , 2012, Appl. Soft Comput..

[13]  H Nakano,et al.  Automatic detection of sleep-disordered breathing from a single-channel airflow record , 2007, European Respiratory Journal.

[14]  Ian T. Nabney,et al.  Netlab: Algorithms for Pattern Recognition , 2002 .

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

[16]  Roberto Hornero,et al.  Automated Prediction of the Apnea-Hypopnea Index from Nocturnal Oximetry Recordings , 2012, IEEE Transactions on Biomedical Engineering.

[17]  Meir Kryger,et al.  Reducing motor-vehicle collisions, costs, and fatalities by treating obstructive sleep apnea syndrome. , 2004, Sleep.

[18]  W. Wootters.  Statistical distance and Hilbert space , 1981 .

[19]  Gunnar Unger,et al.  Diagnostic test evaluation of a nasal flow monitor for obstructive sleep apnea detection in sleep apnea research , 2008, Behavior research methods.

[20]  A. Chesson,et al.  The American Academy of Sleep Medicine (AASM) Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications , 2007 .

[21]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[22]  R. Farré,et al.  Association between obstructive sleep apnea and cancer incidence in a large multicenter Spanish cohort. , 2013, American journal of respiratory and critical care medicine.

[23]  Radhakrishnan Nagarajan,et al.  Quantifying physiological data with Lempel-Ziv complexity-certain issues , 2002, IEEE Transactions on Biomedical Engineering.

[24]  D. Shucard,et al.  Validity of neural network in sleep apnea. , 1999, Sleep.

[25]  Diego Álvarez-Estévez,et al.  Fuzzy reasoning used to detect apneic events in the sleep apnea-hypopnea syndrome , 2009, Expert Syst. Appl..

[26]  B. Everitt,et al.  Applied Multivariate Data Analysis. , 1993 .

[27]  J. D. Jobson,et al.  Regression and experimental design , 1991 .

[28]  R. Hornero,et al.  Nonlinear analysis of time series generated by schizophrenic patients , 1999, IEEE Engineering in Medicine and Biology Magazine.

[29]  S. George,et al.  Evaluation of the accuracy of manual and automatic scoring of a single airflow channel in patients with a high probability of obstructive sleep apnea , 2011, Medical science monitor : international medical journal of experimental and clinical research.

[30]  R. Grebe,et al.  Automated neonatal seizure detection: A multistage classification system through feature selection based on relevance and redundancy analysis , 2006, Clinical Neurophysiology.

[31]  L. Ferini-Strambi,et al.  Epidemiology of obstructive sleep apnea syndrome. , 2004, Minerva medica.

[32]  Osvaldo A. Rosso,et al.  Statistical complexity and disequilibrium , 2003 .

[33]  Roberto Hornero,et al.  Multivariate Analysis of Blood Oxygen Saturation Recordings in Obstructive Sleep Apnea Diagnosis , 2010, IEEE Transactions on Biomedical Engineering.

[34]  J. Victor Marcos,et al.  Linear and nonlinear analysis of airflow recordings to help in sleep apnoea–hypopnoea syndrome diagnosis , 2012, Physiological measurement.

[35]  Abraham Lempel,et al.  On the Complexity of Finite Sequences , 1976, IEEE Trans. Inf. Theory.

[36]  Zoltán Benyó,et al.  A novel method for the detection of apnea and hypopnea events in respiration signals , 2002, IEEE Transactions on Biomedical Engineering.

[37]  J B Korten,et al.  Respiratory waveform pattern recognition using digital techniques. , 1989, Computers in biology and medicine.

[38]  T. Young,et al.  Epidemiology of obstructive sleep apnea: a population health perspective. , 2002, American journal of respiratory and critical care medicine.

[39]  B. Gersh,et al.  Obstructive sleep apnea: implications for cardiac and vascular disease. , 2003, JAMA.

[40]  Steven M. Pincus.  Assessing Serial Irregularity and Its Implications for Health , 2001, Annals of the New York Academy of Sciences.

[41]  Abraham Otero,et al.  Algorithms for the analysis of polysomnographic recordings with customizable criteria , 2011, Expert Syst. Appl..

[42]  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 .

[43]  A. Bahammam,et al.  Comparison of Nasal Prong Pressure and Thermistor Measurements for Detecting Respiratory Events during Sleep , 2004, Respiration.

[44]  Kwang Suk Park,et al.  Detection of apneic events from single channel nasal airflow using 2nd derivative method , 2008, Comput. Methods Programs Biomed..

[45]  Paulo Félix,et al.  Detecting Sleep Apnea by Heart Rate Variability Analysis: Assessing the Validity of Databases and Algorithms , 2011, Journal of Medical Systems.

[46]  G. J. G. Upton,et al.  Applied Multivariate Data Analysis, Volume 1: Regression and Experimental Design , 1994, The Mathematical Gazette.

[47]  D. L. Hudson,et al.  Applying continuous chaotic modeling to cardiac signal analysis , 1996 .

[48]  Qinghua Hu,et al.  An efficient gene selection technique for cancer recognition based on neighborhood mutual information , 2010, Int. J. Mach. Learn. Cybern..

[49]  T. Evans,et al.  Sleep on the cheap : the role of overnight oximetry in the diagnosis of sleep apnoea hypopnoea syndrome , 1999 .