Detection of sleep apnea from heart beat interval and ECG derived respiration signals using sliding mode singular spectrum analysis

Abstract The heartbeat interval (HBI) signal (RR-time series), and electrocardiogram (ECG) derived respiration (EDR) signal quantify the information about the cardiopulmonary activity, and monitoring these two signals simultaneously will provide more information for the sleep apnea detection. This paper proposes a novel approach to detect sleep apnea using both HBI and EDR signals. The approach consists of the decomposition of both HBI and EDR signals into reconstructed components (RCs) or modes using a data-driven signal processing approach namely, the sliding mode singular spectrum analysis (SM-SSA), extraction of features from each RC, and the use of classifier for the detection of sleep apnea. The features such as the mean and the standard deviation values are extracted from the instantaneous amplitude (IA) and instantaneous frequency (IF) of each RC of both HBI and EDR signals. The classifiers, such as the stacked autoencoder based deep neural network (SAE-DNN), and support vector machine (SVM) are considered to classify normal and apnea episodes using the statistical features obtained from the RCs of HBI and EDR signals. The proposed approach is evaluated using different public databases such as apnea-ECG database, University College Dublin (UCD) database, and Physionet challenge database, respectively. The results demonstrate that the combination of the statistical features and SVM classifier has the sensitivity and specificity values of 82.45% and 79.72%, respectively using the 10-fold cross-validation based selection of training and test instances from the apnea-ECG database. Moreover, for subject-specific cross-validation, the proposed method has an average sensitivity and specificity values of 62.87%, and 81.53%, respectively. The proposed method has achieved the accuracy values of 94.3%, and 72% for per-recording based classification of sleep apnea and normal classes using signals from apnea-ECG and UCD databases.

[1]  Ahnaf Rashik Hassan,et al.  Computer-aided obstructive sleep apnea screening from single-lead electrocardiogram using statistical and spectral features and bootstrap aggregating , 2016 .

[2]  Andrés Marino Álvarez-Meza,et al.  Automatic Singular Spectrum Analysis for Time-Series Decomposition , 2013, ESANN.

[3]  Simon J. Doran,et al.  Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[5]  Hlaing Minn,et al.  Real-Time Sleep Apnea Detection by Classifier Combination , 2012, IEEE Transactions on Information Technology in Biomedicine.

[6]  Marimuthu Palaniswami,et al.  Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings , 2009, IEEE Transactions on Information Technology in Biomedicine.

[7]  George B. Moody,et al.  Derivation of Respiratory Signals from Multi-lead ECGs , 2008 .

[8]  Parmjit Singh,et al.  The new AASM criteria for scoring hypopneas: impact on the apnea hypopnea index. , 2009, Sleep.

[9]  Marimuthu Palaniswami,et al.  Automated recognition of patients with obstructive sleep apnoea using wavelet-based features of electrocardiogram recordings , 2009, Comput. Biol. Medicine.

[10]  M. Friedman,et al.  Staging of Obstructive Sleep Apnea/Hypopnea Syndrome: A Guide to Appropriate Treatment , 2004, The Laryngoscope.

[11]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[12]  Theerawit Wilaiprasitporn,et al.  Single Channel ECG for Obstructive Sleep Apnea Severity Detection Using a Deep Learning Approach , 2018, TENCON 2018 - 2018 IEEE Region 10 Conference.

[13]  U. Rajendra Acharya,et al.  Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals , 2017, Comput. Biol. Medicine.

[14]  Philip de Chazal,et al.  A Comparison of obstructive sleep apnoea detection using three different ECG derived respiration algorithms , 2015, 2015 Computing in Cardiology Conference (CinC).

[15]  U. Acharya,et al.  Automated detection of sleep apnea from electrocardiogram signals using nonlinear parameters , 2011, Physiological measurement.

[16]  Patrizia Vergallo,et al.  Empirical Mode Decomposition vs. Wavelet Decomposition for the Extraction of Respiratory Signal From Single-Channel ECG: A Comparison , 2013, IEEE Sensors Journal.

[17]  Saeed Babaeizadeh,et al.  Automatic detection and quantification of sleep apnea using heart rate variability. , 2010, Journal of electrocardiology.

[18]  U. Rajendra Acharya,et al.  Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework , 2018 .

[19]  Qi Cheng,et al.  An Online Sleep Apnea Detection Method Based on Recurrence Quantification Analysis , 2014, IEEE Journal of Biomedical and Health Informatics.

[20]  Philip Langley,et al.  Principal Component Analysis as a Tool for Analyzing Beat-to-Beat Changes in ECG Features: Application to ECG-Derived Respiration , 2010, IEEE Transactions on Biomedical Engineering.

[21]  T. W. Anderson,et al.  Asymptotic Theory of Certain "Goodness of Fit" Criteria Based on Stochastic Processes , 1952 .

[22]  A. Murray,et al.  Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings , 2002, Medical and Biological Engineering and Computing.

[23]  Samarendra Dandapat,et al.  Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction , 2015, IEEE Transactions on Biomedical Engineering.

[24]  Ahnaf Rashik Hassan,et al.  Automatic screening of Obstructive Sleep Apnea from single-lead Electrocardiogram , 2015, 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT).

[25]  U. Rajendra Acharya,et al.  An accurate sleep stages classification system using a new class of optimally time-frequency localized three-band wavelet filter bank , 2018, Comput. Biol. Medicine.

[26]  Ram Bilas Pachori,et al.  Application of TQWT based filter-bank for sleep apnea screening using ECG signals , 2018, J. Ambient Intell. Humaniz. Comput..

[27]  U. Rajendra Acharya,et al.  Automated detection of chronic kidney disease using higher-order features and elongated quinary patterns from B-mode ultrasound images , 2019, Neural Computing and Applications.

[28]  T. Young,et al.  Estimation of the clinically diagnosed proportion of sleep apnea syndrome in middle-aged men and women. , 1997, Sleep.

[29]  B. Koley,et al.  An ensemble system for automatic sleep stage classification using single channel EEG signal , 2012, Comput. Biol. Medicine.

[30]  Anjan Gudigar,et al.  Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images , 2018, Inf. Sci..

[31]  Jing Fang,et al.  HHT based cardiopulmonary coupling analysis for sleep apnea detection. , 2012, Sleep medicine.

[32]  Petros Maragos,et al.  A comparison of the energy operator and the Hilbert transform approach to signal and speech demodulation , 1994, Signal Process..

[33]  S. Kuna.  A 54-year-old man with obstructive sleep apnea. , 2002, JAMA.

[34]  D T Berry,et al.  Sleep apnea syndrome. A critical review of the apnea index as a diagnostic criterion. , 1984, Chest.

[35]  Thomas Penzel,et al.  Sleep Apnea Screening by Autoregressive Models From a Single ECG Lead , 2009, IEEE Transactions on Biomedical Engineering.

[36]  Susan Redline,et al.  Association of nocturnal arrhythmias with sleep-disordered breathing: The Sleep Heart Health Study. , 2006, American journal of respiratory and critical care medicine.

[37]  T Penzel,et al.  A review of signals used in sleep analysis , 2014, Physiological measurement.

[38]  R. K. Tripathy,et al.  Application of intrinsic band function technique for automated detection of sleep apnea using HRV and EDR signals , 2018 .

[39]  Nadi Sadr,et al.  Sleep apnoea classification using heart rate variability, ECG derived respiration and cardiopulmonary coupling parameters , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[40]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[41]  Rodrigo Varejão Andreão,et al.  Spectral analysis of heart rate variability with the autoregressive method: What model order to choose? , 2012, Comput. Biol. Medicine.

[42]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[43]  T. Penzel,et al.  Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography , 2016, Front. Physiol..

[44]  T. Young,et al.  The occurrence of sleep-disordered breathing among middle-aged adults. , 1993, The New England journal of medicine.

[45]  Ram Bilas Pachori,et al.  Discrimination of Focal and Non-Focal Seizures From EEG Signals Using Sliding Mode Singular Spectrum Analysis , 2019, IEEE Sensors Journal.

[46]  R. Thomas,et al.  An electrocardiogram-based technique to assess cardiopulmonary coupling during sleep. , 2005, Sleep.

[47]  H. Fujita,et al.  A REVIEW OF ECG-BASED DIAGNOSIS SUPPORT SYSTEMS FOR OBSTRUCTIVE SLEEP APNEA , 2016 .

[48]  Roberto Hornero,et al.  Improving diagnostic ability of blood oxygen saturation from overnight pulse oximetry in obstructive sleep apnea detection by means of central tendency measure , 2007, Artif. Intell. Medicine.

[49]  L. Tarassenko,et al.  An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram , 2016, Physiological measurement.

[50]  R. Manfredi,et al.  Sleep Disorders of Organic Origin: Narcolepsy and Sleep Apnea , 1987 .

[51]  Hossein Hassani,et al.  Singular Spectrum Analysis: Methodology and Comparison , 2021, Journal of Data Science.

[52]  Daniel J Buysse,et al.  Sleep–Related Breathing Disorders in Adults: Recommendations for Syndrome Definition and Measurement Techniques in Clinical Research , 2000 .

[53]  Conor Heneghan,et al.  Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea , 2003, IEEE Transactions on Biomedical Engineering.

[54]  Xi Zhang,et al.  An Obstructive Sleep Apnea Detection Approach Using a Discriminative Hidden Markov Model From ECG Signals , 2016, IEEE Transactions on Biomedical Engineering.

[55]  G. Moody,et al.  The apnea-ECG database , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[56]  Hui Wang,et al.  An Obstructive Sleep Apnea Detection Approach Using Kernel Density Classification Based on Single-Lead Electrocardiogram , 2015, Journal of Medical Systems.

[57]  S. Redline,et al.  Reliability of scoring respiratory disturbance indices and sleep staging. , 1998, Sleep.

[58]  Sabine Van Huffel,et al.  A Novel Algorithm for the Automatic Detection of Sleep Apnea From Single-Lead ECG , 2015, IEEE Transactions on Biomedical Engineering.

[59]  Patrick Flandrin,et al.  The Sliding Singular Spectrum Analysis: A Data-Driven Nonstationary Signal Decomposition Tool , 2018, IEEE Transactions on Signal Processing.

[60]  Yifan Li,et al.  A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal , 2018, Neurocomputing.

[61]  David G. Stork,et al.  Pattern Classification , 1973 .

[62]  Vivek Kanhangad,et al.  Gabor Filter-Based One-Dimensional Local Phase Descriptors for Obstructive Sleep Apnea Detection Using Single-Lead ECG , 2018, IEEE Sensors Letters.

[63]  Ljubomir J. Buturovic,et al.  Cross-validation pitfalls when selecting and assessing regression and classification models , 2014, Journal of Cheminformatics.

[64]  D. White.  Pathogenesis of obstructive and central sleep apnea. , 2005, American journal of respiratory and critical care medicine.

[65]  Ahnaf Rashik Hassan,et al.  Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting , 2016, Biomed. Signal Process. Control..

[66]  Alan V. Oppenheim,et al.  Discrete-Time Signal Pro-cessing , 1989 .

[67]  U Rajendra Acharya,et al.  Automated detection of sleep apnea using sparse residual entropy features with various dictionaries extracted from heart rate and EDR signals , 2019, Comput. Biol. Medicine.

[68]  Ram Bilas Pachori,et al.  A Novel Approach for Detection of Myocardial Infarction From ECG Signals of Multiple Electrodes , 2019, IEEE Sensors Journal.

[69]  Diego H. Milone,et al.  Screening of obstructive sleep apnea with empirical mode decomposition of pulse oximetry. , 2014, Medical engineering & physics.

[70]  Saeid Sanei,et al.  Detection of periodic signals using a new adaptive line enhancer based on singular spectrum analysis , 2011, 2011 8th International Conference on Information, Communications & Signal Processing.

[71]  Niels Wessel,et al.  Oxygen Saturation and RR Intervals Feature Selection for Sleep Apnea Detection , 2015, Entropy.

[72]  Philip de Chazal,et al.  A fast approximation method for principal component analysis applied to ECG derived respiration for OSA detection , 2016, EMBC.

[73]  U. Rajendra Acharya,et al.  Deep learning for healthcare applications based on physiological signals: A review , 2018, Comput. Methods Programs Biomed..

[74]  J. Elsner.  Analysis of Time Series Structure: SSA and Related Techniques , 2002 .

[75]  Sabine Van Huffel,et al.  Sleep apnea classification using least-squares support vector machines on single lead ECG , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[76]  Nadi Sadr,et al.  A comparison of three ECG-derived respiration methods for sleep apnoea detection , 2019, Biomedical Physics & Engineering Express.

[77]  Abdulnasir Hossen,et al.  Different neural networks approaches for identification of obstructive sleep apnea , 2018, 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE).

[78]  Fernando Morgado Dias,et al.  An Oximetry Based Wireless Device for Sleep Apnea Detection , 2020, Sensors.

[79]  R. Acharya U,et al.  Nonlinear analysis of EEG signals at different mental states , 2004, Biomedical engineering online.

[80]  U Rajendra Acharya,et al.  Automated sleep apnea detection from cardio-pulmonary signal using bivariate fast and adaptive EMD coupled with cross time-frequency analysis , 2020, Comput. Biol. Medicine.