Automated insomnia detection using wavelet scattering network technique with single-channel EEG signals

[1]  Usha R. Acharya,et al.  Computerized detection of cyclic alternating patterns of sleep: A new paradigm, future scope and challenges , 2023, Comput. Methods Programs Biomed..

[2]  U. Acharya,et al.  A novel automated robust dual-channel EEG-based sleep scoring system using optimal half-band pair linear-phase biorthogonal wavelet filter bank , 2023, Applied Intelligence.

[3]  U. Rajendra Acharya,et al.  An automatic sleep-scoring system in elderly women with osteoporosis fractures using frequency localized finite orthogonal quadrature Fejer Korovkin kernels. , 2023, Medical engineering & physics.

[4]  Ruyan Tan,et al.  Automated detection of schizophrenia using deep learning: a review for the last decade , 2023, Physiological measurement.

[5]  Shivam Tiwari,et al.  Detection of insomnia using advanced complexity and entropy features of sleep stage data of EEG recordings , 2022, Measurement: Sensors.

[6]  Jalil Mazloum,et al.  Modified binary salp swarm algorithm in EEG signal classification for epilepsy seizure detection , 2022, Biomed. Signal Process. Control..

[7]  O. Faust,et al.  A review of automated sleep disorder detection , 2022, Comput. Biol. Medicine.

[8]  M. Karabatak,et al.  An Automated Wavelet-Based Sleep Scoring Model Using EEG, EMG, and EOG Signals with More Than 8000 Subjects , 2022, International journal of environmental research and public health.

[9]  E. Nsugbe.  On the Application of Metaheuristics and Deep Wavelet Scattering Decompositions for the Prediction of Adolescent Psychosis Using EEG Brain Wave Signals , 2022, Digital Technologies Research and Applications.

[10]  Ankit A. Bhurane,et al.  Automated classification of cyclic alternating pattern sleep phases in healthy and sleep-disordered subjects using convolutional neural network , 2022, Comput. Biol. Medicine.

[11]  Y. Kaya,et al.  Multi-Layer Co-Occurrence Matrices for Person Identification from ECG Signals , 2022, Traitement du Signal.

[12]  U. Acharya,et al.  Automated detection of obstructive sleep apnea in more than 8000 subjects using frequency optimized orthogonal wavelet filter bank with respiratory and oximetry signals , 2022, Comput. Biol. Medicine.

[13]  U. Acharya,et al.  Automated identification of sleep disorders using wavelet-based features extracted from electrooculogram and electromyogram signals , 2022, Comput. Biol. Medicine.

[14]  V. Jahmunah,et al.  Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals , 2021, Comput. Biol. Medicine.

[15]  Changfa Tian,et al.  Recent progress on two-dimensional neuromorphic devices and artificial neural network , 2021, Current Applied Physics.

[16]  Zheru Chi,et al.  Single-channel EEG based insomnia detection with domain adaptation , 2021, Comput. Biol. Medicine.

[17]  U. Acharya,et al.  Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning Techniques with EEG Signals , 2021, Diagnostics.

[18]  Manish Sharma,et al.  Automated Identification of Sleep Disorder Types Using Triplet Half-Band Filter and Ensemble Machine Learning Techniques with EEG Signals , 2021, Electronics.

[19]  U. Acharya,et al.  Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals , 2021, International journal of environmental research and public health.

[20]  Manish Sharma,et al.  Automatic identification of insomnia using optimal antisymmetric biorthogonal wavelet filter bank with ECG signals , 2021, Comput. Biol. Medicine.

[21]  U. Rajendra Acharya,et al.  Automated detection of abnormal EEG signals using localized wavelet filter banks , 2020, Pattern Recognit. Lett..

[22]  Chih-En Kuo,et al.  A Short-Time Insomnia Detection System Based on Sleep EOG With RCMSE Analysis , 2020, IEEE Access.

[23]  U. Rajendra Acharya,et al.  Automated phase classification in cyclic alternating patterns in sleep stages using Wigner-Ville Distribution based features , 2020, Comput. Biol. Medicine.

[24]  U. Rajendra Acharya,et al.  Automated Detection of Sleep Stages Using Energy-Localized Orthogonal Wavelet Filter Banks , 2020, Arabian Journal for Science and Engineering.

[25]  Koichi Tanno,et al.  Automatic Sleep Disorders Classification Using Ensemble of Bagged Tree Based on Sleep Quality Features , 2020, Electronics.

[26]  U Rajendra Acharya,et al.  Hypertension Diagnosis Index for Discrimination of High-Risk Hypertension ECG Signals Using Optimal Orthogonal Wavelet Filter Bank , 2019, International journal of environmental research and public health.

[27]  B. Malik,et al.  Insomnia: Risk Factor for Neurodegenerative Diseases , 2019, Cureus.

[28]  L. Bonagamba,et al.  Cardiovascular and respiratory profiles during the sleep–wake cycle of rats previously submitted to chronic intermittent hypoxia , 2019, Experimental physiology.

[29]  Bingtao Zhang,et al.  Dual-modal Physiological Feature Fusion-based Sleep Recognition Using CFS and RF Algorithm , 2019, International Journal of Automation and Computing.

[30]  U. Rajendra Acharya,et al.  An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with EEG signals , 2018, Cognitive Systems Research.

[31]  U. Rajendra Acharya,et al.  MMSFL-OWFB: A novel class of orthogonal wavelet filters for epileptic seizure detection , 2018, Knowl. Based Syst..

[32]  U. Rajendra Acharya,et al.  A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank , 2018, Comput. Biol. Medicine.

[33]  M. Sharma,et al.  Application of an optimal class of antisymmetric wavelet filter banks for obstructive sleep apnea diagnosis using ECG signals , 2018, Comput. Biol. Medicine.

[34]  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.

[35]  Laurent Vercueil,et al.  A convolutional neural network for sleep stage scoring from raw single-channel EEG , 2018, Biomed. Signal Process. Control..

[36]  Changshui Zhang,et al.  Deep Defense: Training DNNs with Improved Adversarial Robustness , 2018, NeurIPS.

[37]  N. Zisapel.  New perspectives on the role of melatonin in human sleep, circadian rhythms and their regulation , 2018, British journal of pharmacology.

[38]  Ram Bilas Pachori,et al.  A NOVEL APPROACH TO DETECT EPILEPTIC SEIZURES USING A COMBINATION OF TUNABLE-Q WAVELET TRANSFORM AND FRACTAL DIMENSION , 2017 .

[39]  Ronald M. Aarts,et al.  Measures of cardiovascular autonomic activity in insomnia disorder: A systematic review , 2017, PloS one.

[40]  Habibolah Khazaie,et al.  EEG/EOG/EMG data from a cross sectional study on psychophysiological insomnia and normal sleep subjects , 2017, Data in brief.

[41]  U. Rajendra Acharya,et al.  A novel three-band orthogonal wavelet filter bank method for an automated identification of alcoholic EEG signals , 2017, Applied Intelligence.

[42]  Sajid Saleem,et al.  Mallat’s Scattering Transform Based Anomaly Sensing for Detection of Seizures in Scalp EEG , 2017, IEEE Access.

[43]  Ram Bilas Pachori,et al.  Determination of instantaneous fundamental frequency of speech signals using variational mode decomposition , 2017, Comput. Electr. Eng..

[44]  U. Rajendra Acharya,et al.  A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension , 2017, Pattern Recognit. Lett..

[45]  Ram Bilas Pachori,et al.  A parametrization technique to design joint time-frequency optimized discrete-time biorthogonal wavelet bases , 2017, Signal Process..

[46]  Ram Bilas Pachori,et al.  Optimal duration-bandwidth localized antisymmetric biorthogonal wavelet filters , 2017, Signal Process..

[47]  Ram Bilas Pachori,et al.  Time-frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification , 2017, Digit. Signal Process..

[48]  U. Rajendra Acharya,et al.  An automatic detection of focal EEG signals using new class of time-frequency localized orthogonal wavelet filter banks , 2017, Knowl. Based Syst..

[49]  Sana Tmar-Ben Hamida,et al.  Deep Learning and Insomnia: Assisting Clinicians With Their Diagnosis , 2017, IEEE Journal of Biomedical and Health Informatics.

[50]  Ram Bilas Pachori,et al.  Design of Time–Frequency Optimal Three-Band Wavelet Filter Banks with Unit Sobolev Regularity Using Frequency Domain Sampling , 2016, Circuits Syst. Signal Process..

[51]  D. Hemavathy,et al.  Prevalence of chronic insomnia in adult patients and its correlation with medical comorbidities , 2016, Journal of family medicine and primary care.

[52]  Sana Tmar-Ben Hamida,et al.  How many sleep stages do we need for an efficient automatic insomnia diagnosis? , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[53]  Mohd. Maroof Siddiqui,et al.  Diagnosis of insomnia sleep disorder using short time frequency analysis of PSD approach applied on EEG signal using channel ROC-LOC , 2016, Sleep science.

[54]  Stéphane Mallat,et al.  Understanding deep convolutional networks , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[55]  Sana Tmar-Ben Hamida,et al.  A novel insomnia identification method based on Hjorth parameters , 2015, 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[56]  R. Singh,et al.  Prevalence of insomnia in urban population of West Bengal: A community-based cross-sectional study , 2015 .

[57]  N. Yardi,et al.  A Cross-Sectional Observational Study to Determine the Prevalence of Insomnia amongst Indian Corporate Employees. , 2015, The Journal of the Association of Physicians of India.

[58]  Albert Bifet,et al.  Efficient Online Evaluation of Big Data Stream Classifiers , 2015, KDD.

[59]  Kenneth A Loparo,et al.  Performance evaluation of an automated single-channel sleep–wake detection algorithm , 2014, Nature and science of sleep.

[60]  S. Mazza,et al.  Heart Rate and Heart Rate Variability Modification in Chronic Insomnia Patients , 2014, Behavioral sleep medicine.

[61]  Joakim Andén,et al.  Deep Scattering Spectrum , 2013, IEEE Transactions on Signal Processing.

[62]  L. Stegagno,et al.  Nighttime cardiac sympathetic hyper-activation in young primary insomniacs , 2013, Clinical Autonomic Research.

[63]  Robert T Krafty,et al.  Short-term stability of sleep and heart rate variability in good sleepers and patients with insomnia: for some measures, one night is enough. , 2012, Sleep.

[64]  Stéphane Mallat,et al.  Invariant Scattering Convolution Networks , 2012, IEEE transactions on pattern analysis and machine intelligence.

[65]  Kai Spiegelhalder,et al.  Insomnia as a predictor of depression: a meta-analytic evaluation of longitudinal epidemiological studies. , 2011, Journal of affective disorders.

[66]  E. Walker,et al.  Diagnostic and Statistical Manual of Mental Disorders , 2013 .

[67]  Sadik Kara,et al.  Singular Spectrum Analysis of Sleep EEG in Insomnia , 2011, Journal of Medical Systems.

[68]  C. Morin,et al.  The Insomnia Severity Index: psychometric indicators to detect insomnia cases and evaluate treatment response. , 2011, Sleep.

[69]  Stéphane Mallat,et al.  Group Invariant Scattering , 2011, ArXiv.

[70]  D. Léger,et al.  Societal costs of insomnia. , 2010, Sleep medicine reviews.

[71]  Stéphane Mallat,et al.  Classification with scattering operators , 2010, CVPR 2011.

[72]  O. Buxton,et al.  Short and long sleep are positively associated with obesity, diabetes, hypertension, and cardiovascular disease among adults in the United States. , 2010, Social science & medicine.

[73]  Uzay Kaymak,et al.  Cohen's kappa coefficient as a performance measure for feature selection , 2010, International Conference on Fuzzy Systems.

[74]  B. Feige,et al.  The hyperarousal model of insomnia: a review of the concept and its evidence. , 2010, Sleep medicine reviews.

[75]  Lior Rokach,et al.  Ensemble-based classifiers , 2010, Artificial Intelligence Review.

[76]  Anne Germain,et al.  EEG spectral analysis in primary insomnia: NREM period effects and sex differences. , 2008, Sleep.

[77]  M. Sateia,et al.  Clinical guideline for the evaluation and management of chronic insomnia in adults. , 2008, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[78]  E. Mignot.  Why We Sleep: The Temporal Organization of Recovery , 2008, PLoS biology.

[79]  T. Roth,et al.  Insomnia: definition, prevalence, etiology, and consequences. , 2007, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[80]  Daniel J. Taylor,et al.  Comorbidity of chronic insomnia with medical problems. , 2007, Sleep.

[81]  J. Demšar.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[82]  Maurice M Ohayon,et al.  Epidemiology of insomnia: what we know and what we still need to learn. , 2002, Sleep medicine reviews.

[83]  M Hirshkowitz,et al.  Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep. , 2001, Sleep medicine.

[84]  S. Cole,et al.  Insomnia and heart disease: a review of epidemiologic studies. , 1999, Journal of psychosomatic research.

[85]  C. Sidney Burrus,et al.  Wavelet transform based fast approximate Fourier transform , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[86]  Tai Sing Lee,et al.  Image Representation Using 2D Gabor Wavelets , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[87]  Sahibsingh A. Dudani.  The Distance-Weighted k-Nearest-Neighbor Rule , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[88]  E. Wolpert.  A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. , 1969 .

[89]  U. Acharya,et al.  Automated Explainable Detection of Cyclic Alternating Pattern (CAP) Phases and Sub-Phases Using Wavelet-Based Single-Channel EEG Signals , 2023, IEEE Access.

[90]  Md Belal Bin Heyat,et al.  Ensemble Computational Intelligent for Insomnia Sleep Stage Detection via the Sleep ECG Signal , 2022, IEEE Access.

[91]  Manish Sharma,et al.  Automated identification of insomnia using optimal bi-orthogonal wavelet transform technique with single-channel EEG signals , 2021, Knowl. Based Syst..

[92]  Hongxing Liu,et al.  Automatic Identification of Insomnia Based on Single-Channel EEG Labelled With Sleep Stage Annotations , 2020, IEEE Access.

[93]  U. Rajendra Acharya,et al.  A new approach to identify obstructive sleep apnea using an optimal orthogonal wavelet filter bank with ECG signals , 2019, Informatics in Medicine Unlocked.

[94]  Sana Tmar-Ben Hamida,et al.  A New Era in Sleep Monitoring: The Application of Mobile Technologies in Insomnia Diagnosis , 2015 .

[95]  G. Gururaj,et al.  Sleep-related disorders among a healthy population in South India. , 2012, Neurology India.

[96]  A. Chesson,et al.  The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology, and Techinical Specifications , 2007 .

[97]  D. Léger,et al.  Socioeconomic impact of insomnia in working populations. , 2005, Industrial health.

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

[99]  T. Sejnowski,et al.  Sleep and Memory , 2022 .