Rhythms-Based Coherence Analysis Between Brain and Heart of Sleep Disorder Samples using MSC
Rhythms-Based Coherence Analysis Between Brain and Heart of Sleep Disorder Samples using MSC
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : Polepogu Rajesh, Vaegae Naveen Kumar
|DOI : 10.14445/22315381/IJETT-V70I6P230|
How to Cite?
Polepogu Rajesh, Vaegae Naveen Kumar, "Rhythms-Based Coherence Analysis Between Brain and Heart of Sleep Disorder Samples using MSC," International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 287-299, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I6P230
62% of adults suffer from sleep problems all over the world, and the incidence of these illnesses is anticipated to grow. Unfortunately, many of these problems in scientific practice may go unnoticed and untreated. It has been widely observed in the diagnosis and scientific applications of various sleep disorders that a diligent and fundamental function of watching the heart and brain functions is required. Sleep problems can be studied using various techniques, mainly polysomnography, Electroencephalogram, MSLT, etc. In this work, we propose a new technique for estimating the relationship between cardiology and neurology for identifying cardiac and cerebrovascular anomalies of sleep disorder patients using ECG and EEG rhythms. Sleep problems based on ECG and EEG rhythms are utilized for the analysis and clinical treatment of difficulties associated with heart and brain-related issues. This paper investigated the magnitude squared coherence (MSC) between the ECG and EEG rhythms through a nonparametric power spectrum density estimation (PSDE) using the Welch method in the typical frequency band of 0-35 Hz. All signals are acquired from sleep apnea samples aged 20 to 50, with a sampling rate of thousand samples per second, using physioNet. For each signal, 1000 samples are used for MSC analysis. The MSC between the ECG and EEG rhythms are plotted, and their mean values are computed. ECG and EEG rhythms of two subjects were reported among the 25 subjects investigated. The measurement shows that in sleep apnea sample 1, the value of MSC between the ECG and EEG rhythms are 0.13954, 0.13017, 0.12599, and 0.12197 at beta (β)band, alpha (α)band, theta (θ) and Delta (δ) of EEG rhythms.
Similarly, in sleep apnea sample 2, the value of MSC is 0.13970, 1.3540, 0.13404, and 0.13092 at the beta (β) band, alpha (α) band, theta (θ) band, and delta (δ) band of EEG rhythms respectively. Finally, in the first sample, the MSC value is higher in the beta and the second-highest in the alpha rhythm. In the second, the highest MSC rating and the second-highest MSC value are obtained by the beta rhythm and alpha rhythm, respectively. In sleep apnea, the patient’s brain and heart are closely associated with beta rhythm and the second highest in the alpha rhythms.
Electrocardiogram (ECG), Electroencephalogram (EEG), Rapid eye movement, non-Rapid eye movement, Power Spectrum Density Estimation, Cross Power Spectral Density, Magnitude Squared Coherence (MSC).
 Stranges S, Tigbe W, Gómez-Olivé FX, Thorogood M, Kandala NB. Sleep Problems: An Emerging Global Epidemic? Findings From the in-Depth Who-Sage Study Among More Than 40,000 Older Adults From 8 Countries Across Africa and Asia. Sleep, 35 (2012)1173-81.
 Irish LA, Kline CE, Gunn HE, Buysse DJ, Hall MH. the Role of Sleep Hygiene in Promoting Public Health: A Review of Empirical Evidence. Sleep Med Rev, 22 (2015) 23-36.
 Consensus Conference Panel, Watson NF, Badr MS, Belenky G, Bliwise DL, Buxton OM, Et Al. Recommended Amount of Sleep for a Healthy Adult: A Joint Consensus Statement of the American Academy of Sleep Medicine and Sleep Research Society. J Clin Sleep Med, 11 (2015) 591-2.
 Part Thi S, Brooks LJ, D'Ambrosio C, Hall WA, Kotagal S, Lloyd RM, Et Al. Recommended Amount of Sleep for Pediatric Populations: A Consensus Statement of the American Academy of Sleep Medicine. J Clin Sleep Med, 12 (2016) 785-6.
 Bhaskar S, Hemavathy D, Prasad S. Prevalence of Chronic Insomnia in Adult Patients and Its Correlation with Medical Comorbidities. J Family Med Prim Care, 5 (2016) 780-4.
 Glozier N, Martiniuk A, Patton G, Ivers R, Li Q, Hickie I, Et Al. Short Sleep Duration in Prevalent and Persistent Psychological Distress in Young Adults: the Driving Study. Sleep, 33 (2010) 1139-45.
 Ayas NT, White DP, Manson JE, Stampfer MJ, Speizer FE, Malhotra A, Et Al. A Prospective Study of Sleep Duration and Coronary Heart Disease in Women. Arch Intern Med, 163 (2003) 205-9.
 American Academy of Sleep Medicine Board of Directors, Watson NF, Morgenthaler T, Chervin R, Carden K, Kirsch D, Et Al.Confronting Drowsy Driving: the American Academy of Sleep Medicine Perspective. J Clin Sleep Med, 11(1) (2015) 335-6.
 Witcher LA, Gozal D, Molfese DM, Salathe SM, Spruyt K, Crabtree VM, Et Al. Sleep Hygiene and Problem Behaviors in Snoring and Non-Snoring School-Age Children. Sleep Med, 13 (2012) 802-9.
 Baum KT, Desai A, Field J, Miller LE, Rausch J, Beebe DW. Sleep Restriction Worsens Mood and Emotion Regulation in Adolescents. J Child Psychol Psychiatry 55 (2014) 180-90.
 ICSD-International Classification of Sleep Disorders. Diagnostic and Coding Manual. 2nd Ed. Westchester: American Academy of Sleep Medicine, (2005).
 Devi V, Shankar PK. Ramelteon: A Melatonin Receptor Agonist for the Treatment of Insomnia. J Postgrad Med, 54 (2008) 45-8.
 Kant S, Dixit S, Dubey A, Tewari S. Obstructive Sleep Apnea Syndrome: Genetic and Biochemical Perspective. Indian J Sleep Med, 3 (2008). ISSN 0973-340X.
 Kanwar MS. Coexisting UARS and OSA. Indian J Sleep Med, 3 (2008) ISSN 0973-340X.
 Singh JN. Observations on Sleep-Paralysis.Indian J Psychiatry, 3 (1961) 160-9.
 Bharadwaj R, Kumar S. Somnambulism: Diagnosis and Treatment. Indian J Psychiatry 49 (2007) 123-5.
 Dhanuka AK, Singh G. Periodic Limb Movement Disorder: A Clinical and Polysomnographic Study. Neurol India , 49 (2001) 366-70.
 Krishnan PR, Bhatia M, Behari M. Restless Legs Syndrome in Parkinson's Disease: A Case-Controlled Study. Mov Disord , 18 (2003) 1815.
 Narcolepsy., Scammell TE, the New England Journal of Medicine, (2015).
 Blake, J., Kerr, D. Sleep Disorder Diagnosis: the Design and Implications of Online Tools. Decis. Anal, 1 (2014) 7.
 Polepogu Rajesh, Coherence Analysis Between Heart and Brain of Healthy and Unhealthy Subjects 2017 IEEE 11th International Conference on Intelligent Systems and Control (ISCO), (2017) 351-356.
 S. K. Saini and R. Gupta, A Review on ECG Signal Analysis for Mental Stress Assessment, 2019 6th International Conference on Computing for Sustainable Global Development (Indiacom), New Delhi, India, (2019) 915-918.
 J.W. Hurst, Naming of the Waves in the ECG, with A Brief Account of their Genesis. Circulation, 98 (1998) 1937-1942.
 W. B. Fye, A History of the Origin, Evolution, and Impact of Electrocardiography. Am J Cardiol, 73 (1994) 937-949.
 E. Niedermeyer, Electroencephalography: Basic Principles, Clinical Applications and Related Fields, Lippincott Williams & Wilkins, Philadelphia, 3rd Edition, (1993).
 Https://Www.Bing.Com/Images/ECG and EEG Signals.
 Physionet: the Research Resource for Complex Physiologic Signals//Www.Physionet.Org.//Http://Www.Physionet.Org/Physiobank/Database/Sleep-Edfx/
 S. Bhat, O. Faust, Et Al., Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection, European Neurology, 74( 5-6) (2015) 268–287.
 P. V. Achuth, and U. R. Acharya, An Accurate Sleep Stages Classification System Using A New Class of Optimally Time-Frequency Localized Three-Band Wavelet Filter 14 Computational and Mathematical Methods in Medicine Bank, Computers in Biology and Medicine, 98 (2018) 58–75.
 A. Kales, A Manual of Standardized Terminology, Techniques and Scoring Systems for Sleep Stages of Human Subjects, U. G. P. Office, Public Health Service, Washington DC, USA, (1968).
 K. C. Chua, L. C. Min, and T. Tamura, Analysis and Automatic Identification of Sleep Stages Using Higher Order Spectra,” International Journal of Neural Systems, 20(6) (2010) 509–521.
 Y. F. Sun, Sleep Stages Classification Using Neural Networks with Multi-Channel Neural Data, in Lecture Notes in Computer Science, Springer, Berlin, Germany, (2015) 306–316.
 AH Khandoker, CK Karmakar, M Palaniswami, Interaction Between Sleep EEG and ECG Signals During and After Obstructive Sleep Apnea Events with Or Without Arousals, Computers in Cardiology , the University of Melbourne, Victoria, Australia, (2008) 685−688.
 AK Kokonozi, EM Michxail, IC Chouvarda, NM Maglaveras, ―A Study of Heart Rate and Brain System Complexity and Their Interaction in Sleep-Deprived Subjects, Computers in Cardiology ISSN 0276−6574 969, (2008) 969−971.
 Haslaile Abdullah Et Al., Cross-Correlation of EEG Frequency Bands and Heart Rate Variability for Sleep Apnoea Classification, International Federation for Medical and Biological Engineering, (2010).
 Billy Sulistyo, Nicosurantha, Sani M. Isa, Sleep Apnea Identification Using HRV Features of ECG Signals, International Journal of Electrical and Computer Engineering (IJECE), 8(5) (2018) 3940-3948.
 Tushar R., Hemanth Kumar G., Anitha S., and Shriram S, EEG Wave-Based Identification of Sleep Disorders, AIP Conference Proceedings, 2039(1) (2018) .
 Vijayakumargurrala, Padmarajukoppireddi, and Padmasaiyarlagadda, Detection of Sleep Apnea Based on the Analysis of Sleep Stages Data Using Single-Channel EEG, Treatment Du Signal, 38(2) (2021) 431-436.
 Mahmmudqatmh, Talal Bonny and Ferasbarneih, Sleep Apnea Detection Based on ECG Signals Using Discrete Wavelet Transform and Artificial Neural Network, IEEE Conference on Advances in Science and Engineering Technology International Conferences (ASET), (2022).
 MATLAB R2018b//Www.Mathworks.Com//
 J.G. Proakis, J.G Manolakis, Digitalsignal Processing Principles, Algorithms, and Applications, Prentice-Hall, Inc.2002, 4th Edition, (2002).
 Saeed.Vaseghi, Advanced Digital Signal Processing and Noise Reduction, 2nd Edition