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Current Pharmaceutical Design

Editor-in-Chief

ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

Review Article

Role of Oxidative Stress and Inflammation in Insomnia Sleep Disorder and Cardiovascular Diseases: Herbal Antioxidants and Anti-inflammatory Coupled with Insomnia Detection using Machine Learning

Author(s): Md. Belal Bin Heyat*, Faijan Akhtar, Arshiya Sultana, Saifullah Tumrani, Bibi Nushrina Teelhawod, Rashid Abbasi, Mohammad Amjad Kamal, Abdullah Y. Muaad, Dakun Lai* and Kaishun Wu*

Volume 28, Issue 45, 2022

Published on: 13 December, 2022

Page: [3618 - 3636] Pages: 19

DOI: 10.2174/1381612829666221201161636

Price: $65

Abstract

Insomnia is well-known as trouble in sleeping and enormously influences human life due to the shortage of sleep. Reactive Oxygen Species (ROS) accrue in neurons during the waking state, and sleep has a defensive role against oxidative damage and dissipates ROS in the brain. In contrast, insomnia is the source of inequity between ROS generation and removal by an endogenous antioxidant defense system. The relationship between insomnia, depression, and anxiety disorders damages the cardiovascular systems' immune mechanisms and functions. Traditionally, polysomnography is used in the diagnosis of insomnia. This technique is complex, with a long time overhead. In this work, we have proposed a novel machine learning-based automatic detection system using the R-R intervals extracted from a single-lead electrocardiograph (ECG). Additionally, we aimed to explore the role of oxidative stress and inflammation in sleeping disorders and cardiovascular diseases, antioxidants’ effects, and the psychopharmacological effect of herbal medicine. This work has been carried out in steps, which include collecting the ECG signal for normal and insomnia subjects, analyzing the signal, and finally, automatic classification. We used two approaches, including subjects (normal and insomnia), two sleep stages, i.e., wake and rapid eye movement, and three Machine Learning (ML)-based classifiers to complete the classification. A total number of 3000 ECG segments were collected from 18 subjects. Furthermore, using the theranostics approach, the role of mitochondrial dysfunction causing oxidative stress and inflammatory response in insomnia and cardiovascular diseases was explored. The data from various databases on the mechanism of action of different herbal medicines in insomnia and cardiovascular diseases with antioxidant and antidepressant activities were also retrieved. Random Forest (RF) classifier has shown the highest accuracy (subjects: 87.10% and sleep stage: 88.30%) compared to the Decision Tree (DT) and Support Vector Machine (SVM). The results revealed that the suggested method could perform well in classifying the subjects and sleep stages. Additionally, a random forest machine learning-based classifier could be helpful in the clinical discovery of sleep complications, including insomnia. The evidence retrieved from the databases showed that herbal medicine contains numerous phytochemical bioactives and has multimodal cellular mechanisms of action, viz., antioxidant, anti-inflammatory, vasorelaxant, detoxifier, antidepressant, anxiolytic, and cell-rejuvenator properties. Other herbal medicines have a GABA-A receptor agonist effect. Hence, we recommend that the theranostics approach has potential and can be adopted for future research to improve the quality of life of humans.

Keywords: AI, detection, electrocardiogram, insomnia, oxidative stress, mitochondria, sleep disorder, ROS, diagnosis, nervous system, sleep.

[1]
Heyat MBB, Akhtar F, Khan MH, et al. Detection, treatment planning, and genetic predisposition of bruxism: A systematic mapping process and network visualization technique. CNS Neurol Disord Drug Targets 2021; 20(8): 755-75.
[http://dx.doi.org/10.2174/18715273MTExjMzMh3] [PMID: 33172381]
[2]
Hasan YM, Bin Heyat B, Siddiqui MM, Azad S, Akhtar F. An overview of sleep and stages of sleep. Int J Adv Res Comput Commun Eng 2015; 4: 505-7.
[http://dx.doi.org/10.17148/IJARCCE.2015.412144]
[3]
Bin Heyat MB, Akhtar F, Ansari MA, et al. Progress in detection of insomnia sleep disorder: A comprehensive review. Curr Drug Targets 2021; 22(6): 672-84.
[http://dx.doi.org/10.2174/1389450121666201027125828] [PMID: 33109045]
[4]
Sultana A, Khanam M, Rahman K. Traditional Unani medicine in flu-like epidemics and COVID-19 during pregnancy : A literary research. Cell Med 2021; 11: 1-23.
[http://dx.doi.org/10.5667/CellMed.2021.0020]
[5]
Hillman DR, Lack LC. Public health implications of sleep loss: The community burden. Med J Aust 2013; 199(S8): S7-S10.
[http://dx.doi.org/10.5694/mja13.10620] [PMID: 24138358]
[6]
Bin Heyat MB, Akhtar F, Khan A, et al. A novel hybrid machine learning classification for the detection of bruxism patients using physiological signals. Appl Sci 2020; 10(21): 7410.
[http://dx.doi.org/10.3390/app10217410]
[7]
Allan Hobson J. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Electroencephalogr Clin Neurophysiol 1969; 26(6): 644.
[http://dx.doi.org/10.1016/0013-4694(69)90021-2]
[8]
Grigg-Damberger MM. The AASM scoring manual four years later. J Clin Sleep Med 2012; 8(3): 323-32.
[http://dx.doi.org/10.5664/jcsm.1928] [PMID: 22701392]
[9]
Bb H, Akhtar F, Mehdi A, Azad S, Azad S, Azad S. Normalized power are used in the diagnosis of insomnia medical sleep syndrome through EMG1-EMG2 channel. Austin J Sleep Disord 2017; 4: 2-4.
[10]
Bin Heyat MB. Insomnia: Medical sleep disorder & diagnosis. (1st ed.), Hamburg, Germany: Anchor Academic Publishing 2016.
[11]
Bin Heyat B, Akhtar F, Singh SK, Siddiqui MM. Hamming Window are used in the Prognostic of Insomnia. International Seminar on present scenario & future prospectives of research in engineering & sciences (ISPSFPRES-17) 2017; 65-71.
[12]
Bin Heyat MB, Akhtar F, Sikandar M, Siddiqui H, Azad S. An overview of dalk therapy and treatment of insomnia in dalk therapy. 2015.
[13]
Bin Heyat MB, Lai D, Akhtar F, et al. Bruxism detection using single-channel C4-A1 on human sleep S2 stage recording. In: Gupta D, Bhattacharyya S, Khanna A, Eds. Intell Data Anal. (1st ed.). John Wiley & Sons 2020; pp. 347-67.
[http://dx.doi.org/10.1002/9781119544487.ch17]
[14]
Bin Heyat MB, Akhtar F, Ammar M, Hayat B, Azad S. Power spectral density are used in the investigation of insomnia neurological disorder. XL- Pre Congr Symp, King George Medical University & State Takmeelut-Tib College and Hospital, Lucknow, Uttar Pradesh, India, Lucknow, UP, India. 2016.
[15]
Morin CM, Drake CL, Harvey AG, et al. Insomnia disorder. Nat Rev Dis Primers 2015; 1(1): 15026.
[http://dx.doi.org/10.1038/nrdp.2015.26] [PMID: 27189779]
[16]
Buysse DJ. Insomnia. JAMA 2013; 309(7): 706-16.
[http://dx.doi.org/10.1001/jama.2013.193] [PMID: 23423416]
[17]
Panda S, Taly A, Sinha S, Gururaj G, Girish N, Nagaraja D. Sleep- related disorders among a healthy population in South India. Neurol India 2012; 60(1): 68-74.
[http://dx.doi.org/10.4103/0028-3886.93601] [PMID: 22406784]
[18]
Bittencourt LRA, Santos-Silva R, Taddei JA, Andersen ML, de Mello MT, Tufik S. Sleep complaints in the adult Brazilian population: A national survey based on screening questions. J Clin Sleep Med 2009; 5(5): 459-63.
[http://dx.doi.org/10.5664/jcsm.27603] [PMID: 19961032]
[19]
Lucke-Wold BP, Smith KE, Nguyen L, et al. Sleep disruption and the sequelae associated with traumatic brain injury. Neurosci Biobehav Rev 2015; 55: 68-77.
[http://dx.doi.org/10.1016/j.neubiorev.2015.04.010] [PMID: 25956251]
[20]
Gulec M, Ozkol H, Selvi Y, et al. Oxidative stress in patients with primary insomnia. Prog Neuropsychopharmacol Biol Psychiatry 2012; 37(2): 247-51.
[http://dx.doi.org/10.1016/j.pnpbp.2012.02.011] [PMID: 22401887]
[21]
Zakkar M, Guida G, Suleiman MS, Angelini GD. Cardiopulmonary bypass and oxidative stress. Oxid Med Cell Longev 2015; 2015: 1-8.
[http://dx.doi.org/10.1155/2015/189863] [PMID: 25722792]
[22]
Aghili-Mehrizi S, Williams E, Yan S, Willman M, Willman J, Lucke-Wold B. Secondary mechanisms of neurotrauma: A closer look at the evidence. Diseases 2022; 10(2): 30.
[http://dx.doi.org/10.3390/diseases10020030] [PMID: 35645251]
[23]
Hill VM, O’Connor RM, Sissoko GB, et al. A bidirectional relationship between sleep and oxidative stress in Drosophila. PLoS Biol 2018; 16(7): e2005206.
[http://dx.doi.org/10.1371/journal.pbio.2005206] [PMID: 30001323]
[24]
Javaheri S, Redline S. Insomnia and risk of cardiovascular disease. Chest 2017; 152: 435-44.
[http://dx.doi.org/10.1016/j.chest.2017.01.026]
[25]
Qaseem A, Kansagara D, Forciea MA, et al. Management of chronic insomnia disorder in adults: A clinical practice guideline from the American college of physicians. Ann Intern Med 2016; 165(2): 125-33.
[http://dx.doi.org/10.7326/M15-2175] [PMID: 27136449]
[26]
Sateia MJ, Buysse DJ, Krystal AD, Neubauer DN, Heald JL. Clinical practice guideline for the pharmacologic treatment of chronic insomnia in adults: An American academy of sleep medicine clinical practice guideline. J Clin Sleep Med 2017; 13(2): 307-49.
[http://dx.doi.org/10.5664/jcsm.6470] [PMID: 27998379]
[27]
Ong JC, Manber R, Segal Z, Xia Y, Shapiro S, Wyatt JK. A randomized controlled trial of mindfulness meditation for chronic insomnia. Sleep 2014; 37(9): 1553-63.
[http://dx.doi.org/10.5665/sleep.4010] [PMID: 25142566]
[28]
Zammit G. The prevalence, morbidities, and treatments of insomnia. CNS Neurol Disord Drug Targets 2007; 6(1): 3-16.
[http://dx.doi.org/10.2174/187152707779940754] [PMID: 17305550]
[29]
Wang Y, Zou J, Jia Y, et al. A study on the mechanism of lavender in the treatment of insomnia based on network pharmacology. Comb Chem High Throughput Screen 2020; 23(5): 419-32.
[http://dx.doi.org/10.2174/1386207323666200401095008] [PMID: 32233997]
[30]
Liu L, Liu C, Wang Y, Wang P, Li Y, Li B. Herbal medicine for anxiety, depression and insomnia. Curr Neuropharmacol 2015; 13(4): 481-93.
[http://dx.doi.org/10.2174/1570159X1304150831122734] [PMID: 26412068]
[31]
Ohayon MM, Reynolds CF III. Epidemiological and clinical relevance of insomnia diagnosis algorithms according to the DSM-IV and the International Classification of Sleep Disorders (ICSD). Sleep Med 2009; 10(9): 952-60.
[http://dx.doi.org/10.1016/j.sleep.2009.07.008] [PMID: 19748312]
[32]
Morin CM, Belleville G, Bélanger L, Ivers H. The Insomnia Severity Index: Psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep 2011; 34(5): 601-8.
[http://dx.doi.org/10.1093/sleep/34.5.601] [PMID: 21532953]
[33]
Aydın S, Saraoǧlu HM, Kara S. Singular spectrum analysis of sleep EEG in insomnia. J Med Syst 2011; 35(4): 457-61.
[http://dx.doi.org/10.1007/s10916-009-9381-7] [PMID: 20703545]
[34]
Israel B, Buysse DJ, Krafty RT, Begley A, Miewald J, Hall M. Short-term stability of sleep and heart rate variability in good sleepers and patients with insomnia: For some measures, one night is enough. Sleep 2012; 35(9): 1285-91.
[http://dx.doi.org/10.5665/sleep.2088] [PMID: 22942507]
[35]
Bin Heyat B, Hasan YM, Siddiqui MM. EEG signals and wireless transfer of EEG signals. Int J Adv Res Comput Commun Eng 2015; 4: 10-2.
[http://dx.doi.org/10.17148/IJARCCE.2015.412143]
[36]
Ali L, Rahman A, Khan A, Zhou M, Javeed A, Khan JA. An automated diagnostic system for heart disease prediction based on X2 statistical model and optimally configured deep neural network. IEEE Access 2019; 7: 34938-45.
[http://dx.doi.org/10.1109/ACCESS.2019.2904800]
[37]
AlShorman O, Masadeh M, Bin Heyat MB, et al. Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection. J Integr Neurosci 2022; 21(1): 20.
[http://dx.doi.org/10.31083/j.jin2101020]
[38]
Siddiqui MM, Srivastava G, Saeed SH. Diagnosis of insomnia sleep disorder using short time frequency analysis of PSD approach applied on EEG signal using channel ROC-LOC. Sleep Sci 2016; 9(3): 186-91.
[http://dx.doi.org/10.1016/j.slsci.2016.07.002] [PMID: 28123658]
[39]
Gemignani A, Laurino M, Provini F, et al. Thalamic contribution to sleep slow oscillation features in humans: A single case cross sectional EEG study in Fatal Familial Insomnia. Sleep Med 2012; 13(7): 946-52.
[http://dx.doi.org/10.1016/j.sleep.2012.03.007] [PMID: 22609023]
[40]
Kaplan R, Wang Y, Loparo K, Kelly M, Bootzin R. Performance evaluation of an automated single-channel sleep-wake detection algorithm. Nat Sci Sleep 2014; 6: 113-22.
[http://dx.doi.org/10.2147/NSS.S71159] [PMID: 25342922]
[41]
Hamida ST-B, Ahmed B, Cvetkovic D, Jovanov E, Kennedy G, Penzel T. A new era in sleep monitoring: The application of mobile technologies in insomnia diagnosis. Mobile Health: The Technology Road Map. 2015; pp. 101-27.
[http://dx.doi.org/10.1007/978-3-319-12817-7_5]
[42]
Ben Hamida ST, Ahmed B, Penzel T. A novel insomnia identification method based on Hjorth parameters 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).
[http://dx.doi.org/10.1109/ISSPIT.2015.7394397]
[43]
Redeker NS, Stein S. Characteristics of sleep in patients with stable heart failure versus a comparison group. Heart Lung 2006; 35(4): 252-61.
[http://dx.doi.org/10.1016/j.hrtlng.2005.10.007] [PMID: 16863897]
[44]
Redeker NS, Jeon S, Muench U, Campbell D, Walsleben J, Rapoport DM. Insomnia symptoms and daytime function in stable heart failure. Sleep 2010; 33(9): 1210-6.
[http://dx.doi.org/10.1093/sleep/33.9.1210] [PMID: 20857868]
[45]
Lai D, Zhang X, Zhang Y, Bin Heyat MB. Convolutional neural network based detection of atrial fibrillation combing R-R intervals and F-wave frequency spectrum. Annu Int Conf IEEE Eng Med Biol Soc. 2019: 4897-900.
[http://dx.doi.org/10.1109/EMBC.2019.8856342]
[46]
Hassan AR, Bhuiyan MIH. Computer-aided sleep staging using complete ensemble empirical mode decomposition with adaptive noise and bootstrap aggregating. Biomed Signal Process Control 2016; 24: 1-10.
[http://dx.doi.org/10.1016/j.bspc.2015.09.002]
[47]
Lai D, Heyat MBB, Khan FI, Zhang Y. Prognosis of sleep bruxism using power spectral density approach applied on EEG signal of both EMG1-EMG2 and ECG1-ECG2 channels. IEEE Access 2019; 7: 82553-62.
[http://dx.doi.org/10.1109/ACCESS.2019.2924181]
[48]
Bin Heyat MB, Lai D, Akhtar F, Bin Hayat MA, Azad S. Short Time Frequency Analysis of Theta Activity for the Diagnosis of Bruxism on EEG Sleep Record. Advanced Computational Intelligence Techniques for Virtual Reality in Healthcare Studies in Computational Intelligence. Cham: Springer 2020; 875.
[http://dx.doi.org/10.1007/978-3-030-35252-3_4]
[49]
Goldberger AL, Amaral LAN, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 2000; 101(23): E215-20.
[http://dx.doi.org/10.1161/01.CIR.101.23.e215] [PMID: 10851218]
[50]
Costa M, Moody GB, Henry I, Goldberger AL. PhysioNet: An NIH research resource for complex signals. J Electrocardiol 2003; 36(S1): 139-44.
[http://dx.doi.org/10.1016/j.jelectrocard.2003.09.038]
[51]
Brown RE, Basheer R, McKenna JT, Strecker RE, McCarley RW. Control of sleep and wakefulness. Physiol Rev 2012; 92(3): 1087-187.
[http://dx.doi.org/10.1152/physrev.00032.2011] [PMID: 22811426]
[52]
Heyat MBB, Lai D, Khan FI, Zhang Y. Sleep bruxism detection using decision tree method by the combination of C4-P4 and C4-A1 channels of scalp EEG. IEEE Access 2019; 7: 102542-53.
[http://dx.doi.org/10.1109/ACCESS.2019.2928020]
[53]
Hassan AR, Bhuiyan MIH. An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting. Neurocomputing 2017; 219: 76-87.
[http://dx.doi.org/10.1016/j.neucom.2016.09.011]
[54]
Hassan AR, Bhuiyan MIH. Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting. Comput Methods Programs Biomed 2017; 140: 201-10.
[http://dx.doi.org/10.1016/j.cmpb.2016.12.015] [PMID: 28254077]
[55]
Dodds KL, Miller CB, Kyle SD, Marshall NS, Gordon CJ. Heart rate variability in insomnia patients: A critical review of the literature. Sleep Med Rev 2017; 33: 88-100.
[http://dx.doi.org/10.1016/j.smrv.2016.06.004] [PMID: 28187954]
[56]
Thayer JF, Åhs F, Fredrikson M, Sollers JJ III, Wager TD. A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health. Neurosci Biobehav Rev 2012; 36(2): 747-56.
[http://dx.doi.org/10.1016/j.neubiorev.2011.11.009] [PMID: 22178086]
[57]
Huang S, Li J, Zhang P, Zhang W. Detection of mental fatigue state with wearable ECG devices. Int J Med Inform 2018; 119: 39-46.
[http://dx.doi.org/10.1016/j.ijmedinf.2018.08.010] [PMID: 30342684]
[58]
Schwerdtfeger AR, Schwarz G, Pfurtscheller K, Thayer JF, Jarczok MN, Pfurtscheller G. Heart rate variability (HRV): From brain death to resonance breathing at 6 breaths per minute. Clin Neurophysiol 2020; 131(3): 676-93.
[http://dx.doi.org/10.1016/j.clinph.2019.11.013] [PMID: 31978852]
[59]
Lai D, Zhang Y, Zhang X, Su Y, Bin Heyat MB. An automated strategy for early risk identification of sudden cardiac death by using machine learning approach on measurable arrhythmic risk markers. IEEE Access 2019; 7: 94701-16.
[http://dx.doi.org/10.1109/ACCESS.2019.2925847]
[60]
Panigrahi R, Borah S. Social networks and their uses in the field of secondary education. Social Network Analytics Computational Research Methods and Techniques. Academic Press. Elsevier: Amsterdum 2019; 203-26.
[http://dx.doi.org/10.1016/B978-0-12-815458-8.00001-3]
[61]
Breiman L. Random forests. Mach Learn 2001; 45(1): 5-32.
[http://dx.doi.org/10.1023/A:1010933404324]
[62]
Bin Heyat MB, Akhtar F, Abbas SJ, et al. Wearable flexible electronics based cardiac electrode for researcher mental stress detection system using machine learning models on single lead electrocardiogram signal. Biosensors 2022; 12(6): 427.
[http://dx.doi.org/10.3390/bios12060427]
[63]
Iqbal MS, Abbasi R, Bin Heyat MB, et al. Recognition of mRNA N4 Acetylcytidine (ac4C) by using non-deep vs. deep learning. Appl Sci (Basel) 2022; 12(3): 1344.
[http://dx.doi.org/10.3390/app12031344]
[64]
Sultana A, Begum W, Saeedi R, et al. Experimental and computational approaches for the classification and correlation of temperament (Mizaj) and uterine dystemperament (Su’-I-Mizaj Al-Rahim) in abnormal vaginal discharge (Sayalan Al-Rahim) based on clinical analysis using support vector machine. Complexity 2022; 2022: 1-16.
[http://dx.doi.org/10.1155/2022/5718501]
[65]
Sultana A, Rahman K, Bin Heyat MB, Sumbul F. Role of inflammation, oxidative stress, and mitochondrial changes in premenstrual psychosomatic behavioral symptoms with anti-inflammatory, antioxidant herbs, and nutritional supplements. Oxid Med Cell Longev 2022; 2022: 3599246.
[http://dx.doi.org/10.1155/2022/3599246]
[66]
Ukwuoma CC, Zhiguang Q, Bin Heyat MB, Ali L, Almaspoor Z, Monday HN. Recent advancements in fruit detection and classification using deep learning techniques. Math Probl Eng 2022; 2022: 1-29.
[http://dx.doi.org/10.1155/2022/9210947]
[67]
Ullah H, Bin Heyat MB, AlSalman H, et al. An effective and lightweight deep electrocardiography arrhythmia recognition model using novel special and native structural regularization techniques on cardiac signal. J Healthc Eng 2011; 2022: 3408501.
[http://dx.doi.org/10.1155/2022/3408501] [PMID: 35449862]
[68]
Nawabi AK, Jinfang S, Abbasi R, et al. Segmentation of drug-treated cell image and mitochondrial-oxidative stress using deep convolutional neural network. Oxid Med Cell Longev 2022; 2022: 5641727.
[http://dx.doi.org/10.1155/2022/5641727] [PMID: 35663204]
[69]
Ali L, He Z, Cao W, Rauf HT, Imrana Y, Bin Heyat MB. MMDD-ensemble: A multimodal data–driven ensemble approach for Parkinson’s disease detection. Front Neurosci 2021; 15: 754058.
[http://dx.doi.org/10.3389/fnins.2021.754058] [PMID: 34790091]
[70]
Choi BH, Chung GS, Lee JS, Jeong DU, Park KS. Slow-wave sleep estimation on a load-cell-installed bed: A non-constrained method. Physiol Meas 2009; 30(11): 1163-70.
[http://dx.doi.org/10.1088/0967-3334/30/11/002] [PMID: 19794234]
[71]
Yoon H, Hwang SH, Choi JW, Lee YJ, Jeong DU, Park KS. Slow-wave sleep estimation for healthy subjects and OSA patients using R-R intervals. IEEE J Biomed Health Inform 2018; 22(1): 119-28.
[http://dx.doi.org/10.1109/JBHI.2017.2712861] [PMID: 28600268]
[72]
Shahin M, Ahmed B, Hamida STB, Mulaffer FL, Glos M, Penzel T. Deep learning and insomnia: Assisting clinicians with their diagnosis. IEEE J Biomed Health Inform 2017; 21(6): 1546-53.
[http://dx.doi.org/10.1109/JBHI.2017.2650199] [PMID: 28092583]
[73]
Navarro B, López-Torres J, Andrés F, Latorre JM, Montes MJ, Párraga I. Validation of the insomnia in the elderly scale for the detection of insomnia in older adults. Geriatr Gerontol Int 2013; 13(3): 646-53.
[http://dx.doi.org/10.1111/j.1447-0594.2012.00958.x] [PMID: 23171440]
[74]
Alsaadi SM, McAuley JH, Hush JM, et al. Detecting insomnia in patients with low back pain: Accuracy of four self-report sleep measures. BMC Musculoskelet Disord 2013; 14(1): 196.
[http://dx.doi.org/10.1186/1471-2474-14-196] [PMID: 23805978]
[75]
Binder P, Heintz AL, Haller DM, et al. Detection of adolescent suicidality in primary care: An international utility study of the bullying-insomnia-tobacco-stress test. Early Interv Psychiatry 2020; 14(1): 80-6.
[http://dx.doi.org/10.1111/eip.12828] [PMID: 31058453]
[76]
Felder JN, Hartman AR, Epel ES, Prather AA. Pregnant patient perceptions of provider detection and treatment of insomnia. Behav Sleep Med 2020; 18(6): 787-96.
[http://dx.doi.org/10.1080/15402002.2019.1688153] [PMID: 31694403]
[77]
Gill JM, Lee H, Baxter T, et al. A diagnosis of insomnia is associated with differential expression of sleep-regulating genes in military personnel. Biol Res Nurs 2015; 17(4): 384-92.
[http://dx.doi.org/10.1177/1099800415575343] [PMID: 25767060]
[78]
Zheng X, He Y, Yin F, et al. Pharmacological interventions for the treatment of insomnia: Quantitative comparison of drug efficacy. Sleep Med 2020; 72: 41-9.
[http://dx.doi.org/10.1016/j.sleep.2020.03.022] [PMID: 32544795]
[79]
Bramoweth AD, Lederer LG, Youk AO, Germain A, Chinman MJ. Brief behavioral treatment for insomnia vs. cognitive behavioral therapy for insomnia: Results of a randomized noninferiority clinical trial among veterans. Behav Ther 2020; 51(4): 535-47.
[http://dx.doi.org/10.1016/j.beth.2020.02.002] [PMID: 32586428]
[80]
Längkvist M, Karlsson L, Loutfi A. Sleep stage classification using unsupervised feature learning. Adv Artif Neural Syst 2012; 2012: 1-9.
[http://dx.doi.org/10.1155/2012/107046]
[81]
Boe AJ, McGee Koch LL, O’Brien MK, et al. Automating sleep stage classification using wireless, wearable sensors. NPJ Digit Med 2019; 2(1): 131.
[http://dx.doi.org/10.1038/s41746-019-0210-1] [PMID: 31886412]
[82]
Mitsukura Y, Fukunaga K, Yasui M, Mimura M. Sleep stage detection using only heart rate. Health Informatics J 2020; 26(1): 376-87.
[http://dx.doi.org/10.1177/1460458219827349] [PMID: 30782049]
[83]
Li Q, Li Q, Liu C, Shashikumar SP, Nemati S, Clifford GD. Deep learning in the cross-time frequency domain for sleep staging from a single-lead electrocardiogram. Physiol Meas 2018; 39(12): 124005.
[http://dx.doi.org/10.1088/1361-6579/aaf339] [PMID: 30524025]
[84]
Sridhar N, Shoeb A, Stephens P, et al. Deep learning for automated sleep staging using instantaneous heart rate. NPJ Digit Med 2020; 3(1): 106.
[http://dx.doi.org/10.1038/s41746-020-0291-x] [PMID: 32885052]
[85]
Abdullah H, Penzel T, Cvetkovic D. Detection of insomnia from EEG and ECG. 15th International Conference on Biomedical Engineering (ICBME 2013): Singapore 2014; 687-90.
[http://dx.doi.org/10.1007/978-3-319-02913-9_175]
[86]
Abdullah H, Penzel T, Cvetkovic D. Sleep heart rate variability analysis and k-nearest neighbours classification of primary insomnia. Int J Integr Eng 2018; 10(7): 66-75.
[http://dx.doi.org/10.30880/ijie.2018.10.07.007]
[87]
Radha M, Fonseca P, Moreau A, et al. Sleep stage classification from heart-rate variability using long short-term memory neural networks. Sci Rep 2019; 9(1): 14149.
[http://dx.doi.org/10.1038/s41598-019-49703-y] [PMID: 31578345]
[88]
Fonseca P, van Gilst MM, Radha M, et al. Automatic sleep staging using heart rate variability, body movements, and recurrent neural networks in a sleep disordered population. Sleep 2020; 43(9): zsaa048.
[http://dx.doi.org/10.1093/sleep/zsaa048] [PMID: 32249911]
[89]
Besedovsky L, Lange T, Haack M. The sleep-immune crosstalk in health and disease. Physiol Rev 2019; 99(3): 1325-80.
[http://dx.doi.org/10.1152/physrev.00010.2018] [PMID: 30920354]
[90]
Somerville WF. Sleep and sleeplessness. BMJ 1925; 1(3361): 1020-1.
[http://dx.doi.org/10.1136/bmj.1.3361.1020-a] [PMID: 20772036]
[91]
Opp MR. Cytokines and sleep. Sleep Med Rev 2005; 9(5): 355-64.
[http://dx.doi.org/10.1016/j.smrv.2005.01.002] [PMID: 16102986]
[92]
Vgontzas AN, Fernandez-Mendoza J, Liao D, Bixler EO. Insomnia with objective short sleep duration: The most biologically severe phenotype of the disorder. Sleep Med Rev 2013; 17(4): 241-54.
[http://dx.doi.org/10.1016/j.smrv.2012.09.005] [PMID: 23419741]
[93]
Vgontzas AN, Zoumakis M, Papanicolaou DA, et al. Chronic insomnia is associated with a shift of interleukin-6 and tumor necrosis factor secretion from night time to daytime. Metabolism 2002; 51(7): 887-92.
[http://dx.doi.org/10.1053/meta.2002.33357] [PMID: 12077736]
[94]
Burokas A, Moloney RD, Dinan TG, Cryan JF. Microbiota regulation of the mammalian gut-brain axis. Adv Appl Microbiol 2015; 91: 1-62.
[http://dx.doi.org/10.1016/bs.aambs.2015.02.001] [PMID: 25911232]
[95]
Floam S, Simpson N, Nemeth E, Scott-Sutherland J, Gautam S, Haack M. Sleep characteristics as predictor variables of stress systems markers in insomnia disorder. J Sleep Res 2015; 24(3): 296-304.
[http://dx.doi.org/10.1111/jsr.12259] [PMID: 25524529]
[96]
Savard J, Laroche L, Simard S, Ivers H, Morin CM. Chronic insomnia and immune functioning. Psychosom Med 2003; 65(2): 211-21.
[http://dx.doi.org/10.1097/01.PSY.0000033126.22740.F3] [PMID: 12651988]
[97]
Ader R, Cohen N, Felten DL. Brain, behavior, and immunity. Brain Behav Immun 1987; 1(1): 1-6.
[http://dx.doi.org/10.1016/0889-1591(87)90001-8] [PMID: 3451780]
[98]
Irwin MR. Why sleep is important for health: A psychoneuroimmunology perspective. Annu Rev Psychol 2015; 66(1): 143-72.
[http://dx.doi.org/10.1146/annurev-psych-010213-115205] [PMID: 25061767]
[99]
Rahmani M, Rahmani F, Rezaei N. The brain-derived neurotrophic factor: Missing link between sleep deprivation, insomnia, and depression. Neurochem Res 2020; 45(2): 221-31.
[http://dx.doi.org/10.1007/s11064-019-02914-1] [PMID: 31782101]
[100]
Ramanathan L, Gulyani S, Nienhuis R, Siegel JM. Sleep deprivation decreases superoxide dismutase activity in rat hippocampus and brainstem. Neuroreport 2002; 13(11): 1387-90.
[http://dx.doi.org/10.1097/00001756-200208070-00007] [PMID: 12167758]
[101]
Lopez-Jimenez F, Sert Kuniyoshi FH, Gami A, Somers VK. Obstructive sleep apnea: Implications for cardiac and vascular disease. Chest 2008; 133(3): 793-804.
[http://dx.doi.org/10.1378/chest.07-0800] [PMID: 18321908]
[102]
Suzuki M, Fukuhara K, Unno M, et al. Correlation between plasma and hepatic phosphatidylcholine hydroperoxide, energy charge, and total glutathione content in ischemia reperfusion injury of rat liver. Hepatogastroenterology 2000; 47(34): 1082-9.
[PMID: 11020884]
[103]
Everson CA, Laatsch CD, Hogg N. Antioxidant defense responses to sleep loss and sleep recovery. Am J Physiol Regul Integr Comp Physiol 2005; 288(2): R374-83.
[http://dx.doi.org/10.1152/ajpregu.00565.2004] [PMID: 15472007]
[104]
Shaito A, Thuan DTB, Phu HT, et al. Herbal medicine for cardiovascular diseases: Efficacy, mechanisms, and safety. Front Pharmacol 2020; 11: 422.
[http://dx.doi.org/10.3389/fphar.2020.00422] [PMID: 32317975]
[105]
Logsdon AF, Lucke-Wold BP, Nguyen L, et al. Salubrinal reduces oxidative stress, neuroinflammation and impulsive-like behavior in a rodent model of traumatic brain injury. Brain Res 2016; 1643: 140-51.
[http://dx.doi.org/10.1016/j.brainres.2016.04.063] [PMID: 27131989]
[106]
Sarris J, Panossian A, Schweitzer I, Stough C, Scholey A. Herbal medicine for depression, anxiety and insomnia: A review of psychopharmacology and clinical evidence. Eur Neuropsychopharmacol 2011; 21(12): 841-60.
[http://dx.doi.org/10.1016/j.euroneuro.2011.04.002] [PMID: 21601431]
[107]
Khare CP. Indian Medicinal Plants: An Illustrated Dictionary. Springer Science & Business Media 2007.
[108]
Komaki A, Rasouli B, Shahidi S. Anxiolytic effect of Borago officinalis (Boraginaceae) extract in male rats. Avicenna J Neuropsychophysiol 2015; 2(1)
[http://dx.doi.org/10.17795/ajnpp-27189]
[109]
Moliner C, Cásedas G, Barros L, Finimundy TC, Gómez-Rincón C, López V. Neuroprotective profile of edible flowers of borage (Borago officinalis L.) in two different models: Caenorhabditis elegans and neuro-2a cells. Antioxidants 2022; 11(7): 1244.
[http://dx.doi.org/10.3390/antiox11071244] [PMID: 35883735]
[110]
Zemmouri H, Ammar S, Boumendjel A, Messarah M, El Feki A, Bouaziz M. Chemical composition and antioxidant activity of Borago officinalis L. leaf extract growing in Algeria. Arab J Chem 2019; 12(8): 1954-63.
[http://dx.doi.org/10.1016/j.arabjc.2014.11.059]
[111]
Asad G, Redai A, Hakami A et al. Potential analgesic and anti-inflammatory effect of cuminum cyminum and Borago officinalis in rats and mice. Asian J Pharm Clin Res 2020; 138(1): 216-8.
[http://dx.doi.org/10.22159/ajpcr.2020.v13i1.36107]
[112]
Channa S, Dar A, Anjum S, Yaqoob M, Atta-ur-Rahman . Anti-inflammatory activity of Bacopa monniera in rodents. J Ethnopharmacol 2006; 104(1-2): 286-9.
[http://dx.doi.org/10.1016/j.jep.2005.10.009] [PMID: 16343831]
[113]
Sahoo S, Brijesh S. Anxiolytic activity of Coriandrum sativum seeds aqueous extract on chronic restraint stressed mice and effect on brain neurotransmitters. J Funct Foods 2020; 68: 103884.
[http://dx.doi.org/10.1016/j.jff.2020.103884]
[114]
Tang ELH, Rajarajeswaran J, Fung SY, Kanthimathi MS. Antioxidant activity of Coriandrum sativum and protection against DNA damage and cancer cell migration. BMC Complement Altern Med 2013; 13(1): 347.
[http://dx.doi.org/10.1186/1472-6882-13-347] [PMID: 24517259]
[115]
Salem M, Shaheen M, Tabbara A, Borjac J. Saffron extract and crocin exert anti-inflammatory and anti-oxidative effects in a repetitive mild traumatic brain injury mouse model. Sci Rep 2022; 12(1): 5004.
[http://dx.doi.org/10.1038/s41598-022-09109-9] [PMID: 35322143]
[116]
Silva GLD, Luft C, Lunardelli A, et al. Antioxidant, analgesic and anti-inflammatory effects of lavender essential oil. An Acad Bras Cienc 2015; 87(2 suppl): 1397-408.
[http://dx.doi.org/10.1590/0001-3765201520150056]
[117]
Thippeswamy BS, Mishra B, Veerapur VP, Gupta G. Anxiolytic activity of Nymphaea alba Linn. in mice as experimental models of anxiety. Indian J Pharmacol 2011; 43(1): 50-5.
[http://dx.doi.org/10.4103/0253-7613.75670] [PMID: 21455422]
[118]
Naoi M, Shamoto-Nagai M, Maruyama W. Neuroprotection of multifunctional phytochemicals as novel therapeutic strategy for neurodegenerative disorders: Antiapoptotic and antiamyloidogenic activities by modulation of cellular signal pathways. Future Neurol 2019; 14(1): FNL9.
[http://dx.doi.org/10.2217/fnl-2018-0028]
[119]
Zhivar S, Saeid AM, Ghaderi-Pakdel F. Viola odorata. I Bis Z 1951; 25: 328-30.
[http://dx.doi.org/10.1515/9783112359600-112]
[120]
Nani A, Murtaza B, Sayed Khan A, Khan NA, Hichami A. Antioxidant and anti-inflammatory potential of polyphenols contained in Mediterranean diet in obesity: Molecular mechanisms. Molecules 2021; 26(4): 985.
[http://dx.doi.org/10.3390/molecules26040985] [PMID: 33673390]
[121]
Owen PL, Johns T. Antioxidants in medicines and spices as cardioprotective agents in Tibetan highlanders. Pharm Biol 2002; 40(5): 346-57.
[http://dx.doi.org/10.1076/phbi.40.5.346.8461]
[122]
Ahmadi M, Khalili H, Abbasian L, Ghaeli P. Effect of Valerian in preventing neuropsychiatric adverse effects of Efavirenz in HIV- positive patients: A pilot randomized, placebo-controlled clinical trial. Ann Pharmacother 2017; 51(6): 457-64.
[http://dx.doi.org/10.1177/1060028017696105] [PMID: 28478716]
[123]
Alzoubi KH, Malkawi BS, Khabour OF, El-Elimat T, Alali FQ. Arbutus andrachne L. reverses sleep deprivation-induced memory impairments in rats. Mol Neurobiol 2018; 55(2): 1150-6.
[http://dx.doi.org/10.1007/s12035-017-0387-8] [PMID: 28101814]
[124]
Hieu TH, Dibas M, Surya Dila KA, et al. Therapeutic efficacy and safety of chamomile for state anxiety, generalized anxiety disorder, insomnia, and sleep quality: A systematic review and meta-analysis of randomized trials and quasi-randomized trials. Phytother Res 2019; 33(6): 1604-15.
[http://dx.doi.org/10.1002/ptr.6349] [PMID: 31006899]
[125]
Alzobaidi N, Quasimi H, Emad NA, Alhalmi A, Naqvi M. Bioactive compounds and traditional herbal medicine: Promising approaches for the treatment of dementia. Degener Neurol Neuromuscul Dis 2021; 11: 1-14.
[http://dx.doi.org/10.2147/DNND.S299589] [PMID: 33880073]
[126]
Barbalho SM, Direito R, Laurindo LF, et al. Ginkgo biloba in the aging process: A narrative review. Antioxidants 2022; 11(3): 525.
[http://dx.doi.org/10.3390/antiox11030525] [PMID: 35326176]
[127]
Sharma AK, Basu I, Singh S. Efficacy and safety of ashwagandha root extract in subclinical hypothyroid patients: A double-blind, randomized placebo-controlled trial. J Altern Complement Med 2018; 24(3): 243-8.
[http://dx.doi.org/10.1089/acm.2017.0183] [PMID: 28829155]

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