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      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

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          Abstract

          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.

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          Most cited references115

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          PhysioBank, PhysioToolkit, and PhysioNet

          Circulation, 101(23)
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            The Insomnia Severity Index: psychometric indicators to detect insomnia cases and evaluate treatment response.

            Although insomnia is a prevalent complaint with significant morbidity, it often remains unrecognized and untreated. Brief and valid instruments are needed both for screening and outcome assessment. This study examined psychometric indices of the Insomnia Severity Index (ISI) to detect cases of insomnia in a population-based sample and to evaluate treatment response in a clinical sample. Participants were 959 individuals selected from the community for an epidemiological study of insomnia (Community sample) and 183 individuals evaluated for insomnia treatment and 62 controls without insomnia (Clinical sample). They completed the ISI and several measures of sleep quality, fatigue, psychological symptoms, and quality of life; those in the Clinical sample also completed sleep diaries, polysomnography, and interviews to validate their insomnia/good sleep status and assess treatment response. In addition to standard psychometric indices of reliability and validity, item response theory analyses were computed to examine ISI item response patterns. Receiver operating curves were used to derive optimal cutoff scores for case identification and to quantify the minimally important changes in relation to global improvement ratings obtained by an independent assessor. ISI internal consistency was excellent for both samples (Cronbach α of 0.90 and 0.91). Item response analyses revealed adequate discriminatory capacity for 5 of the 7 items. Convergent validity was supported by significant correlations between total ISI score and measures of fatigue, quality of life, anxiety, and depression. A cutoff score of 10 was optimal (86.1% sensitivity and 87.7% specificity) for detecting insomnia cases in the community sample. In the clinical sample, a change score of -8.4 points (95% CI: -7.1, -9.4) was associated with moderate improvement as rated by an independent assessor after treatment. These findings provide further evidence that the ISI is a reliable and valid instrument to detect cases of insomnia in the population and is sensitive to treatment response in clinical patients.
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              A meta-analysis of heart rate variability and neuroimaging studies: implications for heart rate variability as a marker of stress and health.

              The intimate connection between the brain and the heart was enunciated by Claude Bernard over 150 years ago. In our neurovisceral integration model we have tried to build on this pioneering work. In the present paper we further elaborate our model and update it with recent results. Specifically, we performed a meta-analysis of recent neuroimaging studies on the relationship between heart rate variability and regional cerebral blood flow. We identified a number of regions, including the amygdala and ventromedial prefrontal cortex, in which significant associations across studies were found. We further propose that the default response to uncertainty is the threat response and may be related to the well known negativity bias. Heart rate variability may provide an index of how strongly 'top-down' appraisals, mediated by cortical-subcortical pathways, shape brainstem activity and autonomic responses in the body. If the default response to uncertainty is the threat response, as we propose here, contextual information represented in 'appraisal' systems may be necessary to overcome this bias during daily life. Thus, HRV may serve as a proxy for 'vertical integration' of the brain mechanisms that guide flexible control over behavior with peripheral physiology, and as such provides an important window into understanding stress and health. Copyright © 2011 Elsevier Ltd. All rights reserved.
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                Author and article information

                Journal
                Current Pharmaceutical Design
                CPD
                Bentham Science Publishers Ltd.
                13816128
                December 2022
                December 2022
                : 28
                : 45
                : 3618-3636
                Affiliations
                [1 ]IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
                [2 ]School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
                [3 ]Department of Ilmul Qabalat wa Amraze Niswan, National Institute of Unani Medicine, Ministry of AYUSH, Bengaluru, Karnataka, India
                [4 ]Department of Computer Science, Bahria University, Karachi 75260, Pakistan
                [5 ]School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
                [6 ]Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment of Ministry of Education, School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
                [7 ]Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
                [8 ] King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
                [9 ] Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh
                [10 ] Enzymoics, Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia
                [11 ]Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore 570006, India
                [12 ] Sana’a Community College, Sana’a 5695, Yemen
                [13 ]BMI-EP Laboratory, School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
                Article
                10.2174/1381612829666221201161636
                36464881
                c450cb1d-4e15-4a5c-aefa-37e3c185b5b2
                © 2022
                History

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