In:
Current Pharmaceutical Design, Bentham Science Publishers Ltd., Vol. 28, No. 45 ( 2022-12), p. 3618-3636
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.
Type of Medium:
Online Resource
ISSN:
1381-6128
DOI:
10.2174/1381612829666221201161636
Language:
English
Publisher:
Bentham Science Publishers Ltd.
Publication Date:
2022
SSG:
15,3
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