Jianjun Su¹, Enxiu Wei¹, Cain Clark², Kaixin Liang³, Xiaojiao Sun^4,*

International Journal of Mental Health Promotion, Vol.24, No.5, pp. 759-769, 2022, DOI:10.32604/ijmhp.2022.020152

Abstract Numerous studies links movement activity (e.g., physical activity, sedentary behavior [SB], and sleep) with mental health or illness indicators during the COVID-19 pandemic; however, research has typically examined time-use behaviors independently, rather than considering daily activity as a 24-hour time-use composition. This cross-sectional study aimed to use compositional isotemporal analysis to estimate the association between reallocation of time-use behaviors and depression symptoms in young adults in China. Participants (n = 1475; 68.0% of female; 20.7 [1.60] years) reported their time spent in moderate to vigorous physical activity (MVPA), light physical activity (LPA), SB, and sleep. Replacing SB with sleep, LPA,… More >

Ashit Kumar Dutta^1,*, Yasser Albagory², Manal Al Faraj¹, Yasir A. M. Eltahir³, Abdul Rahaman Wahab Sait⁴

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1517-1529, 2023, DOI:10.32604/csse.2023.026482

Abstract The recently developed machine learning (ML) models have the ability to obtain high detection rate using biomedical signals. Therefore, this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification Model on Electroencephalography (EEG) Biomedical Signals, named OSAE-SSCEEG technique. The major intention of the OSAE-SSCEEG technique is to find the sleep stage disorders using the EEG biomedical signals. The OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization approach. Moreover, the classification of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization (SAE-SR) with softmax (SM) approach. Finally, the parameter optimization of the SAE-SR technique is carried out… More >

Yuezi Liao^1,2,*, Xing Liu^1,2, Mengqin Zhang^1,2, Hao Hua³

Journal of Intelligent Medicine and Healthcare, Vol.1, No.1, pp. 1-10, 2022, DOI:10.32604/jimh.2022.029848

Abstract Aromatherapy is a sort of natural therapy for body maintenance using essential oils and vegetable oils extracted from natural plants. It belongs to the category of homeopathy. Aromatherapy combines the dual functions of art and treatment, comprehensively considers the needs of human physiology and psychology, and is widely used in the field of medical care. Aromatherapy is one of the complementary and alternative treatments extensively studied at home and abroad. It has a relieving effect on postoperative pain, sleep disturbance, nausea, vomiting and preoperative anxiety, and is an important intervention in perioperative care. A large number of research data show… More >

J. Premalatha^*, A. SahayaAnselin Nisha

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 811-826, 2023, DOI:10.32604/iasc.2023.026523

Abstract The traffic activity of fifth generation (5G) networks demand for new energy management techniques that is dynamic deep and longer duration of sleep as compared to the fourth generation (4G) network technologies that demand always for varied control and data signalling based on control base station (CBS) and data base station (DBS). Hence, this paper discusses the energy management in wireless cellular networks using wide range of control for twice the reduction in energy conservation in non-standalone deployment of 5G network. As the new radio (NR) based 5G network is configured to transmit signal blocks for every 20 ms, the proposed… More >

G. Ravikumar¹, K. Venkatachalam², Mohammed A. AlZain³, Mehedi Masud⁴, Mohamed Abouhawwash^5,6,*

Computer Systems Science and Engineering, Vol.44, No.1, pp. 945-959, 2023, DOI:10.32604/csse.2023.024605

Abstract Sleep apnea syndrome (SAS) is a breathing disorder while a person is asleep. The traditional method for examining SAS is Polysomnography (PSG). The standard procedure of PSG requires complete overnight observation in a laboratory. PSG typically provides accurate results, but it is expensive and time consuming. However, for people with Sleep apnea (SA), available beds and laboratories are limited. Resultantly, it may produce inaccurate diagnosis. Thus, this paper proposes the Internet of Medical Things (IoMT) framework with a machine learning concept of fully connected neural network (FCNN) with k-nearest neighbor (k-NN) classifier. This paper describes smart monitoring of a patient’s… More >

Shaoying Liu^1,2,#, Qian Yu^3,#, Md Mahbub Hossain⁴, Scott Doig⁵, Ran Bao⁶, Yaping Zhao⁷, Jin Yan^8,*, Xun Luo³, Jiaxuan Yang³, Arthur F. Kramer^9,10, Liye Zou³

International Journal of Mental Health Promotion, Vol.24, No.1, pp. 13-24, 2022, DOI:10.32604/IJMHP.2021.017660

Abstract This research is designed to investigate the relationship between the 24-h movement guidelines (24-HMG) and self-reported academic achievement (AA) using nationally representative data derived from the 2019 U.S. National Youth Risk Behaviour Survey. A multiple-stage cluster sampling procedure has been adopted to ensure a representative sample (N = 9127 adolescents; mean age = 15.7 years old; male% = 49.8%). Logistic regression has been adopted to obtain the odds ratio (OR) regarding the associations between adherence to 24-HMG and AA while controlling for ethnicity, body mass index, sex and age. The prevalence of meeting the 24-h movement guidelines in isolation and… More >

Wei Pei¹, Yan Li¹, Siuly Siuly^1,*, Peng Wen²

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 889-905, 2022, DOI:10.32604/cmc.2022.021830

Abstract Sleep stage classification plays a significant role in the accurate diagnosis and treatment of sleep-related diseases. This study aims to develop an efficient deep learning based scheme for correctly identifying sleep stages using multi-biological signals such as electroencephalography (EEG), electrocardiogram (ECG), electromyogram (EMG), and electrooculogram (EOG). Most of the prior studies in sleep stage classification focus on hand-crafted feature extraction methods. Traditional hand-crafted feature extraction methods choose features manually from raw data, which is tedious, and these features are limited in their ability to balance efficiency and accuracy. Moreover, most of the existing works on sleep staging are either single… More >

Saadullah Farooq Abbasi^1,2, Harun Jamil³, Wei Chen^2,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4619-4633, 2022, DOI:10.32604/cmc.2022.020318

Abstract Sleep stage classification can provide important information regarding neonatal brain development and maturation. Visual annotation, using polysomnography (PSG), is considered as a gold standard for neonatal sleep stage classification. However, visual annotation is time consuming and needs professional neurologists. For this reason, an internet of things and ensemble-based automatic sleep stage classification has been proposed in this study. 12 EEG features, from 9 bipolar channels, were used to train and test the base classifiers including convolutional neural network, support vector machine, and multilayer perceptron. Bagging and stacking ensembles are then used to combine the outputs for final classification. The proposed… More >

Kye S. Kim¹, Peter M. Kang¹, He Sook N. Kim^2,*

International Journal of Mental Health Promotion, Vol.23, No.3, pp. 303-317, 2021, DOI:10.32604/IJMHP.2021.016470

Abstract The extent of viral spread and strategies in dealing with the COVID-19 pandemic have been different in each country. There are overall increased mental health concerns in many countries but it is unclear what the general public individuals who do not have heighten vulnerability to stressors for existing mental diseases or significant physical illnesses were experiencing during the pandemic. We evaluated the stressors and mental health of general public in South Korea that has a relatively low confirmed cases and deaths. Responses on the on-line survey questions were used to assess the mental and physical symptoms in association with individuals’… More >

W. M. Faizal^1,2, N. N. N. Ghazali^2,*, C. Y. Khor¹, M. Z. Zainon², Irfan Anjum Badruddin^3,4,*, Sarfaraz Kamangar⁴, Norliza Binti Ibrahim⁵, Roziana Mohd Razi⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 583-604, 2021, DOI:10.32604/cmes.2021.015549

Abstract Background: Obstructive sleep apnea is a sleeping disorder that has troubled a sizeable population. There is an active area of research on obstructive sleep apnea that intends to better understand airflow behaviors and therefore treat patients more effectively. This paper aims to investigate the airflow characteristics of the upper airway in an obstructive sleep apnea (OSA) patient under light and heavy breathing conditions by using Turbulent Kinetic Energy (TKE), an accurate method in expressing the flow concentration mechanisms of sleeping disorders. It is important to visualize the concentration of flow in the upper airway in order to identify the severity… More >