Xiaolong Yang¹, Xin Yu¹, Liangbo Xie^1,*, Hao Xue², Mu Zhou¹, Qing Jiang¹

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2793-2806, 2021, DOI:10.32604/cmc.2021.016298

Abstract To address the limitations of traditional sleep monitoring methods that highly rely on sleeping posture without considering sleep apnea, an intelligent apnea monitoring system is designed based on commodity WiFi in this paper. By utilizing linear fitting and wavelet transform, the phase error of channel state information (CSI) of the receiving antenna is eliminated, and the noise of the signal amplitude is removed. Moreover, the short-time Fourier transform (STFT) and sliding window method are combined to segment received wireless signals. Finally, several important statistical characteristics are extracted, and a back propagation (BP) neural network model is built to identify apnea… More >

Jiahui Pan^1,6,*, Jianhao Zhang¹, Fei Wang^1,6, Wuhan Liu², Haiyun Huang^3,6, Weishun Tang³, Huijian Liao⁴, Man Li⁵, Jianhui Wu¹, Xueli Li², Dongming Quan², Yuanqing Li^3,6

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 53-71, 2021, DOI:10.32604/iasc.2021.015970

Abstract In this paper, an automatic sleep scoring system based on electroencephalogram (EEG) and electrooculogram (EOG) signals was proposed for sleep stage classification and depression detection. Our automatic sleep stage classification method contained preprocessing based on independent component analysis, feature extraction including spectral features, spectral edge frequency features, absolute spectral power, statistical features, Hjorth features, maximum-minimum distance and energy features, and a modified ReliefF feature selection. Finally, a support vector machine was employed to classify four states (awake, light sleep [LS], slow-wave sleep [SWS] and rapid eye movement [REM]). The overall accuracy of the Sleep-EDF database reached 90.10 ± 2.68% with… More >

D. Venugopal¹, T. Jayasankar^2,*, N. Krishnaraj³, S. Venkatraman⁴, N. B. Prakash⁵, G. R. Hemalakshmi⁵

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 27-38, 2021, DOI:10.32604/iasc.2021.015205

Abstract Comprehensive evaluation of common complex diseases associated with common gene mutations is currently a hot area of human genome research into causative new developments. A multi-fractal analysis of the genome is performed by placing the entire DNA sequence into smaller fragments and using the chaotic game representation and systematic methods to calculate the general dimensional spectrum of each fragment. This is a time consuming process as it uses floating point to represent large data sets and requires processing time. The proposed Regressive Multi-Array Deep Neural Classifier (RMDNC) system is implemented to reduce the computation time, it is called a polymorphic… More >

Qiang Sun¹, Xuzhi Zhan^2,*

International Journal of Mental Health Promotion, Vol.23, No.1, pp. 75-86, 2021, DOI:10.32604/IJMHP.2021.014634

Abstract Little is known on the association between movement behaviors including physical activity (PA), screen time (ST) and sleep (SLP) with depression in adolescents. This study aimed to explore the associations of PA, ST and SLP with depressive symptoms in adolescents. A total of 1,331 middle school students participated in this survey and provided valid data pertaining to the study variables. Self-reported questionnaires were used to collect information on participants’ sociodemographic parameters. The Health Behavior in School-aged Children Questionnaire was used to assess the PA (days for moderate to vigorous PA), ST (daily hours of ST) and SLP (daily hours of… More >

Guanghui Lei¹, Caihong Yang^2,#, Yan Ge^3,#, Yan Zhang^2,*, Yufei Xie^4,*, Jianwen Chen², Jinyang Wu⁵

International Journal of Mental Health Promotion, Vol.23, No.1, pp. 121-140, 2021, DOI:10.32604/IJMHP.2021.013072

Abstract To explore the relationship between social support and sleep quality of community workers in Wuhan during the coronavirus disease 2019 (the COVID-19 infection epidemic), this research constructed a mediating effect model to explore the mediating psychological mechanism of social support influencing sleep quality of front-line community workers. A total of 500 front-line community workers in Wuhan were investigated. We used the perceived social support scale (PSSS), the Connor-Davidson Resilience Scale (CD-RISC), the perceived stress scale (PSS), and the Pittsburgh sleep quality index (PSQI) to measure social support, psychological resilience, perceived stress and sleep quality. Specifically, the higher the PSQI, the… More >

Rashmi Rai¹, Ambarish Kumar Rai^2,*

International Journal of Mental Health Promotion, Vol.22, No.4, pp. 291-302, 2020, DOI:10.32604/IJMHP.2020.012153

Abstract The intimate partner violence (IPV) against women has been identified as a violation of human rights and a serious public health concern. There is not only the immediate consequence of partner violence, such as injury or death but also the other long-term health consequences. IPV can be associated with psychological effects such as depressive disorder, posttraumatic stress disorder, and substance abuse. The study aims to explore the nature and causes of IPV on women’s life and their personal experiences to deal with. This is an NGO-based study. For better understanding of the issues, Purposive sampling was used in selecting women… More >

Anar A. Hady^∗

Computer Systems Science and Engineering, Vol.35, No.5, pp. 347-355, 2020, DOI:10.32604/csse.2020.35.347

Abstract In this paper, a Duty Cycling Centralized Hierarchical Protocol (DCCHP) has been proposed for wireless sensor networks. DCCHP is an energy efficient protocol that prolongs the lifetime of the network by applying a duty cycling mechanism named DCM that chooses the nodes that send unimportant data in a certain epoch to be candidates to be put to sleep. But if the proposed equations for choosing the cluster head nodes put any of them in a high priority it works in the active mode. When comparing DCCHP to the previously proposed LEACH-CS, LEACH-C protocols, using a simulation study, DCCHP in average… More >

Tianle Zhang¹, Lihua Yin¹, Xiang Cui^{1, *}, Abhishek Behl², Fuqiang Dong³, Ziheng Cheng⁴, Kuo Ma⁴

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 835-845, 2020, DOI:10.32604/cmc.2020.06050

Abstract In Wireless Sensor Network (WSN), because battery and energy supply are constraints, sleep scheduling is always needed to save energy while maintaining connectivity for packet delivery. Traditional schemes have to ensure high duty cycling to ensure enough percentage of active nodes and then derogate the energy efficiency. This paper proposes an RFID based non-preemptive random sleep scheduling scheme with stable low duty cycle. It employs delay tolerant network routing protocol to tackle the frequent disconnections. A low-power RFID based non-preemptive wakeup signal is used to confirm the availability of next-hop before sending packet. It eliminates energy consumption of repeated retransmission… More >

Junbiao Liu^{1, 6}, Duanpo Wu^{2, 3}, Zimeng Wang², Xinyu Jin^{1, *}, Fang Dong⁴, Lurong Jiang⁵, Chenyi Cai⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.1, pp. 401-426, 2020, DOI:10.32604/cmes.2020.08731

Abstract In the field of medical informatics, sleep staging is a challenging and timeconsuming task undertaken by sleep experts. According to the new standard of the American Academy of Sleep Medicine (AASM), the stages of sleep are divided into wakefulness (W), rapid eye movement (REM) and non-rapid eye movement (NREM) which includes three sleep stages (N1, N2 and N3) that describe the depth of sleep. This study aims to establish an automatic sleep staging algorithm based on the improved weighted random forest (WRF) and Hidden Markov Model (HMM) using only the features extracted from double-channel EEG signals. The WRF classification model… More >

N. Kins Burk Sunil^{1, *}, R. Ganesan², B. Sankaragomathi³

CMES-Computer Modeling in Engineering & Sciences, Vol.118, No.2, pp. 351-375, 2019, DOI:10.31614/cmes.2018.04484

Abstract Obstructive Sleep Apnea (OSA) is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation. The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea (SA) activity. In the proposed method, the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted. These features are applied to the Classification and Regression Tree (CART)-Particle Swarm Optimization (PSO) classifier which classifies the signal into normal breathing signal… More >