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Article ; Online: Ear Plaster Therapy as a Safe and Effective Treatment for Gestational Vomiting.

Shao, Shiliang / Zheng, Wenjuan / Wen, Yi

Journal of visualized experiments : JoVE

2023 , Issue 198

Abstract: Nausea and vomiting in pregnancy (NVP) are common symptoms that often complicate early pregnancy for many women. While clinical treatments such as fasting, fluid infusion, and nutritional support are conventionally applied to manage NVP, their ... ...

Abstract	Nausea and vomiting in pregnancy (NVP) are common symptoms that often complicate early pregnancy for many women. While clinical treatments such as fasting, fluid infusion, and nutritional support are conventionally applied to manage NVP, their effectiveness varies. However, traditional ear plaster therapy offers a promising alternative that effectively relieves symptoms and poses no known risk to the development of embryos or fetuses. This therapy is known for its ease of application, cost-effectiveness, and favorable outcomes. Previous studies have demonstrated the efficacy of combining ear plaster therapy with conventional treatments in alleviating symptoms of nausea and vomiting in pregnant women, surpassing the results achieved with conventional treatment alone. The protocol presented herein describes a method to relieve NVP using round, smooth, and hard cowherb seeds applied to specific ear points. These seeds are gently rubbed onto the surface of the ear, utilizing the principles of acupressure. By stimulating the designated ear points, this procedure aims to regulate the body's energy flow and restore balance, thereby reducing the severity and frequency of NVP. The application of cowherb seeds on specific ear points is a straightforward technique that healthcare professionals can easily implement or self-administered by pregnant women under appropriate guidance. Overall, ear plaster therapy presents a safe, effective, and economical approach for managing gestational vomiting, offering women a potential solution to alleviate their discomfort during pregnancy.
MeSH term(s)	Pregnancy ; Female ; Humans ; Treatment Outcome ; Vomiting/therapy ; Nausea/therapy ; Fasting ; Fetus
Language	English
Publishing date	2023-08-04
Publishing country	United States
Document type	Journal Article ; Video-Audio Media ; Research Support, Non-U.S. Gov't
ZDB-ID	2259946-0
ISSN	1940-087X ; 1940-087X
ISSN (online)	1940-087X
ISSN	1940-087X
DOI	10.3791/65549
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: EEG-Based Mental Workload Classification Method Based on Hybrid Deep Learning Model Under IoT.

Shao, Shiliang / Han, Guangjie / Wang, Ting / Lin, Chuan / Song, Chunhe / Yao, Chen

IEEE journal of biomedical and health informatics

2024 Volume 28, Issue 5, Page(s) 2536–2546

Abstract: Automatically detecting human mental workload to prevent mental diseases is highly important. With the development of information technology, remote detection of mental workload is expected. The development of artificial intelligence and Internet of ... ...

Abstract	Automatically detecting human mental workload to prevent mental diseases is highly important. With the development of information technology, remote detection of mental workload is expected. The development of artificial intelligence and Internet of Things technology will also enable the identification of mental workload remotely based on human physiological signals. In this article, a method based on the spatial and time-frequency domains of electroencephalography (EEG) signals is proposed to improve the classification accuracy of mental workload. Moreover, a hybrid deep learning model is presented. First, the spatial domain features of different brain regions are proposed. Simultaneously, EEG time-frequency domain information is obtained based on wavelet transform. The spatial and time-frequency domain features are input into two types of deep learning models for mental workload classification. To validate the performance of the proposed method, the Simultaneous Task EEG Workload public database is used. Compared with the existing methods, the proposed approach shows higher classification accuracy. It provides a novel means of assessing mental workload.
MeSH term(s)	Humans ; Deep Learning ; Electroencephalography/methods ; Electroencephalography/classification ; Signal Processing, Computer-Assisted ; Workload ; Internet of Things ; Brain/physiology ; Wavelet Analysis ; Algorithms
Language	English
Publishing date	2024-05-06
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2023.3281793
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Predicting Cardiovascular and Cerebrovascular Events Based on Instantaneous High-Order Singular Entropy and Deep Belief Network.

Shao, Shiliang / Wang, Ting / Mumtaz, Asad / Song, Chunhe / Yao, Chen

IEEE journal of biomedical and health informatics

2023 Volume 27, Issue 4, Page(s) 1670–1680

Abstract: Automatically predicting cardiovascular and cerebrovascular events (CCEs) is a key technology that can prevent deaths and disabilities. Herein, we propose predicting CCE occurrences based on heart rate variability (HRV) analysis and a deep belief network ...

Abstract	Automatically predicting cardiovascular and cerebrovascular events (CCEs) is a key technology that can prevent deaths and disabilities. Herein, we propose predicting CCE occurrences based on heart rate variability (HRV) analysis and a deep belief network (DBN). The proposed prediction algorithm uses eight novel HRV signal features, which are calculated based on the following steps. First, the instantaneous amplitude (IA), instantaneous frequency (IF), and instantaneous phase (IP) are calculated for the HRV signals. Second, the high-order cumulant is estimated for the HRV and its IA, IF, and IP. Third, a high-order singular entropy is calculated to measure the fluctuation in signals. Fourth, eight novel features are obtained and processed using a DBN classifier designed for CCE prediction. The DBN classification method, with the novel HRV features, outperformed existing methods in terms of accuracy. Thus, the scheme proposed herein provided a novel direction for predicting CCEs.
MeSH term(s)	Humans ; Entropy ; Algorithms ; Heart Rate/physiology
Language	English
Publishing date	2023-04-04
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2022.3162894
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Auricular Acupuncture as a Traditional Chinese Medicine Therapy for Chronic Obstructive Pulmonary Disease Combined with Sleep Disorders.

Xia, Yijia / Wang, Tianbao / Ma, Zhipeng / Ying, Rongtao / Shao, Shiliang / Zeng, Jurong / Zhang, Chuantao

Journal of visualized experiments : JoVE

2023 , Issue 198

Abstract: Chronic obstructive pulmonary disease (COPD) is a clinical syndrome characterized by persistent and irreversible airflow limitation and chronic respiratory symptoms. It has a wide spectrum of complications, and sleep disorders, as part of it, are common ... ...

Abstract	Chronic obstructive pulmonary disease (COPD) is a clinical syndrome characterized by persistent and irreversible airflow limitation and chronic respiratory symptoms. It has a wide spectrum of complications, and sleep disorders, as part of it, are common in severe cases, especially in elderly patients. Long-term lack of sleep may lead to the aggravation of the original disease, reducing patients' quality of life. Benzodiazepines are mainly used for symptomatic treatment of COPD combined with sleep disorders. However, such drugs have the side effect of respiratory central inhibition and could probably aggravate hypoxia symptoms. Auricular acupuncture is a special method of treating physical and psychosomatic dysfunctions by stimulating specific points in the ear. This article explains the specific methods of clinical operation of auricular acupuncture in detail, including assessment of patient eligibility, medical devices used, acupuncture points, course of treatment, post-treatment care, responses to emergencies, etc. The Pittsburgh sleep quality index (PSQI) and chronic obstructive pulmonary disease assessment scale (CAT) were used as the observational index of this method. So far, clinical reports have proved that auricular acupuncture has a definite curative effect in the treatment of COPD combined with sleep disorders, and its advantages of simple operation, few adverse reactions are worthy of further study and promotion, which provide a reference for the clinical treatment of such diseases.
MeSH term(s)	Humans ; Acupuncture, Ear ; Medicine, Chinese Traditional ; Quality of Life ; Pulmonary Disease, Chronic Obstructive/complications ; Pulmonary Disease, Chronic Obstructive/therapy ; Sleep Wake Disorders/etiology ; Sleep Wake Disorders/therapy
Language	English
Publishing date	2023-08-18
Publishing country	United States
Document type	Journal Article ; Video-Audio Media ; Research Support, Non-U.S. Gov't
ZDB-ID	2259946-0
ISSN	1940-087X ; 1940-087X
ISSN (online)	1940-087X
ISSN	1940-087X
DOI	10.3791/65297
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Obstructive Sleep Apnea Detection Scheme Based on Manually Generated Features and Parallel Heterogeneous Deep Learning Model Under IoMT.

Shao, Shiliang / Han, Guangjie / Wang, Ting / Song, Chunhe / Yao, Chen / Hou, Jianxia

IEEE journal of biomedical and health informatics

2022 Volume 26, Issue 12, Page(s) 5841–5850

Abstract: Obstructive sleep apnea (OSA) syndrome is a common sleep disorder and a key cause of cardiovascular and cerebrovascular diseases that seriously affect the lives and health of people. The development of Internet of Medical Things (IoMT) has enabled the ... ...

Abstract	Obstructive sleep apnea (OSA) syndrome is a common sleep disorder and a key cause of cardiovascular and cerebrovascular diseases that seriously affect the lives and health of people. The development of Internet of Medical Things (IoMT) has enabled the remote diagnosis of OSA. The physiological signals of human sleep are sent to the cloud or medical facilities through Internet of Things, after which diagnostic models are employed for OSA detection. In order to improve the detection accuracy of OSA, in this study, a novel OSA detection system based on manually generated features and utilizing a parallel heterogeneous deep learning model in the context of IoMT is proposed, and the accuracy of the proposed diagnostic model is investigated. The OSA recognition scheme used in our model is based on short-term heart rate variability (HRV) signals extracted from ECG signals. First, the HRV signals and the linear and nonlinear features of HRV are combined into a one-dimensional (1-D) sequence. Simultaneously, a two-dimensional (2-D) HRV time-frequency spectrum image is obtained. The 1-D data sequences and 2-D images are coded in different branches of the proposed deep learning network for OSA diagnosis. To validate the performance of the proposed scheme, the Physionet Apnea-ECG public database is used. The proposed scheme outperforms the existing methods in terms of accuracy and provides a novel direction for OSA recognition.
MeSH term(s)	Humans ; Internet of Things ; Deep Learning ; Electrocardiography/methods ; Sleep Apnea, Obstructive/diagnosis ; Sleep
Language	English
Publishing date	2022-12-07
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2022.3166859
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 5483: Show issues

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Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Article: Using Network Pharmacology and Molecular Docking to Explore the Mechanism of Shan Ci Gu (

Wang, Yan / Zhang, Yunwu / Wang, Yujia / Shu, Xinyao / Lu, Chaorui / Shao, Shiliang / Liu, Xingting / Yang, Cheng / Luo, Jingsong / Du, Quanyu

Frontiers in chemistry

2021 Volume 9, Page(s) 682862

Abstract: Background: ...

Abstract	Background:
Language	English
Publishing date	2021-06-09
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2711776-5
ISSN	2296-2646
ISSN	2296-2646
DOI	10.3389/fchem.2021.682862
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Obstructive Sleep Apnea Recognition Based on Multi-Bands Spectral Entropy Analysis of Short-Time Heart Rate Variability.

Shao, Shiliang / Wang, Ting / Song, Chunhe / Chen, Xingchi / Cui, Enuo / Zhao, Hai

Entropy (Basel, Switzerland)

2019 Volume 21, Issue 8

Abstract: Obstructive sleep apnea (OSA) syndrome is a common sleep disorder. As an alternative to polysomnography (PSG) for OSA screening, the current automatic OSA detection methods mainly concentrate on feature extraction and classifier selection based on ... ...

Abstract	Obstructive sleep apnea (OSA) syndrome is a common sleep disorder. As an alternative to polysomnography (PSG) for OSA screening, the current automatic OSA detection methods mainly concentrate on feature extraction and classifier selection based on physiological signals. It has been reported that OSA is, along with autonomic nervous system (ANS) dysfunction and heart rate variability (HRV), a useful tool for ANS assessment. Therefore, in this paper, eight novel indices of short-time HRV are extracted for OSA detection, which are based on the proposed multi-bands time-frequency spectrum entropy (MTFSE) method. In the MTFSE, firstly, the power spectrum of HRV is estimated by the Burg-AR model, and the time-frequency spectrum image (TFSI) is obtained. Secondly, according to the physiological significance of HRV, the TFSI is divided into multiple sub-bands according to frequency. Last but not least, by studying the Shannon entropy of different sub-bands and the relationships among them, the eight indices are obtained. In order to validate the performance of MTFSE-based indices, the Physionet Apnea-ECG database and K-nearest neighbor (KNN), support vector machine (SVM), and decision tree (DT) classification methods are used. The SVM classification method gets the highest classification accuracy, its average accuracy is 91.89%, the average sensitivity is 88.01%, and the average specificity is 93.98%. Undeniably, the MTFSE-based indices provide a novel idea for the screening of OSA disease.
Language	English
Publishing date	2019-08-20
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e21080812
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Distributed Fault Detection and Isolation for Flocking in a Multi-robot System with Imperfect Communication

Shao Shiliang / Wang Ting / Yao Chen / Li Xiaofan / Zhao Hai

International Journal of Advanced Robotic Systems, Vol 11, Iss , p

2014 Volume 86

Abstract: In this paper, we focus on distributed fault detection and isolation (FDI) for a multi-robot system where multiple robots execute a flocking task. Firstly, we propose a fault detection method based on the local-information- exchange and sensor- ... ...

Abstract	In this paper, we focus on distributed fault detection and isolation (FDI) for a multi-robot system where multiple robots execute a flocking task. Firstly, we propose a fault detection method based on the local-information- exchange and sensor-measurement technologies to cover cases of both perfect communication and imperfect communication. The two detection technologies can be adaptively selected according to the packet loss rate (PLR). Secondly, we design a fault isolation method, considering a situation in which faulty robots still influence the behaviours of other robots. Finally, a complete FDI scheme, based on the proposed detection and isolation methods, is simulated in various scenarios. The results demonstrate that our FDI scheme is effective.
Keywords	Multi-Robot ; Flocking ; Fault Detection ; Isolation ; Electronic computers. Computer science ; QA75.5-76.95 ; Instruments and machines ; QA71-90 ; Mathematics ; QA1-939 ; Science ; Q
Subject code	629
Language	English
Publishing date	2014-06-01T00:00:00Z
Publisher	InTech
Document type	Article ; Online
DOI	10.5772/58601
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Distributed Fault Detection and Isolation for Flocking in a Multi-robot System with Imperfect Communication

Shao Shiliang / Wang Ting / Yao Chen / Li Xiaofan / Zhao Hai

International Journal of Advanced Robotic Systems, Vol

2014 Volume 11

Abstract	In this paper, we focus on distributed fault detection and isolation (FDI) for a multi-robot system where multiple robots execute a flocking task. Firstly, we propose a fault detection method based on the local-information-exchange and sensor-measurement technologies to cover cases of both perfect communication and imperfect communication. The two detection technologies can be adaptively selected according to the packet loss rate (PLR). Secondly, we design a fault isolation method, considering a situation in which faulty robots still influence the behaviours of other robots. Finally, a complete FDI scheme, based on the proposed detection and isolation methods, is simulated in various scenarios. The results demonstrate that our FDI scheme is effective.
Keywords	Electronics ; TK7800-8360 ; Electronic computers. Computer science ; QA75.5-76.95
Language	English
Publishing date	2014-06-01T00:00:00Z
Publisher	SAGE Publishing
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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