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Li Y, Li K, Wang S, Chen X, Wen D. Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics. BIOSENSORS 2022; 12:bios12060404. [PMID: 35735552 PMCID: PMC9221330 DOI: 10.3390/bios12060404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 05/31/2022] [Accepted: 06/08/2022] [Indexed: 11/16/2022]
Abstract
With the development of the autopilot system, the main task of a pilot has changed from controlling the aircraft to supervising the autopilot system and making critical decisions. Therefore, the human–machine interaction system needs to be improved accordingly. A key step to improving the human–machine interaction system is to improve its understanding of the pilots’ status, including fatigue, stress, workload, etc. Monitoring pilots’ status can effectively prevent human error and achieve optimal human–machine collaboration. As such, there is a need to recognize pilots’ status and predict the behaviors responsible for changes of state. For this purpose, in this study, 14 Air Force cadets fly in an F-35 Lightning II Joint Strike Fighter simulator through a series of maneuvers involving takeoff, level flight, turn and hover, roll, somersault, and stall. Electro cardio (ECG), myoelectricity (EMG), galvanic skin response (GSR), respiration (RESP), and skin temperature (SKT) measurements are derived through wearable physiological data collection devices. Physiological indicators influenced by the pilot’s behavioral status are objectively analyzed. Multi-modality fusion technology (MTF) is adopted to fuse these data in the feature layer. Additionally, four classifiers are integrated to identify pilots’ behaviors in the strategy layer. The results indicate that MTF can help to recognize pilot behavior in a more comprehensive and precise way.
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Affiliation(s)
| | - Ke Li
- Correspondence: (K.L.); (D.W.)
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2
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Automated Detection of Sudden Cardiac Death by Discrete Wavelet Transform of Electrocardiogram Signal. Symmetry (Basel) 2022. [DOI: 10.3390/sym14030571] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Sudden cardiac death (SCD) results in millions of deaths annually; as it is a fatal heart abnormality, early prediction of SCD could save peoples’ lives to the greatest extent. Symmetry and asymmetry play an important role in many fields. Electrocardiograms (ECG) as a noninvasive process for acquiring the electrical activity of the heart, has both asymmetric and non-stationary characteristics; it is frequently employed to diagnose and evaluate the heart’s condition. In this work, we have detected SCD 14 min (separately for each one-minute interval) prior to its occurrence by analyzing ECG signals using discrete wavelet transform (DWT) and locality preserving projection (LPP). In the experiment, we have performed DWT on ECG signals to obtain coefficients, then LPP as a reduction methodology was used to cut down these obtained coefficients. Then, the acquired LPP features were ranked using various methods, including the T-test, Bhattacharyya, Wilcoxon, and entropy. At last, the highly ranked LPP features were subjected to decision tree, k-nearest neighbor (KNN), and support vector machine classifiers for distinguishing normal from SCD ECG signals. Our proposed technique has achieved a highest accuracy of 97.6% for the detection of SCD 14 min prior using the KNN classifier, compared to the existing works. Our proposed method is capable of predicting the people at risk of developing SCD 14 min before its onset, and, hence, clinicians would have enough time to provide treatment in intensive care units (ICU) for a subject at risk of SCD. Thus, this proposed technique as a useful tool can increase the survival rate of many cardiac patients.
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3
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Ren S, Jin Y, Chen Y, Shen B. CRPMKB: a knowledge base of cancer risk prediction models for systematic comparison and personalized applications. Bioinformatics 2022; 38:1669-1676. [PMID: 34927675 DOI: 10.1093/bioinformatics/btab850] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 12/06/2021] [Accepted: 12/15/2021] [Indexed: 02/05/2023] Open
Abstract
MOTIVATION In the era of big data and precision medicine, accurate risk assessment is a prerequisite for the implementation of risk screening and preventive treatment. A large number of studies have focused on the risk of cancer, and related risk prediction models have been constructed, but there is a lack of effective resource integration for systematic comparison and personalized applications. Therefore, the establishment and analysis of the cancer risk prediction model knowledge base (CRPMKB) is of great significance. RESULTS The current knowledge base contains 802 model data. The model comparison indicates that the accuracy of cancer risk prediction was greatly affected by regional differences, cancer types and model types. We divided the model variables into four categories: environment, behavioral lifestyle, biological genetics and clinical examination, and found that there are differences in the distribution of various variables among different cancer types. Taking 50 genes involved in the lung cancer risk prediction models as an example to perform pathway enrichment analyses and the results showed that these genes were significantly enriched in p53 Signaling and Aryl Hydrocarbon Receptor Signaling pathways which are associated with cancer and specific diseases. In addition, we verified the biological significance of overlapping lung cancer genes via STRING database. CRPMKB was established to provide researchers an online tool for the future personalized model application and developing. This study of CRPMKB suggests that developing more targeted models based on specific demographic characteristics and cancer types will further improve the accuracy of cancer risk model predictions. AVAILABILITY AND IMPLEMENTATION CRPMKB is freely available at http://www.sysbio.org.cn/CRPMKB/. The data underlying this article are available in the article and in its online supplementary material. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shumin Ren
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610212, China
| | - Yanwen Jin
- Center for Systems Biology, Soochow University, Suzhou 215006, China
| | - Yalan Chen
- Department of Medical Informatics, School of Medicine, Nantong University, Nantong 226001, China
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610212, China
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He H, Shi M, Lin Y, Zhan C, Wu R, Bi C, Liu X, Ren S, Shen B. HFBD: a biomarker knowledge database for heart failure heterogeneity and personalized applications. Bioinformatics 2021; 37:4534-4539. [PMID: 34164644 DOI: 10.1093/bioinformatics/btab470] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 04/08/2021] [Accepted: 06/22/2021] [Indexed: 02/05/2023] Open
Abstract
MOTIVATION Heart failure (HF) is a cardiovascular disease with a high incidence around the world. Accumulating studies have focused on the identification of biomarkers for HF precision medicine. To understand the HF heterogeneity and provide biomarker information for the personalized diagnosis and treatment of HF, a knowledge database collecting the distributed and multiple-level biomarker information is necessary. RESULTS In this study, the HF biomarker knowledge database (HFBD) was established by manually collecting the data and knowledge from literature in PubMed. HFBD contains 2618 records and 868 HF biomarkers (731 single and 137 combined) extracted from 1237 original articles. The biomarkers were classified into proteins, RNAs, DNAs, and the others at molecular, image, cellular and physiological levels. The biomarkers were annotated with biological, clinical and article information as well as the experimental methods used for the biomarker discovery. With its user-friendly interface, this knowledge database provides a unique resource for the systematic understanding of HF heterogeneity and personalized diagnosis and treatment of HF in the era of precision medicine. AVAILABILITY The platform is openly available at http://sysbio.org.cn/HFBD/.
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Affiliation(s)
- Hongxin He
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, Sichuan, China.,Center for Systems Biology, Soochow University, Suzhou 215006, China
| | - Manhong Shi
- Center for Systems Biology, Soochow University, Suzhou 215006, China.,College of Information and Network Engineering, Anhui Science and Technology University, Fengyang, Anhui, 233100, China
| | - Yuxin Lin
- Center for Systems Biology, Soochow University, Suzhou 215006, China
| | - Chaoying Zhan
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, Sichuan, China
| | - Rongrong Wu
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, Sichuan, China
| | - Cheng Bi
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, Sichuan, China.,Center for Systems Biology, Soochow University, Suzhou 215006, China
| | - Xingyun Liu
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, Sichuan, China
| | - Shumin Ren
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, Sichuan, China
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, Sichuan, China
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Bi C, Zhou S, Liu X, Zhu Y, Yu J, Zhang X, Shi M, Wu R, He H, Zhan C, Lin Y, Shen B. NDDRF: a risk factor knowledgebase for personalized prevention of neurodegenerative diseases. J Adv Res 2021; 40:223-231. [PMID: 36100329 PMCID: PMC9481935 DOI: 10.1016/j.jare.2021.06.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 06/01/2021] [Accepted: 06/15/2021] [Indexed: 12/20/2022] Open
Abstract
A risk factor knowledgebase (NDDRF) is built for neurodegenerative diseases (NDDs). NDDRF collects the risk factors associated with diagnosis and prevention of NDDs. NDDRF is helpful to the systematic understanding of the heterogeneous NDDs NDDRF provides knowledge for personalized diagnosis and prevention of NDDs. NDDRF can be used to the future explainable artificial intelligent modeling.
Introduction Neurodegenerative diseases (NDDs) are a series of chronic diseases, which are associated with progressive loss of neuronal structure or function. The complex etiologies of the NDDs remain unclear, thus the prevention and early diagnosis of NDDs are critical to reducing the mortality and morbidity of these diseases. Objectives To provide a systematic understanding of the heterogeneity of the risk factors associated with different NDDs (pan-neurodegenerative diseases or pan-NDDs), the knowledgebase is established to facilitate the personalized and knowledge-guided diagnosis, prevention and prediction of NDDs. Methods Before data collection, the medical, life science and informatics experts as well as the potential users of the database were consulted and discussed for the scope of data and the classification of risk factors. The PubMed database was used as the resource of the data and knowledge extraction. Risk factors of NDDs were manually collected from literature published between 1975 and 2020. Results The comprehensive risk factors database for NDDs (NDDRF) was established including 998 single or combined risk factors, 2293 records and 1071 articles relevant to the 14 most common NDDs. The single risk factors are classified into 3 categories, i.e. epidemiological factors (469), genetic factors (324) and biochemical factors (153). Among all the factors, 179 factors are positive and protective, while 880 factors have negative influence for NDDs. The knowledgebase is available at http://sysbio.org.cn/NDDRF/. Conclusion NDDRF provides the structured information and knowledge resource on risk factors of NDDs. It could benefit the future systematic and personalized investigation of pan-NDDs genesis and progression. Meanwhile it may be used for the future explainable artificial intelligence modeling for smart diagnosis and prevention of NDDs.
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Affiliation(s)
- Cheng Bi
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610212, Sichuan, China; Center for Systems Biology, Soochow University, Suzhou 215006, Jiangsu, China
| | - Shengrong Zhou
- Center for Systems Biology, Soochow University, Suzhou 215006, Jiangsu, China
| | - Xingyun Liu
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610212, Sichuan, China; Center for Systems Biology, Soochow University, Suzhou 215006, Jiangsu, China
| | - Yu Zhu
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610212, Sichuan, China; Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, Jiangsu, China
| | - Jia Yu
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610212, Sichuan, China; School of Clinical Medicine, Soochow University, Suzhou 215123, Jiangsu, China
| | - Xueli Zhang
- Center for Systems Biology, Soochow University, Suzhou 215006, Jiangsu, China
| | - Manhong Shi
- Center for Systems Biology, Soochow University, Suzhou 215006, Jiangsu, China
| | - Rongrong Wu
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610212, Sichuan, China; Center for Systems Biology, Soochow University, Suzhou 215006, Jiangsu, China
| | - Hongxin He
- Center for Systems Biology, Soochow University, Suzhou 215006, Jiangsu, China
| | - Chaoying Zhan
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610212, Sichuan, China; Center for Systems Biology, Soochow University, Suzhou 215006, Jiangsu, China
| | - Yuxin Lin
- Center for Systems Biology, Soochow University, Suzhou 215006, Jiangsu, China
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610212, Sichuan, China.
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Shen L, Bai J, Wang J, Shen B. The fourth scientific discovery paradigm for precision medicine and healthcare: Challenges ahead. PRECISION CLINICAL MEDICINE 2021; 4:80-84. [PMID: 35694156 PMCID: PMC8982559 DOI: 10.1093/pcmedi/pbab007] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/13/2021] [Accepted: 04/13/2021] [Indexed: 02/05/2023] Open
Abstract
With the progression of modern information techniques, such as next generation sequencing (NGS), Internet of Everything (IoE) based smart sensors, and artificial intelligence algorithms, data-intensive research and applications are emerging as the fourth paradigm for scientific discovery. However, we face many challenges to practical application of this paradigm. In this article, 10 challenges to data-intensive discovery and applications in precision medicine and healthcare are summarized and the future perspectives on next generation medicine are discussed.
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Affiliation(s)
- Li Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Jinwei Bai
- Library of West-China Hospital, Sichuan University, Chengdu 610041, China
| | - Jiao Wang
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
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7
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Abbas Q, Alsheddy A. Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis. SENSORS (BASEL, SWITZERLAND) 2020; 21:E56. [PMID: 33374270 PMCID: PMC7796320 DOI: 10.3390/s21010056] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 12/17/2020] [Accepted: 12/20/2020] [Indexed: 12/16/2022]
Abstract
Internet of things (IoT) cloud-based applications deliver advanced solutions for smart cities to decrease traffic accidents caused by driver fatigue while driving on the road. Environmental conditions or driver behavior can ultimately lead to serious roadside accidents. In recent years, the authors have developed many low-cost, computerized, driver fatigue detection systems (DFDs) to help drivers, by using multi-sensors, and mobile and cloud-based computing architecture. To promote safe driving, these are the most current emerging platforms that were introduced in the past. In this paper, we reviewed state-of-the-art approaches for predicting unsafe driving styles using three common IoT-based architectures. The novelty of this article is to show major differences among multi-sensors, smartphone-based, and cloud-based architectures in multimodal feature processing. We discussed all of the problems that machine learning techniques faced in recent years, particularly the deep learning (DL) model, to predict driver hypovigilance, especially in terms of these three IoT-based architectures. Moreover, we performed state-of-the-art comparisons by using driving simulators to incorporate multimodal features of the driver. We also mention online data sources in this article to test and train network architecture in the field of DFDs on public available multimodal datasets. These comparisons assist other authors to continue future research in this domain. To evaluate the performance, we mention the major problems in these three architectures to help researchers use the best IoT-based architecture for detecting DFDs in a real-time environment. Moreover, the important factors of Multi-Access Edge Computing (MEC) and 5th generation (5G) networks are analyzed in the context of deep learning architecture to improve the response time of DFD systems. Lastly, it is concluded that there is a research gap when it comes to implementing the DFD systems on MEC and 5G technologies by using multimodal features and DL architecture.
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Affiliation(s)
- Qaisar Abbas
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia;
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8
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Pei Z, Shi M, Guo J, Shen B. Heart Rate Variability Based Prediction of Personalized Drug Therapeutic Response: The Present Status and the Perspectives. Curr Top Med Chem 2020; 20:1640-1650. [PMID: 32493191 DOI: 10.2174/1568026620666200603105002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 02/28/2020] [Accepted: 03/02/2020] [Indexed: 02/08/2023]
Abstract
Heart rate variability (HRV) signals are reported to be associated with the personalized drug
response in many diseases such as major depressive disorder, epilepsy, chronic pain, hypertension, etc.
But the relationships between HRV signals and the personalized drug response in different diseases and
patients are complex and remain unclear. With the fast development of modern smart sensor technologies
and the popularization of big data paradigm, more and more data on the HRV and drug response
will be available, it then provides great opportunities to build models for predicting the association of
the HRV with personalized drug response precisely. We here review the present status of the HRV data
resources and models for predicting and evaluating of personalized drug responses in different diseases.
The future perspectives on the integration of knowledge and personalized data at different levels such as,
genomics, physiological signals, etc. for the application of HRV signals to the precision prediction of
drug therapy and their response will be provided.
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Affiliation(s)
- Zejun Pei
- Nanjing Medical University Affiliated Wuxi Second Hospital, No. 68,Zhongshan road, Wuxi, Jiangsu, China
| | - Manhong Shi
- Centre for Systems Biology, Soochow University, Suzhou 215006, China
| | - Junping Guo
- The Affiliated Yixing Hospital of Jiangsu University, No. 75, Tongzhenguan Road, Yixing, Jiangsu, China
| | - Bairong Shen
- Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China
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9
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Redeker NS. Sensor technology for nursing research. Nurs Outlook 2020; 68:711-719. [PMID: 32580871 DOI: 10.1016/j.outlook.2020.03.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Revised: 03/18/2020] [Accepted: 03/21/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Electronic sensors measuring biological and behavioral aspects of health and the environment are becoming ubiquitous and, with advances in data science and ehealth technology, provide opportunities for new inquiry and innovative approaches to nursing research. PURPOSE To conceptualize the use of sensor technology from the perspective of nursing science. METHODS This review reports the keynote presentation from the Expanding Science of Sensor Technology in Nursing Research Conference presented by the Council for Advancement of Nursing Science in 2019 FINDINGS: Electronic sensors enable collection, recording, and transmission of data in real time in real life settings, remote monitoring, self-monitoring, and communication between health care professionals and patients. A deliberative approach to selecting and applying electronic sensors and analyzing and interpreting the data is needed for successful research. DISCUSSION Electronic sensors have high potential to advance nursing science.
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Affiliation(s)
- Nancy S Redeker
- Yale School of Nursing, Yale School of Medicine, Department of Internal Medicine, Yale University, West Haven CT.
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10
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Shi M, He H, Geng W, Wu R, Zhan C, Jin Y, Zhu F, Ren S, Shen B. Early Detection of Sudden Cardiac Death by Using Ensemble Empirical Mode Decomposition-Based Entropy and Classical Linear Features From Heart Rate Variability Signals. Front Physiol 2020; 11:118. [PMID: 32158399 PMCID: PMC7052183 DOI: 10.3389/fphys.2020.00118] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Accepted: 02/03/2020] [Indexed: 02/05/2023] Open
Abstract
Sudden cardiac death (SCD), which can deprive a person of life within minutes, is a destructive heart abnormality. Thus, providing early warning information for patients at risk of SCD, especially those outside hospitals, is essential. In this study, we investigated the performances of ensemble empirical mode decomposition (EEMD)-based entropy features on SCD identification. EEMD-based entropy features were obtained by using the following technology: (1) EEMD was performed on HRV beats to decompose them into intrinsic mode functions (IMFs), (2) five entropy parameters, namely Rényi entropy (RenEn), fuzzy entropy (FuEn), dispersion Entropy (DisEn), improved multiscale permutation entropy (IMPE), and Renyi distribution entropy(RdisEn), were computed from the first four IMFs obtained, which were named EEMD-based entropy features. Additionally, an automated scheme combining EEMD-based entropy and classical linear (time and frequency domains) features was proposed with the intention of detecting SCD early by analyzing 14 min (at seven successive intervals of 2 min) heart rate variability (HRV) in signals from a normal population and subjects at risk of SCD. Firstly, EEMD-based entropy and classical linear measurements were extracted from HRV beats, and then the integrated measurements were ranked by various methodologies, i.e., t-test, entropy, receiver-operating characteristics (ROC), Wilcoxon, and Bhattacharyya. Finally, these ranked features were fed into a k-Nearest Neighbor algorithm for classification. Compared with several state-of-the-art methods, the proposed scheme firstly predicted subjects at risk of SCD up to 14 min earlier with an accuracy of 96.1%, a sensitivity of 97.5%, and a specificity of 94.4% 14 min before SCD onset. The simulation results exhibited that EEMD-based entropy estimators showed significant difference between SCD patients and normal individuals and outperformed the classical linear estimators in SCD detection, the EEMD-based FuEn and IMPE indexes were particularly useful assessments for identification of patients at risk of SCD and can be used as novel indices to reveal the disorders of rhythm variations of the autonomic nervous system when affected by SCD.
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Affiliation(s)
- Manhong Shi
- Center for Systems Biology, Soochow University, Suzhou, China.,College of Information and Network Engineering, Anhui Science and Technology University, Fengyang, China
| | - Hongxin He
- Center for Systems Biology, Soochow University, Suzhou, China
| | - Wanchen Geng
- Applied Mathematical Sciences, University of Connecticut, Storrs, CT, United States
| | - Rongrong Wu
- Center for Systems Biology, Soochow University, Suzhou, China
| | - Chaoying Zhan
- Center for Systems Biology, Soochow University, Suzhou, China
| | - Yanwen Jin
- Center for Systems Biology, Soochow University, Suzhou, China
| | - Fei Zhu
- School of Computer Science & Technology, Soochow University, Suzhou, China
| | - Shumin Ren
- Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China
| | - Bairong Shen
- Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China
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11
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Shen B, Lin Y, Bi C, Zhou S, Bai Z, Zheng G, Zhou J. Translational Informatics for Parkinson's Disease: from Big Biomedical Data to Small Actionable Alterations. GENOMICS, PROTEOMICS & BIOINFORMATICS 2019; 17:415-429. [PMID: 31786313 PMCID: PMC6943761 DOI: 10.1016/j.gpb.2018.10.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Revised: 08/29/2018] [Accepted: 11/02/2018] [Indexed: 02/05/2023]
Abstract
Parkinson's disease (PD) is a common neurological disease in elderly people, and its morbidity and mortality are increasing with the advent of global ageing. The traditional paradigm of moving from small data to big data in biomedical research is shifting toward big data-based identification of small actionable alterations. To highlight the use of big data for precision PD medicine, we review PD big data and informatics for the translation of basic PD research to clinical applications. We emphasize some key findings in clinically actionable changes, such as susceptibility genetic variations for PD risk population screening, biomarkers for the diagnosis and stratification of PD patients, risk factors for PD, and lifestyles for the prevention of PD. The challenges associated with the collection, storage, and modelling of diverse big data for PD precision medicine and healthcare are also summarized. Future perspectives on systems modelling and intelligent medicine for PD monitoring, diagnosis, treatment, and healthcare are discussed in the end.
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Affiliation(s)
- Bairong Shen
- Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu 610041, China.
| | - Yuxin Lin
- Center for Systems Biology, Soochow University, Suzhou 215006, China
| | - Cheng Bi
- Center for Systems Biology, Soochow University, Suzhou 215006, China
| | - Shengrong Zhou
- Center for Systems Biology, Soochow University, Suzhou 215006, China
| | - Zhongchen Bai
- Center for Translational Biomedical Informatics, Guizhou University School of Medicine, Guiyang 550025, China
| | - Guangmin Zheng
- Center for Translational Biomedical Informatics, Guizhou University School of Medicine, Guiyang 550025, China
| | - Jing Zhou
- Center for Translational Biomedical Informatics, Guizhou University School of Medicine, Guiyang 550025, China
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12
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Goetz LH, Schork NJ. Personalized medicine: motivation, challenges, and progress. Fertil Steril 2019; 109:952-963. [PMID: 29935653 DOI: 10.1016/j.fertnstert.2018.05.006] [Citation(s) in RCA: 234] [Impact Index Per Article: 46.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 05/04/2018] [Accepted: 05/04/2018] [Indexed: 01/07/2023]
Abstract
There is a great deal of hype surrounding the concept of personalized medicine. Personalized medicine is rooted in the belief that since individuals possess nuanced and unique characteristics at the molecular, physiological, environmental exposure, and behavioral levels, they may need to have interventions provided to them for diseases they possess that are tailored to these nuanced and unique characteristics. This belief has been verified to some degree through the application of emerging technologies such as DNA sequencing, proteomics, imaging protocols, and wireless health monitoring devices, which have revealed great inter-individual variation in disease processes. In this review, we consider the motivation for personalized medicine, its historical precedents, the emerging technologies that are enabling it, some recent experiences including successes and setbacks, ways of vetting and deploying personalized medicines, and future directions, including potential ways of treating individuals with fertility and sterility issues. We also consider current limitations of personalized medicine. We ultimately argue that since aspects of personalized medicine are rooted in biological realities, personalized medicine practices in certain contexts are likely to be inevitable, especially as relevant assays and deployment strategies become more efficient and cost-effective.
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Affiliation(s)
| | - Nicholas J Schork
- The Translational Genomics Research Institute, Phoenix, Arizona; The City of Hope/TGen IMPACT Center, Duarte, California; J. Craig Venter Institute, La Jolla, California; The University of California, San Diego, La Jolla, California.
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