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Xu S, Deo RC, Soar J, Barua PD, Faust O, Homaira N, Jaffe A, Kabir AL, Acharya UR. Automated detection of airflow obstructive diseases: A systematic review of the last decade (2013-2022). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 241:107746. [PMID: 37660550 DOI: 10.1016/j.cmpb.2023.107746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 07/07/2023] [Accepted: 08/02/2023] [Indexed: 09/05/2023]
Abstract
BACKGROUND AND OBJECTIVE Obstructive airway diseases, including asthma and Chronic Obstructive Pulmonary Disease (COPD), are two of the most common chronic respiratory health problems. Both of these conditions require health professional expertise in making a diagnosis. Hence, this process is time intensive for healthcare providers and the diagnostic quality is subject to intra- and inter- operator variability. In this study we investigate the role of automated detection of obstructive airway diseases to reduce cost and improve diagnostic quality. METHODS We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance. RESULTS We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent. CONCLUSIONS Our review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings.
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Affiliation(s)
- Shuting Xu
- School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia; Cogninet Australia, Sydney, NSW 2010, Australia
| | - Ravinesh C Deo
- School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia
| | - Jeffrey Soar
- School of Business, University of Southern Queensland, Australia
| | - Prabal Datta Barua
- Cogninet Australia, Sydney, NSW 2010, Australia; School of Business, University of Southern Queensland, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia; Australian International Institute of Higher Education, Sydney, NSW 2000, Australia; School of Science Technology, University of New England, Australia; School of Biosciences, Taylor's University, Malaysia; School of Computing, SRM Institute of Science and Technology, India; School of Science and Technology, Kumamoto University, Japan; Sydney School of Education and Social Work, University of Sydney, Australia.
| | - Oliver Faust
- School of Computing and Information Science, Anglia Ruskin University Cambridge Campus, UK
| | - Nusrat Homaira
- School of Clinical Medicine, University of New South Wales, Australia; Sydney Children's Hospital, Sydney, Australia; James P. Grant School of Public Health, Dhaka, Bangladesh
| | - Adam Jaffe
- School of Clinical Medicine, University of New South Wales, Australia; Sydney Children's Hospital, Sydney, Australia
| | | | - U Rajendra Acharya
- School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia; School of Science and Technology, Kumamoto University, Japan
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Zheng Y, Xu Z, Xiao A. Deep learning in economics: a systematic and critical review. Artif Intell Rev 2023; 56:1-43. [PMID: 36777109 PMCID: PMC9898707 DOI: 10.1007/s10462-022-10272-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
From the perspective of historical review, the methodology of economics develops from qualitative to quantitative, from a small sampling of data to a vast amount of data. Because of the superiority in learning inherent law and representative level, deep learning models assist in realizing intelligent decision-making in economics. After presenting some statistical results of relevant researches, this paper systematically investigates deep learning in economics, including a survey of frequently-used deep learning models in economics, several applications of deep learning models used in economics. Then, some critical reviews of deep learning in economics are provided, including models and applications, why and how to implement deep learning in economics, research gap and future challenges, respectively. It is obvious that several deep learning models and their variants have been widely applied in different subfields of economics, e.g., financial economics, macroeconomics and monetary economics, agricultural and natural resource economics, industrial organization, urban, rural, regional, real estate and transportation economics, health, education and welfare, business administration and microeconomics, etc. We are very confident that decision-making in economics will be more intelligent with the development of deep learning, because the research of deep learning in economics has become a hot and important topic recently.
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Affiliation(s)
- Yuanhang Zheng
- College of Computer Science, Sichuan University, 610064 Chengdu, PR China
| | - Zeshui Xu
- Business School, Sichuan University, 610064 Chengdu, PR China
| | - Anran Xiao
- Business School, Sichuan University, 610064 Chengdu, PR China
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The Effects of Service Quality of Medical Information O2O Platform on Continuous Use Intention: Case of South Korea. INFORMATION 2022. [DOI: 10.3390/info13100486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Digital transformation of the healthcare industry is being accelerated due to the evolution of digital intelligence information technology such as artificial intelligence (AI), Internet of Things (IoT), and big data. As online-to-offline (O2O)-based consumption life, based on platforms, becomes routinized along with the COVID-19 pandemic, the O2O platforms on medical activities are gaining attention. This study targeted the medical information O2O platform users and aimed to verify the effects of service quality factors on the platform users’ continuous use intention with the mediation of perceived usefulness and perceived convenience. Based on previous studies, four such factors: context-based affordability, immediacy of connection, reliability, and safety were defined as the medical information O2O platform service quality components. This study targeted 369 users of medical information O2O platforms with market dominance in Korea and conducted a questionnaire survey. According to analysis results, context-based affordability and immediacy of connection had a positive (+) effect on perceived usefulness and convenience, and they were confirmed to affect continuous-use intention with the mediation of the perceived usefulness and convenience. Meanwhile, reliability did not affect the perceived usefulness and convenience, whereas safety had a positive (+) effect on perceived usefulness but did not have the same effect (+) on perceived convenience. Consequently, it was ascertained that context-based affordability and immediacy of connection are more important factors to the medical information O2O platform consumers than reliability and safety.
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Faust O, Hong W, Loh HW, Xu S, Tan RS, Chakraborty S, Barua PD, Molinari F, Acharya UR. Heart rate variability for medical decision support systems: A review. Comput Biol Med 2022; 145:105407. [DOI: 10.1016/j.compbiomed.2022.105407] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/09/2022] [Accepted: 03/12/2022] [Indexed: 12/22/2022]
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Fuadah YN, Lim KM. Optimal Classification of Atrial Fibrillation and Congestive Heart Failure Using Machine Learning. Front Physiol 2022; 12:761013. [PMID: 35185594 PMCID: PMC8850703 DOI: 10.3389/fphys.2021.761013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 12/29/2021] [Indexed: 11/13/2022] Open
Abstract
Cardiovascular disorders, including atrial fibrillation (AF) and congestive heart failure (CHF), are the significant causes of mortality worldwide. The diagnosis of cardiovascular disorders is heavily reliant on ECG signals. Therefore, extracting significant features from ECG signals is the most challenging aspect of representing each condition of ECG signal. Earlier studies have claimed that the Hjorth descriptor is assigned as a simple feature extraction algorithm capable of class separation among AF, CHF, and normal sinus rhythm (NSR) conditions. However, due to noise interference, certain features do not represent the characteristics of the ECG signals. This study addressed this critical gap by applying the discrete wavelet transform (DWT) to decompose the ECG signals into sub-bands and extracting Hjorth descriptor features and entropy-based features in the DWT domain. Therefore, the calculation of Hjorth descriptor and entropy-based features performed on each sub-band will produce more detailed information of ECG signals. The optimization of various classifier algorithms, including k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), artificial neural network (ANN), and radial basis function network (RBFN), was investigated to provide the best system performance. This study obtained an accuracy of 100% for the k-NN, SVM, RF, and ANN classifiers, respectively, and 97% for the RBFN classifier. The results demonstrated that the optimization of the classifier algorithm could improve the classification accuracy of AF, CHF, and NSR conditions, compared to earlier studies.
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Affiliation(s)
- Yunendah Nur Fuadah
- Computationa Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea
| | - Ki Moo Lim
- Computational Medicine Lab, Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea
- *Correspondence: Ki Moo Lim,
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Gomez Rossi J, Feldberg B, Krois J, Schwendicke F. A systematic scoping review analysing clinical, technical and financial aspects of cost-effectiveness of Artificial Intelligence applied in medicine: A theory and framework of analysis (Preprint). JMIR Med Inform 2021; 10:e33703. [PMID: 35969458 PMCID: PMC9419048 DOI: 10.2196/33703] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 03/29/2022] [Accepted: 05/13/2022] [Indexed: 11/13/2022] Open
Abstract
Background Cost-effectiveness analysis of artificial intelligence (AI) in medicine demands consideration of clinical, technical, and economic aspects to generate impactful research of a novel and highly versatile technology. Objective We aimed to systematically scope existing literature on the cost-effectiveness of AI and to extract and summarize clinical, technical, and economic dimensions required for a comprehensive assessment. Methods A scoping literature review was conducted to map medical, technical, and economic aspects considered in studies on the cost-effectiveness of medical AI. Based on these, a framework for health policy analysis was developed. Results Among 4820 eligible studies, 13 met the inclusion criteria for our review. Internal medicine and emergency medicine were the clinical disciplines most frequently analyzed. Most of the studies included were from the United States (5/13, 39%), assessed solutions requiring market access (9/13, 69%), and proposed optimization of direct resources as the most frequent value proposition (7/13, 53%). On the other hand, technical aspects were not uniformly disclosed in the studies we analyzed. A minority of articles explicitly stated the payment mechanism assumed (5/13, 38%), while it remained unspecified in the majority (8/13, 62%) of studies. Conclusions Current studies on the cost-effectiveness of AI do not allow to determine if the investigated AI solutions are clinically, technically, and economically viable. Further research and improved reporting on these dimensions seem relevant to recommend and assess potential use cases for this technology.
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Affiliation(s)
- Jesus Gomez Rossi
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin, Berlin, Germany
| | - Ben Feldberg
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin, Berlin, Germany
| | - Joachim Krois
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin, Berlin, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin, Berlin, Germany
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Faust O, Kareem M, Ali A, Ciaccio EJ, Acharya UR. Automated Arrhythmia Detection Based on RR Intervals. Diagnostics (Basel) 2021; 11:diagnostics11081446. [PMID: 34441380 PMCID: PMC8391893 DOI: 10.3390/diagnostics11081446] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 07/29/2021] [Accepted: 08/03/2021] [Indexed: 11/16/2022] Open
Abstract
Abnormal heart rhythms, also known as arrhythmias, can be life-threatening. AFIB and AFL are examples of arrhythmia that affect a growing number of patients. This paper describes a method that can support clinicians during arrhythmia diagnosis. We propose a deep learning algorithm to discriminate AFIB, AFL, and NSR RR interval signals. The algorithm was designed with data from 4051 subjects. With 10-fold cross-validation, the algorithm achieved the following results: ACC = 99.98%, SEN = 100.00%, and SPE = 99.94%. These results are significant because they show that it is possible to automate arrhythmia detection in RR interval signals. Such a detection method makes economic sense because RR interval signals are cost-effective to measure, communicate, and process. Having such a cost-effective solution might lead to widespread long-term monitoring, which can help detecting arrhythmia earlier. Detection can lead to treatment, which improves outcomes for patients.
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Affiliation(s)
- Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK;
- Correspondence:
| | - Murtadha Kareem
- Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK;
| | - Ali Ali
- Sheffield Teaching Hospitals NIHR Biomedical Research Centre, Sheffield S10 2JF, UK;
| | - Edward J. Ciaccio
- Department of Medicine—Cardiology, Columbia University, New York, NY 10027, USA;
| | - U. Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore;
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- School of Science and Technology, Singapore University of Social Sciences, Clementi 599494, Singapore
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Kareem M, Lei N, Ali A, Ciaccio EJ, Acharya UR, Faust O. A review of patient-led data acquisition for atrial fibrillation detection to prevent stroke. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102818] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Evaluation of AI-Assisted Telemedicine Service Using a Mobile Pet Application. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11062707] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
This study indirectly verifies the possibility of telemedicine for humans through a mobile application (app) targeting pets. It examined the perception of telemedicine services and the current status of the companion animal industry, the app platform, and its applied technology by industry domain, and four representative types of artificial intelligence (AI) technologies applicable in the medical field. A survey was conducted through an app implementing pet telemedicine, and hypotheses were established and statistically tested based on the adoption period of pets, health status, mobile service utilization (as an index measuring the ease of use of recent AI functions), and positive and negative perceptions of telemedicine services. As revealed by prospect theory, users with a negative perception of pet telemedicine tended to maintain negative perceptions about telemedicine for humans. This study proved that the severity of pet diseases and the ease of use of recent AI technologies act as a moderating effect on the perception of telemedicine services through the verification of reinforcement and additional hypotheses. It suggests a plan to overcome sanctions against telemedicine by utilizing AI technology. A positive effect on changing the medical paradigm to telemedicine and the improvement of the medical legal system were also observed.
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Yeuk-Lan Alice L, Binghe G, Shuang C, Hoyin C, Kawai K, Wenjung L, Jiangang S. Artificial intelligence meets traditional Chinese medicine: a bridge to opening the magic box of sphygmopalpation for pulse pattern recognition. DIGITAL CHINESE MEDICINE 2021. [DOI: 10.1016/j.dcmed.2021.03.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
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Schenck-Gustafsson K, Carnlöf C, Jensen-Urstad M, Insulander P. Improving Efficiency of Clinical Studies Using a Total Digital Approach: Prospective Observational Study. JMIR Form Res 2021; 5:e18385. [PMID: 33599617 PMCID: PMC7932837 DOI: 10.2196/18385] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 10/13/2020] [Accepted: 01/17/2021] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND In general, most clinical studies have long recruitment periods. Signing the informed consent is particularly time-consuming when the participant must meet physically with the researchers. Therefore, introducing fully web-based techniques with the use of eAuthentication (BankID) and new digital electrocardiogram (ECG) monitoring could speed up inclusion time, increase adherence, and also reach out to more remote regions. OBJECTIVE The objectives of this study were to explore whether inclusion of a large number of participants could be realized quickly by using a total digital approach both for information and signing of informed consent, along with ECG monitoring and instant feedback on a mobile device. We also explored whether this approach can increase adherence in registration of ECG recordings and answering questionnaires, and if it would result in a more geographically uniform distribution of participants covering a wide age span. METHODS Women with palpitations were intensively studied over 2 months by means of a handheld ECG monitoring device (Coala Heart Monitor). The device connects to a smartphone or tablet, which allows the participants to obtain the results immediately. Recruitment, study information, and signing the informed consent form with the help of BankID were performed in a completely digital manner. RESULTS Between March and May 2018, 2424 women indicated their interest in participating in the study. On June 19, 2018, presumptive participants were invited to log in and register. After 25 days, 1082 women were included in the study; among these, 1020 women fulfilled the inclusion criteria, 913 of whom completed all phases of the study: recording ECG using the handheld device, completion of the prestudy questionnaires, and completion of the poststudy questionnaires 2 months after the ECG recordings. The dropout rate was 9%. In total, 101,804 ECG recordings were made. The mean age was 56 (SD 11) years (range 21-88 years) and 35 participants were 75 years or older. The participants were evenly distributed between living in the countryside and in cities. CONCLUSIONS Total digital inclusion recruitment of 1082 participants was achieved in only 25 days, and resulted in a good geographical distribution, excellent adherence, and ability to reach a vast age span, including elderly women. Studies using a total digital design would be particularly appealing during a pandemic since physical contact should be avoided as much as possible. TRIAL REGISTRATION ISRCTN Registry ISRCTN22495299; http://www.isrctn.com/ISRCTN22495299.
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Affiliation(s)
- Karin Schenck-Gustafsson
- Institute of Medicine, Karolinska Institutet, Stockholm, Sweden.,Heart and Vascular Theme, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Carina Carnlöf
- Heart and Vascular Theme, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Mats Jensen-Urstad
- Heart and Vascular Theme, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Per Insulander
- Heart and Vascular Theme, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
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Lei N, Kareem M, Moon SK, Ciaccio EJ, Acharya UR, Faust O. Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:813. [PMID: 33477887 PMCID: PMC7833442 DOI: 10.3390/ijerph18020813] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/08/2021] [Accepted: 01/11/2021] [Indexed: 11/16/2022]
Abstract
In this paper, we discuss hybrid decision support to monitor atrial fibrillation for stroke prevention. Hybrid decision support takes the form of human experts and machine algorithms working cooperatively on a diagnosis. The link to stroke prevention comes from the fact that patients with Atrial Fibrillation (AF) have a fivefold increased stroke risk. Early diagnosis, which leads to adequate AF treatment, can decrease the stroke risk by 66% and thereby prevent stroke. The monitoring service is based on Heart Rate (HR) measurements. The resulting signals are communicated and stored with Internet of Things (IoT) technology. A Deep Learning (DL) algorithm automatically estimates the AF probability. Based on this technology, we can offer four distinct services to healthcare providers: (1) universal access to patient data; (2) automated AF detection and alarm; (3) physician support; and (4) feedback channels. These four services create an environment where physicians can work symbiotically with machine algorithms to establish and communicate a high quality AF diagnosis.
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Affiliation(s)
- Ningrong Lei
- College of Business, Technology and Engineering, Sheffield Hallam University, Sheffield S1 1WB, UK;
| | - Murtadha Kareem
- Materials & Engineering Research Institute, Sheffield Hallam University, Sheffield S1 1WB, UK;
| | - Seung Ki Moon
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore;
| | - Edward J. Ciaccio
- Department of Medicine-Cardiology, Columbia University, New York, NY 10027, USA;
| | - U Rajendra Acharya
- Ngee Ann Polytechnic, Singapore 598269, Singapore;
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- School of Management and Enterprise, University of Southern Queensland, Toowoomba 4350, Australia
| | - Oliver Faust
- College of Business, Technology and Engineering, Sheffield Hallam University, Sheffield S1 1WB, UK;
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