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Alassafi MO, Aziz W, AlGhamdi R, Alshdadi AA, Nadeem MSA, Khan IR, Bahaddad A, Altalbe A, Albishry N. Complexity reduction of oxygen saturation variability signals in COVID-19 patients: Implications for cardiorespiratory control. J Infect Public Health 2024; 17:601-608. [PMID: 38377633 DOI: 10.1016/j.jiph.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) is a respiratory illness that leads to severe acute respiratory syndrome and various cardiorespiratory complications, contributing to morbidity and mortality. Entropy analysis has demonstrated its ability to monitor physiological states and system dynamics during health and disease. The main objective of the study is to extract information about cardiorespiratory control by conducting a complexity analysis of OSV signals using scale-based entropy measures following a two-month timeframe after recovery. METHODS This prospective study collected data from subjects meeting specific criteria, using a Beurer PO-80 pulse oximeter to measure oxygen saturation (SpO2) and pulse rate. Excluding individuals with a history of pulmonary/cardiovascular issues, the study analyzed 88 recordings from 44 subjects (26 men, 18 women, mean age 45.34 ± 14.40) during COVID-19 and two months post-recovery. Data preprocessing and scale-based entropy analysis were applied to assess OSV signals. RESULTS The study found a significant difference in mean OSV during illness (95.08 ± 0.15) compared to post-recovery (95.59 ± 1.03), indicating reduced cardiorespiratory dynamism during COVID-19. Multiscale entropy analyses (MSE, MPE, MFE) confirmed lower entropy values during illness across all time scales, particularly at higher scales. Notably, the maximum distinction between illness and recovery phases was seen at specific time scales and similarity criteria for each entropy measure, showing statistically significant differences. CONCLUSIONS The study demonstrates that the loss of complexity in OSV signals, quantified using scale-based entropy measures, has the potential to detect malfunctioning of cardiorespiratory control in COVID-19 patients. This finding suggests that OSV signals could serve as a valuable indicator for assessing the cardiorespiratory status of COVID-19 patients and monitoring their recovery progress.
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Affiliation(s)
- Madini O Alassafi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Wajid Aziz
- Department of Computer Science and Information Technology, King Abdullah Campus, University of Azad Jammu and Kashmir, Muzaffarabad, AK, Pakistan
| | - Rayed AlGhamdi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
| | - Abdulrahman A Alshdadi
- Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
| | - Malik Sajjad Ahmed Nadeem
- Department of Computer Science and Information Technology, King Abdullah Campus, University of Azad Jammu and Kashmir, Muzaffarabad, AK, Pakistan
| | | | - Adel Bahaddad
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ali Altalbe
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Nabeel Albishry
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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Alassafi MO, Aziz W, AlGhamdi R, Alshdadi AA, Nadeem MSA, Khan IR, Albishry N, Bahaddad A, Altalbe A. Scale based entropy measures and deep learning methods for analyzing the dynamical characteristics of cardiorespiratory control system in COVID-19 subjects during and after recovery. Comput Biol Med 2024; 170:108032. [PMID: 38310805 DOI: 10.1016/j.compbiomed.2024.108032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 01/20/2024] [Accepted: 01/25/2024] [Indexed: 02/06/2024]
Abstract
COVID-19, known as Coronavirus Disease 2019 primarily targets the respiratory system and can impact the cardiovascular system, leading to a range of cardiorespiratory complications. The current forefront in analyzing the dynamical characteristics of physiological systems and aiding clinical decision-making involves the integration of entropy-based complexity techniques with artificial intelligence. Entropy-based measures offer promising prospects for identifying disturbances in cardiorespiratory control system (CRCS) among COVID-19 patients by assessing the oxygen saturation variability (OSV) signals. In this investigation, we employ scale-based entropy (SBE) methods, including multiscale entropy (MSE), multiscale permutation entropy (MPE), and multiscale fuzzy entropy (MFE), to characterize the dynamical characteristics of OSV signals. These measurements serve as features for the application of traditional machine learning (ML) and deep learning (DL) approaches in the context of classifying OSV signals from COVID-19 patients during their illness and subsequent recovery. We use the Beurer PO-80 pulse oximeter which non-invasively acquired OSV and pulse rate data from COVID-19 infected patients during the active infection phase and after a two-month recovery period. The dataset comprises of 88 recordings collected from 44 subjects(26 men and 18 women), both during their COVID-19 illness and two months post-recovery. Prior to analysis, data preprocessing is performed to remove artifacts and outliers. The application of SBE measures to OSV signals unveils a reduction in signal complexity during the course of COVID-19. Leveraging these SBE measures as feature sets, we employ two DL techniques, namely the radial basis function network (RBFN) and RBFN with dynamic delay algorithm (RBFNDDA), for the classification of OSV data collected during and after COVID-19 recovery. To evaluate the classification performance, we employ standard metrics such as sensitivity, specificity, false positive rate (FPR), and the area under the receiver operator characteristic curve (AUC). Among the three scale-based entropy measures, MFE outperformed MSE and MPE by achieving the highest classification performance using RBFN with 13 best features having sensitivity (0.84), FPR (0.30), specificity (0.70) and AUC (0.77). The outcomes of our study demonstrate that SBE measures combined with DL methods offer a valuable approach for categorizing OSV signals obtained during and after COVID-19, ultimately aiding in the detection of CRCS dysfunction.
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Affiliation(s)
- Madini O Alassafi
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Wajid Aziz
- Department of Computer Science and Information Technology, King Abdullah Campus, University of Azad Jammu and Kashmir Muzaffarabad (AK), Pakistan
| | - Rayed AlGhamdi
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
| | | | - Malik Sajjad Ahmed Nadeem
- Department of Computer Science and Information Technology, King Abdullah Campus, University of Azad Jammu and Kashmir Muzaffarabad (AK), Pakistan
| | | | - Nabeel Albishry
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Adel Bahaddad
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ali Altalbe
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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Alassafi MO, Khan IR, AlGhamdi R, Aziz W, Alshdadi AA, Dessouky MM, Bahaddad A, Altalbe A, Albishry N. Studying Dynamical Characteristics of Oxygen Saturation Variability Signals Using Haar Wavelet. Healthcare (Basel) 2023; 11:2280. [PMID: 37628478 PMCID: PMC10454822 DOI: 10.3390/healthcare11162280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 08/02/2023] [Accepted: 08/11/2023] [Indexed: 08/27/2023] Open
Abstract
An aim of the analysis of biomedical signals such as heart rate variability signals, brain signals, oxygen saturation variability (OSV) signals, etc., is for the design and development of tools to extract information about the underlying complexity of physiological systems, to detect physiological states, monitor health conditions over time, or predict pathological conditions. Entropy-based complexity measures are commonly used to quantify the complexity of biomedical signals; however novel complexity measures need to be explored in the context of biomedical signal classification. In this work, we present a novel technique that used Haar wavelets to analyze the complexity of OSV signals of subjects during COVID-19 infection and after recovery. The data used to evaluate the performance of the proposed algorithms comprised recordings of OSV signals from 44 COVID-19 patients during illness and after recovery. The performance of the proposed technique was compared with four, scale-based entropy measures: multiscale entropy (MSE); multiscale permutation entropy (MPE); multiscale fuzzy entropy (MFE); multiscale amplitude-aware permutation entropy (MAMPE). Preliminary results of the pilot study revealed that the proposed algorithm outperformed MSE, MPE, MFE, and MMAPE in terms of better accuracy and time efficiency for separating during and after recovery the OSV signals of COVID-19 subjects. Further studies are needed to evaluate the potential of the proposed algorithm for large datasets and in the context of other biomedical signal classifications.
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Affiliation(s)
- Madini O. Alassafi
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (M.O.A.); (A.B.); (A.A.); (N.A.)
| | - Ishtiaq Rasool Khan
- College of Computer Science and Engineering, University of Jeddah, Jeddah 21725, Saudi Arabia; (I.R.K.); (A.A.A.); (M.M.D.)
| | - Rayed AlGhamdi
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (M.O.A.); (A.B.); (A.A.); (N.A.)
| | - Wajid Aziz
- Department of Computer Science and Information Technology, King Abdullah Campus, University of Azad Jammu and Kashmir Muzaffarabad (AK), Azad Jammu and Kashmir 13100, Pakistan;
| | - Abdulrahman A. Alshdadi
- College of Computer Science and Engineering, University of Jeddah, Jeddah 21725, Saudi Arabia; (I.R.K.); (A.A.A.); (M.M.D.)
| | - Mohamed M. Dessouky
- College of Computer Science and Engineering, University of Jeddah, Jeddah 21725, Saudi Arabia; (I.R.K.); (A.A.A.); (M.M.D.)
- Department of Computer Science & Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 12548, Egypt
| | - Adel Bahaddad
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (M.O.A.); (A.B.); (A.A.); (N.A.)
| | - Ali Altalbe
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (M.O.A.); (A.B.); (A.A.); (N.A.)
| | - Nabeel Albishry
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (M.O.A.); (A.B.); (A.A.); (N.A.)
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Alarood AA, Faheem M, Al‐Khasawneh MA, Alzahrani AIA, Alshdadi AA. Secure medical image transmission using deep neural network in e-health applications. Healthc Technol Lett 2023; 10:87-98. [PMID: 37529409 PMCID: PMC10388229 DOI: 10.1049/htl2.12049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/13/2023] [Accepted: 07/03/2023] [Indexed: 08/03/2023] Open
Abstract
Recently, medical technologies have developed, and the diagnosis of diseases through medical images has become very important. Medical images often pass through the branches of the network from one end to the other. Hence, high-level security is required. Problems arise due to unauthorized use of data in the image. One of the methods used to secure data in the image is encryption, which is one of the most effective techniques in this field. Confusion and diffusion are the two main steps addressed here. The contribution here is the adaptation of the deep neural network by the weight that has the highest impact on the output, whether it is an intermediate output or a semi-final output in additional to a chaotic system that is not detectable using deep neural network algorithm. The colour and grayscale images were used in the proposed method by dividing the images according to the Region of Interest by the deep neural network algorithm. The algorithm was then used to generate random numbers to randomly create a chaotic system based on the replacement of columns and rows, and randomly distribute the pixels on the designated area. The proposed algorithm evaluated in several ways, and compared with the existing methods to prove the worth of the proposed method.
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Affiliation(s)
| | - Muhammad Faheem
- School of Technology and InnovationsUniversity of VaasaVaasaFinland
| | - Mahmoud Ahmad Al‐Khasawneh
- School of Information TechnologySkyline University CollegeUniversity City SharjahSharjahUnited Arab Emirates
| | - Abdullah I. A. Alzahrani
- Department of Computer Science, Collage of Science and Humanities in Al QuwaiiyahShaqra UniversityShaqraSaudi Arabia
| | - Abdulrahman A. Alshdadi
- Department of Information Systems and Technology, College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia
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Alshdadi AA, Usman M, Alassafi MO, Afzal MT, AlGhamdi R. Formulation of rules for the scientific community using deep learning. Scientometrics 2023. [DOI: 10.1007/s11192-023-04633-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Almalki M, Alsulami MH, Alshdadi AA, Almuayqil SN, Alsaqer MS, Atkins AS, Choukou MA. Delivering Digital Healthcare for Elderly: A Holistic Framework for the Adoption of Ambient Assisted Living. Int J Environ Res Public Health 2022; 19:16760. [PMID: 36554640 PMCID: PMC9779582 DOI: 10.3390/ijerph192416760] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/13/2022] [Accepted: 11/20/2022] [Indexed: 06/17/2023]
Abstract
Adoption of Ambient Assisted Living (AAL) technologies for geriatric healthcare is suboptimal. This study aims to present the AAL Adoption Diamond Framework, encompassing a set of key enablers/barriers as factors, and describe our approach to developing this framework. A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. SCOPUS, IEEE Xplore, PubMed, ProQuest, Science Direct, ACM Digital Library, SpringerLink, Wiley Online Library and grey literature were searched. Thematic analysis was performed to identify factors reported or perceived to be important for adopting AAL technologies. Of 3717 studies initially retrieved, 109 were thoroughly screened and 52 met our inclusion criteria. Nineteen unique technology adoption factors were identified. The most common factor was privacy (50%) whereas data accuracy and affordability were the least common factors (4%). The highest number of factors found per a given study was eleven whereas the average number of factors across all studies included in our sample was four (mean = 3.9). We formed an AAL technology adoption framework based on the retrieved information and named it the AAL Adoption Diamond Framework. This holistic framework was formed by organising the identified technology adoption factors into four key dimensions: Human, Technology, Business, and Organisation. To conclude, the AAL Adoption Diamond Framework is holistic in term of recognizing key factors for the adoption of AAL technologies, and novel and unmatched in term of structuring them into four overarching themes or dimensions, bringing together the individual and the systemic factors evolving around the adoption of AAL technology. This framework is useful for stakeholders (e.g., decision-makers, healthcare providers, and caregivers) to adopt and implement AAL technologies.
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Affiliation(s)
- Manal Almalki
- College of Public Health and Tropical Medicine, Jazan University, Jazan 45142, Saudi Arabia
| | | | | | - Saleh N. Almuayqil
- College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia
| | - Mohammed S. Alsaqer
- College of Computer Science, King Khalid University, Abha 62529, Saudi Arabia
| | - Anthony S. Atkins
- School of Computing and Digital Technologies, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
| | - Mohamed-Amine Choukou
- Department of Occupational Therapy, College of Rehabilitation Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
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Alharbey R, Kim JI, Daud A, Song M, Alshdadi AA, Hayat MK. Indexing important drugs from medical literature. Scientometrics 2022. [DOI: 10.1007/s11192-022-04340-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Daud A, Hayat MK, Alshdadi AA, Banjar A, Alharbi WM. Measuring the impact of co-author count on citation count of research publications. COLLNET Journal of Scientometrics and Information Management 2022. [DOI: 10.1080/09737766.2021.2016356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Affiliation(s)
- Ali Daud
- Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Malik Khizar Hayat
- Department of Computing Faculty of Science and, Engineering, Macquarie University, New South Wales, Sydney, Australia
- Department of Information Technology, Faculty of Information Technology and Engineering, The University of Haripur, Haripur, Pakistan
| | - Abdulrahman A. Alshdadi
- Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
- Big Data Centre, Makkah Province, Makkah, Saudi Arabia
| | - Ameen Banjar
- Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Wael Mansour Alharbi
- Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
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Abstract
Web spam is the unwanted request on websites, low-quality backlinks, emails, and reviews which is generated by an automated program. It is the big threat for website owners; because of it, they can lose their top keywords ranking from search engines, which will result in huge financial loss to the business. Over the years, researchers have tried to identify malicious domains based on specific features. However, lighthouse plugin, Ahrefs tool, and social media platforms features are ignored. In this paper, the authors are focused on detection of the spam domain name from a mixture of legit and spam domain name dataset. The dataset is taken from Google webmaster tools. Machine learning models are applied on individual, distributed, and hybrid features, which significantly improved the performance of existing malicious domain machine learning techniques. Better accuracy is achieved for support vector machine (SVM) classifier, as compared to Naïve Bayes, C4.5, AdaBoost, LogitBoost.
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Affiliation(s)
| | | | - Ali Daud
- University of Jeddah, Saudi Arabia
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Abstract
People are afraid about COVID-19 and are actively talking about it on social media platforms such as Twitter. People are showing their emotions openly in their tweets on Twitter. It's very important to perform sentiment analysis on these tweets for finding COVID-19's impact on people's lives. Natural language processing, textual processing, computational linguists, and biometrics are applied to perform sentiment analysis to identify and extract the emotions. In this work, sentiment analysis is carried out on a large Twitter dataset of English tweets. Ten emotional themes are investigated. Experimental results show that COVID-19 has spread fear/anxiety, gratitude, happiness and hope, and other mixed emotions among people for different reasons. Specifically, it is observed that positive news from top officials like Trump of chloroquine as cure to COVID-19 has suddenly lowered fear in sentiment, and happiness, gratitude, and hope started to rise. But, once FDA said, chloroquine is not effective cure, fear again started to rise.
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Affiliation(s)
| | | | - Ali Daud
- University of Jeddah, Saudi Arabia
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Alshdadi AA, AlGhamdi R, Alassafi MO, Alfakeeh AS, Alsulami MH. A validation of a cloud migration readiness assessment instrument: case studies. SN Appl Sci 2020. [DOI: 10.1007/s42452-020-3162-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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Alghamdi AS, Polat K, Alghoson A, Alshdadi AA, Abd El-Latif AA. A novel blood pressure estimation method based on the classification of oscillometric waveforms using machine-learning methods. Applied Acoustics 2020; 164:107279. [DOI: 10.1016/j.apacoust.2020.107279] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Alghamdi AS, Polat K, Alghoson A, Alshdadi AA, Abd El-Latif AA. Gaussian process regression (GPR) based non-invasive continuous blood pressure prediction method from cuff oscillometric signals. Applied Acoustics 2020; 164:107256. [DOI: 10.1016/j.apacoust.2020.107256] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Affiliation(s)
- Farah Naz
- Department of Computer Science, COMSATS University Islamabad – Wah Campus, Wah Cantt, Pakistan
| | - Muhammad Kamran
- Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, KSA
| | - Waqar Mehmood
- Department of Computer Science, COMSATS University Islamabad – Wah Campus, Wah Cantt, Pakistan
| | - Wilayat Khan
- Department of Electrical and Computer Engineering COMSATS University Islamabad – Wah Campus, Wah Cantt, Pakistan
| | - Mohammed Saeed Alkatheiri
- Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, KSA
| | - Ahmed S. Alghamdi
- Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, KSA
| | - Abdulrahman A. Alshdadi
- Department of Information Science and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, KSA
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Hussain L, Aziz W, Alshdadi AA, Abbasi AA, Majid A, Marchal AR. Multiscale entropy analysis to quantify the dynamics of motor movement signals with fist or feet movement using topographic maps. Technol Health Care 2019; 28:259-273. [PMID: 31594269 DOI: 10.3233/thc-191803] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Brain neural activity is measured using electroencephalography (EEG) recording from the scalp. The EEG motor/imagery tasks help disabled people to communicate with the external environment. OBJECTIVE In this paper, robust multiscale sample entropy (MSE) and wavelet entropy measures are employed using topographic maps' analysis and tabulated form to quantify the dynamics of EEG motor movements tasks with actual and imagery opening and closing of fist or feet movements. METHODS To distinguish these conditions, we used the topographic maps which visually show the significance level of the brain regions and probes for dominant activities. The paired t-test and Posthoc Tukey test are used to find the significance levels. RESULTS The topographic maps results obtained using MSE reveal that maximum electrodes show the significance in frontpolar, frontal, and few frontal and parietal brain regions at temporal scales 3, 4, 6 and 7. Moreover, it was also observed that the distribution of significance is from frontoparietal brain regions. Using wavelet entropy, the significant results are obtained at frontpolar, frontal, and few electrodes in right hemisphere. The highest significance is obtained at frontpolar electrodes followed by frontal and few central and parietal electrodes.
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Affiliation(s)
- Lal Hussain
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad 13100, Pakistan
| | - Wajid Aziz
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad 13100, Pakistan.,College of Computer Sciences and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia
| | - Abdulrahman A Alshdadi
- College of Computer Sciences and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia
| | - Adeel Ahmed Abbasi
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad 13100, Pakistan
| | - Abdul Majid
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad 13100, Pakistan
| | - Ali Raza Marchal
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad 13100, Pakistan
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