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Al-hajjar ALN, Al-Qurabat AKM. An overview of machine learning methods in enabling IoMT-based epileptic seizure detection. THE JOURNAL OF SUPERCOMPUTING 2023; 79:1-48. [PMID: 37359338 PMCID: PMC10123593 DOI: 10.1007/s11227-023-05299-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 04/12/2023] [Indexed: 06/28/2023]
Abstract
The healthcare industry is rapidly automating, in large part because of the Internet of Things (IoT). The sector of the IoT devoted to medical research is sometimes called the Internet of Medical Things (IoMT). Data collecting and processing are the fundamental components of all IoMT applications. Machine learning (ML) algorithms must be included into IoMT immediately due to the vast quantity of data involved in healthcare and the value that precise forecasts have. In today's world, together, IoMT, cloud services, and ML techniques have become effective tools for solving many problems in the healthcare sector, such as epileptic seizure monitoring and detection. One of the biggest hazards to people's lives is epilepsy, a lethal neurological condition that has become a global issue. To prevent the deaths of thousands of epileptic patients each year, there is a critical necessity for an effective method for detecting epileptic seizures at their earliest stage. Numerous medical procedures, including epileptic monitoring, diagnosis, and other procedures, may be carried out remotely with the use of IoMT, which will reduce healthcare expenses and improve services. This article seeks to act as both a collection and a review of the different cutting-edge ML applications for epilepsy detection that are presently being combined with IoMT.
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Affiliation(s)
| | - Ali Kadhum M. Al-Qurabat
- Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq
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2
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Ein Shoka AA, Dessouky MM, El-Sayed A, Hemdan EED. EEG seizure detection: concepts, techniques, challenges, and future trends. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-31. [PMID: 37362745 PMCID: PMC10071471 DOI: 10.1007/s11042-023-15052-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 08/07/2022] [Accepted: 02/27/2023] [Indexed: 06/28/2023]
Abstract
A central nervous system disorder is usually referred to as epilepsy. In epilepsy brain activity becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of awareness. Consequently, epilepsy patients face problems in daily life due to precautions they must take to adapt to this condition, particularly when they use heavy equipment, e.g., vehicle derivation. Epilepsy studies rely primarily on electroencephalography (EEG) signals to evaluate brain activity during seizures. It is troublesome and time-consuming to manually decide the location of seizures in EEG signals. The automatic detection framework is one of the principal tools to help doctors and patients take appropriate precautions. This paper reviews the epilepsy mentality disorder and the types of seizure, preprocessing operations that are performed on EEG data, a generally extracted feature from the signal, and a detailed view on classification procedures used in this problem and provide insights on the difficulties and future research directions in this innovative theme. Therefore, this paper presents a review of work on recent methods for the epileptic seizure process along with providing perspectives and concepts to researchers to present an automated EEG-based epileptic seizure detection system using IoT and machine learning classifiers for remote patient monitoring in the context of smart healthcare systems. Finally, challenges and open research points in EEG seizure detection are investigated.
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Affiliation(s)
- Athar A. Ein Shoka
- Faculty of Electronic Engineering, Computer Science and Engineering Department, Menoufia University, Menouf, Egypt
| | - Mohamed M. Dessouky
- Faculty of Electronic Engineering, Computer Science and Engineering Department, Menoufia University, Menouf, Egypt
- Department of Computer Science & Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Ayman El-Sayed
- Faculty of Electronic Engineering, Computer Science and Engineering Department, Menoufia University, Menouf, Egypt
| | - Ezz El-Din Hemdan
- Faculty of Electronic Engineering, Computer Science and Engineering Department, Menoufia University, Menouf, Egypt
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3
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Hei Y, Yuan T, Fan Z, Yang B, Hu J. Sleep staging classification based on a new parallel fusion method of multiple sources signals. Physiol Meas 2022; 43. [PMID: 35381584 DOI: 10.1088/1361-6579/ac647b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 04/05/2022] [Indexed: 11/12/2022]
Abstract
APPROACH First, the heart rate variability (HRV) is extracted from EOG with the Weight Calculation Algorithm (WCA) and an "HYF" RR interval detection algorithm. Second, three feature sets were extracted from HRV segments and EOG segments: time-domain features, frequency domain features and nonlinear-domain features. The frequency domain features and nonlinear-domain features were extracted by using Discrete Wavelet Transform (DWT), Autoregressive (AR), and Power Spectral entropy (PSE), and Refined Composite Multiscale Dispersion Entropy (RCMDE). Third, a new "Parallel Fusion Method" (PFM) for sleep stage classification is proposed. Three kinds of feature sets from EOG and HRV segments are fused by using PFM. Fourth, Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM) classification models is employed for sleep staging. MAIN RESULTS Our experimental results show significant performance improvement on automatic sleep staging on the target domains achieved with the new sleep staging approach. The performance of the proposed method is testedby evaluating the average accuracy, Kappa coefficient. The average accuracy of sleep classification results by using XGBoost classification model with PFM is 82.7% and the kappa coefficient is 0.711, also by using SVM classification model with the PFM is 83.7%, and the kappa coefficient is 0.724. Experimental results show that the performance of the proposed method is competitive with the most current methods and results, and the recognition rate of S1 stage is significantly improved. Significance: As a consequence, it would enable one to improve the quality of automatic sleep staging models when the EOG and HRV signals are fused, which can be beneficial for monitor sleep quality and keep abreast of health conditions. Besides, our study provides good research ideas and methods for scholars, doctors and individuals.
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Affiliation(s)
- Yafang Hei
- College of Applied Mathematics, Chengdu University of Information Technology, Xuefu road 24, Shuangliu, Chengdu, Chengdu, Sichuan, 610225, CHINA
| | - Tuming Yuan
- College of Applied Mathematics, Chengdu University of Information Technology, Xuefu road 24, Chengdu, Sichuan, 610225, CHINA
| | - Zhigao Fan
- School of Atmospheric Sciences, Chengdu University of Information Technology, Xuefu road 24, Chengdu, Sichuan, 610225, CHINA
| | - Bo Yang
- College of Electronic Engineering, Chengdu University of Information Technology, Xuefu road 24, , , 610225, CHINA
| | - Jiancheng Hu
- College of Applied Mathematics, Chengdu University of Information Technology, Xuefu road 24, , , 610225, CHINA
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4
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Advanced Authentication Scheme with Bio-Key Using Artificial Neural Network. SUSTAINABILITY 2022. [DOI: 10.3390/su14073950] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The improvements in the field of health monitoring have revolutionized our daily lifestyle by developing various applications that did not exist before. However, these applications have serious security concerns; they also can be taken good care of by utilizing the Electrocardiogram (ECG) as potential biometrics. The ECG provides robustness against forgery attacks unlike conventional methods of authentication. Therefore, it has attained the utmost attention and is utilized in several authentication solutions. In this paper, we have presented an efficient architecture for an advanced authentication scheme that utilized a binarized form (bio-key) of ECG signal along with an Artificial Neural Network (ANN) to enhance the authentication process. In order to prove the concept, we have developed the testbed and acquired ECG signals using the AD8232 ECG recording module under a controlled environment. The variable-length bio-keys are extracted using an algorithm after the feature extraction process. The extracted features along with bio-keys are utilized for template formation and also for training/testing of the ANN model to enhance the accuracy of the authentication process. The performance of authentication results depicted high authentication accuracy of 98% and minimized the equal error rate (EER) to 2%. Moreover, our scheme outperformed comparative peers’ work in terms of accuracy and EER.
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Almarshad MA, Islam MS, Al-Ahmadi S, BaHammam AS. Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review. Healthcare (Basel) 2022; 10:healthcare10030547. [PMID: 35327025 PMCID: PMC8950880 DOI: 10.3390/healthcare10030547] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/03/2022] [Accepted: 03/11/2022] [Indexed: 02/04/2023] Open
Abstract
Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications. All these encourage the researchers to estimate its feasibility as an alternative to many expansive, time-wasting, and invasive methods. This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020. To this aim, papers from 1981 to date are reviewed and categorized in terms of the healthcare application domain. Along with consolidated research areas, recent topics that are growing in popularity are also discovered. We also highlight the potential impact of using PPG signals on an individual’s quality of life and public health. The state-of-the-art studies suggest that in the years to come PPG wearables will become pervasive in many fields of medical practices, and the main domains include cardiology, respiratory, neurology, and fitness. Main operation challenges, including performance and robustness obstacles, are identified.
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Affiliation(s)
- Malak Abdullah Almarshad
- Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; (M.S.I.); (S.A.-A.)
- Computer Science Department, College of Computer and Information Sciences, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia
- Correspondence:
| | - Md Saiful Islam
- Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; (M.S.I.); (S.A.-A.)
| | - Saad Al-Ahmadi
- Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; (M.S.I.); (S.A.-A.)
| | - Ahmed S. BaHammam
- The University Sleep Disorders Center, Department of Medicine, College of Medicine, King Saud University, Riyadh 11324, Saudi Arabia;
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Xu S, Chen X, Fu Y, Xu H, Hong K. Research on Weigh-in-Motion Algorithm of Vehicles Based on BSO-BP. SENSORS 2022; 22:s22062109. [PMID: 35336283 PMCID: PMC8948758 DOI: 10.3390/s22062109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 03/01/2022] [Accepted: 03/04/2022] [Indexed: 12/10/2022]
Abstract
Weigh-in-motion (WIM) systems are used to measure the weight of moving vehicles. Aiming at the problem of low accuracy of the WIM system, this paper proposes a WIM model based on the beetle swarm optimization (BSO) algorithm and the error back propagation (BP) neural network. Firstly, the structure and principle of the WIM system used in this paper are analyzed. Secondly, the WIM signal is denoised and reconstructed by wavelet transform. Then, a BP neural network model optimized by BSO algorithm is established to process the WIM signal. Finally, the predictive ability of BP neural network models optimized by different algorithms are compared and conclusions are drawn. The experimental results show that the BSO-BP WIM model has fast convergence speed, high accuracy, the relative error of the maximum gross weight is 1.41%, and the relative error of the maximum axle weight is 6.69%.
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Affiliation(s)
- Suan Xu
- Correspondence: ; Tel.: +86-0571-86914559
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Zhang S, Liu G, Xiao R, Cui W, Cai J, Hu X, Sun Y, Qiu J, Qi Y. A combination of statistical parameters for epileptic seizure detection and classification using VMD and NLTWSVM. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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8
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Arpaia P, Crauso F, De Benedetto E, Duraccio L, Improta G, Serino F. Soft Transducer for Patient's Vitals Telemonitoring with Deep Learning-Based Personalized Anomaly Detection. SENSORS 2022; 22:s22020536. [PMID: 35062496 PMCID: PMC8777728 DOI: 10.3390/s22020536] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 01/05/2022] [Accepted: 01/07/2022] [Indexed: 12/25/2022]
Abstract
This work addresses the design, development and implementation of a 4.0-based wearable soft transducer for patient-centered vitals telemonitoring. In particular, first, the soft transducer measures hypertension-related vitals (heart rate, oxygen saturation and systolic/diastolic pressure) and sends the data to a remote database (which can be easily consulted both by the patient and the physician). In addition to this, a dedicated deep learning algorithm, based on a Long-Short-Term-Memory Autoencoder, was designed, implemented and tested for providing an alert when the patient’s vitals exceed certain thresholds, which are automatically personalized for the specific patient. Furthermore, a mobile application (EcO2u) was developed to manage the entire data flow and facilitate the data fruition; this application also implements an innovative face-detection algorithm that ensures the identity of the patient. The robustness of the proposed soft transducer was validated experimentally on five individuals, who used the system for 30 days. The experimental results demonstrated an accuracy in anomaly detection greater than 93%, with a true positive rate of more than 94%.
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Affiliation(s)
- Pasquale Arpaia
- Interdepartmental Research Center in Health Management and Innovation in Healthcare (CIRMIS), University of Naples Federico II, 80125 Naples, Italy;
- Department of Information Technology and Electrical Engineering (DIETI), University of Naples Federico II, 80125 Naples, Italy
| | - Federica Crauso
- Department of Public Health, University of Naples Federico II, 80125 Naples, Italy; (F.C.); (G.I.)
| | - Egidio De Benedetto
- Department of Information Technology and Electrical Engineering (DIETI), University of Naples Federico II, 80125 Naples, Italy
- Correspondence:
| | - Luigi Duraccio
- Department of Electronics and Telecommunications, Polytechnic University of Turin, 10129 Turin, Italy;
| | - Giovanni Improta
- Department of Public Health, University of Naples Federico II, 80125 Naples, Italy; (F.C.); (G.I.)
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Formoso MA, Ortiz A, Martinez-Murcia FJ, Gallego N, Luque JL. Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis. SENSORS 2021; 21:s21217061. [PMID: 34770378 PMCID: PMC8588444 DOI: 10.3390/s21217061] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/21/2021] [Accepted: 10/22/2021] [Indexed: 01/07/2023]
Abstract
Objective Dyslexia diagnosis is a challenging task, since traditional diagnosis methods are not based on biological markers but on behavioural tests. Although dyslexia diagnosis has been addressed by these tests in clinical practice, it is difficult to extract information about the brain processes involved in the different tasks and, then, to go deeper into its biological basis. Thus, the use of biomarkers can contribute not only to the diagnosis but also to a better understanding of specific learning disorders such as dyslexia. In this work, we use Electroencephalography (EEG) signals to discover differences among controls and dyslexic subjects using signal processing and artificial intelligence techniques. Specifically, we measure phase synchronization among channels, to reveal the functional brain network activated during auditory processing. On the other hand, to explore synchronicity patterns risen by low-level auditory processing, we used specific stimuli consisting in band-limited white noise, modulated in amplitude at different frequencies. The differential information contained in the functional (i.e., synchronization) network has been processed by an anomaly detection system that addresses the problem of subjects variability by an outlier-detection method based on vector quantization. The results, obtained for 7 years-old children, show that the proposed method constitutes an useful tool for clinical use, with the area under ROC curve (AUC) values up to 0.95 in differential diagnosis tasks.
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Affiliation(s)
- Marco A. Formoso
- Communications Engineering Department, University of Málaga, 29071 Málaga, Spain; (M.A.F.); (N.G.)
| | - Andrés Ortiz
- Communications Engineering Department, University of Málaga, 29071 Málaga, Spain; (M.A.F.); (N.G.)
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), 18014 Granada, Spain;
- Correspondence: ; Tel.: +34-952133353
| | - Francisco J. Martinez-Murcia
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), 18014 Granada, Spain;
- Department of Signal Theory, Networking and Communications, University of Granada, 18014 Granada, Spain
| | - Nicolás Gallego
- Communications Engineering Department, University of Málaga, 29071 Málaga, Spain; (M.A.F.); (N.G.)
| | - Juan L. Luque
- Department of Basic Psychology, University of Malaga, 29019 Málaga, Spain;
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10
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Saminu S, Xu G, Shuai Z, Abd El Kader I, Jabire AH, Ahmed YK, Karaye IA, Ahmad IS. A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal. Brain Sci 2021; 11:668. [PMID: 34065473 PMCID: PMC8160878 DOI: 10.3390/brainsci11050668] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 05/14/2021] [Accepted: 05/16/2021] [Indexed: 02/07/2023] Open
Abstract
The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.
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Affiliation(s)
- Sani Saminu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (Z.S.); (I.A.E.K.); (I.A.K.); (I.S.A.)
- Biomedical Engineering Department, University of Ilorin, P.M.B 1515, Ilorin 240003, Nigeria;
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (Z.S.); (I.A.E.K.); (I.A.K.); (I.S.A.)
| | - Zhang Shuai
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (Z.S.); (I.A.E.K.); (I.A.K.); (I.S.A.)
| | - Isselmou Abd El Kader
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (Z.S.); (I.A.E.K.); (I.A.K.); (I.S.A.)
| | - Adamu Halilu Jabire
- Department of Electrical and Electronics Engineering, Taraba State University, Jalingo 660242, Nigeria;
| | - Yusuf Kola Ahmed
- Biomedical Engineering Department, University of Ilorin, P.M.B 1515, Ilorin 240003, Nigeria;
| | - Ibrahim Abdullahi Karaye
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (Z.S.); (I.A.E.K.); (I.A.K.); (I.S.A.)
| | - Isah Salim Ahmad
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (Z.S.); (I.A.E.K.); (I.A.K.); (I.S.A.)
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11
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Navaz AN, Serhani MA, El Kassabi HT, Al-Qirim N, Ismail H. Trends, Technologies, and Key Challenges in Smart and Connected Healthcare. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:74044-74067. [PMID: 34812394 PMCID: PMC8545204 DOI: 10.1109/access.2021.3079217] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 05/05/2021] [Indexed: 05/04/2023]
Abstract
Cardio Vascular Diseases (CVD) is the leading cause of death globally and is increasing at an alarming rate, according to the American Heart Association's Heart Attack and Stroke Statistics-2021. This increase has been further exacerbated because of the current coronavirus (COVID-19) pandemic, thereby increasing the pressure on existing healthcare resources. Smart and Connected Health (SCH) is a viable solution for the prevalent healthcare challenges. It can reshape the course of healthcare to be more strategic, preventive, and custom-designed, making it more effective with value-added services. This research endeavors to classify state-of-the-art SCH technologies via a thorough literature review and analysis to comprehensively define SCH features and identify the enabling technology-related challenges in SCH adoption. We also propose an architectural model that captures the technological aspect of the SCH solution, its environment, and its primary involved stakeholders. It serves as a reference model for SCH acceptance and implementation. We reflected the COVID-19 case study illustrating how some countries have tackled the pandemic differently in terms of leveraging the power of different SCH technologies, such as big data, cloud computing, Internet of Things, artificial intelligence, robotics, blockchain, and mobile applications. In combating the pandemic, SCH has been used efficiently at different stages such as disease diagnosis, virus detection, individual monitoring, tracking, controlling, and resource allocation. Furthermore, this review highlights the challenges to SCH acceptance, as well as the potential research directions for better patient-centric healthcare.
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Affiliation(s)
- Alramzana Nujum Navaz
- Department of Information Systems and SecurityCollege of Information TechnologyUnited Arab Emirates UniversityAl AinUnited Arab Emirates
| | - Mohamed Adel Serhani
- Department of Information Systems and SecurityCollege of Information TechnologyUnited Arab Emirates UniversityAl AinUnited Arab Emirates
| | - Hadeel T. El Kassabi
- Department of Computer Science and Software EngineeringCollege of Information TechnologyUAE UniversityAl AinUnited Arab Emirates
| | - Nabeel Al-Qirim
- Department of Information Systems and SecurityCollege of Information TechnologyUnited Arab Emirates UniversityAl AinUnited Arab Emirates
| | - Heba Ismail
- Department of Computer Science and Information Technology (CS-IT)College of EngineeringAbu Dhabi UniversityAl AinUnited Arab Emirates
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12
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Formica D, Schena E. Smart Sensors for Healthcare and Medical Applications. SENSORS 2021; 21:s21020543. [PMID: 33466591 PMCID: PMC7828709 DOI: 10.3390/s21020543] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 01/08/2021] [Indexed: 11/16/2022]
Abstract
This special issue on "Smart Sensors for Healthcare and Medical Applications" focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare [...].
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Affiliation(s)
- Domenico Formica
- Unit of Neurophysiology and Neuroengineering of Human Technology Interaction (NeXT), Università Campus Bio-Medico di Roma, 00128 Rome, Italy
- Correspondence:
| | - Emiliano Schena
- Laboratory of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, 00128 Rome, Italy;
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13
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Khan MU, Aziz S, Akram T, Amjad F, Iqtidar K, Nam Y, Khan MA. Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme. SENSORS 2021; 21:s21010247. [PMID: 33401652 PMCID: PMC7794944 DOI: 10.3390/s21010247] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 12/22/2020] [Accepted: 12/24/2020] [Indexed: 12/27/2022]
Abstract
Hypertension is an antecedent to cardiac disorders. According to the World Health Organization (WHO), the number of people affected with hypertension will reach around 1.56 billion by 2025. Early detection of hypertension is imperative to prevent the complications caused by cardiac abnormalities. Hypertension usually possesses no apparent detectable symptoms; hence, the control rate is significantly low. Computer-aided diagnosis based on machine learning and signal analysis has recently been applied to identify biomarkers for the accurate prediction of hypertension. This research proposes a new expert hypertension detection system (EHDS) from pulse plethysmograph (PuPG) signals for the categorization of normal and hypertension. The PuPG signal data set, including rich information of cardiac activity, was acquired from healthy and hypertensive subjects. The raw PuPG signals were preprocessed through empirical mode decomposition (EMD) by decomposing a signal into its constituent components. A combination of multi-domain features was extracted from the preprocessed PuPG signal. The features exhibiting high discriminative characteristics were selected and reduced through a proposed hybrid feature selection and reduction (HFSR) scheme. Selected features were subjected to various classification methods in a comparative fashion in which the best performance of 99.4% accuracy, 99.6% sensitivity, and 99.2% specificity was achieved through weighted k-nearest neighbor (KNN-W). The performance of the proposed EHDS was thoroughly assessed by tenfold cross-validation. The proposed EHDS achieved better detection performance in comparison to other electrocardiogram (ECG) and photoplethysmograph (PPG)-based methods.
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Affiliation(s)
- Muhammad Umar Khan
- Department of Electronics Engineering, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (M.U.K.); (F.A.)
| | - Sumair Aziz
- Department of Electronics Engineering, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (M.U.K.); (F.A.)
- Correspondence: (S.A.); (Y.N.)
| | - Tallha Akram
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Wah Cantonment, Islamabad 45550, Pakistan;
| | - Fatima Amjad
- Department of Electronics Engineering, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (M.U.K.); (F.A.)
| | - Khushbakht Iqtidar
- Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan;
| | - Yunyoung Nam
- Department of Computer Science and Engineering, Soonchunhyang University, Asan 31538, Korea
- Correspondence: (S.A.); (Y.N.)
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