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Rehman SU, Sadek I, Huang B, Manickam S, Mahmoud LN. IoT-based emergency cardiac death risk rescue alert system. MethodsX 2024; 13:102834. [PMID: 39071997 PMCID: PMC11278581 DOI: 10.1016/j.mex.2024.102834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 06/28/2024] [Indexed: 07/30/2024] Open
Abstract
The use of technology in healthcare is one of the most critical application areas today. With the development of medical applications, people's quality of life has improved. However, it is impractical and unnecessary for medium-risk people to receive specialized daily hospital monitoring. Due to their health status, they will be exposed to a high risk of severe health damage or even life-threatening conditions without monitoring. Therefore, remote, real-time, low-cost, wearable, and effective monitoring is ideal for this problem. Many researchers mentioned that their studies could use electrocardiogram (ECG) detection to discover emergencies. However, how to respond to discovered emergencies in household life is still a research gap in this field.•This paper proposes a real-time monitoring of ECG signals and sending them to the cloud for Sudden Cardiac Death (SCD) prediction.•Unlike previous studies, the proposed system has an additional emergency response mechanism to alert nearby community healthcare workers when SCD is predicted to occur.
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Affiliation(s)
| | - Ibrahim Sadek
- Biomedical Engineering Dept, Faculty of Engineering, Helwan University, Helwan, Cairo, Egypt
| | - Binhua Huang
- National Advanced IPv6 Centre, Universiti Sains Malaysia, Malaysia
| | | | - Lamees N Mahmoud
- Biomedical Engineering Dept, Faculty of Engineering, Helwan University, Helwan, Cairo, Egypt
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de Faria GM, Lopes EG, Tobaldini E, Montano N, Cunha TS, Casali KR, de Amorim HA. Advances in Non-Invasive Neuromodulation: Designing Closed-Loop Devices for Respiratory-Controlled Transcutaneous Vagus Nerve Stimulation. Healthcare (Basel) 2023; 12:31. [PMID: 38200937 PMCID: PMC10778699 DOI: 10.3390/healthcare12010031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/11/2023] [Accepted: 12/13/2023] [Indexed: 01/12/2024] Open
Abstract
Studies suggest non-invasive transcutaneous auricular vagus nerve stimulation (taVNS) as a potential therapeutic option for various pathological conditions, such as epilepsy and depression. Exhalation-controlled taVNS, which synchronizes stimulation with internal body rhythms, holds promise for enhanced neuromodulation, but there is no closed-loop system in the literature capable of performing such integration in real time. In this context, the objective was to develop real-time signal processing techniques and an integrated closed-loop device with sensors to acquire physiological data. After a conditioning stage, the signal is processed and delivers synchronized electrical stimulation during the patient's expiratory phase. Additional modules were designed for processing, software-controlled selectors, remote and autonomous operation, improved analysis, and graphical visualization. The signal processing method effectively extracted respiratory cycles and successfully attenuated signal noise. Heart rate variability was assessed in real time, using linear statistical evaluation. The prototype feedback stimulator device was physically constructed. Respiratory peak detection achieved an accuracy of 90%, and the real-time processing resulted in a small delay of up to 150 ms in the detection of the expiratory phase. Thus, preliminary results show promising accuracy, indicating the need for additional tests to optimize real-time processing and the application of the prototype in clinical studies.
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Affiliation(s)
- Gabriella Maria de Faria
- Institute of Science and Technology, Universidade Federal de São Paulo, São José dos Campos 12231-280, Brazil; (G.M.d.F.); (E.G.L.); (T.S.C.); (H.A.d.A.)
| | - Eugênia Gonzales Lopes
- Institute of Science and Technology, Universidade Federal de São Paulo, São José dos Campos 12231-280, Brazil; (G.M.d.F.); (E.G.L.); (T.S.C.); (H.A.d.A.)
| | - Eleonora Tobaldini
- Department of Clinical Sciences and Community Health, Università degli Studi di Milano, 20122 Milan, Italy; (E.T.); (N.M.)
| | - Nicola Montano
- Department of Clinical Sciences and Community Health, Università degli Studi di Milano, 20122 Milan, Italy; (E.T.); (N.M.)
| | - Tatiana Sousa Cunha
- Institute of Science and Technology, Universidade Federal de São Paulo, São José dos Campos 12231-280, Brazil; (G.M.d.F.); (E.G.L.); (T.S.C.); (H.A.d.A.)
| | - Karina Rabello Casali
- Institute of Science and Technology, Universidade Federal de São Paulo, São José dos Campos 12231-280, Brazil; (G.M.d.F.); (E.G.L.); (T.S.C.); (H.A.d.A.)
| | - Henrique Alves de Amorim
- Institute of Science and Technology, Universidade Federal de São Paulo, São José dos Campos 12231-280, Brazil; (G.M.d.F.); (E.G.L.); (T.S.C.); (H.A.d.A.)
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Rath A, Mishra D, Panda G. Imbalanced ECG signal-based heart disease classification using ensemble machine learning technique. Front Big Data 2022; 5:1021518. [PMID: 36299660 PMCID: PMC9589052 DOI: 10.3389/fdata.2022.1021518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 09/07/2022] [Indexed: 01/07/2023] Open
Abstract
The machine learning (ML)-based classification models are widely utilized for the automated detection of heart diseases (HDs) using various physiological signals such as electrocardiogram (ECG), magnetocardiography (MCG), heart sound (HS), and impedance cardiography (ICG) signals. However, ECG-based HD identification is the most common one used by clinicians. In the current investigation, the ECG records or subjects have been sampled and are used as inputs to the classification model to distinguish between normal and abnormal patients. The study has employed an imbalanced number of ECG samples for training the various classification models. Few ML methods such as support vector machine (SVM), logistic regression (LR), and adaptive boosting (AdaBoost) which have been rarely used for HD detection have been selected. The performance of the developed model has been evaluated in terms of accuracy, F1-score, and area under curve (AUC) values using ECG signals of subjects given in publicly available (PTB-ECG, MIT-BIH) datasets. Ranking of the models has been assigned based on these performance metrics and it is found that the AdaBoost and LR classifiers stand in first and second positions. These two models have been ensembled based on the majority voting principle and the performance measure of this ensemble model has also been determined. It is, in general, observed that the proposed ensemble model demonstrates the best HD detection performance of 0.946, 0.949, and 0.951 for the PTB-ECG dataset and 0.921, 0.926, and 0.950 for the MIT-BIH dataset in terms of accuracy, F1-score, and AUC, respectively. The proposed methodology can also be employed for the classification of HD using ICG, MCG, and HS signals as inputs. Further, the proposed methodology can also be applied to the detection of other diseases.
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Affiliation(s)
- Adyasha Rath
- Department of Computer Science and Engineering, Siksha O Anusandhan (Deemed to be) University, Bhubaneswar, Odisha, India
| | - Debahuti Mishra
- Department of Computer Science and Engineering, Siksha O Anusandhan (Deemed to be) University, Bhubaneswar, Odisha, India
| | - Ganapati Panda
- Department of Electronics and Tele Communication, C. V. Raman Global University, Bhubaneswar, Odisha, India,*Correspondence: Ganapati Panda
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Fathi IS, Makhlouf MAA, Osman E, Ahmed MA. An Energy-Efficient Compression Algorithm of ECG Signals in Remote Healthcare Monitoring Systems. IEEE ACCESS 2022; 10:39129-39144. [DOI: 10.1109/access.2022.3166476] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Affiliation(s)
- Islam S. Fathi
- Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia, Egypt
| | - Mohamed Abd Allah Makhlouf
- Department of Information System, Faculty of Computer and Informatics, Suez Canal University, Ismailia, Egypt
| | - Elsaeed Osman
- Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Cairo, Egypt
| | - Mohamed Ali Ahmed
- Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia, Egypt
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Kadhim OR, Taha ZK, Abd AL- Majeed SW. Low-Cost Cloud-based Intelligent Remote Patient Monitoring for Heart Patient. 2021 INTERNATIONAL CONFERENCE ON COMMUNICATION & INFORMATION TECHNOLOGY (ICICT) 2021. [DOI: 10.1109/icict52195.2021.9568458] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Advances of ECG Sensors from Hardware, Software and Format Interoperability Perspectives. ELECTRONICS 2021. [DOI: 10.3390/electronics10020105] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
It is well-known that cardiovascular disease is one of the major causes of death worldwide nowadays. Electrocardiogram (ECG) sensor is one of the tools commonly used by cardiologists to diagnose and detect signs of heart disease with their patients. Since fast, prompt and accurate interpretation and decision is important in saving the life of patients from sudden heart attack or cardiac arrest, many innovations have been made to ECG sensors. However, the use of traditional ECG sensors is still prevalent in the clinical settings of many medical institutions. This article provides a comprehensive survey on ECG sensors from hardware, software and data format interoperability perspectives. The hardware perspective outlines a general hardware architecture of an ECG sensor along with the description of its hardware components. The software perspective describes various techniques (denoising, machine learning, deep learning, and privacy preservation) and other computer paradigms used in the software development and deployment for ECG sensors. Finally, the format interoperability perspective offers a detailed taxonomy of current ECG formats and the relationship among these formats. The intention is to help researchers towards the development of modern ECG sensors that are suitable and approved for adoption in real clinical settings.
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Xie L, Li Z, Zhou Y, He Y, Zhu J. Computational Diagnostic Techniques for Electrocardiogram Signal Analysis. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6318. [PMID: 33167558 PMCID: PMC7664289 DOI: 10.3390/s20216318] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 10/27/2020] [Accepted: 11/04/2020] [Indexed: 12/25/2022]
Abstract
Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina, myocardial infarction, and ischemic heart failure, are the leading cause of death globally. Early detection and treatment of CVDs significantly contribute to the prevention or delay of cardiovascular death. Electrocardiogram (ECG) records the electrical impulses generated by heart muscles, which reflect regular or irregular beating activity. Computer-aided techniques provide fast and accurate tools to identify CVDs using a patient's ECG signal, which have achieved great success in recent years. Latest computational diagnostic techniques based on ECG signals for estimating CVDs conditions are summarized here. The procedure of ECG signals analysis is discussed in several subsections, including data preprocessing, feature engineering, classification, and application. In particular, the End-to-End models integrate feature extraction and classification into learning algorithms, which not only greatly simplifies the process of data analysis, but also shows excellent accuracy and robustness. Portable devices enable users to monitor their cardiovascular status at any time, bringing new scenarios as well as challenges to the application of ECG algorithms. Computational diagnostic techniques for ECG signal analysis show great potential for helping health care professionals, and their application in daily life benefits both patients and sub-healthy people.
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Affiliation(s)
- Liping Xie
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China; (Z.L.); (Y.Z.); (Y.H.); (J.Z.)
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Serhani MA, T. El Kassabi H, Ismail H, Nujum Navaz A. ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges. SENSORS (BASEL, SWITZERLAND) 2020; 20:E1796. [PMID: 32213969 PMCID: PMC7147367 DOI: 10.3390/s20061796] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 03/17/2020] [Accepted: 03/19/2020] [Indexed: 02/01/2023]
Abstract
Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems' components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems' value chain is conducted, and a thorough review of the relevant literature, classified against the experts' taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.
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Affiliation(s)
- Mohamed Adel Serhani
- Department of Information Systems and Security, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates;
| | - Hadeel T. El Kassabi
- Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates; (H.T.E.K.)
| | - Heba Ismail
- Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates; (H.T.E.K.)
| | - Alramzana Nujum Navaz
- Department of Information Systems and Security, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates;
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Shao M, Zhou Z, Bin G, Bai Y, Wu S. A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection. SENSORS (BASEL, SWITZERLAND) 2020; 20:E606. [PMID: 31979184 PMCID: PMC7038204 DOI: 10.3390/s20030606] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 01/16/2020] [Accepted: 01/20/2020] [Indexed: 11/19/2022]
Abstract
In this paper we proposed a wearable electrocardiogram (ECG) telemonitoring system for atrial fibrillation (AF) detection based on a smartphone and cloud computing. A wearable ECG patch was designed to collect ECG signals and send the signals to an Android smartphone via Bluetooth. An Android APP was developed to display the ECG waveforms in real time and transmit every 30 s ECG data to a remote cloud server. A machine learning (CatBoost)-based ECG classification method was proposed to detect AF in the cloud server. In case of detected AF, the cloud server pushed the ECG data and classification results to the web browser of a doctor. Finally, the Android APP displayed the doctor's diagnosis for the ECG signals. Experimental results showed the proposed CatBoost classifier trained with 17 selected features achieved an overall F1 score of 0.92 on the test set (n = 7,270). The proposed wearable ECG monitoring system may potentially be useful for long-term ECG telemonitoring for AF detection.
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Affiliation(s)
- Minggang Shao
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China; (M.S.); (Z.Z.); (G.B.); (Y.B.)
- Smart City College, Beijing Union University, Beijing 100101, China
| | - Zhuhuang Zhou
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China; (M.S.); (Z.Z.); (G.B.); (Y.B.)
| | - Guangyu Bin
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China; (M.S.); (Z.Z.); (G.B.); (Y.B.)
| | - Yanping Bai
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China; (M.S.); (Z.Z.); (G.B.); (Y.B.)
| | - Shuicai Wu
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China; (M.S.); (Z.Z.); (G.B.); (Y.B.)
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Bazi Y, Al Rahhal MM, AlHichri H, Ammour N, Alajlan N, Zuair M. Real-Time Mobile-Based Electrocardiogram System for Remote Monitoring of Patients with Cardiac Arrhythmias. INT J PATTERN RECOGN 2019. [DOI: 10.1142/s0218001420580136] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this study, we propose an electrocardiogram (ECG) system for the simultaneous and remote monitoring of multiple heart patients. It consists of three main components: patient, sever, and monitoring units. The patient unit uses a wearable miniature sensor that continuously measures ECG signals and sends them to a smart mobile phone via a Bluetooth connection. In the mobile device, the ECG signals can be stored, displayed on screen, and automatically transmitted to a distant server unit over the internet; the server stores ECG data from several patients. Health care stakeholders use a monitoring unit to retrieve the ECG signals of multiple patients at any time from the server for display and real-time automatic analysis. The analysis includes segmentation of the ECG signal into separate heartbeats followed by arrhythmia detection and classification. When compared to existing real-time ECG systems, where the detection of abnormalities is usually performed using simple rules, the proposed system implements a real-time classification module that is based on a support vector machine (SVM) classifier. Extensive experimental results on ECG data obtained from a TechPatientTM simulator, a real person, and 20 records from the MIT arrhythmia database are reported and discussed.
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Affiliation(s)
- Yakoub Bazi
- Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Mohamad M. Al Rahhal
- Information Science Department, College of Applied Computer Science, King Saud University, Riyadh 11543, Saudi Arabia
| | - Haikel AlHichri
- Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Nassim Ammour
- Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Naif Alajlan
- Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Mansour Zuair
- Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
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