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Iqbal U, Almakki R, Usman M, Altameem A, Albathan M, Jilani AK. Methodological identification of anomalies episodes in ECG streams: a systematic mapping study. BMC Med Res Methodol 2024; 24:127. [PMID: 38834955 DOI: 10.1186/s12874-024-02251-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Accepted: 05/20/2024] [Indexed: 06/06/2024] Open
Abstract
An electrocardiogram is a medical examination tool for measuring different patterns of heart blood flow circle either in the form of usual or non-invasive patterns. These patterns are useful for the identification of morbidity condition of the heart especially in certain conditions of heart abnormality and arrhythmia. Myocardial infarction (MI) is one of them that happened due to sudden blockage of blood by the cause of malfunction of heart. In electrocardiography (ECG) intensity of MI is highlighted on the basis of unusual patterns of T wave changes. Various studies have contributed for MI through T wave's classification, but more to the point of T wave has always attracted the ECG researchers. Methodology. This Study is primarily designed for proposing the combination of latest methods that are worked for the solutions of pre-defined research questions. Such solutions are designed in the form of the systematic review process (SLR) by following the Kitchen ham guidance. The literature survey is a two phase's process, at first phase collect the articles that were published in IEEE Xplore, Scopus, science direct and Springer from 2008 to 2023. It consist of steps; the first level is executed by filtrating the articles on the basis of keyword phase of title and abstract filter. Similarly, at two level the manuscripts are scanned through filter of eligibility criteria of articles selection. The last level belongs to the quality assessment of articles, in such level articles are rectified through evaluation of domain experts. Results. Finally, the selected articles are addressed with research questions and briefly discuss these selected state-of-the-art methods that are worked for the T wave classification. These address units behave as solutions to research problems that are highlighted in the form of research questions. Conclusion and future directions. During the survey process for these solutions, we got some critical observations in the form of gaps that reflected the other directions for researchers. In which feature engineering, different dependencies of ECG features and dimensional reduction of ECG for the better ECG analysis are reflection of future directions.
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Affiliation(s)
- Uzair Iqbal
- Department of Artificial Intelligence and Data Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan.
| | - Riyad Almakki
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), 11432, Riyadh, Saudi Arabia.
| | - Muhammad Usman
- Department of Computer Science and Technology, Harbin Institue of Technology, Harbin, Heilongjiang, China
| | - Abdullah Altameem
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), 11432, Riyadh, Saudi Arabia
| | - Mubarak Albathan
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), 11432, Riyadh, Saudi Arabia
| | - Abdul Khader Jilani
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
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Cuesta-Morales P, Perez-Schofield BG, Rodríguez-Liñares L, Lado MJ, Méndez AJ, Vila XA. VARSE: Android app for real-time acquisition and analysis of heart rate signals. Int J Med Inform 2022; 160:104692. [DOI: 10.1016/j.ijmedinf.2022.104692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 11/23/2021] [Accepted: 01/15/2022] [Indexed: 11/28/2022]
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Wal A, Khandai M, Vig H, Srivastava P, Agarwal A, Wadhwani S, Wal P. Evidence-Based Treatment, assisted by Mobile Technology to Deliver, and Evidence-Based Drugs in South Asian Countries. ARCHIVES OF PHARMACY PRACTICE 2022. [DOI: 10.51847/d5zeajvk6x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
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Liu Y, Jin Y, Liu J, Qin C, Lin K, Shi H, Tao J, Zhao L, Liu C. Precise and efficient heartbeat classification using a novel lightweight-modified method. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102771] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Murugappan M, Murugesan L, Jerritta S, Adeli H. Sudden Cardiac Arrest (SCA) Prediction Using ECG Morphological Features. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-020-04765-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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Early Detection of Atrial Fibrillation Based on ECG Signals. Bioengineering (Basel) 2020; 7:bioengineering7010016. [PMID: 32069949 PMCID: PMC7148541 DOI: 10.3390/bioengineering7010016] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 01/29/2020] [Accepted: 02/11/2020] [Indexed: 01/23/2023] Open
Abstract
Atrial fibrillation, often called AF is considered to be the most common type of cardiac arrhythmia, which is a major healthcare challenge. Early detection of AF and the appropriate treatment is crucial if the symptoms seem to be consistent and persistent. This research work focused on the development of a heart monitoring system which could be considered as a feasible solution in early detection of potential AF in real time. The objective was to bridge the gap in the market for a low-cost, at home use, noninvasive heart health monitoring system specifically designed to periodically monitor heart health in subjects with AF disorder concerns. The main characteristic of AF disorder is the considerably higher heartbeat and the varying period between observed R waves in electrocardiogram (ECG) signals. This proposed research was conducted to develop a low cost and easy to use device that measures and analyzes the heartbeat variations, varying time period between successive R peaks of the ECG signal and compares the result with the normal heart rate and RR intervals. Upon exceeding the threshold values, this device creates an alert to notify about the possible AF detection. The prototype for this research consisted of a Bitalino ECG sensor and electrodes, an Arduino microcontroller, and a simple circuit. The data was acquired and analyzed using the Arduino software in real time. The prototype was used to analyze healthy ECG data and using the MIT-BIH database the real AF patient data was analyzed, and reasonable threshold values were found, which yielded a reasonable success rate of AF detection.
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Wongvibulsin S, Martin SS, Steinhubl SR, Muse ED. Connected Health Technology for Cardiovascular Disease Prevention and Management. CURRENT TREATMENT OPTIONS IN CARDIOVASCULAR MEDICINE 2019; 21:29. [PMID: 31104157 PMCID: PMC7263827 DOI: 10.1007/s11936-019-0729-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
PURPOSE OF THE REVIEW Advances in computing power and wireless technologies have reshaped our approach to patient monitoring. Medical grade sensors and apps that were once restricted to hospitals and specialized clinic are now widely available. Here, we review the current evidence supporting the use of connected health technologies for the prevention and management of cardiovascular disease in an effort to highlight gaps and future opportunities for innovation. RECENT FINDINGS Initial studies in connected health for cardiovascular disease prevention and management focused primarily on activity tracking and blood pressure monitoring but have since expanded to include a full panoply of novel sensors and pioneering smartphone apps with targeted interventions in diet, lipid management and risk assessment, smoking cessation, cardiac rehabilitation, heart failure, and arrhythmias. While outfitting patients with sensors and devices alone is infrequently a lasting solution, monitoring programs that include personalized insights based on patient-level data are more likely to lead to improved outcomes. Advances in this space have been driven by patients and researchers while healthcare systems remain slow to fully integrate and adequately adapt these new technologies into their workflows. Cardiovascular disease prevention and management continue to be key focus areas for clinicians and researchers in the connected health space. Exciting progress has been made though studies continue to suffer from small sample size and limited follow-up. Efforts that combine home patient monitoring, engagement, and personalized feedback are the most promising. Ultimately, combining patient-level ambulatory sensor data, electronic health records, and genomics using machine learning analytics will bring precision medicine closer to reality.
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Affiliation(s)
- Shannon Wongvibulsin
- Department of Biomedical Engineering, Johns Hopkins University, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Seth S Martin
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Steven R Steinhubl
- Scripps Research Translational Institute, 3344 N. Torrey Pines Ct, Suite 300, La Jolla, San Diego, CA, 92037, USA
| | - Evan D Muse
- Scripps Research Translational Institute, 3344 N. Torrey Pines Ct, Suite 300, La Jolla, San Diego, CA, 92037, USA.
- Division of Cardiovascular Disease, Scripps Clinic-Scripps Health, La Jolla, San Diego, CA, USA.
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Shi H, Wang H, Zhang F, Huang Y, Zhao L, Liu C. Inter-patient heartbeat classification based on region feature extraction and ensemble classifier. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.02.012] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Acharya UR, Hagiwara Y, Adeli H. Automated seizure prediction. Epilepsy Behav 2018; 88:251-261. [PMID: 30317059 DOI: 10.1016/j.yebeh.2018.09.030] [Citation(s) in RCA: 71] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 09/16/2018] [Accepted: 09/22/2018] [Indexed: 11/16/2022]
Abstract
In the past two decades, significant advances have been made on automated electroencephalogram (EEG)-based diagnosis of epilepsy and seizure detection. A number of innovative algorithms have been introduced that can aid in epilepsy diagnosis with a high degree of accuracy. In recent years, the frontiers of computational epilepsy research have moved to seizure prediction, a more challenging problem. While antiepileptic medication can result in complete seizure freedom in many patients with epilepsy, up to one-third of patients living with epilepsy will have medically intractable epilepsy, where medications reduce seizure frequency but do not completely control seizures. If a seizure can be predicted prior to its clinical manifestation, then there is potential for abortive treatment to be given, either self-administered or via an implanted device administering medication or electrical stimulation. This will have a far-reaching impact on the treatment of epilepsy and patient's quality of life. This paper presents a state-of-the-art review of recent efforts and journal articles on seizure prediction. The technologies developed for epilepsy diagnosis and seizure detection are being adapted and extended for seizure prediction. The paper ends with some novel ideas for seizure prediction using the increasingly ubiquitous machine learning technology, particularly deep neural network machine learning.
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Affiliation(s)
- U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia
| | - Yuki Hagiwara
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Hojjat Adeli
- Department of Neuroscience, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, United States; Department of Neurology, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, United States; Department of Biomedical Informatics, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, United States.
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Chen Y, Wang X, Jung Y, Abedi V, Zand R, Bikak M, Adibuzzaman M. Classification of short single-lead electrocardiograms (ECGs) for atrial fibrillation detection using piecewise linear spline and XGBoost. Physiol Meas 2018; 39:104006. [PMID: 30183685 DOI: 10.1088/1361-6579/aadf0f] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Detection of atrial fibrillation is important for risk stratification of stroke. We developed a novel methodology to classify electrocardiograms (ECGs) to normal, atrial fibrillation and other cardiac dysrhythmias as defined by the PhysioNet Challenge 2017. APPROACH More specifically, we used piecewise linear splines for the feature selection and a gradient boosting algorithm for the classifier. In the algorithm, the ECG waveform is fitted by a piecewise linear spline, and morphological features relating to the piecewise linear spline coefficients are extracted. XGBoost is used to classify the morphological coefficients and heart rate variability features. MAIN RESULTS The performance of the algorithm was evaluated by the PhysioNet Challenge database (3658 ECGs classified by experts). Our algorithm achieved an average F 1 score of 81% for a 10-fold cross-validation and also achieved 81% for F 1 score on the independent testing set. This score is similar to the top 9th score (81%) in the official phase of the PhysioNet Challenge 2017. SIGNIFICANCE Our algorithm presents a good performance on multi-label short ECG classification with selected morphological features.
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Affiliation(s)
- Yao Chen
- Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, IN, United States of America. Department of Statistics, Purdue University, West Lafayette, IN, United States of America
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Iftikhar Z, Lahdenoja O, Jafari Tadi M, Hurnanen T, Vasankari T, Kiviniemi T, Airaksinen J, Koivisto T, Pänkäälä M. Multiclass Classifier based Cardiovascular Condition Detection Using Smartphone Mechanocardiography. Sci Rep 2018; 8:9344. [PMID: 29921933 PMCID: PMC6008477 DOI: 10.1038/s41598-018-27683-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 05/31/2018] [Indexed: 01/05/2023] Open
Abstract
Cardiac translational and rotational vibrations induced by left ventricular motions are measurable using joint seismocardiography (SCG) and gyrocardiography (GCG) techniques. Multi-dimensional non-invasive monitoring of the heart reveals relative information of cardiac wall motion. A single inertial measurement unit (IMU) allows capturing cardiac vibrations in sufficient details and enables us to perform patient screening for various heart conditions. We envision smartphone mechanocardiography (MCG) for the use of e-health or telemonitoring, which uses a multi-class classifier to detect various types of cardiovascular diseases (CVD) using only smartphone's built-in internal sensors data. Such smartphone App/solution could be used by either a healthcare professional and/or the patient him/herself to take recordings from their heart. We suggest that smartphone could be used to separate heart conditions such as normal sinus rhythm (SR), atrial fibrillation (AFib), coronary artery disease (CAD), and possibly ST-segment elevated myocardial infarction (STEMI) in multiclass settings. An application could run the disease screening and immediately inform the user about the results. Widespread availability of IMUs within smartphones could enable the screening of patients globally in the future, however, we also discuss the possible challenges raised by the utilization of such self-monitoring systems.
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Affiliation(s)
- Zuhair Iftikhar
- University of Turku, Department of Future Technologies, Turku, Finland
| | - Olli Lahdenoja
- University of Turku, Department of Future Technologies, Turku, Finland
| | - Mojtaba Jafari Tadi
- University of Turku, Department of Future Technologies, Turku, Finland.
- University of Turku, Faculty of Medicine, Turku, Finland.
| | - Tero Hurnanen
- University of Turku, Department of Future Technologies, Turku, Finland
| | | | | | | | - Tero Koivisto
- University of Turku, Department of Future Technologies, Turku, Finland
| | - Mikko Pänkäälä
- University of Turku, Department of Future Technologies, Turku, Finland
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Tomasic I, Tomasic N, Trobec R, Krpan M, Kelava T. Continuous remote monitoring of COPD patients-justification and explanation of the requirements and a survey of the available technologies. Med Biol Eng Comput 2018; 56:547-569. [PMID: 29504070 PMCID: PMC5857273 DOI: 10.1007/s11517-018-1798-z] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Accepted: 01/30/2018] [Indexed: 01/03/2023]
Abstract
Remote patient monitoring should reduce mortality rates, improve care, and reduce costs. We present an overview of the available technologies for the remote monitoring of chronic obstructive pulmonary disease (COPD) patients, together with the most important medical information regarding COPD in a language that is adapted for engineers. Our aim is to bridge the gap between the technical and medical worlds and to facilitate and motivate future research in the field. We also present a justification, motivation, and explanation of how to monitor the most important parameters for COPD patients, together with pointers for the challenges that remain. Additionally, we propose and justify the importance of electrocardiograms (ECGs) and the arterial carbon dioxide partial pressure (PaCO2) as two crucial physiological parameters that have not been used so far to any great extent in the monitoring of COPD patients. We cover four possibilities for the remote monitoring of COPD patients: continuous monitoring during normal daily activities for the prediction and early detection of exacerbations and life-threatening events, monitoring during the home treatment of mild exacerbations, monitoring oxygen therapy applications, and monitoring exercise. We also present and discuss the current approaches to decision support at remote locations and list the normal and pathological values/ranges for all the relevant physiological parameters. The paper concludes with our insights into the future developments and remaining challenges for improvements to continuous remote monitoring systems. Graphical abstract ᅟ.
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Affiliation(s)
- Ivan Tomasic
- Division of Intelligent Future Technologies, Mälardalen University, Högskoleplan 1, 72123, Västerås, Sweden.
| | - Nikica Tomasic
- Department of Clinical Sciences, Lund University, Lund, Sweden
- Department of Neonatology, Karolinska University Hospital, Stockholm, Sweden
| | - Roman Trobec
- Department of Communication Systems, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Miroslav Krpan
- Department of Cardiology, University Hospital Centre, Zagreb, Croatia
| | - Tomislav Kelava
- Department of Physiology, School of Medicine, University of Zagreb, Zagreb, Croatia
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Cibis T, McEwan A, Sieber A, Eskofier B, Lippmann J, Friedl K, Bennett M. Diving Into Research of Biomedical Engineering in Scuba Diving. IEEE Rev Biomed Eng 2017; 10:323-333. [PMID: 28600260 DOI: 10.1109/rbme.2017.2713300] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The physiologic response of the human body to different environments is a complex phenomenon to ensure survival. Immersion and compressed gas diving, together, trigger a set of responses. Monitoring those responses in real time may increase our understanding of them and help us to develop safety procedures and equipment. This review outlines diving physiology and diseases and identifies physiological parameters worthy of monitoring. Subsequently, we have investigated technological approaches matched to those in order to evaluated their capability for underwater application. We focused on wearable biomedical monitoring technologies, or those which could be transformed to wearables. We have also reviewed current safety devices, including dive computers and their underlying decompression models and algorithms. The review outlines the necessity for biomedical monitoring in scuba diving and should encourage research and development of new methods to increase diving safety.
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Mozos OM, Sandulescu V, Andrews S, Ellis D, Bellotto N, Dobrescu R, Ferrandez JM. Stress Detection Using Wearable Physiological and Sociometric Sensors. Int J Neural Syst 2016; 27:1650041. [DOI: 10.1142/s0129065716500416] [Citation(s) in RCA: 100] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and [Formula: see text]-nearest neighbor. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real-time stress detection. Finally, we present an study of the most discriminative features for stress detection.
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Affiliation(s)
- Oscar Martinez Mozos
- DETCP, Technical University of Cartagena, Plaza del Hospital, n1, 30202 Cartagena, Spain
| | - Virginia Sandulescu
- Department of Automatic Control and Computer Science, Politehnica University of Bucharest, 313 Splaiul Independentei, Bucharest 060042, Romania
| | - Sally Andrews
- Division of Psychology, Nottingham Trent University, Burton Street, Nottingham, NG1 4BU, UK
| | - David Ellis
- Department of Psychology, Lancaster University, Bailrigg, Lancaster, LA1 4YW, UK
| | - Nicola Bellotto
- School of Computer Science, University of Lincoln, Brayford Pool, Lincoln, LN67TS, UK
| | - Radu Dobrescu
- Department of Automatic Control and Computer Science, Politehnica University of Bucharest, 313 Splaiul Independentei, Bucharest 060042, Romania
| | - Jose Manuel Ferrandez
- DETCP, Technical University of Cartagena, Plaza del Hospital, n1, 30202 Cartagena, Spain
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Serhani MA, Menshawy ME, Benharref A. SME2EM: Smart mobile end-to-end monitoring architecture for life-long diseases. Comput Biol Med 2016; 68:137-54. [PMID: 26654871 DOI: 10.1016/j.compbiomed.2015.11.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2015] [Revised: 11/05/2015] [Accepted: 11/16/2015] [Indexed: 10/22/2022]
Abstract
Monitoring life-long diseases requires continuous measurements and recording of physical vital signs. Most of these diseases are manifested through unexpected and non-uniform occurrences and behaviors. It is impractical to keep patients in hospitals, health-care institutions, or even at home for long periods of time. Monitoring solutions based on smartphones combined with mobile sensors and wireless communication technologies are a potential candidate to support complete mobility-freedom, not only for patients, but also for physicians. However, existing monitoring architectures based on smartphones and modern communication technologies are not suitable to address some challenging issues, such as intensive and big data, resource constraints, data integration, and context awareness in an integrated framework. This manuscript provides a novel mobile-based end-to-end architecture for live monitoring and visualization of life-long diseases. The proposed architecture provides smartness features to cope with continuous monitoring, data explosion, dynamic adaptation, unlimited mobility, and constrained devices resources. The integration of the architecture׳s components provides information about diseases׳ recurrences as soon as they occur to expedite taking necessary actions, and thus prevent severe consequences. Our architecture system is formally model-checked to automatically verify its correctness against designers׳ desirable properties at design time. Its components are fully implemented as Web services with respect to the SOA architecture to be easy to deploy and integrate, and supported by Cloud infrastructure and services to allow high scalability, availability of processes and data being stored and exchanged. The architecture׳s applicability is evaluated through concrete experimental scenarios on monitoring and visualizing states of epileptic diseases. The obtained theoretical and experimental results are very promising and efficiently satisfy the proposed architecture׳s objectives, including resource awareness, smart data integration and visualization, cost reduction, and performance guarantee.
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Affiliation(s)
- Mohamed Adel Serhani
- College of Information Technology, United Arab Emirates University, United Arab Emirates.
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Ho TW, Huang CW, Lin CM, Lai F, Ding JJ, Ho YL, Hung CS. A telesurveillance system with automatic electrocardiogram interpretation based on support vector machine and rule-based processing. JMIR Med Inform 2015; 3:e21. [PMID: 25953306 PMCID: PMC4440896 DOI: 10.2196/medinform.4397] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2015] [Accepted: 04/03/2015] [Indexed: 01/19/2023] Open
Abstract
Background Telehealth care is a global trend affecting clinical practice around the world. To mitigate the workload of health professionals and provide ubiquitous health care, a comprehensive surveillance system with value-added services based on information technologies must be established. Objective We conducted this study to describe our proposed telesurveillance system designed for monitoring and classifying electrocardiogram (ECG) signals and to evaluate the performance of ECG classification. Methods We established a telesurveillance system with an automatic ECG interpretation mechanism. The system included: (1) automatic ECG signal transmission via telecommunication, (2) ECG signal processing, including noise elimination, peak estimation, and feature extraction, (3) automatic ECG interpretation based on the support vector machine (SVM) classifier and rule-based processing, and (4) display of ECG signals and their analyzed results. We analyzed 213,420 ECG signals that were diagnosed by cardiologists as the gold standard to verify the classification performance. Results In the clinical ECG database from the Telehealth Center of the National Taiwan University Hospital (NTUH), the experimental results showed that the ECG classifier yielded a specificity value of 96.66% for normal rhythm detection, a sensitivity value of 98.50% for disease recognition, and an accuracy value of 81.17% for noise detection. For the detection performance of specific diseases, the recognition model mainly generated sensitivity values of 92.70% for atrial fibrillation, 89.10% for pacemaker rhythm, 88.60% for atrial premature contraction, 72.98% for T-wave inversion, 62.21% for atrial flutter, and 62.57% for first-degree atrioventricular block. Conclusions Through connected telehealth care devices, the telesurveillance system, and the automatic ECG interpretation system, this mechanism was intentionally designed for continuous decision-making support and is reliable enough to reduce the need for face-to-face diagnosis. With this value-added service, the system could widely assist physicians and other health professionals with decision making in clinical practice. The system will be very helpful for the patient who suffers from cardiac disease, but for whom it is inconvenient to go to the hospital very often.
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Affiliation(s)
- Te-Wei Ho
- National Taiwan University, Graduate Institute of Biomedical Electronics and Bioinformatics, Taipei, Taiwan
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Faust O, Acharya UR, Adeli H, Adeli A. Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure 2015; 26:56-64. [DOI: 10.1016/j.seizure.2015.01.012] [Citation(s) in RCA: 206] [Impact Index Per Article: 22.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2014] [Revised: 01/15/2015] [Accepted: 01/18/2015] [Indexed: 11/25/2022] Open
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Mukhopadhyay SK, Mitra S, Mitra M. A combined application of lossless and lossy compression in ECG processing and transmission via GSM-based SMS. J Med Eng Technol 2014; 39:105-22. [PMID: 25534118 DOI: 10.3109/03091902.2014.990159] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
This paper presents a software-based scheme for reliable and robust Electrocardiogram (ECG) data compression and its efficient transmission using Second Generation (2G) Global System for Mobile Communication (GSM) based Short Message Service (SMS). To achieve a firm lossless compression in high standard deviating QRS complex regions and an acceptable lossy compression in the rest of the signal, two different algorithms have been used. The combined compression module is such that it outputs only American Standard Code for Information Interchange (ASCII) characters and, hence, SMS service is found to be most suitable for transmitting the compressed signal. At the receiving end, the ECG signal is reconstructed using just the reverse algorithm. The module has been tested to all the 12 leads of different types of ECG signals (healthy and abnormal) collected from the PTB Diagnostic ECG Database. The compression algorithm achieves an average compression ratio of ∼22.51, without any major alteration of clinical morphology.
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Affiliation(s)
- S K Mukhopadhyay
- Department of Applied Physics, Faculty of Technology, University of Calcutta , 92 A.P.C. Road, Kolkata , India and
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Martis RJ, Acharya UR, Adeli H, Prasad H, Tan JH, Chua KC, Too CL, Yeo SWJ, Tong L. Computer aided diagnosis of atrial arrhythmia using dimensionality reduction methods on transform domain representation. Biomed Signal Process Control 2014. [DOI: 10.1016/j.bspc.2014.04.001] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Martis RJ, Acharya UR, Adeli H. Current methods in electrocardiogram characterization. Comput Biol Med 2014; 48:133-49. [DOI: 10.1016/j.compbiomed.2014.02.012] [Citation(s) in RCA: 100] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2013] [Revised: 02/15/2014] [Accepted: 02/17/2014] [Indexed: 10/25/2022]
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21
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Honeyman E, Ding H, Varnfield M, Karunanithi M. Mobile health applications in cardiac care. Interv Cardiol 2014. [DOI: 10.2217/ica.14.4] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
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22
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FU RONGRONG, WANG HONG. DETECTION OF DRIVING FATIGUE BY USING NONCONTACT EMG AND ECG SIGNALS MEASUREMENT SYSTEM. Int J Neural Syst 2014; 24:1450006. [DOI: 10.1142/s0129065714500063] [Citation(s) in RCA: 79] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Driver fatigue can be detected by constructing a discriminant mode using some features obtained from physiological signals. There exist two major challenges of this kind of methods. One is how to collect physiological signals from subjects while they are driving without any interruption. The other is to find features of physiological signals that are of corresponding change with the loss of attention caused by driver fatigue. Driving fatigue is detected based on the study of surface electromyography (EMG) and electrocardiograph (ECG) during the driving period. The noncontact data acquisition system was used to collect physiological signals from the biceps femoris of each subject to tackle the first challenge. Fast independent component analysis (FastICA) and digital filter were utilized to process the original signals. Based on the statistical analysis results given by Kolmogorov–Smirnov Z test, the peak factor of EMG (p < 0.001) and the maximum of the cross-relation curve of EMG and ECG (p < 0.001) were selected as the combined characteristic to detect fatigue of drivers. The discriminant criterion of fatigue was obtained from the training samples by using Mahalanobis distance, and then the average classification accuracy was given by 10-fold cross-validation. The results showed that the method proposed in this paper can give well performance in distinguishing the normal state and fatigue state. The noncontact, onboard vehicle drivers' fatigue detection system was developed to reduce fatigue-related risks.
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Affiliation(s)
- RONGRONG FU
- Laboratory of Bio-Mechatronic Engineering, School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110189, P.R. China
| | - HONG WANG
- Laboratory of Bio-Mechatronic Engineering, School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110189, P.R. China
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Abstract
Adherence to long-term therapy in outpatient setting is required to reduce the prevalence of chronic diseases such as HIV/AIDS, Diabetes, Tuberculosis and Malaria. This paper presents a mobile technology-based medical alert system for outpatient adherence in Nigeria. The system makes use of the SMS and voice features of mobile phones. The system has the potential of improving adherence to medication in outpatient setting by reminding patients of dosing schedules and attendance to scheduled appointments through SMS and voice calls. It will also inform patients of benefits and risks associated with adherence. Interventions aimed at improving adherence would provide significant positive return on investment through primary prevention (of risk factors) and secondary prevention of adverse health outcomes.
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MARTIS ROSHANJOY, ACHARYA URAJENDRA, LIM CHOOMIN, MANDANA KM, RAY AK, CHAKRABORTY CHANDAN. APPLICATION OF HIGHER ORDER CUMULANT FEATURES FOR CARDIAC HEALTH DIAGNOSIS USING ECG SIGNALS. Int J Neural Syst 2013; 23:1350014. [DOI: 10.1142/s0129065713500147] [Citation(s) in RCA: 119] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Electrocardiogram (ECG) is the electrical activity of the heart indicated by P, Q-R-S and T wave. The minute changes in the amplitude and duration of ECG depicts a particular type of cardiac abnormality. It is very difficult to decipher the hidden information present in this nonlinear and nonstationary signal. An automatic diagnostic system that characterizes cardiac activities in ECG signals would provide more insight into these phenomena thereby revealing important clinical information. Various methods have been proposed to detect cardiac abnormalities in ECG recordings. Application of higher order spectra (HOS) features is a seemingly promising approach because it can capture the nonlinear and dynamic nature of the ECG signals. In this paper, we have automatically classified five types of beats using HOS features (higher order cumulants) using two different approaches. The five types of ECG beats are normal (N), right bundle branch block (RBBB), left bundle branch block (LBBB), atrial premature contraction (APC) and ventricular premature contraction (VPC). In the first approach, cumulant features of segmented ECG signal were used for classification; whereas in the second approach cumulants of discrete wavelet transform (DWT) coefficients were used as features for classifiers. In both approaches, the cumulant features were subjected to data reduction using principal component analysis (PCA) and classified using three layer feed-forward neural network (NN) and least square — support vector machine (LS-SVM) classifiers. In this study, we obtained the highest average accuracy of 94.52%, sensitivity of 98.61% and specificity of 98.41% using first approach with NN classifier. The developed system is ready clinically to run on large datasets.
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Affiliation(s)
- ROSHAN JOY MARTIS
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - U. RAJENDRA ACHARYA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, University of Malaya, Malaysia
| | - CHOO MIN LIM
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - K. M. MANDANA
- Department of Cardiothoracic Surgery, Fortis Hospitals, Kolkata, India
| | - A. K. RAY
- Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, Kharagpur, India 721302, India
| | - CHANDAN CHAKRABORTY
- School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, Kharagpur, India 721302, India
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McManus DD, Lee J, Maitas O, Esa N, Pidikiti R, Carlucci A, Harrington J, Mick E, Chon KH. A novel application for the detection of an irregular pulse using an iPhone 4S in patients with atrial fibrillation. Heart Rhythm 2013; 10:315-9. [PMID: 23220686 PMCID: PMC3698570 DOI: 10.1016/j.hrthm.2012.12.001] [Citation(s) in RCA: 162] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2012] [Indexed: 10/27/2022]
Abstract
BACKGROUND Atrial fibrillation (AF) is common and associated with adverse health outcomes. Timely detection of AF can be challenging using traditional diagnostic tools. Smartphone use is increasing and may provide an inexpensive and user-friendly means to diagnoseAF. OBJECTIVE To test the hypothesis that a smartphone-based application could detect an irregular pulse fromAF. METHODS Seventy-six adults with persistent AF were consented for participation in our study. We obtained pulsatile time series recordings before and after cardioversion using an iPhone 4S camera. A novel smartphone application conducted real-time pulse analysis using 2 statistical methods: root mean square of successive RR difference (RMSSD/mean) and Shannon entropy (ShE). We examined the sensitivity, specificity, and predictive accuracy of both algorithms using the 12-lead electrocardiogram as the gold standard. RESULTS RMSDD/mean and ShE were higher in participants in AF than in those with sinus rhythm. The 2 methods were inversely related to AF in regression models adjusting for key factors including heart rate and blood pressure (beta coefficients per SD increment in RMSDD/mean and ShE were-0.20 and-0.35; P<.001). An algorithm combining the 2 statistical methods demonstrated excellent sensitivity (0.962), specificity (0.975), and accuracy (0.968) for beat-to-beat discrimination of an irregular pulse during AF from sinus rhythm. CONCLUSIONS In a prospectively recruited cohort of 76 participants undergoing cardioversion for AF, we found that a novel algorithm analyzing signals recorded using an iPhone 4S accurately distinguished pulse recordings during AF from sinus rhythm. Data are needed to explore the performance and acceptability of smartphone-based applications for AF detection.
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Affiliation(s)
- David D McManus
- Cardiac Electrophysiology Section, Cardiovascular Medicine Division, Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts 01655, USA.
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26
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A comprehensive survey of wearable and wireless ECG monitoring systems for older adults. Med Biol Eng Comput 2013; 51:485-95. [DOI: 10.1007/s11517-012-1021-6] [Citation(s) in RCA: 98] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2012] [Accepted: 12/17/2012] [Indexed: 10/27/2022]
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Baig MM, Gholamhosseini H. Smart health monitoring systems: an overview of design and modeling. J Med Syst 2013; 37:9898. [PMID: 23321968 DOI: 10.1007/s10916-012-9898-z] [Citation(s) in RCA: 102] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2012] [Accepted: 09/18/2012] [Indexed: 11/25/2022]
Abstract
Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way health care is currently delivered. Although smart health monitoring systems automate patient monitoring tasks and, thereby improve the patient workflow management, their efficiency in clinical settings is still debatable. This paper presents a review of smart health monitoring systems and an overview of their design and modeling. Furthermore, a critical analysis of the efficiency, clinical acceptability, strategies and recommendations on improving current health monitoring systems will be presented. The main aim is to review current state of the art monitoring systems and to perform extensive and an in-depth analysis of the findings in the area of smart health monitoring systems. In order to achieve this, over fifty different monitoring systems have been selected, categorized, classified and compared. Finally, major advances in the system design level have been discussed, current issues facing health care providers, as well as the potential challenges to health monitoring field will be identified and compared to other similar systems.
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Affiliation(s)
- Mirza Mansoor Baig
- Department of Electrical and Electronic Engineering, School of Engineering, Auckland University of Technology, Private Bag 92006, Auckland, 1142, New Zealand,
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Current world literature. Curr Opin Cardiol 2011; 27:62-5. [PMID: 22146379 DOI: 10.1097/hco.0b013e32834f4ed9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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