Real-Time Mobile-Based Electrocardiogram System for Remote Monitoring of Patients with Cardiac Arrhythmias

Bazi, Yakoub; Rahhal, Mohamad Mahmoud Al; Alhichri, Haikel; Ammour, Nassim; Alajlan, Naif

doi:10.1142/s0218001420580136

Cited by 11 publications

(5 citation statements)

References 75 publications

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“…For example, in [ 45 , 47 , 55 , 68 , 70 , 80 , 85 , 98 , 103 , 107 , 108 , 112 ], the authors used smartwatches and smart wristbands to record vital signs. In addition, the authors in [ 40 , 44 , 49 , 63 , 64 , 76 , 78 , 83 , 91 , 93 , 96 , 102 , 110 , 120 , 121 ] used various wearable ECG devices such as smart vests and patches. In addition, the authors in [ 90 , 99 , 105 , 106 ] used smart belts to collect ECG data.…”

Section: Resultsmentioning

confidence: 99%

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Moshawrab

Adda

Bouzouane

et al. 2023

Sensors

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Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.

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Section: Resultsmentioning

confidence: 99%

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Moshawrab

Adda

Bouzouane

et al. 2023

Sensors

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“…the availability of point-of-care 12-lead ECGs taken on smart devices owned by the general public and uploaded practically instantly to a common cloudbased server present a chance to alter how healthcare is given and delivered. In order to improve access and timeliness of care, local hospitals or healthcare systems, EMS services, and communities may decide to develop and sign up for services that include monitoring, alarms, and two-way communication [17].…”

Section: Discussionmentioning

confidence: 99%

Cross-Sectional, Non-Randomized, Single-Blinded, and Single-Center Study for the Accuracy of 12 Lead Smartphone ECG in the Detection of Ventricular Arrhythmias

Chandola,

Mahajan,

Garg

et al. 2023

JICOA

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Since ventricular arrhythmias are frequently transitory, diagnosing them can be difficult. It has been investigated in the past to diagnose atrial fibrillation (AF) using a smartphone-based ECG. Data on the diagnostic efficacy of smartphone-based ECGs for ventricular arrhythmias, however, is insufficient. Objective: Objectives of this study were a) to evaluate the accuracy of the spandan 12 lead ECG device in detection of the ventricular arrhythmias in comparison to the cardiologist, b) to evaluate the arrhythmia detection accuracy of the conventional ECG machine and spandan smartphone ECG machine to cardiologist diagnosis, and c) to detect spontaneous ventricular arrhythmias (VAs), namely ventricular tachycardia (VT) and supraventricular ectopic (SVE), ventricular ectopics (VE), ventricular premature complexes (VPCs), AV-block and ventricular ectopics (VE) can be fatal. Patients who are susceptible to VT/SVTs always have a risk of sudden cardiac death. Methods: This cross-sectional study, single blinded and single-centric study was carried out at Shri Mahant Indresh Hospital (SMIH), Dehradun from 02-Aug-2022 to 29-Dec-2022. Patients with (n=1137) chest pain, syncope, palpitation, shortness of breath were enrolled from ECG department. A final total of 84 participants considered for the accuracy of interpretation of ventricular arrhythmia detected by the gold standard 12 lead ECG and smartphone-based ECG device along with the cardiologist’s diagnosis. Results: Mean age (SD) was 54.42±14.58 years. The male gender (65.89%) shows the maximum frequency than female gender. Confusion matrix was referred to derive true positive cases for 12 lead standard ECG and smartphone ECG along with the cardiologist’s diagnosis was 46 as compared to 30 from 12 lead gold standard. Sensitivity of smartphone spandan ECG (35.38%) was better than gold standard 12 lead ECG (15.625%), and, PPV and NPV of smartphone spandan ECG was recorded to be better than gold standard 12 lead ECG. Ventricular arrhythmia was detected correctly in 46 (54.7%) cases and 30 (36.58%) cases by smartphone ECG and 12 lead gold standard, respectively. Conclusion: Mobile ECG based devices can be used for detecting the arrhythmias as its overall accuracy of smartphone ECG in detecting the arrhythmias increase by 66.8%, i.e. the significance rise in accuracy of computer interpretation when compared to the cardiologist’s diagnosis.

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“…The study included a user experience questionnaire, showing that 77% of participants preferred the chest belt to a standard 3-lead Holter monitor. Additional studies detecting AF have been performed using commercially available heart rate monitors and ECG systems [ 30 , 31 , 32 , 39 , 40 , 41 ].…”

Section: Cardiovascular Systemmentioning

confidence: 99%

“…ML approaches often include the use of decision tree ensembles such as Random Forest [ 13 , 47 ] or support vector machines (SVMs) [ 40 , 48 ] for arrhythmia classification. Multi-stage and multi-level classification systems derive local features of atrial and ventricular activity through a combination of SVMs and decision trees and global features from the raw ECG recording, ultimately leading to classification through linear SVMs.…”

Section: Cardiovascular Systemmentioning

confidence: 99%

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Neri

Oberdier

Abeelen

et al. 2023

Sensors

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Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.

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Real-Time Mobile-Based Electrocardiogram System for Remote Monitoring of Patients with Cardiac Arrhythmias

Cited by 11 publications

References 75 publications

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Cross-Sectional, Non-Randomized, Single-Blinded, and Single-Center Study for the Accuracy of 12 Lead Smartphone ECG in the Detection of Ventricular Arrhythmias

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

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