A Systematic Review of Noninvasive Electrocardiogram Monitoring Devices for the Evaluation of Suspected Cardiovascular Syncope

Solbiati, Monica; Trombetta, Lucia; Sacco, R.; Erba, Luca; Bozzano, Viviana; Costantino, Giorgio; Raj, Satish R.; Barbic, Franca; Casazza, Giovanni; Dipaola, Franca; Furlan, Raffaello; Kenny, Rose Anne; Quinn, James V.; Sheldon, Robert S.; Shen, Win Kuang; Sun, Benjamin; Thiruganasambandamoorthy, Venkatesh; Reed, Matthew J

doi:10.1115/1.4042795

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…Long term in-situ ECG monitoring is an area that is rapidly growing and very promising for early diagnosis and follow-up of CVD. In particular, ECG monitoring for arrhythmia detection has received a great deal of attention with a plethora of remote ECG monitoring devices such as patches, wired and wireless Holters, and event recorders [55]. Event recorders are the devices that record an ECG after or during an arrhythmia event.…”

Section: Related Workmentioning

confidence: 99%

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mCardia: A Context-Aware ECG Collection System for Ambulatory Arrhythmia Screening

Kumar

Maharjan

Maxhuni

et al. 2022

ACM Trans. Comput. Healthcare

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This article presents the design, technical implementation, and feasibility evaluation of mCardia —a context-aware, mobile electrocardiogram (ECG) collection system for longitudinal arrhythmia screening under free-living conditions. Along with ECG, mCardia also records active and passive contextual data, including patient-reported symptoms and physical activity. This contextual data can provide a more accurate understanding of what happens before, during, and after an arrhythmia event, thereby providing additional information in the diagnosis of arrhythmia. By using a plugin-based architecture for ECG and contextual sensing, mCardia is device-agnostic and can integrate with various wireless ECG devices and supports cross-platform deployment. We deployed the mCardia system in a feasibility study involving 24 patients who used the system over a two-week period. During the study, we observed high patient acceptance and compliance with a satisfactory yield of collected ECG and contextual data. The results demonstrate the high usability and feasibility of mCardia for longitudinal ambulatory monitoring under free-living conditions. The article also reports from two clinical cases, which demonstrate how a cardiologist can utilize the collected contextual data to improve the accuracy of arrhythmia analysis. Finally, the article discusses the lessons learned and the challenges found in the mCardia design and the feasibility study.

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Section: Related Workmentioning

confidence: 99%

“…They operate in two modes; real-time transmission or storing data for later analysis. Patches are more comfortable than Holters and mostly wireless [55]. However, most of the above-mentioned ECG collection systems only collect ECG and do not capture the user's context in which the ECG was collected.…”

Section: Related Workmentioning

confidence: 99%

mCardia: A Context-Aware ECG Collection System for Ambulatory Arrhythmia Screening

Kumar

Maharjan

Maxhuni

et al. 2022

ACM Trans. Comput. Healthcare

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“…Wearable devices collect behavioural data on an ongoing daily basis so that it allows health care providers to monitor patients and to provide personalized intervention. In addition, more employers and insurers are likely to offer wearable devices as part of a wellness program [41,42].…”

Section: Wearable Devices: Potential and Criticality In The Working Cmentioning

confidence: 99%

The Role of Electrocardiography in Occupational Medicine, from Einthoven’s Invention to the Digital Era of Wearable Devices

Baldassarre

Mucci

Padovan

et al. 2020

IJERPH

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Clinical-instrumental investigations, such as electrocardiography (ECG), represent a corollary of a procedures that, nowadays, is called upon as part of the principles of precision medicine. However when carrying out the professional routine examinations, most tend to ignore how a “simple” instrument can offer indispensable support in clinical practice, even in occupational medicine. The advent of the digital age, made of silicon and printed circuit boards, has allowed the miniaturization of the electronic components of these electro-medical devices. Finally, the adoption of patient wearables in medicine has been rapidly expanding worldwide for a number of years. This has been driven mainly by consumers’ demand to monitor their own health. With the ongoing research and development of new features capable of assessing and transmitting real-time biometric data, the impact of wearables on cardiovascular management has become inevitable. Despite the potential offered by this technology, as evident from the scientific literature, the application of these devices in the field of health and safety in the workplace is still limited. This may also be due to the lack of targeted scientific research. While offering great potential, it is very important to consider and evaluate ethical aspects related to the use of these smart devices, such as the management of the collected data relating to the physiological parameters and the location of the worker. This technology is to be considered as being aimed at monitoring the subject’s physiological parameters, and not at the diagnosis of any pathological condition, which should always be on charge of the medical specialist We conducted a review of the evolution of the role that electrophysiology plays as part of occupational health and safety management and on its possible future use, thanks to ongoing technological innovation.

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“…The extracted features are however biased with noise effects which lead to misclassification. In classification model, various classifier such as support vector machine (SVM), probabilistic neural network (PNN) and multi-layer perceptron (MLP) back propagated network is outlined in [14,15,25]. Artificial intelligence (AI) methods have been used in the diagnosis and early alarming of cardiovascular diseases.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Classification Approach using Feature Fusion Model for Heart Disease Diagnosis

Vasantrao¹,

Rangasamy²,

Shelke³

2022

IJACSA

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Early Diagnosis has a very critical role in medical data processing and automated system. In medical diagnosis, automation is focused in different area of applications, in which heart disease diagnosis is a prominent domain. An early detection of heart disease can save many lives or criticality issues in diagnosing patients. In the process of heart disease diagnosis spatial and frequency domain features are used in making decision by the automation system. The processing features are observed to time variant or invariant in nature and the criticality of the observing feature varies with the diagnosis need. Wherein, the current automation system utilizes the features extracted in a large count to attain a higher accuracy, the processing overhead, and delay are considerable. Different regression approaches were developed in recent past to minimize the processing feature overhead the features are optimized based on gain performance or distance factors. The characteristic variation of feature and the significance of the feature vector are not addressed. This paper outlines a method of feature selection for heart disease diagnosis, based on weighted method of feature vector in consideration of feature significance and probability of estimate. A new optimizing function for feature selection is proposed as a dual function of probability factor and feature weight value. Simulation results illustrate the improvement of accuracy and speed of computation using proposed method compared to other existing methods.

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A Systematic Review of Noninvasive Electrocardiogram Monitoring Devices for the Evaluation of Suspected Cardiovascular Syncope

Cited by 4 publications

References 17 publications

mCardia: A Context-Aware ECG Collection System for Ambulatory Arrhythmia Screening

mCardia: A Context-Aware ECG Collection System for Ambulatory Arrhythmia Screening

The Role of Electrocardiography in Occupational Medicine, from Einthoven’s Invention to the Digital Era of Wearable Devices

A Deep Learning Classification Approach using Feature Fusion Model for Heart Disease Diagnosis

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