Ambulatory electrocardiography: indications and devices

Sampson, Michael

doi:10.12968/bjca.2019.14.3.114

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…Ambulatory ECG. Ambulatory ECG (AECG) devices allow the cardiac rhythm to be monitored and recorded over days, weeks, or years and are used primarily in the outpatient setting [30]. Recording may take place continuously or occur intermittently in response to patient activation or autosensing of rhythm disturbances.…”

Section: Electrocardiogrammentioning

confidence: 99%

IoT Based System for Heart Monitoring and Arrhythmia Detection Using Machine Learning

Cañón-Clavijo

Herrera‐Viedma

Gaona-García

et al. 2023

Journal of Healthcare Engineering

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Internet of Things (IoT) technologies allow building a digital representation of people, objects, or physical phenomena to be available on the Internet. Thus, stakeholders can access this information from remote places or computational systems could analyze this data to find patterns, make decisions, or execute actions. For instance, a doctor could diagnose patients by analyzing the received data from an IoT system even when patients are located in a remote place. This article proposes an IoT system for monitoring electrocardiogram (ECG) signal and processing heart data in order to generate an alert when an arrhythmia is present. This system involves a Polar H10 heart sensor, machine-learning models to classify heart events, and communication technology to share and store patient’s information. In the first place, the architecture of the IoT monitoring system and the communication between the components are described by discussing the designing criteria. Second, the experimentation process performs the training and the assessment of three classification algorithms, random forest, convolutional neural network, and k-nearest neighbors. The results show that k-nearest neighbor has the best accuracy percentage classifying the arrhythmias under study (premature ventricular contraction 94%, fusion of ventricular beat 81%, and supraventricular premature beat 82%); also, it is able to discern normal and unclassifiable beats with 93% and 97%, respectively.

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Section: Electrocardiogrammentioning

confidence: 99%

IoT Based System for Heart Monitoring and Arrhythmia Detection Using Machine Learning

Cañón-Clavijo

Herrera‐Viedma

Gaona-García

et al. 2023

Journal of Healthcare Engineering

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show abstract

“…Currently, understanding these factors relies on diaries, which are subjective and often either not completed, lost, or of insufficient accuracy to be useful. 1 , 2 For example, without a highly accurate patient diary, it is currently not possible to know if a person’s episodes of atrial fibrillation (AF) occur only during exercise, or, alternatively, whether someone’s sinus pauses only occur during sleep, which could be daytime sleep. The ability to provide objective physiologic data could reduce or eliminate the reliance on diary information for rhythm and symptom interpretation.…”

Section: Introductionmentioning

confidence: 99%

Next-generation Mobile Cardiac Telemetry: Clinical Value of Combining Electrocardiographic and Physiologic Parameters

Yoo¹,

Bhalla²,

Manyam³

et al. 2022

J Innov Cardiac Rhythm Manage.

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A hybrid cardiovascular arrhythmia disease detection using ConvNeXt-X models on electrocardiogram signals

Talukder,

Khalid,

Kazi

et al. 2024

Sci Rep

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Ambulatory electrocardiography: indications and devices

Abstract: Cardiac electrical abnormalities are common, and result in considerable morbidity and mortality. Although diagnosis can sometimes be made from the resting 12-lead ECG, a longer period of monitoring is often required to capture diagnostic information.

Cited by 5 publications

References 21 publications

IoT Based System for Heart Monitoring and Arrhythmia Detection Using Machine Learning

IoT Based System for Heart Monitoring and Arrhythmia Detection Using Machine Learning

Next-generation Mobile Cardiac Telemetry: Clinical Value of Combining Electrocardiographic and Physiologic Parameters

A hybrid cardiovascular arrhythmia disease detection using ConvNeXt-X models on electrocardiogram signals

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