Continuous ECG monitoring versus mobile telemetry: A comparison of arrhythmia diagnostics in human- versus algorithmic-dependent systems

Willcox, Mark E.; Compton, Steven J.; Bardy, Gust H.

doi:10.1016/j.hroo.2021.09.008

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…Abdou and Krishnan summarize the current wearable ECG remote monitoring devices on the market [42]. These algorithms can also be applied to ECG data for similar detection purposes [28,43]. Overall, the algorithms to date only examine a small fraction of all available clinical data.…”

Section: Cardiac Electrophysiologymentioning

confidence: 99%

An Update on the Use of Artificial Intelligence in Cardiovascular Medicine

Rao,

Iqbal,

Isath

et al. 2024

Hearts

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Artificial intelligence, specifically advanced language models such as ChatGPT, have the potential to revolutionize various aspects of healthcare, medical education, and research. In this review, we evaluate the myriad applications of artificial intelligence in diverse healthcare domains. We discuss its potential role in clinical decision-making, exploring how it can assist physicians by providing rapid, data-driven insights for diagnosis and treatment. We review the benefits of artificial intelligence such as ChatGPT in personalized patient care, particularly in geriatric care, medication management, weight loss and nutrition, and physical activity guidance. We further delve into its potential to enhance medical research, through the analysis of large datasets, and the development of novel methodologies. In the realm of medical education, we investigate the utility of artificial intelligence as an information retrieval tool and personalized learning resource for medical students and professionals.

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Section: Cardiac Electrophysiologymentioning

confidence: 99%

An Update on the Use of Artificial Intelligence in Cardiovascular Medicine

Rao,

Iqbal,

Isath

et al. 2024

Hearts

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“…The T 89 until 1990, from 2008 to 2018, and the years 2020 and 2021; the T 28 from 1990 to 2007; the T 46 in 2019; and the T 64 in 2022. Between domains T 93 and T 89 , the most relevant works are: a paper on community preparedness for cardiovascular emergencies during the COVID-19 pandemic [68]; and a comparison of arrhythmia diagnosis in human-dependent versus algorithm-dependent systems [69]. At the date of these articles (2021), as we mentioned previously, the labels of the T 89 topic are "assay" and "detection".…”

Section: Cross-domain Document Validationmentioning

confidence: 99%

Dynamic Topic Modelling for Exploring the Scientific Literature on Coronavirus: An Unsupervised Labelling Technique

Guillén-Pacho,

Badenes-Olmedo,

Corcho

2023

Preprint

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Purpose: The work presented in this article focusses on improving the interpretability of probabilistic topic models created from a large collection of scientific documents that evolve over time. Methods: Several time-dependent approaches based on topic models were compared to analyse the annual evolution of latent concepts in the CORD-19 corpus: Dynamic Topic Model, Dynamic Embedded Topic Model, and BERTopic. Then COVID-19 period (December 2019 - present) has been analysed in greater depth, month by month, to explore the evolution of what is written about the disease. Results: The evaluations suggest that the Dynamic Topic Model is the best choice to analyse the CORD-19 corpus. A novel topic labelling strategy is proposed for dynamic topic models to analyse the evolution of latent concepts. It incorporates content changes in both the annual evolution of the corpus and the monthly evolution of the COVID-19 disease. The generated labels are manually validated using two approaches: through the most relevant documents on the topic, and through the documents that share the most semantically similar label topics. Conclusions: The labelling enables the interpretation of topics. The novel method for dynamic topic labelling fits the content of each topic and supports the semantics of the topics.

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“…The core component of this new diagnostic system is a commercially available Bluetooth low-energy (BLE) HR monitor sensor (Movesense HR+, Suunto, Finland), commercialized for use during sports, working through a chest-strap, although it can also be connected to the skin with small adhesive patch-type electrodes. The sensor can reliably and continuously acquire peak RR cardiac interval data [13] through the electrodes in contact with the skin. The integrated circuits filter out noise and non-cardiac electrical potentials based on frequency or amplitude, robustly selecting and transmitting only R-R interval data in real-time through a BLE standard protocol.…”

Section: The Custom Programmed Sensormentioning

confidence: 99%

“…Studies have demonstrated that prolonging the monitoring period yields incremental detection of arrhythmias [11,12], but Holter ECG recordings remain limited in the maximal duration of continuous monitoring, often not sufficient to detect rarely occurring arrhythmias [13].…”

Section: Introductionmentioning

confidence: 99%

Commercially Available Heart Rate Monitor Repurposed for Automatic Arrhythmia Detection with Snapshot Electrocardiographic Capability: A Pilot Validation

Martini

Maria²,

Reverberi³

et al. 2022

Diagnostics

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The usefulness of opportunistic arrhythmia screening strategies, using an electrocardiogram (ECG) or other methods for random “snapshot” assessments is limited by the unexpected and occasional nature of arrhythmias, leading to a high rate of missed diagnosis. We have previously validated a cardiac monitoring system for AF detection pairing simple consumer-grade Bluetooth low-energy (BLE) heart rate (HR) sensors with a smartphone application (RITMIA™, Heart Sentinel srl, Italy). In the current study, we test a significant upgrade to the above-mentioned system, thanks to the technical capability of new HR sensors to run algorithms on the sensor itself and to acquire, and store on-board, single-lead ECG strips. We have reprogrammed an HR monitor intended for sports use (Movensense HR+) to run our proprietary RITMIA algorithm code in real-time, based on RR analysis, so that if any type of arrhythmia is detected, it triggers a brief retrospective recording of a single-lead ECG, providing tracings of the specific arrhythmia for later consultation. We report the initial data on the behavior, feasibility, and high diagnostic accuracy of this ultra-low weight customized device for standalone automatic arrhythmia detection and ECG recording, when several types of arrhythmias were simulated under different baseline conditions. Conclusions: The customized device was capable of detecting all types of simulated arrhythmias and correctly triggered a visually interpretable ECG tracing. Future human studies are needed to address real-life accuracy of this device.

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Continuous ECG monitoring versus mobile telemetry: A comparison of arrhythmia diagnostics in human- versus algorithmic-dependent systems

Cited by 8 publications

References 14 publications

An Update on the Use of Artificial Intelligence in Cardiovascular Medicine

An Update on the Use of Artificial Intelligence in Cardiovascular Medicine

Dynamic Topic Modelling for Exploring the Scientific Literature on Coronavirus: An Unsupervised Labelling Technique

Commercially Available Heart Rate Monitor Repurposed for Automatic Arrhythmia Detection with Snapshot Electrocardiographic Capability: A Pilot Validation

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