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Wearable Electrocardiogram Technology: Help or Hindrance to the Modern Doctor?

Wearable Electrocardiogram Technology: Help or Hindrance to the Modern Doctor?

This article will review the fundamentals of the ECG before examining the potential impacts of the digital age on electrocardiography for the modern doctor. This history of the ECG is really the history of electrophysiology, which can be traced back to Galvani’s [7] experimentation in the 18th century on the role of electricity in the frog nervous system.

Samuel Smith, Shalisa Maisrikrod

JMIR Cardio 2025;9:e62719

Comparative Evaluation of Consumer Wearable Devices for Atrial Fibrillation Detection: Validation Study

Comparative Evaluation of Consumer Wearable Devices for Atrial Fibrillation Detection: Validation Study

The underlying CWD technology is mainly based on electrocardiography or photoplethysmography (PPG). While an ECG measures electrical signals from the heart using surface electrodes on the skin, PPG analyzes the heart rhythm through an optical technique that measures the peripheral pulse [10]. This PPG technology has been adopted by smartwatches and smartphones, using light-emitting diodes and sensors or smartphone flashlights and cameras to generate PPG waveforms.

Femke Wouters, Henri Gruwez, Christophe Smeets, Anessa Pijalovic, Wouter Wilms, Julie Vranken, Zoë Pieters, Hugo Van Herendael, Dieter Nuyens, Maximo Rivero-Ayerza, Pieter Vandervoort, Peter Haemers, Laurent Pison

JMIR Form Res 2025;9:e65139

Artificial Intelligence–Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study

Artificial Intelligence–Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study

Reference 7: Detection of left atrial myopathy using artificial intelligence-enabled electrocardiography interpretation of printed ECG images: diagnostic performance of ST-elevation myocardial infarction on electrocardiography Reference 14: Feasibility of artificial intelligence-based electrocardiography analysis for the prediction Reference 24: Development and validation of deep-learning algorithm for electrocardiography-based heartelectrocardiography

Youngjin Cho, Minjae Yoon, Joonghee Kim, Ji Hyun Lee, Il-Young Oh, Chan Joo Lee, Seok-Min Kang, Dong-Ju Choi

J Med Internet Res 2024;26:e52139

Corrected QT Interval (QTc) Diagnostic App for the Oncological Routine: Development Study

Corrected QT Interval (QTc) Diagnostic App for the Oncological Routine: Development Study

Reference 31: QTc prolongation measured by standard 12-lead electrocardiography is an independent risk Reference 42: Validation of a smartphone-based electrocardiography in the screening of QT intervals in Reference 43: Diagnostic accuracy of a smartphone-operated, single-lead electrocardiography device forelectrocardiography

Kristina Klier, Yash J Patel, Timo Schinköthe, Nadia Harbeck, Annette Schmidt

JMIR Cardio 2023;7:e48096

Feasibility of Artificial Intelligence–Based Electrocardiography Analysis for the Prediction of Obstructive Coronary Artery Disease in Patients With Stable Angina: Validation Study

Feasibility of Artificial Intelligence–Based Electrocardiography Analysis for the Prediction of Obstructive Coronary Artery Disease in Patients With Stable Angina: Validation Study

Although electrocardiography (ECG) has been used as a primary noninvasive modality in patients with suspected CAD, its diagnostic value is limited to acute coronary syndrome (ACS), such as ST-elevation myocardial infarction (STEMI) [2]. In patients presenting with stable angina, the initial ECG often shows nonspecific or normal findings, resulting in a low-diagnostic accuracy [2,3].

Jiesuck Park, Yeonyee Yoon, Youngjin Cho, Joonghee Kim

JMIR Cardio 2023;7:e44791

Intervention in the Timeliness of Two Electrocardiography Types for Patients in the Emergency Department With Chest Pain: Randomized Controlled Trial

Intervention in the Timeliness of Two Electrocardiography Types for Patients in the Emergency Department With Chest Pain: Randomized Controlled Trial

Twelve-lead electrocardiography (ECG) is an essential diagnostic tool in the emergency department (ED) for patients with chest pain [1]. The most important step to be taken for a patient who complains of chest pain is to identify the location of the pain. ECG should be performed to determine if the pain is caused by a cardiovascular disease [2].

Suyoung Yoo, Hansol Chang, Taerim kim, Hee yoon, Sung Yeon Hwang, Tae Gun Shin, Min Seob Sim, Ik joon Jo, Jin-Ho Choi, Won Chul Cha

Interact J Med Res 2022;11(2):e36335

Research on Digital Technology Use in Cardiology: Bibliometric Analysis

Research on Digital Technology Use in Cardiology: Bibliometric Analysis

Smartphone and smartwatch apps could already readily distinguish atrial fibrillation from sinus rhythm and detect them with high sensitivity and specificity comparable to 12-lead electrocardiography (ECG) [95,96]. Authoritative bodies such as the European Society of Cardiology have also developed smartphone apps for patient education on atrial fibrillation [97].

Andy Wai Kan Yeung, Stefan Tino Kulnik, Emil D Parvanov, Anna Fassl, Fabian Eibensteiner, Sabine Völkl-Kernstock, Maria Kletecka-Pulker, Rik Crutzen, Johanna Gutenberg, Isabel Höppchen, Josef Niebauer, Jan David Smeddinck, Harald Willschke, Atanas G Atanasov

J Med Internet Res 2022;24(5):e36086