e.g. mhealth
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Electrocardiogram (ECG) is an old tool in clinical medicine but has re-emerged for the prediction of low left ventricular ejection fraction [17], arrhythmia [18], dyskalemia [19], or even longer-term mortality [20].
J Med Internet Res 2025;27:e67576
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Overwhelmingly, real-world studies have opted to use wrist-worn photoplethysmography sensors to evaluate HRV over other more accurate methods such as chest-worn electrocardiogram (ECG) sensors, likely improving patient compliance in these studies. As a result, we must determine how to best assess data from wrist-worn photoplethysmography sensors, including accounting for missing data and assessing data validity compared with ECG HRV data.
JMIR Form Res 2025;9:e53645
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use of the included studies.
a PVCT: pulmonary vein computed tomography.
b CT: computed tomography.
c LAV: left atrial volume.
d AF: atrial fibrillation.
e LVEF: left ventricular ejection fraction.
f LGE-MRI: late gadolinium-enhanced cardiac magnetic resonance.
g NR: not reported.
h ERP: effective refractory period.
i PVI: pulmonary vein isolation.
j HF: heart failure.
ku EGM: unipolar endocardial electrogram.
lb EGM: bipolar endocardial electrogram.
mu LAT: unipolar localized activation time.
n EGM: electrogram.
o ECG: electrocardiogram
J Med Internet Res 2025;27:e60888
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Wearable Electrocardiogram Technology: Help or Hindrance to the Modern Doctor?
The electrocardiogram (ECG) is one of the most commonly obtained test results in medical practice [1,2]. By measuring the electrical activity of the heart, an ECG can indicate cardiac arrhythmias and structural defects, respiratory disease, electrolyte disturbances, and even noncardiac events such as subarachnoid hemorrhage [1].
JMIR Cardio 2025;9:e62719
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Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach
Ada Boost: adaptive boosting; EDA: electrodermal activity; EMG: electromyogram; ECG: electrocardiogram; ML: machine learning; PPG: photoplethysmography; SMOTE: synthetic minority oversampling technique; SVM: support vector machine; RF: random forest; KNN: k-nearest neighbors.
The ECG channel was filtered using a Butterworth band-pass filter with a frequency range of 0.1-250 Hz.
JMIR Form Res 2025;9:e67969
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Hence, there are data-driven approaches based on noninvasive signals for the detection of hypertension, such as electrocardiogram (ECG) or photoplethysmogram (PPG), that are easily accessible from wearable sensors [2]. Subsequently, wearable monitoring can continuously monitor patients’ physiological conditions 24 hours a day.
JMIR Cardio 2025;9:e60238
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The diagnosis of AF requires an electrocardiogram (ECG) showing irregular R-R intervals (when atrioventricular conduction is not impaired) and the absence of distinct repeating P waves [4].
Historically, ECG diagnostics were confined to medical settings, using devices administered or prescribed by health care professionals. However, the 21st century has witnessed a rapid surge in the availability and use of consumer-oriented wearable devices (CWDs) capable of monitoring heart rhythm [5-8].
JMIR Form Res 2025;9:e65139
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Each search consisted of 3 elements: cardiac electrodiagnostic methods (eg, “electrocardiogram”), cardiac fibrosis (eg, “myocardial fibrosis”), or known ECG identification methods of fibrosis (eg, “Selvester score”), and ML methods (eg, “deep learning”). The strategy was adjusted to the constraints of the respective database. Details of the search strategy used for each database are available (Multimedia Appendix 2). The search was conducted in October 2024.
JMIR Cardio 2024;8:e60697
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ECG: electrocardiogram; SW: study watch; PPG: photoplethysmography; IBI: interbeat interval; VSW: Verily Study Watch; RHR: resting heart rate.
The processing steps to calculate the VSW RHR for each participant are shown in Figure 1 B. First, we gathered PPG IBIs, actigraphy counts, and on-wrist states in the 2-minute window mentioned above.
J Med Internet Res 2024;26:e60493
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ACC: accelerometer; ECG: electrocardiogram; PSG: polysomnography.
Figure 1 shows that ACC data were sampled over 14 seconds within every 20-second interval, which provided three 14-second blocks of ACC data per minute.
JMIR Ment Health 2024;11:e62959
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