Comparative study of a single lead ECG in a wearable device

Funston, Rebecca; Gibbs, Austin; Diven, Jordan; Francey, Jonathan; Easlea, Holly; Murray, Stacey; Fitzpatrick, Matthew; Condon, Adrian; Mitchell, Andrew R.J.

doi:10.1016/j.jelectrocard.2022.08.004

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…ECG data from our retrospective cohorts correspond to high-quality Lead I ECG signals. However, the signal quality from wrist-worn ECG devices can be poor relative to the Lead I ECG 23 . By contrast, bipolar single lead wearable ECG patch-monitors, which are applied to the chest wall, typically yield signals comparable to what is obtained with multi-lead high-quality ECG devices 36,37 .…”

Section: Discussionmentioning

confidence: 99%

“…Previous applications include the detection of atrial fibrillation 17-19 , cardiac ischemia 20 , long-QT syndrome 21 , and reduced ejection fraction 22 . Many of these studies have focused on ECG signals that are acquired from smartwatch devices, which can yield noisy signals when the subject is not at rest 23 . Wearable patch-monitors, which are applied to the chest wall, provide higher quality data, but the utility of ECG signals arising from these devices for machine learning tasks has not been widely explored.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence for Hemodynamic Monitoring with a Wearable Electrocardiogram Monitor

Schlesinger,

Alam,

Ringel

et al. 2024

Preprint

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BackgroundThe ability to non-invasively measure left atrial pressure would facilitate the identification of patients at risk of pulmonary congestion and guide proactive heart failure care. Wearable cardiac monitors, which record single-lead electrocardiogram data, provide information that can be leveraged to infer left atrial pressures.MethodsWe developed a deep neural network using single-lead electrocardiogram data to determine when the left atrial pressure is elevated. The model was developed and internally evaluated using a cohort of 6739 samples from the Massachusetts General Hospital (MGH) and externally validated on a cohort of 4620 samples from a second institution. We then evaluated model on patch-monitor electrocardiographic data on a small prospective cohort.ResultsThe model achieves an area under the receiver operating characteristic curve of 0.80 for detecting elevated left atrial pressures on an internal holdout dataset from MGH and 0.76 on an external validation set from a second institution. A further prospective dataset was obtained using single-lead electrocardiogram data with a patch-monitor from patients who underwent right heart catheterization at MGH. Evaluation of the model on this dataset yielded an area under the receiver operating characteristic curve of 0.875 for identifying elevated left atrial pressures for electrocardiogram signals acquired close to the time of the right heart catheterization procedure.ConclusionsThese results demonstrate the utility and the potential of ambulatory cardiac hemodynamic monitoring with electrocardiogram patch-monitors.Plain Language SummaryHeart failure is a prevalent disorder that is challenging to manage. Part of what makes heart failure management challenging is that the onset of symptoms can be insidious as there are few robust tools that a clinical can leverage to estimate when a patient is likely to experience an episode of acute heart failure. While elevated intracardiac pressures are a reliable harbinger of worsening heart failure, these pressures are typically measured using an invasive, gold-standard approach, which can only be performed in an inpatient setting. A non-invasive method for detecting higher pressures inside of heart would be especially helpful for identifying worsening heart failure in an expeditious manner in the home environment. For this reason, we developed an artificial intelligence model to detect elevated pressures inside the heart using a non-invasive signal, the electrocardiogram (ECG, or EKG), which can be acquired from a wearable patch monitor device. Our results demonstrate that the model provides a reliable platform for the non-invasive assessment of cardiac pressures using data that can be obtained in the outpatient setting.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Hemodynamic Monitoring with a Wearable Electrocardiogram Monitor

Schlesinger,

Alam,

Ringel

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…ECG data from our dataset correspond to high-quality Lead-I ECG signals, rather than an ambulatory ECG monitor. This is important because the signal quality from wrist-worn ECG devices can be poor relative to the Lead-I ECG [ 30 ]. However, bipolar single-lead wearable ECG patch monitors, which are applied to the chest wall, yield signals comparable to what is obtained with multi-lead high-quality ECG devices [ 31 , 32 ].…”

Section: Discussionmentioning

confidence: 99%

Detecting QT prolongation from a single-lead ECG with deep learning

Alam,

Aguirre,

Stultz

2024

PLOS Digit Health

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For a number of antiarrhythmics, drug loading requires a 3-day hospitalization with continuous monitoring for QT-prolongation. Automated QT monitoring with wearable ECG monitors would enable out-of-hospital care. We therefore develop a deep learning model that infers QT intervals from ECG Lead-I—the lead that is often available in ambulatory ECG monitors—and use this model to detect clinically meaningful QT-prolongation episodes during Dofetilide drug loading. QTNet–a deep neural network that infers QT intervals from Lead-I ECG–was trained using over 3 million ECGs from 653 thousand patients at the Massachusetts General Hospital and tested on an internal-test set consisting of 633 thousand ECGs from 135 thousand patients. QTNet is further evaluated on an external-validation set containing 3.1 million ECGs from 667 thousand patients at another healthcare institution. On both evaluations, the model achieves mean absolute errors of 12.63ms (internal-test) and 12.30ms (external-validation) for estimating absolute QT intervals. The associated Pearson correlation coefficients are 0.91 (internal-test) and 0.92 (external-validation). Finally, QTNet was used to detect Dofetilide-induced QT prolongation in a publicly available database (ECGRDVQ-dataset) containing ECGs from subjects enrolled in a clinical trial evaluating the effects of antiarrhythmic drugs. QTNet detects Dofetilide-induced QTc prolongation with 87% sensitivity and 77% specificity. The negative predictive value of the model is greater than 95% when the pre-test probability of drug-induced QTc prolongation is below 25%. These results show that drug-induced QT prolongation risk can be tracked from ECG Lead-I using deep learning.

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