Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor

Daphne E Schlesinger; Ridwan Alam; Roey Ringel; Eugene Pomerantsev; Srikanth Devireddy; Pinak Shah; Joseph Garasic; Collin M Stultz

doi:10.1038/s43856-024-00730-5

Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor

Commun Med (Lond). 2025 Jan 7;5(1):4. doi: 10.1038/s43856-024-00730-5.

Authors

Daphne E Schlesinger^#^{1

2

3

4}, Ridwan Alam^#^{2

3

4}, Roey Ringel^{4

5}, Eugene Pomerantsev^{4

6}, Srikanth Devireddy^{6

7}, Pinak Shah^{6

7}, Joseph Garasic^{6

7}, Collin M Stultz^{8

9

10

11

12

13

14}

Affiliations

¹ Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA.
² Research Laboratory of Electronics, MIT, Cambridge, MA, USA.
³ Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.
⁴ Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA.
⁵ Boston University School of Medicine, Boston, MA, USA.
⁶ Harvard Medical School, Boston, MA, USA.
⁷ Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, USA.
⁸ Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA. [email protected].
⁹ Research Laboratory of Electronics, MIT, Cambridge, MA, USA. [email protected].
¹⁰ Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA. [email protected].
¹¹ Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA. [email protected].
¹² Harvard Medical School, Boston, MA, USA. [email protected].
¹³ Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA. [email protected].
¹⁴ Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA. [email protected].

^# Contributed equally.

Abstract

Background: The 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.

Methods: We 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.

Results: The 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.

Conclusions: These results demonstrate the utility and the potential of ambulatory cardiac hemodynamic monitoring with electrocardiogram patch-monitors.

Plain language summary

Heart failure is a common disorder that is challenging to manage. Appearance of symptoms can be subtle and dangerous and there are few tools for clinicians to estimate when a patient is likely to experience an episode of heart failure. Current methods to detect elevated pressure in the heart (one sign of oncoming failure) are invasive and can only be performed in an inpatient setting. A non-invasive, quick method for detecting higher heart pressure would be helpful for identifying worsening heart failure in the home environment. For this reason, we developed a computer method to detect elevated pressures inside the heart using a non-invasive signal from a wearable patch monitor device, the electrocardiogram (ECG, or EKG). Our results show our method provides a reliable, non-invasive way to measure heart pressures using data that can be obtained in the outpatient setting.