Wearable Seismocardiography-Based Assessment of Stroke Volume in Congenital Heart Disease

Venu G. Ganti, Asim H. Gazi, Sungtae An, Adith V. Srivatsa, Brandi N. Nevius, Christopher J. Nichols, Andrew M. Carek, Munes Fares, Mubeena Abdulkarim, Mohammad T Hussain, F. Gerald Greil, Mozziyar Etemadi, Omer T. Inan, Animesh Tandon

Research output: Contribution to journalArticlepeer-review

Abstract

BACKGROUND: Patients with congenital heart disease (CHD) are at risk for the development of low cardiac output and other physiologic derangements, which could be detected early through continuous stroke volume (SV) measurement. Unfortunately, existing SV measurement methods are limited in the clinic because of their invasiveness (eg, thermodilution), location (eg, cardiac magnetic resonance imaging), or unreliability (eg, bioimpedance). Multimodal wearable sensing, leveraging the seismocardiogram, a sternal vibration signal associated with cardiomechanical activity, offers a means to monitoring SV conveniently, affordably, and continuously. However, it has not been evaluated in a population with significant anatomical and physiological differences (ie, children with CHD) or compared against a true gold standard (ie, cardiac magnetic resonance). Here, we present the feasibility of wearable estimation of SV in a diverse CHD population (N=45 patients). METHODS AND RESULTS: We used our chest-worn wearable biosensor to measure baseline ECG and seismocardiogram signals from patients with CHD before and after their routine cardiovascular magnetic resonance imaging, and derived features from the measured signals, predominantly systolic time intervals, to estimate SV using ridge regression. Wearable signal features achieved acceptable SV estimation (28% error with respect to cardiovascular magnetic resonance imaging) in a held-out test set, per cardiac output measurement guidelines, with a root-mean-square error of 11.48 mL and R2 of 0.76. Additionally, we observed that using a combination of electrical and cardiomechanical features surpassed the performance of either modality alone. CONCLUSIONS: A convenient wearable biosensor that estimates SV enables remote monitoring of cardiac function and may potentially help identify decompensation in patients with CHD.

Original languageEnglish (US)
Article numbere026067
JournalJournal of the American Heart Association
Volume11
Issue number18
DOIs
StatePublished - Sep 20 2022

Keywords

  • cardiac output
  • machine learning
  • multimodal
  • noninvasive
  • pediatrics

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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