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

被引:21
作者
Ganti, Venu G. [1 ]
Gazi, Asim H. [2 ]
An, Sungtae [3 ]
Srivatsa, Adith, V [4 ]
Nevius, Brandi N. [5 ]
Nichols, Christopher J. [4 ]
Carek, Andrew M. [6 ,7 ]
Fares, Munes [8 ]
Abdulkarim, Mubeena [8 ]
Hussain, Tarique [8 ]
Greil, F. Gerald [8 ]
Etemadi, Mozziyar [6 ,7 ]
Inan, Omer T. [1 ,2 ]
Tandon, Animesh [8 ,9 ]
机构
[1] Georgia Inst Technol, Bioengn Grad Program, 85 5th St NW, Atlanta, GA 30308 USA
[2] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30308 USA
[3] Georgia Inst Technol, Sch Interact Comp, Atlanta, GA 30308 USA
[4] Georgia Inst Technol, Wallace H Coulter Dept Biomed Engn, Atlanta, GA 30308 USA
[5] Georgia Inst Technol, Sch Mech Engn, Atlanta, GA 30308 USA
[6] Northwestern Univ, Dept Biomed Engn, McCormick Sch Engn, Evanston, IL 60208 USA
[7] Northwestern Univ, Feinberg Sch Med, Dept Anesthesiol, Evanston, IL USA
[8] Univ Texas Southwestern Med Ctr Dallas, Dept Pediat, Dallas, TX USA
[9] Cleveland Clin Childrens, Cleveland, OH USA
来源
JOURNAL OF THE AMERICAN HEART ASSOCIATION | 2022年 / 11卷 / 18期
基金
美国国家科学基金会;
关键词
cardiac output; machine learning; multimodal; noninvasive; pediatrics; CARDIAC-OUTPUT; MAGNETIC-RESONANCE; ACCURACY; INFANTS; DEATH; CARE;
D O I
10.1161/JAHA.122.026067
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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 R-2 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.
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页数:20
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