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

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.

Keywords: cardiac output; machine learning; multimodal; noninvasive; pediatrics.

Figure 1. Concept overview.

Study design showing wearable biosensor placement when supine and asynchronous reference cardiovascular magnetic resonance imaging (CMR) measurement. Seismocardiogram (SCG) mechanistic overview detailing modulation due to cardiac physiology, acquisition with an accelerometer, and sensing axes for ECG (negative, positive, and right‐leg‐drive [RLD] electrodes), and triaxial SCG signals. Analysis pipeline, from sensor input to model estimation of stroke volume, for wearable (blue), demographic (green), and CMR (purple) data. H indicates the transfer function between the input internal sources of cardiomechanical vibration and the output SCG waveform measured on the surface of the torso; MRI, magnetic resonance imaging; and SCG_dv, dorso‐ventral SCG.

Figure 2. Wearable multimodal hardware engineering mechanics.

A, Pertinent multimodal hardware diagram. Final wearable biosensor iteration with exploded view detailing photoplethysmogram (PPG) components, gel‐electrode ECG connectors, lithium‐polymer battery, and printed circuit boards (PCBs). Main PCB with ATSAM4LS8 microcontroller (μC), BMG250 triaxial gyroscope and BME280 environmental sensor, micro secure digital card (μSD), and BQ24232 battery charger. Sensor PCB, connected to main PCB via flexible connector, with ADXL355 accelerometer, ADS1291 analog front end, and magnetic wire connections to separate PCB containing SFH7016 multichip light‐emitting diode (LED) and SFH 2703 photodiode (PD) used to acquire triaxial seismocardiogram (SCG), single‐lead ECG, and multiwavelength sternum PPG signals, respectively. B, Sample 5 seconds of filtered wearable signal data from a single‐ventricle patient with corresponding amplitudes are shown. In order from top to bottom: ECG, lateral SCG (SCG_lat), head‐to‐foot SCG (SCG_hf), dorso‐ventral SCG (SCG_dv), green PPG (PPG_g), red PPG (PPG_r), and infrared PPG (PPG_i) signals. The darker blue ECG and SCGdv signals are those used in this work. USB indicates universal serial bus.

Figure 3. Wearable stroke volume (SV) estimation results.

Correlation and Bland‐Altman plots between wearable signal estimated SV and the cardiovsacular magnetic resonance imaging (CMR) imaging SV for held‐out test set of 9 patients. The coefficient of determination (R ², 0.76) and root‐mean‐square error (RMSE, 11.48 mL) are shown.

Figure 4. Importance of features for stroke volume estimation model.

Importance of features for wearable system from magnitude of ridge regression weights ranked in order from top to bottom and color‐coded by wearable sensing modality, ECG and seismocardiogram (SCG) signals. AC indicates aortic valve closure; CMR, cardiovascular magnetic resonance imaging; HR, heart rate; PEP, pre‐ejection period; RMS, root‐mean‐square; and VET, ventricular ejection time.