Cardiac output estimation using ballistocardiography: a feasibility study in healthy subjects

Abstract

There is no reliable automated non-invasive solution for monitoring circulation and guiding treatment in prehospital emergency medicine. Cardiac output (CO) monitoring might provide a solution, but CO monitors are not feasible/practical in the prehospital setting. Non-invasive ballistocardiography (BCG) measures heart contractility and tracks CO changes. This study analyzed the feasibility of estimating CO using morphological features extracted from BCG signals. In 20 healthy subjects ECG, carotid/abdominal BCG, and invasive arterial blood pressure based CO were recorded. BCG signals were adaptively processed to isolate the circulatory component from carotid (CCc) and abdominal (CCa) BCG. Then, 66 features were computed on a beat-to-beat basis to characterize amplitude/duration/area/length of the fluctuation in CCc and CCa. Subjects' data were split into development set (75%) to select the best feature subset with which to build a machine learning model to estimate CO and validation set (25%) to evaluate model's performance. The model showed a mean absolute error, percentage error and 95% limits of agreement of 0.83 L/min, 30.2% and - 2.18-1.89 L/min respectively in the validation set. BCG showed potential to reliably estimate/track CO. This method is a promising first step towards an automated, non-invasive and reliable CO estimator that may be tested in prehospital emergencies.

Figure 1

Overview of biosensor placements and study flow. (a) Subject placed on a stretcher. (b) BCG biosensor placed on the skin over the carotid artery. (c) FloTrac transducer for measuring arterial blood pressure. (d) Radial arterial line connected to the FloTrac sensor and transducer. (e) Defibrillation pads connected to the LIFEPAK® 15 monitor showing ECG (example of red ECG waves shown over blue BCG waves). (f) BCG biosensor placed on the skin over the abdominal aorta. (g) Flow of the study from left to right showing each phase with corresponding BCG waves. CCA, chest circumference at armpit level; CCX, chest circumference at the xiphoid process level; DJX, distance from the jugular notch to the xiphoid process.

Figure 2

Adaptive filtering of BCG signals to extract the circulatory-related component. Preprocessed ECG, BCG_c and BCG_a are represented from top to bottom in the left panel. While preprocessed ECG and circulatory-related components of the BCG_c (CC_c) and BCG_a (CC_a) are depicted from top to bottom in the right panel. Dark blue circles represent the instants of the QRS complexes.

Figure 3

Example of a 60-s segment analyzed corresponding to Phase 4 (Trendelenburg). From top to bottom, the CO, preprocessed ECG, CC_c and CC_a are depicted. Dark blue filled dots in CC_c and CC_a represent the maxima of the fluctuations caused by each heartbeat. The large shaded rectangles in the axes illustrate the first window analyzed, whereas smaller rectangles at the bottom represent the relative position of consecutive analysis windows.

Figure 4

The mean squared error (MSE) in the development set as a function of the number of features included in the model.

Figure 5

Bland–Altman plots representing the error defined as [real CO—estimated CO] as a function of the ground truth (CO computed by the HemoSphere, HS) for development (left) and validation (right) sets, respectively. Dashed line represents the mean error and dash-dotted lines represent the LOA_95%.

Figure 6

Boxplots showing the absolute error distribution across phases for development (left) and validation (right) sets, respectively.