Detection of Aortic Valve Opening and Estimation of Pre-Ejection Period in Forcecardiography Recordings

Abstract

Forcecardiography (FCG) is a novel technique that measures the local forces induced on the chest wall by the mechanical activity of the heart. Specific piezoresistive or piezoelectric force sensors are placed on subjects’ thorax to measure these very small forces. The FCG signal can be divided into three components: low-frequency FCG, high-frequency FCG (HF-FCG) and heart sound FCG. HF-FCG has been shown to share a high similarity with the Seismocardiogram (SCG), which is commonly acquired via small accelerometers and is mainly used to locate specific fiducial markers corresponding to essential events of the cardiac cycle (e.g., heart valves opening and closure, peaks of blood flow). However, HF-FCG has not yet been demonstrated to provide the timings of these markers with reasonable accuracy. This study addresses the detection of the aortic valve opening (AO) marker in FCG signals. To this aim, simultaneous recordings from FCG and SCG sensors were acquired, together with Electrocardiogram (ECG) recordings, from a few healthy subjects at rest, both during quiet breathing and apnea. The AO markers were located in both SCG and FCG signals to obtain pre-ejection periods (PEP) estimates, which were compared via statistical analyses. The PEPs estimated from FCG and SCG showed a strong linear relationship (r > 0.95) with a practically unit slope, and 95% of their differences were found to be distributed within ± 4.6 ms around small biases of approximately 1 ms, corresponding to percentage differences lower than 5% of the mean measured PEP. These preliminary results suggest that FCG can provide accurate AO timings and PEP estimates.

Keywords: cardiac function; cardiac monitoring; forcecardiography; mechanocardiography; pre-ejection period; seismocardiography; systolic time intervals.

Figure 1

Sensors assembly: piezoelectric FCG sensor with a dome and an MMA7361 accelerometer.

Figure 2

Sensors assembly placement on a subject: (a) frontal view; (b) lateral view.

Figure 3

Examples of HF-FCG, dHF-FCG, SCG and ECG signals from (a) subject #1 and (b) subject #3.

Figure 4

(a) Ensemble averages of HF-FCG, SCG and ECG of subject #1; (b) ensemble averages of dHF-FCG, SCG and ECG of subject #1; (c) ensemble averages of HF-FCG, SCG and ECG of subject #3; (d) ensemble averages of dHF-FCG, SCG and ECG of subject #3. The ensemble averages are depicted as solid lines, while the limits of the ± SD ranges are depicted as dashed lines.

Figure 4

(a) Ensemble averages of HF-FCG, SCG and ECG of subject #1; (b) ensemble averages of dHF-FCG, SCG and ECG of subject #1; (c) ensemble averages of HF-FCG, SCG and ECG of subject #3; (d) ensemble averages of dHF-FCG, SCG and ECG of subject #3. The ensemble averages are depicted as solid lines, while the limits of the ± SD ranges are depicted as dashed lines.

Figure 5

Statistical analyses on PEP estimates related to signals acquired during apneas: (a) results of regression and correlation analyses; (b) results of Bland–Altman analysis.

Figure 6

Statistical analyses on PEP estimates related to signals acquired during quiet breathing: (a) results of regression and correlation analyses; (b) results of Bland–Altman analysis.