The purpose of this work is to define fiducial points in the seismocardiogram (SCG) and to correlate them with physiological events identified in ultrasound images. For 45 healthy subjects the SCG and the electrocardiogram (ECG) were recorded simultaneously at rest. Immediately following the SCG and ECG recordings ultrasound images of the heart were also obtained at rest. For all subjects a mean SCG signal was calculated and all fiducial points (peaks and valleys) were identified and labeled in the same way across all signals. Eight physiologic events, including the valve openings and closings, were annotated from ultrasound as well and the fiducial points were correlated with those physiologic events. A total of 42 SCG signals were used in the data analysis. The smallest mean differences (±SD) between the eight events found in the ultrasound images and the fiducial points, together with their correlation coefficients (r) were: atrial systolic onset: −2 (±16) ms, r = 0.75 (p < 0.001); peak atrial inflow: 13 (±19) ms, r = 0.63 (p < 0.001); mitral valve closure: 4 (±11) ms, r = 0.71 (p < 0.01); aortic valve opening: −3 (±11) ms, r = 0.60 (p < 0.001); peak systolic inflow: 13 (±23) ms, r = 0.42 (p < 0.01); aortic valve closure: −5 (±12) ms, r = 0.94 (p < 0.001); mitral valve opening: −7 (±19) ms, r = 0.87 (p < 0.001) and peak early ventricular filling: −18 (±28 ms), r = 0.79 (p < 0.001). In conclusion eight physiologic events characterizeing the cardiac cycle, are associated with reproducible, well-defined fiducial points in the SCG.

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Abstract

Objective Conduction-induced heart failure in patients with left bundle branch block (LBBB) can benefit from cardiac resynchronization therapy (CRT). However, some patients are non-responders to the therapy with one contributing factor being poor optimization of the atrioventricular (AV) pacing delay. In this study, we have investigated the pacing-induced changes in the seismocardiogram (SCG). Approach 14 patients with heart failure, LBBB, and CRT were included. SCG was recorded with pacing turned on and off. Based on a mean SCG heartbeat from each patient, fiducial points were annotated, and cardiac timing intervals (CTI) and amplitudes were derived. These were compared between the CRT group and a group of healthy normal subjects (n = 14). Echocardiography was also used to derive CTI. Intervals derived from the SCG and echocardiogram were correlated. Main Results The isovolumetric contraction time (IVCT) derived from SCG was significantly shorter in the CRT group when the pacemaker was turned on (63.2 to 52.6 ms, p = 0.027). The first peak-to-peak amplitude in the systolic complex was significantly larger with the pacemaker turned on (p = 0.002), as well as the |max-min| amplitude in the systolic complex (p = 0.003). Isovolumetric relaxation time and left ventricular ejection time (LVET) were not significantly different between pacemaker settings. Compared to normal subjects, IVCT was significantly prolonged with the pacemaker turned off. All amplitudes were significantly larger in the healthy subject group. IVCT and LVET derived from SCG were significantly correlated to the echocardiogram. Significance IVCT shortened and SCG amplitudes increased in response to CRT, indicating a more efficient ventricular contraction. This demonstrates the possibility to detect cardio-mechanic changes in response to treatment with the SCG. However, for the patients the systolic part of the SCG was abnormal and difficult to characterize, raising concerns about the correct interpretation of the SCG.

IntroductionThe isolated papillary muscle is a standard preparation for investigation of drug induced effects on the ventricular AP. To facilitate model based analysis of mechanisms underlying drug effects in this preparation, we adapted the rabbit ventricular AP model published by Shannon et al.[1] to reproduce parameters of our baseline transmembrane AP recordings. The adapted model will be applied in future investigation of drug effects. Methods and materialsData collection was previously described in detail [2]. APs were recorded in right ventricular papillary muscles isolated from 21 female New Zealand white rabbits. For all rabbits, APs were recorded at baseline without any drugs at pacing rates of 0.5 and 2.0 Hz. Sequences of baseline recordings were obtained from approximately eight different cells from each rabbit, and a median AP was calculated for each cell. We used a current MatLab implementation of the Shannon rabbit ventricular AP model published online. Adaptation of the model was done by changes in the maximal current conductance of membrane currents. The major transmembrane currents investigated were: the fast inward sodium current (INa), the rapid and slow components of the delayed rectifier potassium current (I Kr , I Ks ), the fast and slow components of the transient outward potassium current (I tof , I tos ), the inward rectifier potassium current (I K1 ), the L-type calcium current (I CaL ), the sodium-potassium pump (I NaK ), and the sodium-calcium exchanger (I NaCa ). Baseline adaptationThe default model AP displayed longer APD 90 and greater amplitude compared to experiment. The mean and 95 % confidence interval of measured AP amplitude (defined as the maximal value of the upstroke) was 21.4 ± 2.5 mV and 23.8 ± 2.4 mV at 2.0 and 0.5 Hz pacing respectively. The default model AP amplitude was 42.7 mV and 43.7 mV. Before adaptation to APD, the model AP amplitude was reduced by reduction in INa current conductance determined by minimization of the error between model and measured AP amplitude.The mean and 95 % confidence interval of measured APD 90 was 113.9 ± 11.2 ms and 150.2 ± 13.1 ms at 2.0 and 0.5 Hz pacing respectively. The model APD 90 was 190 ms and 221.5 ms and was adapted to experiment by reduction of the I CaL , I NaK , and I NaCa currents by multiplication of the current conductances with an identical factor to minimize the error between model and measured APD 90 across both frequencies. This approach was based in part on the results of the sensitivity analysis as described in the discussion. ISSN 2325-8861Computing in Cardiology 2015; 42:429-432.