Comparison of Different Methods for Estimating Cardiac Timings: A Comprehensive Multimodal Echocardiography Investigation

Background Remote monitoring of heart failure (HF) patients using wearable devices can allow patient-specific adjustments to treatments and thereby potentially reduce hospitalizations. We aimed to assess HF state using wearable measurements of electrical and mechanical aspects of cardiac function in the context of exercise. Methods and Results Patients with compensated (outpatient) and decompensated (hospitalized) HF were fitted with a wearable electrocardiogram (ECG) and seismocardiogram (SCG) sensing patch. Patients stood at rest for an initial recording, performed a six-minute walk test (6MWT), and then stood at rest for five minutes of recovery. The protocol was performed at the time of outpatient visit or at two time points (admission and discharge) during an HF hospitalization. To assess patient state, we devised a method based on comparing the similarity of the structure of SCG signals following exercise compared to rest using graph mining (Graph Similarity Score, GSS). We found that GSS can assess HF patient state, and correlates to clinical improvement in 45 patients (13 decompensated, 32 compensated). A significant difference was found between the groups in the GSS metric (44.4±4.9 [Decompensated HF] vs. 35.2±10.5 [Compensated HF], p<0.001). In the six decompensated patients with longitudinal data we found a significant change in GSS from admission (decompensated) to discharge (compensated) (44±4.1 [Admitted] vs. 35±3.9 [Discharged], p<0.05). Conclusions Wearable technologies recording cardiac function and machine learning algorithms can assess compensated and decompensated HF states by analyzing cardiac response to sub-maximal exercise. These techniques can be tested in the future to track the clinical status of outpatients with HF and their response to pharmacological interventions.

Seismocardiogram (SCG) is the measure of the micro-vibrations produced by the heart contraction and blood ejection into the vascular tree. Over time, a large body of evidence has been collected on the ability of SCG to reflect cardiac mechanical events such as opening and closure of mitral and aortic valves, atrial filling and point of maximal aortic blood ejection. We recently developed a smart garment, named MagIC-SCG, that allows the monitoring of SCG, electrocardiogram (ECG) and respiration out of the laboratory setting in ambulant subjects. The present pilot study illustrates the results of two different experiments performed to obtain a first evaluation on whether a dynamical assessment of indexes of cardiac mechanics can be obtained from SCG recordings obtained by MagIC-SCG. In the first experiment, we evaluated the consistency of the estimates of two indexes of cardiac contractility, the pre-ejection period, PEP, and the left ventricular ejection time, LVET. This was done in the lab, by reproducing an experimental protocol well known in literature, so that our measures derived from SCG could have been compared with PEP and LVET reference values obtained by traditional techniques. Six healthy subjects worn MagIC-SCG while assuming two different postures (supine and standing); PEP was estimated as the time interval between the Q wave in ECG and the SCG wave corresponding to the opening of aortic valve; LVET was the time interval between the SCG waves corresponding to the opening and closure of the aortic valve. The shift from supine to standing posture produced a significant increase in PEP and PEP/LVET ratio, a reduction in LVET and a concomitant rise in the LF/HF ratio in the RR interval (RRI) power spectrum. These results are in line with data available in literature thus providing a first support to the validity of our estimates. In the second experiment, we evaluated in one subject the feasibility of the beat-by-beat assessment of LVET during spontaneous behavior. The subject was continuously monitored by the smart garment from 8 am to 8 pm during a workday. From the whole recording, three data segments were selected: while the subject was traveling to work (M1), during work in the office (O) and while traveling back home (M2). LVET was estimated on a beat-by-beat basis from SCG and the RRI influence was removed by regression analysis. The LVET series displayed marked beat-by-beat fluctuations at the respiratory frequency. The amplitude of these fluctuations changed in the three periods and was lower when the LF/HF RRI power ratio was higher, at O, thus suggesting a possible influence of the autonomic nervous system on LVET short-term variability. To the best of our knowledge this case report provides for the first time a representation of the beat-by-beat dynamics of a systolic time interval during daily activity. The statistical characterization of these findings remains to be explored on a larger population.