Optimal Algorithm Switching for the Estimation of Systole Period From Cardiac Microacceleration Signals (SonR)

Analysis of Cardiac Vibration Signals Acquired From a Novel Implant Placed on the Gastric Fundus

The analysis of cardiac vibration signals has been shown as an interesting tool for the follow-up of chronic pathologies involving the cardiovascular system, such as heart failure (HF). However, methods to obtain high-quality, real-world and longitudinal data, that do not require the involvement of the patient to correctly and regularly acquire these signals, remain to be developed. Implantable systems may be a solution to this observability challenge. In this paper, we evaluate the feasibility of acquiring useful electrocardiographic (ECG) and accelerometry (ACC) data from an innovative implant located in the gastric fundus. In a first phase, we compare data acquired from the gastric fundus with gold standard data acquired from surface sensors on 2 pigs. A second phase investigates the feasibility of deriving useful hemodynamic markers from these gastric signals using data from 4 healthy pigs and 3 pigs with induced HF with longitudinal recordings. The following data processing chain was applied to the recordings: (1) ECG and ACC data denoising, (2) noise-robust real-time QRS detection from ECG signals and cardiac cycle segmentation, (3) Correlation analysis of the cardiac cycles and computation of coherent mean from aligned ECG and ACC, (4) cardiac vibration components segmentation (S1 and S2) from the coherent mean ACC data, and (5) estimation of signal context and a signal-to-noise ratio (SNR) on both signals. Results show a high correlation between the markers acquired from the gastric and thoracic sites, as well as pre-clinical evidence on the feasibility of chronic cardiovascular monitoring from an implantable cardiac device located at the gastric fundus, the main challenge remains on the optimization of the signal-to-noise ratio, in particular for the handling of some sources of noise that are specific to the gastric acquisition site.

A novel bimodal stethoscope for gastric collection of heart sounds: preliminary results

Heart sound analysis is commonly used by physicians during auscultations to evaluate cardiac activity as a first line. These sounds originate from heartbeats and the resulting blood flow, and can provide important information about heart function and hemodynamics. The monitoring of heart sounds in patients suffering from chronic cardiac pathologies can be useful to detect or prevent cardiac events. For this purpose, a bimodal implanted gastric stethoscope was developed allowing home monitoring of electrophysiological and mechanical parameters.An in-vivo experiment in pigs was carried out to validate the feasibility of heart sound detection from an accelerometer embedded in a stethoscope prototype implanted in the submucosal layer of the gastric wall. Data recorded over several weeks validate the tolerance and the sensitivity of the device. These promising preliminary results confirm the interest of considering the stomach as a strategic implantation site for heart sound monitoring.

An algorithm for the beat-to-beat assessment of cardiac mechanics during sleep on Earth and in microgravity from the seismocardiogram

Seismocardiogram, SCG, is the measure of precordial vibrations produced by the beating heart, from which cardiac mechanics may be explored on a beat-to-beat basis. We recently collected a large amount of SCG data (>69 recording hours) from an astronaut to investigate cardiac mechanics during sleep aboard the International Space Station and on Earth. SCG sleep recordings are characterized by a prolonged duration and wide heart rate swings, thus a specific algorithm was developed for their analysis. In this article we describe the new algorithm and its performance. The algorithm is composed of three parts: 1) artifacts removal, 2) identification in each SCG waveform of four fiducial points associated with the opening and closure of the aortic and mitral valves, 3) beat-to-beat computation of indexes of cardiac mechanics from the SCG fiducial points. The algorithm was tested on two sleep recordings and yielded the identification of the fiducial points in more than 36,000 beats with a precision, quantified by the Positive Predictive Value, ≥99.2%. These positive findings provide the first evidence that cardiac mechanics may be explored by the automatic analysis of SCG long-lasting recordings, taken out of the laboratory setting, and in presence of significant heart rate modulations.

Automatic and Robust Delineation of the Fiducial Points of the Seismocardiogram Signal for Non-invasive Estimation of Cardiac Time Intervals

OBJECTIVE

The purpose of this research was to design a delineation algorithm that could detect specific fiducial points of the seismocardiogram (SCG) signal with or without using the electrocardiogram (ECG) R-wave as the reference point. The detected fiducial points were used to estimate cardiac time intervals. Due to complexity and sensitivity of the SCG signal, the algorithm was designed to robustly discard the low-quality cardiac cycles, which are the ones that contain unrecognizable fiducial points.

METHOD

The algorithm was trained on a dataset containing 48,318 manually annotated cardiac cycles. It was then applied to three test datasets: 65 young healthy individuals (dataset 1), 15 individuals above 44 years old (dataset 2), and 25 patients with previous heart conditions (dataset 3).

RESULTS

The algorithm accomplished high prediction accuracy with the rootmean- square-error of less than 5 ms for all the test datasets. The algorithm overall mean detection rate per individual recordings (DRI) were 74, 68, and 42 percent for the three test datasets when concurrent ECG and SCG were used. For the standalone SCG case, the mean DRI was 32, 14 and 21 percent.

CONCLUSION

When the proposed algorithm applied to concurrent ECG and SCG signals, the desired fiducial points of the SCG signal were successfully estimated with a high detection rate. For the standalone case, however, the algorithm achieved high prediction accuracy and detection rate for only the young individual dataset.

SIGNIFICANCE

The presented algorithm could be used for accurate and non-invasive estimation of cardiac time intervals.

Analysis of endocardial acceleration during intraoperative optimization of cardiac resynchronization therapy

Cardiac resynchronization therapy (CRT) is the therapy of choice for selected patients suffering from drug-refractory congestive heart failure and presenting an interventricular desynchronization. CRT is delivered by an implantable biventricular pacemaker, which stimulates the right atrium and both ventricles at specific timings. The optimization and personalization of this therapy requires to quantify both the electrical and the mechanical cardiac functions during the intraoperative and postoperative phases. The objective of this paper is to evaluate the feasibility of the calculation of features extracted from endocardial acceleration (EA) signals and the potential utility of these features for the intraoperative optimization of CRT. Endocardial intraoperative data from one patient are analyzed for 33 different pacing configurations, including changes in the atrio-ventricular and inter-ventricular delays and different ventricular stimulation sites. The main EA features are extracted for each pacing configuration and analyzed so as to estimate the intra-configuration and inter-configuration variability. Results show the feasibility of the proposed approach and suggest the potential utility of EA for intraoperative monitoring of the cardiac function and defining optimal, adaptive pacing configurations.