Abstract
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.
Highlights
Patients suffering from chronic pathologies involving the cardiovascular system, such as heart failure (HF), may benefit from a long-term remote monitoring of the main cardiovascular parameters in order to early diagnose decompensation events or to adapt their therapy in a personalized and continuous fashion (Cleland et al, 2006; Desai et al, 2017)
The acquisition of accelerometry signals from the chest of the patient, using in particular these Mechanical Systems (MEMS) devices, leads to the observation of the seismocardiography (SCG) signal, that is characterized by the presence of two main components, S1 and S2, which correspond to the first and second heart sounds in the PCG, respectively
In Donal et al (2011) and Giorgis et al (2012), a number of features were extracted from chest accelerometry signals and compared to classical hemodynamic echocardiography markers, in order to optimize parameters of cardiac resynchronization therapy devices implanted on HF patients
Summary
Patients suffering from chronic pathologies involving the cardiovascular system, such as heart failure (HF), may benefit from a long-term remote monitoring of the main cardiovascular parameters in order to early diagnose decompensation events or to adapt their therapy in a personalized and continuous fashion (Cleland et al, 2006; Desai et al, 2017). The acquisition of accelerometry signals from the chest of the patient, using in particular these MEMS devices, leads to the observation of the seismocardiography (SCG) signal, that is characterized by the presence of two main components, S1 and S2, which correspond to the first and second heart sounds in the PCG, respectively. In Donal et al (2011) and Giorgis et al (2012), a number of features were extracted from chest accelerometry signals and compared to classical hemodynamic echocardiography markers, in order to optimize parameters of cardiac resynchronization therapy devices implanted on HF patients. Methods to obtain high-quality, chronic and longitudinal cardiac vibration data, that do not require the involvement of a medical practitioner or the patient to correctly and regularly acquire these signals, remain to be developed
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