Endocardial acceleration (sonR) vs. ultrasound-derived time intervals in recipients of cardiac resynchronization therapy systems

Donal, Erwan; Giorgis, L.; Cazeau, Serge; Leclercq, Christophe; Senhadji, Lotfi; Amblard, Amel; Jauvert, Gaël; Burban, M.; Hernández, Alfredo; Mabo, Philippe

doi:10.1093/europace/euq411

Cited by 23 publications

(29 citation statements)

References 20 publications

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“…The original detection method proposed in [14] analyzed the SonR signal and output the timing of SonR1 and SonR2. Briefly, after ensemble averaging and high-pass filtering, we applied signal envelope estimation and identified the onset of SonR1 and SonR2 by testing when the envelope exceeded specific thresholds.…”

Section: A Brief Description Of the Original Methods And Its Parametersmentioning

confidence: 99%

“…In this work we considered a subset of the database used in [14], containing 31 HF patients implanted with a biventricular system and enrolled by the Rennes University Hospital (CHU Rennes, Service de Cardiologie et Maladies Vasculaires). This database (DB) is composed of N = 103 records containing two standard ECG leads, surface SonR and pulsed Doppler echocardiography, acquired during various pacing configurations (biventricular pacing with various VV delays, single ventricular pacing, and spontaneous rhythm, when possible).…”

Section: ) Databasementioning

confidence: 99%

“…For instance, a correlation coefficient of 0.85 with an average absolute estimation error of 13.4 ms was obtained for the estimation of the systole period. More details on the performance obtained for the different valve closing instants are provided in [14]. However, we also noticed that, according to the signal context (signal-to-noise ratio, instantaneous heart rate, etc...) of each record, the SonR signal processing methods providing the best performance were not the same.…”

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confidence: 99%

“…This method was then clinically evaluated in the context of CRT optimization with data from 75 HF patients, under different pacing configurations [14]. We showed that satisfactory systolic/diastolic time interval estimations can be obtained from the SonR signal.…”

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confidence: 99%

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

Giorgis

Frogerais

Amblard³

et al. 2012

IEEE Trans. Biomed. Eng.

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Abstract-Previous studies have shown that cardiac microacceleration signals, recorded either cutaneously, or embedded into the tip of an endocardial pacing lead, provide meaningful information to characterize the cardiac mechanical function. This information may be useful to personalize and optimize the cardiac resynchronization therapy, delivered by a biventricular pacemaker, for patients suffering from chronic heart failure. The present paper focuses on the improvement of a previously proposed method for the estimation of the systole period from a signal acquired with a cardiac micro-accelerometer (SonR sensor, Sorin CRM SAS, France). We propose an optimal algorithm switching approach, to dynamically select the best configuration of the estimation method, as a function of different control variables, such as signal-to-noise ratio or heart rate. This method was evaluated on a database containing recordings from 31 patients suffering from chronic heart failure and implanted with a biventricular pacemaker, for which various cardiac pacing configurations were tested. Ultrasound measurements of the systole period were used as a reference and the improved method was compared with the original estimator. A reduction of 11% on the absolute estimation error was obtained for the systole period with the proposed algorithm switching approach.

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Section: A Brief Description Of the Original Methods And Its Parametersmentioning

confidence: 99%

Section: ) Databasementioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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

Giorgis

Frogerais

Amblard³

et al. 2012

IEEE Trans. Biomed. Eng.

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“…This amplitude has also been shown to decrease in humans during acute myocardial ischemia, where myocardial contractility is known to be decreased [3]. More recently, our group has proposed EA processing methods allowing for the estimation of cardiac electro-mechanical parameters, which have been applied to the improvement of patient selection and to optimize device configuration in the context of Cardiac Resynchronization Therapy (CRT) [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Endocardial Micro:Accelerometry during Valsalva Maneuvers

Gallet

Rolle

Bonnet

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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The endocardial acceleration (EA) signal is a particular kind of cardiac mechanogram, acquired through a microaccelerometer, embedded at the tip of a cardiac pacing lead, that can be chronically implanted inside a cardiac chamber. In this work, we analyze the evolution of the main EA-derived markers during severe cardio-respiratory modifications. Intracardiac electrograms (EGM), intraventricular pressure and EA data were acquired from 6 sheep during a set of Valsalva-like maneuvers. These data have been processed to segment the main EA components during each Valsalva phase, in a beat-to-beat basis. Results confirm the correlation between the energy of the EA1 component and the dP/dtmax. Significant modifications are observed in the main components of the EA signal during and just after the maneuver. These modifications may be useful for the characterization of respiratory events from the analysis of intracardiac EA signals.

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