Continuous Estimation of Acute Changes in Preload Using Epicardially Attached Accelerometers

Krogh, Magnus Reinsfelt; Halvorsen, Per Steinar; Grymyr, Ole-Johannes; Bergsland, Jacob; Elle, Ole Jakob; Fosse, Erik; Remme, Espen W.

doi:10.1109/tbme.2020.3020358

Cited by 7 publications

(17 citation statements)

References 24 publications

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“…These results significantly extend the application of mechano-sensors in estimating cardiac function, which is so far largely limited to optimization of pacemaker settings by the SonR system ( 15 , 23 ), without the knowledge of absolute values of pressures. Another study on pigs showed that an epicardially placed accelerometer can be used to assess changes in preload, and so filling status, using the frequency of myocardial acceleration ( 24 ). In preclinical studies, Thakur et al showed that analysis of S1 and S3 amplitude signals from accelerometers integrated into an implanted pacemaker device was able to monitor the change of absolute left atrial pressure over time ( 25 ).…”

Section: Discussionmentioning

confidence: 99%

Left Ventricular Pressure Estimation Using Machine Learning-Based Heart Sound Classification

Westphal

Luo

Shahmohammadi

et al. 2022

Front. Cardiovasc. Med.

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ObjectiveA method to estimate absolute left ventricular (LV) pressure and its maximum rate of rise (LV dP/dtmax) from epicardial accelerometer data and machine learning is proposed.MethodsFive acute experiments were performed on pigs. Custom-made accelerometers were sutured epicardially onto the right ventricle, LV, and right atrium. Different pacing configurations and contractility modulations, using isoflurane and dobutamine infusions, were performed to create a wide variety of hemodynamic conditions. Automated beat-by-beat analysis was performed on the acceleration signals to evaluate amplitude, time, and energy-based features. For each sensing location, bootstrap aggregated classification tree ensembles were trained to estimate absolute maximum LV pressure (LVPmax) and LV dP/dtmax using amplitude, time, and energy-based features. After extraction of acceleration and pressure-based features, location specific, bootstrap aggregated classification ensembles were trained to estimate absolute values of LVPmax and its maximum rate of rise (LV dP/dtmax) from acceleration data.ResultsWith a dataset of over 6,000 beats, the algorithm narrowed the selection of 17 predefined features to the most suitable 3 for each sensor location. Validation tests showed the minimal estimation accuracies to be 93% and 86% for LVPmax at estimation intervals of 20 and 10 mmHg, respectively. Models estimating LV dP/dtmax achieved an accuracy of minimal 93 and 87% at estimation intervals of 100 and 200 mmHg/s, respectively. Accuracies were similar for all sensor locations used.ConclusionUnder pre-clinical conditions, the developed estimation method, employing epicardial accelerometers in conjunction with machine learning, can reliably estimate absolute LV pressure and its first derivative.

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Section: Discussionmentioning

confidence: 99%

Left Ventricular Pressure Estimation Using Machine Learning-Based Heart Sound Classification

Westphal

Luo

Shahmohammadi

et al. 2022

Front. Cardiovasc. Med.

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“…Additional data from previous studies in 24 porcines (44 ± 4 kg SD) were used for validation of the accuracy of the accelerometer-based method for estimations of the LV pressure trace and pressure-displacement loop. These studies included measurements from 15 open chest 9,17 and 9 closed chest porcines 18 , the latter included to test the method also under closed chest conditions. The animals were anesthetized and surgically prepared as previously described 17 .…”

Section: Methodsmentioning

confidence: 99%

Automatic detection of valve events by epicardial accelerometer allows estimation of the left ventricular pressure trace and pressure–displacement loop area

Wajdan

Krogh

Villegas-Martínez

et al. 2020

Sci Rep

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Measurements of the left ventricular (LV) pressure trace are rarely performed despite high clinical interest. We estimated the LV pressure trace for an individual heart by scaling the isovolumic, ejection and filling phases of a normalized, averaged LV pressure trace to the time-points of opening and closing of the aortic and mitral valves detected in the individual heart. We developed a signal processing algorithm that automatically detected the time-points of these valve events from the motion signal of a miniaturized accelerometer attached to the heart surface. Furthermore, the pressure trace was used in combination with measured displacement from the accelerometer to calculate the pressure–displacement loop area. The method was tested on data from 34 animals during different interventions. The accuracy of the accelerometer-detected valve events was very good with a median difference of 2 ms compared to valve events defined from hemodynamic reference recordings acquired simultaneously with the accelerometer. The average correlation coefficient between the estimated and measured LV pressure traces was r = 0.98. Finally, the LV pressure–displacement loop areas calculated using the estimated and measured pressure traces showed very good correlation (r = 0.98). Hence, the pressure–displacement loop area can be assessed solely from accelerometer recordings with very good accuracy.

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“…Data comprised recordings from 19 ID: 8628 and 9303]. They were acute experiments where the animals were ventilated and surgically prepared as previously described [3], [27], [28]. The experiments followed different instrumentation protocols, but all of them had left ventricular pressure (LVP) and acceleration signals recorded simultaneously during different interventions.…”

Section: A Data Acquisitionmentioning

confidence: 99%

Automatic Detection of Aortic Valve Events Using Deep Neural Networks on Cardiac Signals From Epicardially Placed Accelerometer

Wajdan

Jahren

Villegas-Martínez

et al. 2022

IEEE J. Biomed. Health Inform.

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Background: Miniaturized accelerometers incorporated in pacing leads attached to the myocardium, are used to monitor cardiac function. For this purpose functional indices must be extracted from the acceleration signal. A method that automatically detects the time of aortic valve opening (AVO) and aortic valve closure (AVC) will be helpful for such extraction. We tested if deep learning can be used to detect these valve events from epicardially attached accelerometers, using high fidelity pressure measurements to establish ground truth for these valve events. Method: A deep neural network consisting of a CNN, an RNN, and a multi-head attention module was trained and tested on 130 recordings from 19 canines and 159 recordings from 27 porcines covering different interventions. Due to limited data, nested cross-validation was used to assess the accuracy of the method. Result: The correct detection rates were 98.9% and 97.1% for AVO and AVC in canines and 98.2% and 96.7% in porcines when defining a correct detection as a prediction closer than 40 ms to the ground truth. The incorrect detection rates were 0.7% and 2.3% for Manuscript

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Continuous Estimation of Acute Changes in Preload Using Epicardially Attached Accelerometers

Cited by 7 publications

References 24 publications

Left Ventricular Pressure Estimation Using Machine Learning-Based Heart Sound Classification

Left Ventricular Pressure Estimation Using Machine Learning-Based Heart Sound Classification

Automatic detection of valve events by epicardial accelerometer allows estimation of the left ventricular pressure trace and pressure–displacement loop area

Automatic Detection of Aortic Valve Events Using Deep Neural Networks on Cardiac Signals From Epicardially Placed Accelerometer

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