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

Abstract

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.

Accelerometer Detects Pump Thrombosis and Thromboembolic Events in an In vitro HVAD Circuit

“…Our group has been developing algorithms to monitor aortic valve opening and preload estimations based on accelerometer signals in healthy hearts. 103,104…”

“…Our group has been working on detection of valve events using accelerometer. 104 This algorithm should be tested under LVAD physiology.…”

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

“…We observed large variations in the signal as seen in Fig. 1, and our previous signal method [20] performed poorly in several of the tested interventions. On the other hand, the deep learning neural network detected the aortic valve events with high accuracy despite the large signal variation and with no use of ECG, demonstrating the ability of this method to identify patterns in such complex and varying signals.…”

“…We previously developed and tested an approach for detection of the valve events using a conventional signal processing method [20]. The method employed different filter types (e.g.…”

“…Several recent studies on automatic detection of valve events by prominent features in SCG signals have been performed using different approaches such as temporal enveloping [13]- [15], continuous wavelet transform [16], machine learning [17], [18], or signal processing combinations [19]. We have also proposed a signal processing method to detect mitral and aortic valve events on measurements from epicardially attached accelerometers [20]. Typically, these SCG studies have used expert opinion based annotation of recognizable features in the acceleration signals and the methods have been tested mainly in normal individuals and in few types of conditions with abnormal cardiac motion.…”