Tracking Early Systolic Motion for Assessing Acute Response to Cardiac Resynchronization Therapy in Real Time.

An abnormal systolic motion is frequently observed in patients with left bundle branch block (LBBB), and it has been proposed as a predictor of response to cardiac resynchronization therapy (CRT). Our goal was to investigate if this motion can be monitored with miniaturized sensors feasible for clinical use to identify response to CRT in real time. Motion sensors were attached to the septum and the left ventricular (LV) lateral wall of eighteen anesthetized dogs. Recordings were performed during baseline, after induction of LBBB, and during biventricular pacing. The abnormal contraction pattern in LBBB was quantified by the septal flash index (SFI) equal to the early systolic shortening of the LV septal-to-lateral wall diameter divided by the maximum shortening achieved during ejection. In baseline, with normal electrical activation, there was limited early-systolic shortening and SFI was low (9 ± 8%). After induction of LBBB, this shortening and the SFI significantly increased (88 ± 34%, p < 0.001). Subsequently, CRT reduced it approximately back to baseline values (13 ± 13%, p < 0.001 vs. LBBB). The study showed the feasibility of using miniaturized sensors for continuous monitoring of the abnormal systolic motion of the LV in LBBB and how such sensors can be used to assess response to pacing in real time to guide CRT implantation.

Continuous Estimation of Acute Changes in Preload Using Epicardially Attached Accelerometers.

OBJECTIVE: A miniaturized accelerometer can be incorporated in temporary pacemaker leads which are routinely attached to the epicardium during cardiac surgery and provide continuous monitoring of cardiac motion during and following surgery. We tested if such a sensor could be used to assess volume status, which is essential in hemodynamically unstable patients. METHODS: An accelerometer was attached to the epicardium of 9 pigs and recordings performed during baseline, fluid loading, and phlebotomy in a closed chest condition. Alterations in left ventricular (LV) preload alter myocardial tension which affects the frequency of myocardial acceleration associated with the first heart sound ( fS1). The accuracy of fS1 as an estimate of preload was evaluated using sonomicrometry measured end-diastolic volume (EDV[Formula: see text]). Standard clinical estimates of global end-diastolic volume using pulse index continuous cardiac output (PiCCO) measurements (GEDV[Formula: see text]) and pulmonary artery occlusion pressure (PAOP) were obtained for comparison. The diagnostic accuracy of identifying fluid responsiveness was analyzed for fS1, stroke volume variation (SVV[Formula: see text]), pulse pressure variation (PPV[Formula: see text]), GEDV[Formula: see text], and PAOP. RESULTS: Changes in fS1 correlated well to changes in EDV[Formula: see text] ( r2=0.81, 95%CI: [0.68, 0.89]), as did GEDV[Formula: see text] ( r2=0.59, 95%CI: [0.36, 0.76]) and PAOP ( r2=0.36, 95%CI: [0.01, 0.73]). The diagnostic accuracy [95%CI] in identifying fluid responsiveness was 0.79 [0.66, 0.94] for fS1, 0.72 [0.57, 0.86] for SVV[Formula: see text], and 0.63 (0.44, 0.82) for PAOP. CONCLUSION: An epicardially placed accelerometer can assess changes in preload in real-time. SIGNIFICANCE: This novel method can facilitate continuous monitoring of the volemic status in open-heart surgery patients and help guiding fluid resuscitation.

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

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.

Dynamic gravity compensation does not increase detection of myocardial ischemia in combined accelerometer and gyro sensor measurements.

Previous studies have shown that miniaturised accelerometers can be used to monitor cardiac function and automatically detect ischemic events. However, accelerometers cannot differentiate between acceleration due to motion and acceleration due to gravity. Gravity filtering is essential for accurate integration of acceleration to yield velocity and displacement. Heart motion is cyclic and mean acceleration over time is zero. Thus, static gravity filtering is performed by subtracting mean acceleration. However, the heart rotates during the cycle, the gravity component is therefore not constant, resulting in overestimation of motion by static filtering. Accurate motion can be calculated using dynamic gravity filtering by a combined gyro and accelerometer. In an animal model, we investigated whether increased accuracy using dynamic filtering, compared to using static filtering, would enhance the ability to detect ischemia. Additionally, we investigated how well the gyro alone could detect ischemia based on the heart's rotation. Dynamic filtering tended towards lower sensitivity and specificity, using receiver operating characteristics analysis, for ischemia-detection compared to static filtering (area under the curve (AUC): 0.83 vs 0.93, p = 0.125). The time-varying gravity component indirectly reflects the heart's rotation. Hence, static filtering has the advantage of indirectly including rotation, which alone demonstrated excellent sensitivity to ischemia (AUC = 0.98).

Monitoring cardiac function by accelerometer - detecting start systole from the acceleration signal makes additional ECG recordings for R-peak detection redundant.

A miniaturized accelerometer attached to the heart has been used for monitoring functional parameters such as early systolic velocity and displacement. Currently, processing of the accelerometer signal for derival of these functional parameters depends on determining start systole by detecting the ECG R-peaks. This study proposes an alternative method using only the accelerometer signal to detect start systole, making additional ECG recordings for this purpose redundant. A signal processing method for automatic detection of start systole by accelerometer alone was developed and compared with detected R-peaks in 15 pigs during 5 different interventions showing a difference of 30 ± 17 ms. Furthermore, the derived early systolic velocity and displacement using only accelerometer measurements correlated well (r2=0.91 and 0.82, respectively) with minor differences compared to the current method using ECG R-peaks as time reference. The results show that an accelerometer can be used to monitor cardiac function without the need to measure ECG which can simplify the monitoring system.

Gravity Compensation Method for Combined Accelerometer and Gyro Sensors Used in Cardiac Motion Measurements.

A miniaturized accelerometer fixed to the heart can be used for monitoring of cardiac function. However, an accelerometer cannot differentiate between acceleration caused by motion and acceleration due to gravity. The accuracy of motion measurements is therefore dependent on how well the gravity component can be estimated and filtered from the measured signal. In this study we propose a new method for estimating the gravity, based on strapdown inertial navigation, using a combined accelerometer and gyro. The gyro was used to estimate the orientation of the gravity field and thereby remove it. We compared this method with two previously proposed gravity filtering methods in three experimental models using: (1) in silico computer simulated heart motion; (2) robot mimicked heart motion; and (3) in vivo measured motion on the heart in an animal model. The new method correlated excellently with the reference (r 2 > 0.93) and had a deviation from reference peak systolic displacement (6.3 ± 3.9 mm) below 0.2 ± 0.5 mm for the robot experiment model. The new method performed significantly better than the two previously proposed methods (p < 0.001). The results show that the proposed method using gyro can measure cardiac motion with high accuracy and performs better than existing methods for filtering the gravity component from the accelerometer signal.