A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time.

This work presents a data-driven method to simulate, in real-time, the biomechanical behavior of the breast tissues in some image-guided interventions such as biopsies or radiotherapy dose delivery as well as to speed up multimodal registration algorithms. Ten real breasts were used for this work. Their deformation due to the displacement of two compression plates was simulated off-line using the finite element (FE) method. Three machine learning models were trained with the data from those simulations. Then, they were used to predict in real-time the deformation of the breast tissues during the compression. The models were a decision tree and two tree-based ensemble methods (extremely randomized trees and random forest). Two different experimental setups were designed to validate and study the performance of these models under different conditions. The mean 3D Euclidean distance between nodes predicted by the models and those extracted from the FE simulations was calculated to assess the performance of the models in the validation set. The experiments proved that extremely randomized trees performed better than the other two models. The mean error committed by the three models in the prediction of the nodal displacements was under 2 mm, a threshold usually set for clinical applications. The time needed for breast compression prediction is sufficiently short to allow its use in real-time (<0.2 s).

Quantification of the modifications in the dominant frequency of ventricular fibrillation under conditions of ischemia and reperfusion: an experimental study.

The characteristics of ventricular fibrillatory signals vary as a function of the time elapsed from the onset of arrhythmia and the maneuvers used to maintain coronary perfusion. The dominant frequency (FrD) of the power spectrum of ventricular fibrillation (VF) is known to decrease after interrupting coronary perfusion, though the corresponding recovery process upon reestablishing coronary flow has not been quantified to date. With the aim of investigating the recovery of the FrD during reperfusion after a brief ischemic period, 11 isolated and perfused rabbit heart preparations were used to analyze the signals obtained with three unipolar epicardial electrodes (E1-E3) and a bipolar electrode immersed in the thermostatized organ bath (E4), following the electrical induction of VF. Recordings were made under conditions of maintained coronary perfusion (5 min), upon interrupting perfusion (15 min), and after reperfusion (5 min). FrD was determined using Welch's method. The variations in FrD were quantified during both ischemia and reperfusion, based on an exponential model deltaFrD = A exp (-t/C). During ischemia deltaFrD is the difference between FrD and the minimum value, while t is the time elapsed from the interruption of coronary perfusion. During reperfusion deltaFrD is the difference between the maximum value and FrD, while t is the time elapsed from the restoration of perfusion. A is one of the constants of the model, and C is the time constant. FrD exhibited respective initial values of 16.20 +/- 1.67, 16.03 +/- 1.38, and 16.03 +/- 1.80 Hz in the epicardial leads, and 15.09 +/- 1.07 Hz in the bipolar lead within the bath. No significant variations were observed during maintained coronary perfusion. The fit of the FrD variations to the model during ischemia and reperfusion proved significant in nine experiments. The mean time constants C obtained on fitting to the model during ischemia were as follows: E1 = 294.4 +/- 75.6, E2 = 225.7 +/- 48.5, E3 = 327.4 +/- 79.7, and E4 = 298.7 +/- 43.9 seconds. The mean values of C obtained during reperfusion, and the significance of the differences with respect to the ischemic period were: E1 = 57.5 +/- 8.4 (P < 0.01), E2 = 64.5 +/- 11.2 (P < 0.01), E3 = 80.7 +/- 13.3 (P < 0.01), and E4 = 74.9 +/- 13.6 (P < 0.0001). The time course variations of the FrD of the VF power spectrum fit an exponential model during ischemia and reperfusion. The time constants of the model during reperfusion after a brief ischemic period are significantly shorter than those obtained during ischemia.

Application of adaptive signal processing for determining the limits of P and T waves in an ECG.

A new algorithm for the determination of the limits of P and T waves is proposed, and its foundations are mathematically analyzed. The algorithm performs an adaptive filtering so that the searched point corresponds to a minimum. Crucial properties of its performance are discussed, i.e., immunity to base line drifts and full adaptation to any cardiological criteria. A series of tests are made involving real registers with different morphologies for P and T-waves.

[The automatic, beat-to-beat determination of the QT interval and analysis of its spontaneous variability under basal conditions]. / Determinación automática, latido a latido, del intervalo QT y análisis de su variabilidad espontánea en condiciones basales.

OBJECTIVES: An analysis is made of the automatic beat-by-beat measurement of QT and other intervals related to ventricular repolarization. The variability pattern of these intervals is investigated in normal subjects at rest, along with their relation to RR cycle variability. MATERIAL AND METHODS: The electrocardiographic signals (LII) from 11 normal subjects (mean age 31 +/- 10 years) were recorded over 5 min and processed by applying specific algorithms to determine beat-by-beat the RR, QT, RT, QTm and RTm intervals (Tm = peak of T wave). An analysis was made of the variability of these intervals in the time (standard deviation, variation coefficient, difference between maximum and minimum values) and frequency domains (spectral analysis applying the Fourier transform). RESULTS: The differences between the automatic measurements and those performed by two observers (n = 110) were respectively -1.3 +/- 6.4 and -3.7 +/- 6.5 ms for QT, - 1.0 +/- 1.4 and -1.0 +/- 2.3 ms for QTm, -0.3 +/- 1.4 and -0.2 +/- 1.8 ms for RTm, and 0.7 +/- 6.5 and -2.8 +/- 10.3 ms for RT. The QT and RT intervals exhibited greater variability (SD = 6 +/- 1 ms) than QTm and RTm (SD = 3 +/- 1 ms, p < 0.0001). These differences persisted on comparing the corresponding variation coefficients. The differences between the maximum and minimum measurements were 45 +/- 24 ms for QT and RT, the values being significantly less in the case of QTm (21 +/- 26 ms, p < 0.05) and RTm (20 +/- 27 ms, p < 0.05). In the frequency domain, the high- (HF) and low-frequency (LF) band energies were low in the series formed by the ventricular repolarization intervals, and the LF band normalized amplitude was significantly lower than in the RR series. There were no significant differences in the frequencies of the maximum values of the LF and HF bands of the RR series with respect to the QT series. The correlations between the RR intervals and the subsequent repolarization intervals obtained in each subject were not significant in 7 of the 11 subjects studied. CONCLUSIONS: The automatic beat-by-beat determination of the ventricular repolarization intervals is precise, particularly when considering the intervals defined by the T wave peak. Repolarization variability during the sinus rhythm at rest is small, and is not linearly related to modifications of the previous RR interval. Neurovegetative and humoral influences are postulated to explain QT variations. The neurovegetative and humoral influences that regulate cardiac cycle and ventricular repolarization variability at rest, are found to be quantitatively different.