Nanoscale Dielectric Imaging by 3D-Electrostatic Force Microscopy
Martí Checa, Nanoscale bioelectrical characterization
We present Electrostatic Force Volume Microscopy (EFVM) for nanoscale dielectric imaging. EFVM is a new 3D-SPM technique, based in the acquisition of electrostatic force approach curves at each point of a sample and its post-processing and quantification to obtain both Electrostatic Force Microscopy (EFM) images and dielectric constant maps. We show that with a single set of EFVM data one can obtain EFM images in all currently available EFM imaging modes (e.g. constant height, lift mode, constant electric force, etc.) and at any desired tip-sample distance or electric force set point. EFVM enables, in addition, obtaining EFM images under acquisition settings that cannot be implemented in any existing EFM instrument. Finally, EFVM allows obtaining maps of the dielectric constant of the sample with unparalleled accuracy and spatial resolution, irrespectively of the sample topography. We report applications of EFVM to thin oxide films, silver nanowires and single bacterial cells to show the broad applicability of the technique. EFVM is expected to have an important impact in the nanoscale dielectric mapping of topographically complex samples in Materials and Life Sciences.
Sudden cardiac death risk stratification of idiopathic cardiomyopathy patients by the application of cardiovascular coupling analysis
Javier Rodríguez, Biomedical signal processing and interpretation
Cardiovascular diseases are one of the most common cause of death. Early detection of patients at high risk of sudden cardiac death (SCD) is still an issue. The aim of this study was to analyze the cardio-vascular couplings based on heart rate variability (HRV) and blood pressure variability (BPV) analysis in order to introduce new indices that allow noninvasive risk stratification in idiopathic dilated cardiomyopathy patients (IDC).
High-resolution electrocardiogram (ECG) and continuous noninvasive blood pressure (BP) signals were recorded from 91 IDC patients and 49 healthy subjects (CON) for 30 minutes. During a follow-up period of 2 years, 14 patients either died or suffered life-threatening complications due to their cardiac condition. From the ECG and BP signals, the beat-to-beat interval, and systolic and diastolic blood pressure values were extracted. All this new information was analyzed, in univariate and bivariate ways, using the segmented Poincaré plot analysis, the high resolution joint symbolic dynamics and the normalized short time partial directed coherence methods. Indices with statistical significance between different SCD risk levels were selected. Support vector machine (SVM) models were built in order to classify these patients by their level of SCD risk. Patients at high risk of SCD (IDCHR) presented lowered HRV and increased BPV compared to both the low risk patients (IDCLR) and the control subjects, suggesting a depression in their vagal activity and a compensation from the sympathetic activity. The coupling strength from both, the systolic and diastolic blood pressure to the cardiac activity were stronger in high risk patients. Additionally, the cardio-systolic coupling analysis revealed that the systolic influence over the heart rate gets weaker as the risk increases. The SVM IDCLR vs IDCHR model achieved 98.9% accuracy with an area under the curve (AUC) of 0.96. When comparing IDC vs CON groups, 93.6% and 0.94 accuracy and AUC were obtained, respectively. In order to simulate the case were the original status of the subject is unknown, a cascade model was built fusing the aforementioned models, achieving 94.4% accuracy.
In conclusion, this study introduced a novel method of SCD risk stratification of IDC patients based on new indices from coupling analysis and non-linear HRV and BPV. We uncovered some of the complex interactions within the autonomic regulation in this type of patients.