Background - Atrial fibrillation (AF) can be maintained by localized intramural reentrant drivers. However, AF driver detection by clinical surface-only multi-electrode mapping (MEM) has relied on subjective interpretation of activation maps. We hypothesized that application of Machine Learning (ML) to electrogram frequency spectra may accurately automate driver detection by MEM and add some objectivity to the interpretation of MEM findings. Methods - Temporally and spatially stable single AF drivers were mapped simultaneously in explanted human atria (n=11) by subsurface near-infrared optical mapping (NIOM) (0.3mm 2 resolution) and 64-electrode MEM (Higher-Density (HD) or Lower-Density (LD) with 3mm 2 and 9mm 2 resolution, respectively). Unipolar MEM and NIOM recordings were processed by Fourier Transform analysis into 28407 total Fourier spectra. Thirty-five features for ML were extracted from each Fourier spectrum. Results - Targeted driver ablation and NIOM activation maps efficiently defined the center and periphery of AF driver preferential tracks and provided validated classifications for driver vs non-driver electrodes in MEM arrays. Compared to analysis of single electrogram frequency features, averaging the features for each surrounding 8 electrodes neighborhood, significantly improved classification of AF driver electrograms. The classification metrics increased when less strict annotation including driver periphery electrodes were added to driver center annotation. Notably, f1-score for the binary classification of HD catheter dataset were significantly higher than that of LD catheter (0.81 ± 0.02 vs 0.66 ± 0.04, p<0.05). The trained algorithm correctly highlighted 86% of driver regions with HD but only 80% with LD MEM arrays (81% for LD+HD arrays together). Conclusions - The ML model pre-trained on Fourier spectrum features allows efficient classification of electrograms recordings as AF driver or non-driver compared to the NIOM gold-standard. Future application of NIOM-validated ML approach may improve the accuracy of AF driver detection for targeted ablation treatment in patients.

Purposes. Monopolar energy (ME) is routinely used in appendectomy. This study aimed to investigate the degree of lateral thermal spread generated by ME and to evaluate the thermal injury sustained by the close-lying tissues. Materials and methods. Appendectomy with a monopolar Maryland dissector was performed in 8 rabbits (at 30 and 60 W power settings). A high-resolution infrared camera was used to record tissue heating during the intervention. After autopsy macroscopic changes were evaluated and tissue samples were subjected to myeloperoxidase (MPO) assay and histological examination. Results. No significant differences in the extent of thermal spread, MPO activity and histological signs of inflammation were observed between groups. Regardless of the power settings, the heat spread exceeded 2 cm laterally along the mesoappendix when application time exceeded 3 s. The spread of heat through tubular structures in both groups caused a significant temperature rise in the nearby intestinal loop, resulting in perforation (n=3) and necrosis (n=1). Conclusions. Application time is critical in thermal spread during appendectomy aided by ME. Tubular anatomic structures can enhance thermal injury on distant tissues. The observed effects of ME bear clinical relevance that need further investigation.

It is presented hardware- program complex for eyes, head and hand separate movement's check-up, and their coordination check-up in the integrated movement act. Patients with movement disorders are surveyed by using this complex. As an example there are shown differences in movement parameters value (a stage of latency, duration, frequency etc.), of patients with different stages of Parkinson disease (PD) by using several tests. Analysis of head, eyes and hand movement's trajectories can be used for early PD diagnostics.