Supercomputer framework for reverse engineering firing patterns of neuron populations to identify their synaptic inputs.

In this study, we develop new reverse engineering (RE) techniques to identify the organization of the synaptic inputs generating firing patterns of populations of neurons. We tested these techniques in silico to allow rigorous evaluation of their effectiveness, using remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. We chose spinal motoneurons as our target neural system, since motoneurons process all motor commands and have well-established input-output properties.

Inter-Patient Atrial Flutter Classification Using FFT-Based Features and a Low-Variance Stacking Classifier.

Objective: Atrial Flutter (AFL) is a supraventricular tachyarrhythmia typically arising from a macroreentry circuit that can have variable atrial anatomy. It is often treated by catheter ablation, the success of which depends upon the correct determination of the electroanatomic circuit, generally through invasive electrophysiological (EP) study. We hypothesized that machine learning (ML) methods applied to the diagnostic 12-lead surface electrocardiogram (ECG) could determine the specific circuit prior to any invasive EP study.

Early and Late Fusion Machine Learning on Multi-Frequency Electrical Impedance Data to Improve Radiofrequency Ablation Monitoring.

Radiofrequency ablation (RFA) is a popular modality for tumor treatment. However, inexpensive real-time monitoring of RFA within multiple tissue types is still an ongoing research topic. The objective of this study is to utilize multi-frequency electrical impedance data within real-time RFA depth estimation through data fusion schemes that include non-linear machine learning (ML) models.

Real-Time Radiofrequency Ablation Lesion Depth Estimation Using Multi-frequency Impedance With a Deep Neural Network and Tree-Based Ensembles.

Objective: Design and optimization of statistical models for use in methods for estimating radiofrequency ablation (RFA) lesion depths in soft real-time performance.

Methods: Using tissue multi-frequency complex electrical impedance data collected from a low-cost embedded system, a deep neural network (NN) and tree-based ensembles (TEs) were trained for estimating the RFA lesion depth via regression.

Results: Addition of frequency sweep data, previous depth data, and previous RF power state data boosted accuracy of the statistical models.

Real-time monitoring radiofrequency ablation using tree-based ensemble learning models.

Background: Radiofrequency ablation is a minimally-invasive treatment method that aims to destroy undesired tissue by exposing it to alternating current in the 100 kHz-800 kHz frequency range and heating it until it is destroyed via coagulative necrosis. Ablation treatment is gaining momentum especially in cancer research, where the undesired tissue is a malignant tumor. While ablating the tumor with an electrode or catheter is an easy task, real-time monitoring the ablation process is a must in order to maintain the reliability of the treatment.