A5058786380- Advanced Memory and Neural Computing
- Neural dynamics and brain function
- Neuroscience and Neural Engineering
- Electrical and Bioimpedance Tomography
- Atrial Fibrillation Management and Outcomes
- Cardiac Arrhythmias and Treatments
- Neural Networks and Applications
- Mobile Crowdsensing and Crowdsourcing
- ECG Monitoring and Analysis
- Data Stream Mining Techniques
- Target Tracking and Data Fusion in Sensor Networks
- Microfluidic and Bio-sensing Technologies
- Pulsars and Gravitational Waves Research
- Gamma-ray bursts and supernovae
- Non-Destructive Testing Techniques
- Gaussian Processes and Bayesian Inference
- Advanced MRI Techniques and Applications
- Spectroscopy Techniques in Biomedical and Chemical Research
- Control Systems and Identification
- Advanced machining processes and optimization
- AI in cancer detection
- Thyroid Disorders and Treatments
- Music and Audio Processing
- Anomaly Detection Techniques and Applications
- Seismology and Earthquake Studies
Northwestern University
2015-2023
Sağlık Bilimleri Üniversitesi
2023
University of Health Sciences
2023
University of Health Sciences Antigua
2023
Science North
2016
With the first direct detection of gravitational waves, advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field astronomy by providing an alternative means sensing universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation all sensitive components LIGO from non-gravitational-wave disturbances. Nonetheless, still susceptible variety instrumental and environmental sources noise that contaminate data. Of...
In this study, we develop new reverse engineering (RE) techniques to identify the organization of synaptic inputs generating firing patterns populations neurons. We tested these in silico allow rigorous evaluation their effectiveness, using remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. chose spinal motoneurons as our target neural system, since process all motor commands and have well-established input-output properties. One set...
Design and optimization of statistical models for use in methods estimating radiofrequency ablation (RFA) lesion depths soft real-time performance.Using tissue multi-frequency complex electrical impedance data collected from a low-cost embedded system, deep neural network (NN) tree-based ensembles (TEs) were trained the RFA depth via regression.Addition frequency sweep data, previous RF power state boosted accuracy models. The root mean square errors 2 mm NN 0.5 TEs 0.4 0.04 presented this...
Background: Radiofrequency ablation is a minimally-invasive treatment method that aims to destroy undesired tissue by exposing it alternating current in the 100 kHz–800 kHz frequency range and heating until destroyed via coagulative necrosis. Ablation gaining momentum especially cancer research, where malignant tumor. While ablating tumor with an electrode or catheter easy task, real-time monitoring process must order maintain reliability of treatment. Common methods for this task have...
Atrial Flutter (AFL) is a supraventricular tachyarrhythmia typically arising from macroreentry circuit that can have variable atrial anatomy. It often treated by catheter ablation, the success of which depends upon correct determination electroanatomic circuit, generally through invasive electrophysiological (EP) study. We hypothesized machine learning (ML) methods applied to diagnostic 12-lead surface electrocardiogram (ECG) could determine specific prior any EP study.The ECGs were reduced...
Radiofrequency ablation (RFA) is a popular modality for tumor treatment. However, inexpensive real-time monitoring of RFA within multiple tissue types still an ongoing research topic. The objective this study to utilize multi-frequency electrical impedance data depth estimation through fusion schemes that include non-linear machine learning (ML) models. Multi-frequency complex measurements are used provide input the schemes. Our results show significantly decrease both spread residuals and...
In this study, we develop new reverse engineering (RE) techniques to identify the organization of synaptic inputs generating firing patterns populations neurons. We tested these in silico allow rigorous evaluation their effectiveness, using remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. chose spinal motoneurons as our target neural system, since process all motor commands and have well-established input-output properties. One set...
In this paper we address supervised learning problems where, instead of having a single annotator who provides the ground truth, multiple annotators, usually with varying degrees expertise, provide conflicting labels for same sample. Once Gaussian Process classification has been adapted to problem propose and describe how Variational Bayes inference can be used to, given observed labels, approximate posterior distribution latent classifier also estimate each annotator's reliability....
In this paper, we introduce a new Gaussian Process (GP) classification method for multisensory data. The proposed approach can deal with noisy and missing It is also capable of estimating the contribution each sensor towards task. We use Bayesian modeling to build GP-based classifier which combines information provided by all sensors approximates posterior distribution GP using variational inference. During its training phase, algorithm estimates sensor's weight then uses assign label...
In this paper we address the crowdsourcing problem, where a classifier must be trained without knowing real labels. For each sample, labels (which may not same) are provided by different annotators (usually with degrees of expertise). The problem is formulated using Bayesian modeling, and considers scenarios annotator label subset training set samples only. Although approaches have been previously proposed in literature, introduce Variational Bayes inference to develop an iterative algorithm...
In this study, we develop new reverse engineering (RE) techniques to identify the organization of synaptic inputs generating firing patterns populations neurons. We tested these in silico allow rigorous evaluation their effectiveness, using remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. chose spinal motoneurons as our target neural system, since process all motor commands and have well established input-output properties. One set...
This study aims to investigate the factors affecting development of acute kidney injury (AKI) in patients with severe hypothyroidism.
Abstract In this study, we develop new reverse engineering (RE) techniques to identify the organization of synaptic inputs generating firing patterns populations neurons. We tested these in silico allow rigorous evaluation their effectiveness, using remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. chose spinal motoneurons as our target neural system, since process all motor commands and have well established input-output properties. One set...
In this study, we develop new reverse engineering (RE) techniques to identify the organization of synaptic inputs generating firing patterns populations neurons. We tested these in silico allow rigorous evaluation their effectiveness, using remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. chose spinal motoneurons as our target neural system, since process all motor commands and have well established input-output properties. One set...
Atypical atrial flutter (AFL) is an increasingly encountered arrythmia. Unlike typical AFL, it may arise from either atrium. There are currently no accurate methods for distinguishing atrium of origin on surface ECG, which has important implications procedural planning and risk-benefit discussions.