Real-Time Postural Disturbance Detection Through Sensor Fusion of EEG and Motion Data Using Machine Learning.

Millions of people around the globe are impacted by falls annually, making it a significant public health concern. Falls are particularly challenging to detect in real time, as they often occur suddenly and with little warning, highlighting the need for innovative detection methods. This study aimed to assist in the advancement of an accurate and efficient fall detection system using electroencephalogram (EEG) data to recognize the reaction to a postural disturbance.

Automated Seizure Detection Based on State-Space Model Identification.

In this study, we developed a machine learning model for automated seizure detection using system identification techniques on EEG recordings. System identification builds mathematical models from a time series signal and uses a small number of parameters to represent the entirety of time domain signal epochs. Such parameters were used as features for the classifiers in our study.

Localizing EEG Recordings Associated With a Balance Threat During Unexpected Postural Translations in Young and Elderly Adults.

Balance perturbations are accompanied by global cortical activation that increases in magnitude when postural perturbations are unexpected, potentially due to the addition of a startle response. A specific site for best recording the response to unexpected destabilization has not been identified. We hypothesize that a single sensor located near to subcortical brainstem mechanisms could serve as a marker for the response to unpredictable postural events.

An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features.

The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and release the medical expert from labor-consumption work. In this paper, novel improved model based essence features (IMBEFs) were proposed combining locality energy (LE) and dual state space models (DSSMs) for automatic sleep stage detection on single-channel electroencephalograph (EEG) signals. Firstly, each EEG epoch is decomposed into low-level sub-bands (LSBs) and high-level sub-bands (HSBs) by wavelet packet decomposition (WPD), separately.

Complexity reduction in human atrial modeling using extended Kalman filter.

Human atrial tissue electrophysiology is modeled upon biophysical details obtained from cellular level measurements. Data collected for this purpose typically represent a unique state of the tissue. As reproducing dynamic cases such as subject-varied and/or disorder-varied electrophysiological properties is in question, such complex models are typically hard to use.

Spiral waves characterization: Implications for an automated cardiodynamic tissue characterization.

Background And Objective: Spiral waves are phenomena observed in cardiac tissue especially during fibrillatory activities. Spiral waves are revealed through in-vivo and in-vitro studies using high density mapping that requires special experimental setup. Also, in-silico spiral wave analysis and classification is performed using membrane potentials from entire tissue.

Spiral wave classification using normalized compression distance: Towards atrial tissue spatiotemporal electrophysiological behavior characterization.

Analysis of electrical activation patterns such as re-entries during atrial fibrillation (Afib) is crucial in understanding arrhythmic mechanisms and assessment of diagnostic measures. Spiral waves are a phenomena that provide intuitive basis for re-entries occurring in cardiac tissue. Distinct spiral wave behaviors such as stable spiral waves, meandering spiral waves, and spiral wave break-up may have distinct electrogram manifestations on a mapping catheter.

Computational fluid dynamics modeling of the upper airway of children with obstructive sleep apnea syndrome in steady flow.

Computational fluid dynamic (CFD) analysis was used to model the effect of airway geometry on internal pressure in the upper airway of three children with obstructive sleep apnea syndrome (OSAS), and three controls. Model geometry was reconstructed from magnetic resonance images obtained during quiet tidal breathing, meshed with an unstructured grid, and solved at normative peak resting flow. The unsteady Reynolds-averaged Navier-Stokes equations were solved with steady flow boundary conditions in inspiration and expiration, using a two-equation low-Reynolds number turbulence model.