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A Narrative Review on Different Novel Machine Learning Techniques for Detecting Pathologies in Infants From Born Baby Cries.
J Voice
May 2024
Department of Electronics and Communication Engineering, Birla Institute of Technology Mesra, Ranchi, Jharkhand, India. Electronic address:
This paper reviews the research work on the analysis and classification of pathological infant cries in the last 50 years. The literature review mainly covers the need and role of early clinical diagnosis, pathologies detected from cry samples, challenges in pathological cry signal data acquisition, signal processing techniques, and signal classifiers. The signal processing techniques include preprocessing, feature extraction from domains, such as time, spectral, time-frequency, prosodic, wavelet, etc, and feature selection for selecting dominant features.
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- The study investigates the relationship between sleep neural circuits and early signs of Alzheimer's disease (AD) by analyzing sleep EEG patterns in aging adults.
- Data from 205 participants revealed that cognitive impairment correlates with reduced sleep oscillations (specifically, theta bursts and sleep spindles) and lower coupling precision between specific neural circuits.
- Findings suggest that disruptions in sleep-related memory processing circuits may signal the onset of AD, as these changes are linked to amyloid positivity and elevated levels of AD-related biomarkers.
A Discriminative Long Short Term Memory Network with Metric Learning Applied to Multispectral Time Series Classification.
J Imaging
July 2020
Department of Computer Engineering, Istanbul Technical University, Maslak, Istanbul 34469, Turkey.
In this article, we propose an end-to-end deep network for the classification of multi-spectral time series and apply them to crop type mapping. Long short-term memory networks (LSTMs) are well established in this regard, thanks to their capacity to capture both long and short term temporal dependencies. Nevertheless, dealing with high intra-class variance and inter-class similarity still remain significant challenges.
View Article and Find Full Text PDFBiological aerosols, such as bacteria, fungal spores, and pollens, play an important role on various atmospheric processes, whereas their inherent optical property is one of the most uncertainties that limit our ability to assess their effects on weather and climate. A numerical model with core-shell structure, hexagonal grids and barbs is developed to represent one kind of realistic pollen particles, and their inherent optical properties are simulated using a pseudo-spectral time domain method. Both the hexagonal grids and barbs substantially affect the modeled pollen optical properties.
View Article and Find Full Text PDFComparison of spatial filters and features for the detection and classification of movement-related cortical potentials in healthy individuals and stroke patients.
J Neural Eng
October 2015
Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Fredrik Bajers vej 7D, D2-212, DK-9220, Aalborg, Denmark.
Objective: The possibility of detecting movement-related cortical potentials (MRCPs) at the single trial level has been explored for closing the motor control loop with brain-computer interfaces (BCIs) for neurorehabilitation. A distinct feature of MRCPs is that the movement kinetic information is encoded in the brain potential prior to the onset of the movement, which makes it possible to timely drive external devices to provide sensory feedback according to the efferent activity from the brain. The aim of this study was to compare methods for the detection (different spatial filters) and classification (features extracted from various domains) of MRCPs from continuous electroencephalography recordings from executed and imagined movements from healthy subjects (n = 24) and attempted movements from stroke patients (n = 6) to optimize the performance of MRCP-based BCIs for neurorehabilitation.
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