Results 71 to 80 of about 1,173 (164)

Case Report: Three cases involving the use of endovascular intervention for artery of Percheron infarction with a unilateral fetal-type posterior cerebral artery

Frontiers in Surgery
BackgroundOcclusion of the artery of Percheron (AOP) in the context of a unilateral fetal-type posterior cerebral artery (fPCA) is a rare cerebrovascular etiology for bilateral thalamic infarction and is associated with distinctive clinical features ...
Yue Xu, Yue Xu, Minjia Yang, Minjia Yang
doaj   +1 more source

Functional principal component analysis (FPCA) on remote sensing data and longitudinal studies

, 2021
This dissertation consists of three main pieces of my Ph.D. dissertation, which shapes my research on multivariate functional data, change-point detection, asynchronous longitudinal data, and remote sensing data applications. The first project is motivated by NASA's first dedicated \ce{CO2} monitoring satellite, the Orbiting Carbon Observatory-2 (OCO-2)
openaire   +4 more sources

Explainable data mining model for hyperinsulinemia diagnostics

Connection Science
In our research, we present a data mining model for the early diagnosis of hyperinsulinemia, potentially reducing the risk of diabetes, heart disease, and other chronic conditions.
Nevena Rankovic, Dragica Rankovic, Mirjana Ivanovic, Igor Lukic   +3 more
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A Classification Method for Multichannel MI-EEG Signal with FPCA and DNN

Journal of Physics: Conference Series
Abstract A new accurate identification method has been proposed to address the lack of interpretability in current deep learning-based feature extraction methods for motor imagery electroencephalogram (MI-EEG) signals. This method combines functional principal component analysis (FPCA) and deep neural networks (DNN) for four ...
Yunhui Hou, Na Shen, Yubin Lin
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Dimensionality Reduction of Multivariate Functional Data Using FPCA

Proceedings of the International Conference on Applied Innovations in IT
In many scientific and engineering applications, data are collected over discrete, often equidistant, time intervals. While such data can be analyzed using traditional statistical methods, these methods are often very limited in their ability to capture ...
Alaa H. Jalob, Lekaa Ali Mohamed
doaj   +1 more source

Unvealing the Principal Modes of Human Upper Limb Movements through Functional Analysis

Frontiers in Robotics and AI, 2017
The rich variety of human upper limb movements requires an extraordinary coordination of different joints according to specific spatio-temporal patterns. However, unvealing these motor schemes is a challenging task.
Giuseppe Averta, Giuseppe Averta, Cosimo Della Santina, Edoardo Battaglia, Federica Felici, Matteo Bianchi, Matteo Bianchi, Antonio Bicchi, Antonio Bicchi   +8 more
doaj   +1 more source

FPCA applied to flight paths optimization

, 2020
In this paper, we detail the steps that lead to optimized trajectories according to a selected criterion, in a low dimensional space. After presenting the main techniques for optimizing flight paths, as well as methods for reducing the size of the state space, we precise the modeling of our problem.
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Predicting health outcomes with intensive longitudinal data collected by mobile health devices: a functional principal component regression approach

BMC Medical Research Methodology
Background Intensive longitudinal data (ILD) collected in near real time by mobile health devices provide a new opportunity for monitoring chronic diseases, early disease risk prediction, and disease prevention in health research.
Qing Yang, Meilin Jiang, Cai Li, Sheng Luo, Matthew J. Crowley, Ryan J. Shaw   +5 more
doaj   +1 more source

Integrating statistical parametric mapping, functional principal component analysis, explainable machine learning, and equivalence testing for multimodal gait signal analysis

BMC Biomedical Engineering
Background Gait speed is a key clinical indicator in neurological and orthopaedic conditions, yet waveform-level adaptations in ground reaction forces (GRF) and multi-muscle electromyography (EMG) remain poorly characterised.
Kofi Nyantakyi Appiah, Edward Wilson Ansah, Frank Sarfo Dofour   +2 more
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Research on Action Recognition of Multi-Component Motion Sensors Based on FPCA and Ensemble Learning

Highlights in Science, Engineering and Technology
In the cutting-edge fields of health monitoring, intelligent security, and human - computer interaction, the technology for recognizing human motion states plays a crucial role. However, there is an inherent contradiction between the continuity of human motion and the discrete data collection, which restricts the accuracy and operational efficiency of ...
Yuyang Zhang, Bingtao Yang, Runqian Zhao
openaire   +1 more source