A Markov-Switching Model Approach to Heart Sound Segmentation and Classification.

Objective: We consider challenges in accurate segmentation of heart sound signals recorded under noisy clinical environments for subsequent classification of pathological events. Existing state-of-the-art solutions to heart sound segmentation use probabilistic models such as hidden Markov models (HMMs), which, however, are limited by its observation independence assumption and rely on pre-extraction of noise-robust features.

Methods: We propose a Markov-switching autoregressive (MSAR) process to model the raw heart sound signals directly, which allows efficient segmentation of the cyclical heart sound states according to the distinct dependence structure in each state.

Estimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models.

We consider the challenges in estimating the state-related changes in brain connectivity networks with a large number of nodes. Existing studies use the sliding-window analysis or time-varying coefficient models, which are unable to capture both smooth and abrupt changes simultaneously, and rely on ad-hoc approaches to the high-dimensional estimation. To overcome these limitations, we propose a Markov-switching dynamic factor model, which allows the dynamic connectivity states in functional magnetic resonance imaging (fMRI) data to be driven by lower-dimensional latent factors.

A Unified Estimation Framework for State-Related Changes in Effective Brain Connectivity.

Objective: This paper addresses the critical problem of estimating time-evolving effective brain connectivity. Current approaches based on sliding window analysis or time-varying coefficient models do not simultaneously capture both slow and abrupt changes in causal interactions between different brain regions.

Methods: To overcome these limitations, we develop a unified framework based on a switching vector autoregressive (SVAR) model.

Electroencephalographic Motor Imagery Brain Connectivity Analysis for BCI: A Review.

Recent research has reached a consensus on the feasibility of motor imagery brain-computer interface (MI-BCI) for different applications, especially in stroke rehabilitation. Most MI-BCI systems rely on temporal, spectral, and spatial features of single channels to distinguish different MI patterns. However, no successful communication has been established for a completely locked-in subject.

Is First-Order Vector Autoregressive Model Optimal for fMRI Data?

We consider the problem of selecting the optimal orders of vector autoregressive (VAR) models for fMRI data. Many previous studies used model order of one and ignored that it may vary considerably across data sets depending on different data dimensions, subjects, tasks, and experimental designs. In addition, the classical information criteria (IC) used (e.

Morphological study of the newly designed cementless femoral stem.

A morphology study was essential to the development of the cementless femoral stem because accurate dimensions for both the periosteal and endosteal canal ensure primary fixation stability for the stem, bone interface, and prevent stress shielding at the calcar region. This paper focused on a three-dimensional femoral model for Asian patients that applied preoperative planning and femoral stem design. We measured various femoral parameters such as the femoral head offset, collodiaphyseal angle, bowing angle, anteversion, and medullary canal diameters from the osteotomy level to 150 mm below the osteotomy level to determine the position of the isthmus.

Primary stability recognition of the newly designed cementless femoral stem using digital signal processing.

Stress shielding and micromotion are two major issues which determine the success of newly designed cementless femoral stems. The correlation of experimental validation with finite element analysis (FEA) is commonly used to evaluate the stress distribution and fixation stability of the stem within the femoral canal. This paper focused on the applications of feature extraction and pattern recognition using support vector machine (SVM) to determine the primary stability of the implant.

Design process of cementless femoral stem using a nonlinear three dimensional finite element analysis.

Background: Minimal available information concerning hip morphology is the motivation for several researchers to study the difference between Asian and Western populations. Current use of a universal hip stem of variable size is not the best option for all femur types. This present study proposed a new design process of the cementless femoral stem using a three dimensional model which provided more information and accurate analysis compared to conventional methods.

Fabrication of low-cost, cementless femoral stem 316L stainless steel using investment casting technique.

Total hip arthroplasty is a flourishing orthopedic surgery, generating billions of dollars of revenue. The cost associated with the fabrication of implants has been increasing year by year, and this phenomenon has burdened the patient with extra charges. Consequently, this study will focus on designing an accurate implant via implementing the reverse engineering of three-dimensional morphological study based on a particular population.

Linear dynamic models for classification of single-trial EEG.

This paper investigates the use of linear dynamic models (LDMs) to improve classification of single-trial EEG signals. Existing dynamic classification of EEG uses discrete-state hidden Markov models (HMMs) based on piecewise-stationary assumption, which is inadequate for modeling the highly non-stationary dynamics underlying EEG. The continuous hidden states of LDMs could better describe this continuously changing characteristic of EEG, and thus improve the classification performance.

Discriminative tandem features for HMM-based EEG classification.

We investigate the use of discriminative feature extractors in tandem configuration with generative EEG classification system. Existing studies on dynamic EEG classification typically use hidden Markov models (HMMs) which lack discriminative capability. In this paper, a linear and a non-linear classifier are discriminatively trained to produce complementary input features to the conventional HMM system.

EMG-based facial gesture recognition through versatile elliptic basis function neural network.

Background: Recently, the recognition of different facial gestures using facial neuromuscular activities has been proposed for human machine interfacing applications. Facial electromyograms (EMGs) analysis is a complicated field in biomedical signal processing where accuracy and low computational cost are significant concerns. In this paper, a very fast versatile elliptic basis function neural network (VEBFNN) was proposed to classify different facial gestures.

An expectation-maximization algorithm based Kalman smoother approach for single-trial estimation of event-related potentials.

This paper applies an expectation-maximization (EM) based Kalman smoother (KS) approach for single-trial event-related potential (ERP) estimation. Existing studies assume a Markov diffusion process for the dynamics of ERP parameters which is recursively estimated by optimal filtering approaches such as Kalman filter (KF). However, these studies only consider estimation of ERP state parameters while the model parameters are pre-specified using manual tuning, which is time-consuming for practical usage besides giving suboptimal estimates.

Human facial neural activities and gesture recognition for machine-interfacing applications.

The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human-machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures.

An artifacts removal post-processing for epiphyseal region-of-interest (EROI) localization in automated bone age assessment (BAA).

Background: Segmentation is the most crucial part in the computer-aided bone age assessment. A well-known type of segmentation performed in the system is adaptive segmentation. While providing better result than global thresholding method, the adaptive segmentation produces a lot of unwanted noise that could affect the latter process of epiphysis extraction.

Spectral estimation of nonstationary EEG using particle filtering with application to event-related desynchronization (ERD).

This paper proposes non-Gaussian models for parametric spectral estimation with application to event-related desynchronization (ERD) estimation of nonstationary EEG. Existing approaches for time-varying spectral estimation use time-varying autoregressive (TVAR) state-space models with Gaussian state noise. The parameter estimation is solved by a conventional Kalman filtering.