Classifying Unstable and Stable Walking Patterns Using Electroencephalography Signals and Machine Learning Algorithms

Analyzing unstable gait patterns from Electroencephalography (EEG) signals is vital to develop real-time brain-computer interface (BCI) systems to prevent falls and associated injuries. This study investigates the feasibility of classification algorithms to detect walking instability utilizing EEG signals. A 64-channel Brain Vision EEG system was used to acquire EEG signals from 13 healthy adults. Participants performed walking trials for four different stable and unstable conditions: (i) normal walking, (ii) normal walking with medial-lateral perturbation (MLP), (iii) normal walking with dual-tasking (Stroop), (iv) normal walking with center of mass visual feedback. Digital biomarkers were extracted using wavelet energy and entropies from the EEG signals. Algorithms like the ChronoNet, SVM, Random Forest, gradient boosting and recurrent neural networks (LSTM) could classify with 67 to 82% accuracy. The classification results show that it is possible to accurately classify different gait patterns (from stable to unstable) using EEG-based digital biomarkers. This study develops various machine-learning-based classification models using EEG datasets with potential applications in detecting unsteady gait neural signals and intervening by preventing falls and injuries

Predicting dental implant failures by integrating multiple classifiers

El campo de la ciencia de datos ha tenido muchos avances respecto a la aplicación y desarrollo de técnicas en el sector de la salud. Estos avances se ven reflejados en la predicción de enfermedades, clasificación de imágenes, identificación y reducción de riesgos, así como muchos otros. Este trabajo tiene por objetivo investigar el beneficio de la utilización de múltiples algoritmos de clasificación, para la predicción de fracasos en implantes dentales de la provincia de Misiones, Argentina y proponer un procedimiento validado por expertos humanos. El modelo abarca la combinación de los clasificadores: Random Forest, C-Support Vector, K-Nearest Neighbors, Multinomial Naive Bayes y Multi-layer Perceptron. La integración de los modelos se realiza con el weighted soft voting method. La experimentación es realizada con cuatro conjuntos de datos, un conjunto de implantes dentales confeccionado para el estudio de caso, un conjunto generado artificialmente y otros dos conjuntos obtenidos de distintos repositorios de datos. Los resultados arrojados del enfoque propuesto sobre el conjunto de datos de implantes dentales, es validado con el desempeño en la clasificación por expertos humanos. Nuestro enfoque logra un porcentaje de acierto del 93% de casos correctamente identificados, mientras que los expertos humanos consiguen un 87% de precisión.The field of data science has made many advances in the application and development of techniques in several aspects of the health sector, such as in disease prediction, image classification, risk identification and risk reduction. Based on this, the objectives of this work were to investigate the benefit of using multiple classification algorithms to predict dental implant failures in patients from Misiones province, Argentina, and to propose a procedure validated by human experts. The model used the integration of several types of classifiers.The experimentation was performed with four data sets: a data set of dental implants made for the case study, an artificially generated data set, and two other data sets obtained from different data repositories. The results of the approach proposed were validated by the performance in classification made by human experts. Our approach achieved a success rate of 93% of correctly identified cases, whereas human experts achieved 87% accuracy. Based on this, we can argue that multi-classifier systems are a good approach to predict dental implant failures.Fil: Ganz, Nancy Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Materiales de Misiones. Universidad Nacional de Misiones. Facultad de Ciencias Exactas Químicas y Naturales. Instituto de Materiales de Misiones; ArgentinaFil: Ares, Alicia Esther. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Materiales de Misiones. Universidad Nacional de Misiones. Facultad de Ciencias Exactas Químicas y Naturales. Instituto de Materiales de Misiones; ArgentinaFil: Kuna, Horacio Daniel. Universidad Nacional de Misiones. Facultad de Cs.exactas Quimicas y Naturales. Instituto de Investigacion Desarrollo E Innovacion En Informatica.; Argentin

Advanced Learning Methodologies for Biomedical Applications

University of Minnesota Ph.D. dissertation. October 2017. Major: Electrical/Computer Engineering. Advisor: Vladimir Cherkassky. 1 computer file (PDF); ix, 109 pages.There has been a dramatic increase in application of statistical and machine learning methods for predictive data-analytic modeling of biomedical data. Most existing work in this area involves application of standard supervised learning techniques. Typical methods include standard classification or regression techniques, where the goal is to estimate an indicator function (classification decision rule) or real-valued function of input variables, from finite training sample. However, real-world data often contain additional information besides labeled training samples. Incorporating this additional information into learning (model estimation) leads to nonstandard/advanced learning formalizations that represent extensions of standard supervised learning. Recent examples of such advanced methodologies include semi-supervised learning (or transduction) and learning through contradiction (or Universum learning). This thesis investigates two new advanced learning methodologies along with their biomedical applications. The first one is motivated by modeling complex survival data which can incorporate future, censored, or unknown data, in addition to (traditional) labeled training data. Here we propose original formalization for predictive modeling of survival data, under the framework of Learning Using Privileged Information (LUPI) proposed by Vapnik. Survival data represents a collection of time observations about events. Our modeling goal is to predict the state (alive/dead) of a subject at a pre-determined future time point. We explore modeling of survival data as binary classification problem that incorporates additional information (such as time of death, censored/uncensored status, etc.) under LUPI framework. Then we propose two advanced constructive Support Vector Machine (SVM)-based formulations: SVM+ and Loss-Order SVM (LO-SVM). Empirical results using simulated and real-life survival data indicate that the proposed LUPI-based methods are very effective (versus classical Cox regression) when the survival time does not follow classical probabilistic assumptions. Second advanced methodology investigates a new learning paradigm for classification called Group Learning. This approach is motivated by modeling high-dimensional data when the number of input features is much larger than the number of training samples. There are two main approaches to solving such ill-posed problems: (a) selecting a small number of informative features via feature selection; (b) using all features but imposing additional complexity constraints, e.g., ridge regression, SVM, LASSO, etc. The proposed Group Learning method takes a different approach, by splitting all features into many (t) groups, and then estimating a classifier in reduced space (of dimensionality d/t). This approach effectively uses all features, but implements training in a lower-dimensional input space. Note that the formation of groups reflects application-domain knowledge. For example, in classifying of two-dimensional images represented as a set of pixels (original high-dimensional input space), appropriate groups can be formed by grouping adjacent pixels or “local patches” because adjacent pixels are known to be highly correlated. We provide empirical validation of this new methodology for two real-life applications: (a) handwritten digit recognition, and (b) predictive classification of univariate signals, e.g., prediction of epileptic seizures from intracranial electroencephalogram (iEEG) signal. Prediction of epileptic seizures is particularly challenging, due to highly unbalanced data (just 4–5 observed seizures) and patient-specific modeling. In a joint project with Mayo Clinic, we have incorporated the Group Learning approach into an SVM-based system for seizure prediction. This system performs subject-specific modeling and achieves robust prediction performance

Detection of Mild Cognitive Impairment with MEG Functional Connectivity using Wavelet-based Neuromarkers

Studies on developing effective neuromarkers based on magnetoencephalographic (MEG) signals have been drawing increasing attention in the neuroscience community. This study explores the idea of using source-based magnitude-squared spectral coherence as a spatial indicator for effective regions of interest (ROIs) localization, subsequently discriminating the participants with mild cognitive impairment (MCI) from a group of age-matched healthy control (HC) elderly participants. We found that the cortical regions could be divided into two distinctive groups based on their coherence indices. Compared to HC, some ROIs showed increased connectivity (hyper-connected ROIs) for MCI participants, whereas the remaining ROIs demonstrated reduced connectivity (hypo-connected ROIs). Based on these findings, a series of wavelet-based source-level neuromarkers for MCI detection are proposed and explored, with respect to the two distinctive ROI groups. It was found that the neuromarkers extracted from the hyper-connected ROIs performed significantly better for MCI detection than those from the hypo-connected ROIs. The neuromarkers were classified using support vector machine (SVM) and k-NN classifiers and evaluated through Monte Carlo cross-validation. An average recognition rate of 93.83% was obtained using source-reconstructed signals from the hyper-connected ROI group. To better conform to clinical practice settings, a leave-one-out cross-validation (LOOCV) approach was also employed to ensure that the data for testing was from a participant that the classifier has never seen. Using LOOCV, we found the best average classification accuracy was reduced to 83.80% using the same set of neuromarkers obtained from the ROI group with functional hyper-connections. This performance surpassed the results reported using wavelet-based features by approximately 15%. Overall, our work suggests that (1) certain ROIs are particularly effective for MCI detection, especially when multi-resolution wavelet biomarkers are employed for such diagnosis; (2) there exists a significant performance difference in system evaluation between research-based experimental design and clinically accepted evaluation standards

Survey of deep representation learning for speech emotion recognition

Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual eort, which impedes generalisability and slows the pace of innovation. This has motivated the adoption of representation learning techniques that can automatically learn an intermediate representation of the input signal without any manual feature engineering. Representation learning has led to improved SER performance and enabled rapid innovation. Its effectiveness has further increased with advances in deep learning (DL), which has facilitated \textit{deep representation learning} where hierarchical representations are automatically learned in a data-driven manner. This paper presents the first comprehensive survey on the important topic of deep representation learning for SER. We highlight various techniques, related challenges and identify important future areas of research. Our survey bridges the gap in the literature since existing surveys either focus on SER with hand-engineered features or representation learning in the general setting without focusing on SER

Sistema de clasificación para predicción de fracasos en implantes dentales validado por expertos humanos

Hoy en día, la predicción del éxito o fracaso de un implante dental está determinado a través de una evaluación clínica y radiológica. Por esta razón, las predicciones dependen en gran medida de la experiencia del implantólogo. Este trabajo tiene por objetivo investigar el beneficio de la utilización de múltiples algoritmos de clasificación, para la predicción de fracasos en implantes dentales de la provincia de Misiones, Argentina validado por expertos humanos. El modelo abarca la combinación de los clasificadores Random Forest, SVM, KNN, Naive Bayes y perceptrón multicapa. La experimentación es realizada con cuatro conjuntos de datos, un conjunto de implantes dentales confeccionado para el estudio de caso, un conjunto generado artificialmente y otros dos conjuntos obtenidos de distintos repositorios de datos. Nuestro enfoque logra sobre el conjunto de datos de implantes un porcentaje de acierto del 93% de casos correctamente identificados, mientras que los expertos humanos consiguen un 86% de precisión. En base a esto podemos alegar, que los sistemas de múltiple clasificadores son un buen enfoque para la predicción de fracasos en implantes dentales.Sociedad Argentina de Informátic