1,275 research outputs found
Flexible Time Series Matching for Clinical and Behavioral Data
Time Series data became broadly applied by the research community in the last decades after
a massive explosion of its availability. Nonetheless, this rise required an improvement
in the existing analysis techniques which, in the medical domain, would help specialists
to evaluate their patients condition. One of the key tasks in time series analysis is pattern
recognition (segmentation and classification). Traditional methods typically perform subsequence
matching, making use of a pattern template and a similarity metric to search
for similar sequences throughout time series. However, real-world data is noisy and variable
(morphological distortions), making a template-based exact matching an elementary
approach. Intending to increase flexibility and generalize the pattern searching tasks
across domains, this dissertation proposes two Deep Learning-based frameworks to solve
pattern segmentation and anomaly detection problems.
Regarding pattern segmentation, a Convolution/Deconvolution Neural Network is
proposed, learning to distinguish, point-by-point, desired sub-patterns from background
content within a time series. The proposed framework was validated in two use-cases:
electrocardiogram (ECG) and inertial sensor-based human activity (IMU) signals. It outperformed
two conventional matching techniques, being capable of notably detecting the
targeted cycles even in noise-corrupted or extremely distorted signals, without using any
reference template nor hand-coded similarity scores.
Concerning anomaly detection, the proposed unsupervised framework uses the reconstruction
ability of Variational Autoencoders and a local similarity score to identify
non-labeled abnormalities. The proposal was validated in two public ECG datasets (MITBIH
Arrhythmia and ECG5000), performing cardiac arrhythmia identification. Results
indicated competitiveness relative to recent techniques, achieving detection AUC scores
of 98.84% (ECG5000) and 93.32% (MIT-BIH Arrhythmia).Dados de séries temporais tornaram-se largamente aplicados pela comunidade cientÃfica
nas últimas decadas após um aumento massivo da sua disponibilidade. Contudo, este
aumento exigiu uma melhoria das atuais técnicas de análise que, no domÃnio clÃnico, auxiliaria
os especialistas na avaliação da condição dos seus pacientes. Um dos principais
tipos de análise em séries temporais é o reconhecimento de padrões (segmentação e classificação).
Métodos tradicionais assentam, tipicamente, em técnicas de correspondência em
subsequências, fazendo uso de um padrão de referência e uma métrica de similaridade
para procurar por subsequências similares ao longo de séries temporais. Todavia, dados
do mundo real são ruidosos e variáveis (morfologicamente), tornando uma correspondência
exata baseada num padrão de referência uma abordagem rudimentar. Pretendendo
aumentar a flexibilidade da análise de séries temporais e generalizar tarefas de procura
de padrões entre domÃnios, esta dissertação propõe duas abordagens baseadas em Deep
Learning para solucionar problemas de segmentação de padrões e deteção de anomalias.
Acerca da segmentação de padrões, a rede neuronal de Convolução/Deconvolução
proposta aprende a distinguir, ponto a ponto, sub-padrões pretendidos de conteúdo de
fundo numa série temporal. O modelo proposto foi validado em dois casos de uso: sinais
eletrocardiográficos (ECG) e de sensores inerciais em atividade humana (IMU). Este superou
duas técnicas convencionais, sendo capaz de detetar os ciclos-alvo notavelmente,
mesmo em sinais corrompidos por ruÃdo ou extremamente distorcidos, sem o uso de
nenhum padrão de referência nem métricas de similaridade codificadas manualmente.
A respeito da deteção de anomalias, a técnica não supervisionada proposta usa a
capacidade de reconstrução dos Variational Autoencoders e uma métrica de similaridade
local para identificar anomalias desconhecidas. A proposta foi validada na identificação
de arritmias cardÃacas em duas bases de dados públicas de ECG (MIT-BIH Arrhythmia e
ECG5000). Os resultados revelam competitividade face a técnicas recentes, alcançando
métricas AUC de deteção de 93.32% (MIT-BIH Arrhythmia) e 98.84% (ECG5000)
Clustering of Time Series Data: Measures, Methods, and Applications
Clustering is an essential branch of data mining and statistical analysis that could help us explore the distribution of data and extract knowledge. With the broad accumulation and application of time series data, the study of its clustering is a natural extension of existing unsupervised learning heuristics. We discuss the components which configure the clustering of time series data, specifically, the similarity measure, the clustering heuristic, the evaluation of cluster quality, and the applications of said heuristics. Being the groundwork for the task of data analysis, we propose a scalable and efficient time series similarity measure: segmented-Dynamic Time Warping. For time series clustering, we formulate the Distance Density Clustering heuristic, a deterministic clustering algorithm that adopts concepts from both density and distance separation. In addition, we explored the characteristics and discussed the limitations of existing cluster evaluation methods. Finally, all components lead to the goal of real-world applications
Classification of Time-Series Images Using Deep Convolutional Neural Networks
Convolutional Neural Networks (CNN) has achieved a great success in image
recognition task by automatically learning a hierarchical feature
representation from raw data. While the majority of Time-Series Classification
(TSC) literature is focused on 1D signals, this paper uses Recurrence Plots
(RP) to transform time-series into 2D texture images and then take advantage of
the deep CNN classifier. Image representation of time-series introduces
different feature types that are not available for 1D signals, and therefore
TSC can be treated as texture image recognition task. CNN model also allows
learning different levels of representations together with a classifier,
jointly and automatically. Therefore, using RP and CNN in a unified framework
is expected to boost the recognition rate of TSC. Experimental results on the
UCR time-series classification archive demonstrate competitive accuracy of the
proposed approach, compared not only to the existing deep architectures, but
also to the state-of-the art TSC algorithms.Comment: The 10th International Conference on Machine Vision (ICMV 2017
Clustering of clinical multivariate time-series utilizing recent advances in machine-learning
The purpose of this thesis is to set the groundwork for future research on developing
a machine-learning based anomaly detection system for hospitalized patients.
Our first step was to study and analyze the project’s needs, background,
and literature examining similar criteria. In the second step, we interviewed
medical experts and researchers. Based on our research and the suggestions
received in our interviews, we explored methods that could be utilized to approach
the issue based on the data we collected. The results of these approaches
were then discussed.
According to the results, the K-means algorithm, which utilizes principle components
to cluster, obtained the highest quality. We then discussed how other
algorithms have been influenced more by the shape of the data than by the values
of the data. Afterward, we made some suggestions about how this research
could be approached in the future as we move forward
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