714 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)
Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data
In medicine, one often bases decisions upon a comparative analysis of patient data. In this paper, we build upon this observation and describe similarity-based algorithms to risk stratify patients for major adverse cardiac events. We evolve the traditional approach of comparing patient data in two ways. First, we propose similarity-based algorithms that compare patients in terms of their long-term physiological monitoring data. Symbolic mismatch identifies functional units in long-term signals and measures changes in the morphology and frequency of these units across patients. Second, we describe similarity-based algorithms that are unsupervised and do not require comparisons to patients with known outcomes for risk stratification. This is achieved by using an anomaly detection framework to identify patients who are unlike other patients in a population and may potentially be at an elevated risk. We demonstrate the potential utility of our approach by showing how symbolic mismatch-based algorithms can be used to classify patients as being at high or low risk of major adverse cardiac events by comparing their long-term electrocardiograms to that of a large population. We describe how symbolic mismatch can be used in three different existing methods: one-class support vector machines, nearest neighbor analysis, and hierarchical clustering. When evaluated on a population of 686 patients with available long-term electrocardiographic data, symbolic mismatch-based comparative approaches were able to identify patients at roughly a two-fold increased risk of major adverse cardiac events in the 90 days following acute coronary syndrome. These results were consistent even after adjusting for other clinical risk variables.National Science Foundation (U.S.) (CAREER award 1054419
Evaluation of spatial-temporal anomalies in the analysis of human movement
The dissemination of Internet of Things solutions, such as smartphones, lead to the
appearance of devices that allow to monitor the activities of their users. In manufacture,
the performed tasks consist on sets of predetermined movements that are exhaustively
repeated, forming a repetitive behaviour. Additionally, there are planned and unplanned events on manufacturing production lines which cause the repetitive behaviour to stop. The execution of improper movements and the existence of events that might prejudice the productive system are regarded as anomalies.
In this work, it was investigated the feasibility of the evaluation of spatial-temporal
anomaly detection in the analysis of human movement. It is proposed a framework capable of detecting anomalies in generic repetitive time series, thus being adequate to handle Human motion from industrial scenarios. The proposed framework consists of (1) a new unsupervised segmentation algorithm; (2) feature extraction, selection and dimensionality reduction; (3) unsupervised classification based on DBSCAN used to distinguish normal and anomalous instances.
The proposed solution was applied in four different datasets. Two of those datasets
were synthetic and two were composed of real-world data, namely, electrocardiography
data and human movement in manufacture. The yielded results demonstrated not only
that anomaly detection in human motion is possible, but that the developed framework
is generic and, with examples, it was shown that it may be applied in general repetitive
time series with little adaptation effort for different domains.
The results showed that the proposed framework has the potential to be applied in
manufacturing production lines to monitor the employees movements, acting as a tool to detect both planned and unplanned events, and ultimately reduce the risk of appearance of musculoskeletal disorders in industrial settings in long-term
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach
Since edge device failures (i.e., anomalies) seriously affect the production
of industrial products in Industrial IoT (IIoT), accurately and timely
detecting anomalies is becoming increasingly important. Furthermore, data
collected by the edge device may contain the user's private data, which is
challenging the current detection approaches as user privacy is calling for the
public concern in recent years. With this focus, this paper proposes a new
communication-efficient on-device federated learning (FL)-based deep anomaly
detection framework for sensing time-series data in IIoT. Specifically, we
first introduce a FL framework to enable decentralized edge devices to
collaboratively train an anomaly detection model, which can improve its
generalization ability. Second, we propose an Attention Mechanism-based
Convolutional Neural Network-Long Short Term Memory (AMCNN-LSTM) model to
accurately detect anomalies. The AMCNN-LSTM model uses attention
mechanism-based CNN units to capture important fine-grained features, thereby
preventing memory loss and gradient dispersion problems. Furthermore, this
model retains the advantages of LSTM unit in predicting time series data.
Third, to adapt the proposed framework to the timeliness of industrial anomaly
detection, we propose a gradient compression mechanism based on Top-\textit{k}
selection to improve communication efficiency. Extensive experiment studies on
four real-world datasets demonstrate that the proposed framework can accurately
and timely detect anomalies and also reduce the communication overhead by 50\%
compared to the federated learning framework that does not use a gradient
compression scheme.Comment: IEEE Internet of Things Journa
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