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UNSUPERVISED REAL-TIME DIAGNOSIS SYSTEM FOR ECG STREAMING DAT

Eman Maghawry, Rasha Ismail, Tarek Gharib
2021 International Journal of Intelligent Computing and Information Sciences  
We aim to diagnose ECG by investigating healthy ECG and ECG with cardiological disorders by detecting anomalies in ECG signals.  ...  An accurate ECG streaming analytics approach requires continuous learning and adaptation in changing data behaviors.  ...  Also, in [10] , the authors used a combination of supervised active learning and unsupervised learning with autoencoder and softmax for feature learning and a deep neural network for classification according  ... 
doi:10.21608/ijicis.2021.69762.1077 fatcat:g3b6n22wqjbnvafiufqymu7yc4

Semantic Anomaly Detection in Medical Time Series [chapter]

Sven Festag, Cord Spreckelsen
2021 Studies in Health Technology and Informatics  
The main goal of this project was to define and evaluate a new unsupervised deep learning approach that can differentiate between normal and anomalous intervals of signals like the electrical activity  ...  This corresponds to a precision and recall regarding the detection task of around 0.72.  ...  task of unsupervised anomaly detection in medical time series.  ... 
doi:10.3233/shti210059 pmid:34042884 fatcat:msgt2leys5awhcrrmg5c4o73he

DeepFIB: Self-Imputation for Time Series Anomaly Detection [article]

Minhao Liu, Zhijian Xu, Qiang Xu
2021 arXiv   pre-print
Due to the inherently unpredictable and highly varied nature of anomalies and the lack of anomaly labels in historical data, the AD problem is typically formulated as an unsupervised learning problem.  ...  Time series (TS) anomaly detection (AD) plays an essential role in various applications, e.g., fraud detection in finance and healthcare monitoring.  ...  As the anomalies in 2d-gesture, Power demand, and ECG are mainly sequence outliers, we apply the DeepFIB-s model on these datasets.  ... 
arXiv:2112.06247v1 fatcat:k7gmfcsnajafdp5qgwq4iuq4f4

Improving Generalization of Deep Models for Cardiac Disease Detection Using Limited Channel ECG

Deepta Rajan, David Beymer, Girish Narayan
2018 2018 Computing in Cardiology Conference (CinC)  
In this work, we consider such a challenging problem in machine learning driven diagnosis detecting a gamut of cardiovascular conditions (e.g. infarction, dysrhythmia etc.) from limited channel ECG measurements  ...  Acceleration of machine learning research in healthcare is challenged by lack of large annotated and balanced datasets.  ...  Our approach uses an unsupervised generative model to construct latent features, followed by a discriminative model (1-D ResNet [9] ) to detect anomalies.  ... 
doi:10.22489/cinc.2018.378 dblp:conf/cinc/RajanBN18 fatcat:wl2r2wcupbhqrdfrzumxekylzq

Generalization Studies of Neural Network Models for Cardiac Disease Detection Using Limited Channel ECG [article]

Deepta Rajan, David Beymer, Girish Narayan
2019 arXiv   pre-print
In this work, we consider such a challenging problem in machine learning driven diagnosis: detecting a gamut of cardiovascular conditions (e.g. infarction, dysrhythmia etc.) from limited channel ECG measurements  ...  Acceleration of machine learning research in healthcare is challenged by lack of large annotated and balanced datasets.  ...  Our approach uses an unsupervised generative model to construct latent features, followed by a discriminative model (1-D ResNet [9] ) to detect anomalies.  ... 
arXiv:1901.03295v1 fatcat:pbfqxwkzvfe7nc676pjs7ydyla

Recognising Cardiac Abnormalities in Wearable Device Photoplethysmography (PPG) with Deep Learning [article]

Stewart Whiting, Samuel Moreland, Jason Costello, Glen Colopy, Christopher McCann
2018 arXiv   pre-print
We propose an automatic method for recognising these anomalies in PPG signal alone, without the need for ECG.  ...  ECG monitors are typically used to detect these events in electrical heart activity, however they are impractical for continuous long-term use.  ...  Anomaly detection and classification approaches have long been applied to ECG and electroencephalography (EEG) signals. For example, [8] used deep learning to identify cardiac events in ECG.  ... 
arXiv:1807.04077v1 fatcat:hdquo2rwxnacrmo7cftgeomrde

Reconstruct Anomaly to Normal: Adversarial Learned and Latent Vector-constrained Autoencoder for Time-series Anomaly Detection [article]

Chunkai Zhang, Wei Zuo, Xuan Wang
2020 arXiv   pre-print
In recent years, anomaly detection algorithms are mostly based on deep-learning generative models and use the reconstruction error to detect anomalies.  ...  In this paper, we propose RAN based on the idea of Reconstruct Anomalies to Normal and apply it for unsupervised time series anomaly detection.  ...  The aims of AE are: 1) for normal data, learning a good representation in the latent space and generating good reconstructions in the original data space; 2) for anomaly data, learning a representation  ... 
arXiv:2010.06846v1 fatcat:xdcos5djrzcgvjpqcir245oevu

Iterative Bilinear Temporal-Spectral Fusion for Unsupervised Time-Series Representation Learning [article]

Ling Yang, Shenda Hong, Luxia Zhang
2022 arXiv   pre-print
Unsupervised/self-supervised time series representation learning is a challenging problem because of its complex dynamics and sparse annotations.  ...  We firstly conducts downstream evaluations on three major tasks for time series including classification, forecasting and anomaly detection.  ...  Related Work Unsupervised representation learning for time series A relevant direction of research about representation learning on sequence data has been well-studied [Chung et al., 2015 , Fraccaro et  ... 
arXiv:2202.04770v2 fatcat:pfkrhpifprbota4hzyp7o5srga

Unsupervised Anomaly Detection on Temporal Multiway Data [article]

Duc Nguyen, Phuoc Nguyen, Kien Do, Santu Rana, Sunil Gupta, Truyen Tran
2020 arXiv   pre-print
Leveraging recent advances in matrix-native recurrent neural networks, we investigated strategies for data arrangement and unsupervised training for temporal multiway anomaly detection.  ...  Unsupervised temporal models employed thus far typically work on sequences of feature vectors, and much less on temporal multiway data.  ...  RELATED WORK Anomaly detection (AD) in sequential data has been studied widely in the literature, with learning methods ranging from supervised, semi-supervised to unsupervised [1] .  ... 
arXiv:2009.09443v1 fatcat:jpv2pmituzdsdhv3hq4c5dxieu

Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery [article]

Shawn Tan and Guillaume Androz and Ahmad Chamseddine and Pierre Fecteau and Aaron Courville and Yoshua Bengio and Joseph Paul Cohen
2019 arXiv   pre-print
We release the largest public ECG dataset of continuous raw signals for representation learning containing 11 thousand patients and 2 billion labelled beats.  ...  To this end, we propose an unsupervised representation learning task, evaluated in a semi-supervised fashion. We provide a set of baselines for different feature extractors that can be built upon.  ...  ACKNOWLEDGMENTS We thank Leon Glass, Yannick Le Devehat, GermainÉthier, and Margaux Luck, Kris Sankaran, and Gabriele Prato for useful discussions.  ... 
arXiv:1910.09570v1 fatcat:cwwyctxnivf6bcwyfbcixrhak4

Attention Autoencoder for Generative Latent Representational Learning in Anomaly Detection

Ariyo Oluwasanmi, Muhammad Umar Aftab, Edward Baagyere, Zhiguang Qin, Muhammad Ahmad, Manuel Mazzara
2021 Sensors  
To accomplish such frontiers, we propose three artificial intelligence models through the application of deep learning algorithms to analyze and detect anomalies in human heartbeat signals.  ...  The three proposed models displayed outstanding ability to detect anomalies on the evaluated five thousand electrocardiogram (ECG5000) signals with 99% accuracy and 99.3% precision score in detecting healthy  ...  Lastly, the LSTM network models the data sequence using a gated hidden state to learn the essential time-step features for detecting anomalies in the ECG heartbeats.  ... 
doi:10.3390/s22010123 pmid:35009666 pmcid:PMC8747546 fatcat:glnwwinczjd23hbmyy2gtvpkwm

Use of Hierarchical Temporal Memory Algorithm in Heart Attack Detection

Tesnim Charrad, Kaouther Nouira, Ahmed Ferchichi
2019 Zenodo  
It is powerful in predicting unusual patterns, anomaly detection and classification. In this paper, HTM have been implemented and tested on ECG datasets in order to detect cardiac anomalies.  ...  HTM is a cortical learning algorithm based on neocortex used for anomaly detection. In other words, it is based on a conceptual theory of how the human brain can work.  ...  [6] developed a Sequence to Sequence Pattern Learning Algorithm for Real-Time Anomaly Detection in Network Traffic.  ... 
doi:10.5281/zenodo.3298882 fatcat:zs5xspk6sndrpjypffrcv7xcg4

Warping Resilient Scalable Anomaly Detection in Time Series [article]

Abilasha S, Sahely Bhadra, Deepak P, Anish Mathew
2021 arXiv   pre-print
We will illustrate that WaRTEm-AD is designed to detect two types of time series anomalies: point and sequence anomalies.  ...  In this paper, we propose a novel unsupervised time series anomaly detection method, WaRTEm-AD, that operates in two stages.  ...  The work is supported by project titled "Robust Multiview Learning for Extreme Events Detection and Prediction in Time Series Data", IITPKD/2021/013/CSE/SAB funded by ICSR  ... 
arXiv:1906.05205v2 fatcat:yse2nf5d45akzdytutkd63lf5i

System Light-Loading Technology for mHealth: Manifold-Learning-Based Medical Data Cleansing and Clinical Trials in WE-CARE Project

Anpeng Huang, Wenyao Xu, Zhinan Li, Linzhen Xie, Majid Sarrafzadeh, Xiaoming Li, Jason Cong
2014 IEEE journal of biomedical and health informatics  
Our clinical trials verify that our proposal can detect anomalies with a recognition rate of up to 94% which is highly valuable in daily public health-risk alert applications based on clinical criteria  ...  In this study, our main objective is to get a system light-loading technology to enable mHealth with a benchmarked ECG anomaly recognition rate.  ...  He is currently an Assistant Professor with the Department of Computer Science and Engineering, University at Buffalo, the State University of New  ... 
doi:10.1109/jbhi.2013.2292576 pmid:25192569 fatcat:fyvbhdofzrfrpodn4qvhbndth4

A framework for end-to-end deep learning-based anomaly detection in transportation networks

Neema Davis, Gaurav Raina, Krishna Jagannathan
2020 Transportation Research Interdisciplinary Perspectives  
We develop an end-to-end deep learning-based anomaly detection model for temporal data in transportation networks.  ...  Experiments on seven diverse real-world data sets demonstrate the superior anomaly detection performance of our proposed model over the other models considered in the comparison study.  ...  Hybrid deep anomaly detection Neural networks can perform unsupervised modeling and learn complex time-sequences, which makes them suitable candidates for anomaly detection in large real-world data sets  ... 
doi:10.1016/j.trip.2020.100112 fatcat:4fnnhrd23jgfjj477fdmwblfcm
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