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This paper presents detailed anomaly detection evaluation on operational time-series data of Internet of Things (IoT) based household devices in general and Heating, Ventilation and Air Conditioning (HVAC) systems in specific. Due to the number of issues observed during evaluation of widely used distance-based, statistical-based, and cluster-based anomaly detection techniques, we also present a pattern-based...
This study proposes a robust similarity score-based time series feature extraction method that is termed as Window-based Time series Feature ExtraCtion (WTC). Specifically, WTC generates domain-interpretable results and involves significantly low computational complexity thereby rendering itself useful for densely sampled and populated time series datasets. In this study, WTC is applied to a proprietary...
This work is devoted to the prediction of epileptic seizures using heart rate variability (HRV) characteristics. Several HRV features were extracted (statistical, spectral, histogram, polynomial approximation coefficients) for various durations of sliding time windows and various lengths of preictal intervals. The data from 14 subjects with generalized epileptic seizures was used. Support Vector Machine...
A large number of obstructive sleep apnea (OSA) cases are under-diagnosed due unavailability, inconvenience or expense of sleep labs. Hence, an automated detection by applying computational techniques to multivariate signals has already become a well-researched subject. However, the best-known techniques that use various features have not achieved the gold standard of polysomnography (PSG) tests....
Since the emergence of extensive multivariate data, feature fusion has been more and more important. Most conventional methods of feature fusion do not pay sufficient attention to inherent geometric properties of data, even in the case where the data have spatial features. Therefore, how to fuse multiple features in a more intuitive way is still an open problem. A new strategy of parallel graphical...
Atrial Fibrillation (AF) is the most common chronic arrhythmia. Effective detection of the AF would avoid serious consequences like stroke. Conventional AF detection methods need heuristic or hand-craft feature extraction. In this paper, A deep neural network named multi-scale convolutional neural networks (MCNN) based AF detector is proposed. Instant heart rate sequence is extracted from ECG signal,...
Symbolic dynamics of electrocardiograms (ECG) carries the information about functioning of various human body systems. A method for distinguishing the men with low hemoglobin value from the men with normal hemoglobin value by analyzing the symbolic dynamics of the heart rate variability was proposed in the research. The method has got an acceptable sensitivity (0.67–0.80) and specificity (0.80–1.00)...
The article is devoted to searching for a time series fragment of an electrocardiogram. The purpose of this study is to identify defects in the work of the human heart. First of all, the desired pattern is selected. Next, a sampling window is arbitrarily selected in the electrocardiogram from the entire time series. The ST-index is used to find the fragment that looks like the given pattern. As a...
One reason for researching new biometric modalities is to improve the capabilities of security systems against threats. Biometric modalities based on biomedical signals, in particular the electrocardiogram signal (ECG), have been widely adopted. These can be represented by time series. However, in this context, a critical issue is how to extract features from ECG signals effectively. Several techniques...
In this paper, we review the current state of implementation of the MULTISAB platform, a web platform whose main goal is to provide a user with detailed analysis capabilities for heterogeneous biomedical time series. These time series are often encumbered by noise that prohibits accurate calculation of clinically significant features. The goal of preprocessing is either to completely remove the noise...
Sharing outsourced data between owners and data mining experts is becoming a challenging issue in biomedical and healthcare fields. Watermarking has been proved as a right-protection mechanism that can provide detectable evidence for the legal ownership of a shared dataset, without compromising its usability. However, the main disadvantage of these conventional techniques is unintelligent, rule-based...
As technology evolves, its consumers gain considerable advantages to bring prosperity for all humankind. It is so in medical environment. Even though there always has been standards in hospital or other related medical sites, it is possible for people with the help of technology to study about simple theory of pathology and find another mechanism to meet those standards, i.e., to create new method...
The development of accurate fault detection and diagnosis (FDD) techniques are an important aspect of monitoring system health, whether it be an industrial machine or human system. In FDD systems where real-time or mobile monitoring is required there is a need to minimise computational overhead whilst maintaining detection and diagnosis accuracy. Symbolic Aggregate Approximation (SAX) is one such...
Early detection of cardiovascular diseases can prevent the premature deaths caused by abnormal heartbeat problems. Application of unsupervised classification by Extreme learning machine is addressed for ElectroCardiogram (ECG) heart-beat time series clustering by a hybrid of Extreme learning machine and Decision rule using full heart-beat time series by alignment of R-peaks of all beats is proposed...
This paper presents the canonical correlation analysis (CCA) method for dimensional reduction of sleep apnea features extracted from the electrocardiogram single lead. The feature extraction belong to the linear and nonlinear techniques of variance of heart rhythm or heart rate variability and respiratory waveform from electrocardiography signal. These are benefit to evaluate the sleep apnea in noninvasive...
This paper proposes a method for grouping singular spectrum analysis (SSA) components via the empirical mode decomposition (EMD) approach. To perform the grouping, the total number of the groups of the singular spectrum analysis components is equal to the total number of the intrinsic mode functions (IMFs). The SSA components are assigned to the group where the 2-norm between the IMFs and the grouped...
Different types of artifacts contaminate the electroencephalography (EEG) signals in brain computer interface (BCI) application. Electrocardiography (ECG) is such potential artifact which negatively affects the BCI performance. This paper presents a novel method for ECG artifact elimination from EEG using stationary subspace analysis (SSA). It is based on the consideration that the ECG components...
The heart rate variability (HRV) is the variation in the pulsing frequency of the human heart. Measuring this parameter can reveal important information on the real-time interaction between the autonomic nervous system and the cardiocirculatory system. It can provide useful insight on an individual's state of stress or well being even outside a clinical setting, thanks to inexpensive and unobtrusive...
Nowadays, temporal data is generated at an unprecedentedspeed from a variety of applications, such as wearable devices, sensor networks, wireless networks, etc. In contrast to suchlarge amount of temporal data, it is usually the case that onlya small portion of them contains information of interest. Forexample, for the ECG signals collected by wearable devices, most of them collected from healthy...
Electrocardiography is a common tool for detecting cardiovascular system diseases. In clinical, as the individual difference is an intrinsic feature of ECG, data distribution difference between training and testing data impacts on the accuracy of classifier. Automatic ECG classification satisfied clinical demand is urgently required. QRS is a main waves in a heartbeat. In this paper, we propose a...
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