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Due to the sporadic occurrence of cardiovascular disease, long term continuous monitoring is needed. An embedded wireless health monitoring system is designed and implemented, which is made up of wearable monitoring nodes with Bluetooth Low Energy (BLE) wireless transmission, a sensor data gateway and smart terminal etc. The wearable monitoring nodes are integrated electrocardiography (ECG) and impedance...
Currently, as a effort to reduce a rate of death by cardiovascular diseases, a lot of researches have been studied regarding real-time diagnosis system. So, we implement a prototype which is contained of stream data processor and incremental data mining module for automatic diagnosis of cardiovascular diseases. In the prototype, (i)ECG signal data which is transported from body-attached sensor is...
Visual stress which can induce headache, migraines and eyestrain affects our body often detrimentally. Heart rate variability (HRV) analysis is commonly used as a quantitative marker depicting the activity of autonomic nervous system (ANS) that may be related to visual stress. In this paper, we proposed an improved HRV methodology for HRV features extraction and analysis. Firstly, a multi-channel...
Arrhythmia diagnosis is commonly conducted through visual analysis of human electrocardiograms, a very resource consuming task for physicians. In this paper we present a computational approach for arrhythmia detection based on heart rate variability signal analysis and the application of a neuro-fuzzy classification model called SONFIS. The aforementioned method generates a set of linguistically interpretable...
Long-term monitoring of health is essential in many chronic conditions, but automatic monitoring is not yet utilized routinely with mental stress. Accelerometers, magnetometers, ECG, respiratory effort, skin temperature and pulse oximetry were used with 12 health volunteers in this study for monitoring 1) heavy mental load, 2) normal mental load, 3) walking, 4) running and 5) lying. Heavy mental load...
This paper details the process of oral food challenges (`allergy tests') and steps followed to investigate whether automatic classification of the tests is possible. It has been observed by trained staff that the mood and physiological signals of a subject being tested for allergies can change during the test if they are sensitive to the allergen they are being tested against. Data from thirteen subjects...
Sustained attention is an important requirement when we are doing vigilant works. The detection of whether a worker is in sustained attention stage is important to maintain the safety of the worker. Thus this paper performs classification of sustained attention and non sustained attention phases based on the heart rate variability (HRV). To achieve this purpose several features are derived from time...
Signal segmentation plays an important role in Electrocardiogram (ECG) feature extraction. In ECG signals, there are two kinds of dependencies: the dependencies in a single ECG cycle and the dependencies across ECG cycles. The proposed investigation focus on multiple cardiac cycle fusion for ECG feature extraction. Five different feature sets were generated using different ECG segmentation methods...
In this paper, a bispectrum analysis technique is suggested for quantitative analysis of Congestive Heart Failure (CHF). The bispectrum is estimated using an autoregressive model, and the frequency coupling of the bispectrum is extracted as a quantitative measure to classify normal and CHF subjects. According to the further study of the foot to peak interval variability (FPIV), the peak to notch interval...
Heart rate variability (HRV) is an established indicator of cardiac health. Recent developments have shown the potential of nonlinear metrics for pattern classification of various heart conditions. Evidence indicates that the combination of multiple linear and nonlinear features leads to increased classification accuracy. In our paper, we demonstrate HRV classification using two dynamic nonlinear...
The problem of patient disorder classification and prediction from biological signals is addressed. We approach the problem from the perspective of nonlinear dynamical systems. Explored signals are ECG and EEG. We propose a combination of linear and nonlinear features for classification of four different types of heart rhythms through heart rate variability analysis. Classification accuracy is evaluated...
Sleep is a circadian rhythm essential for human life. Many events occur in the body during this state. In the past, significant efforts have been made to provide clinicians with reliable and less intrusive tools to automatically classify the sleep stages and detect apnea events. A few systems are available in the market to accomplish this task. However, sleep specialists may not have full confidence...
Heart rate, heart rate variability (HRV), and respiratory effort have been proposed in numerous studies with the goal of correlating physiological parameters with mental workload. Aim of this study was to analyze the cardio-respiratory response to a mental task (Sternberg Task) from a single lead ambulatory ECG recording, in healthy subjects. Under no assumptions on stationarity, HRV was analyzed...
In this paper, we propose a classifier that can predict ventricular tachycardia (VT) events using artificial neural networks (ANNs) trained with parameters from heart rate variability (HRV) analysis. The Spontaneous Ventricular Tachyarrhythmia Database (Medtronic Version 1.0), comprising 106 pre-VT records and 126 control data, was used. Each data set was subjected to preprocessing and parameter extraction...
In this paper, a novel method for representation of heart rate has been introduced which is obtaining by using RR interval time series signal to plot the Triangle mapping consist of all the ordered pairs: (RRi, abs ((RR)̅ - RRi), i = 1, ..., N where (RR)̅ is the mean of RR intervals. We obtained a triangle from the distribution of these points and by analyzing it, we could extracted some geometric...
This paper presents the performance of support vector machine to classify the multi-class arrhythmia dataset by pre-selecting sets of feature that best suit the training data set in two-class fashion. By allowing freedom of feature dimension selection in different grouping in classification procedure, the classification performance is comparable to one that uses constant feature dimension but with...
This paper presents a methodology for Obstructive Sleep Apnea (OSA) detection based on the HRV analysis, where as a measure of relevance PLS is used. Besides, two different combining approaches for the selection of the best set of contours are studied. Attained results can be oriented in research focused on finding alternative methods minimizing the HRV-derived parameters used for OSA diagnosing,...
This work proposes a novel foetal electrocardiogram (FECG) extraction approach based on the cyclostationary properties of the signal of interest. The problem of FECG extraction can easily fit in a blind source separation (BSS) framework; taking into account specific statistical nature of the signal, that one wants to extract, leads to an algorithm able to estimate the FECG contribution to ECG recordings...
This paper describes the performance of beat detection and heart rate variability (HRV) feature extraction on electrocardiogram signals which have been compressed and reconstructed with a lossy compression algorithm. The set partitioning in hierarchical trees (SPIHT) compression algorithm was used with sixteen compression ratios (CR) between 2 and 50 over the records of the MIT/BIH arrhythmia database...
This study presents different methods for automatic sleep classification based on heart rate variability (HRV), respiration and movement signals recorded through bed sensors. Two methods for feature extraction have been implemented: time variant-autoregressive model (TVAM) and wavelet discrete transform (WDT); the obtained features are fed into two classifiers: Quadratic (QD) and Linear (LD) discriminant...
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