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Several medical studies reveal that alcohol consumption has pronounced effects on the heart rate variability (HRV) of the consumer. In this paper, machine learning algorithms use the features extracted through HRV analysis performed on ECG samples of chronic alcohol users and normative subjects, in order to classify them. To carry out the classification, a Support Vector Machine (SVM) and an Extreme...
In medical care, it is essential to assess and manage acute painful conditions adequately. Heart rate variability (HRV) analysis is based on the acquisition of electrocardiogram (ECG), which is available from both patient monitor and wearable device. As HRV analysis can reflect autonomic nervous system activity which is unconsciously regulated, HRV analysis in ultra-short-term is getting attention...
Electrocardiogram (ECG), Electrodermal Activity (EDA), Electromyogram (EMG) and Impedance Cardiography (ICG) are among physiological signals widely used in various biomedical applications including health tracking, sleep quality assessment, early disease detection/diagnosis and human affective state recognition. This paper presents the development of a biosignal-specific processing and feature extraction...
In this study we address an important pediatric cardiopulmonary resuscitation problem to identify the cause of a cardiac arrest during the beginning of cardiopulmonary resuscitation. A support vector algorithm was trained and tested using a feature set constructed through wavelet transform analysis of experimental electrocardiography and heart rate data provided by Children's Hospital of Philadelphia...
In biomedical signal processing, Power Line Interference (50Hz) is one of the most and commonly types of electrical noises that often corrupt the quality of a biomedical data. In this paper, we present a simple tool for ECG signal enhancement approach based on Power Line Interference (PLI) reduction algorithm in Undecimated Wavelet Transform and Interval Thresholding. In our scheme, we use the Undecimated...
Early detection and monitoring of heart diseases increase human quality of life and this can prevent negative consequences. It is even more important because it can prevent sudden deaths. In today's technology, these operations can be done with telemedicine systems. In this work, appropriate methods have been proposed for telemedicine systems. The proposed system is of two classes and is based on...
Electrocardiogram (ECG) diagnosis is a widely-used clinical approach because it has been proven as an efficient way to diagnose cardiac disease. However, to get an accurate ECG diagnosis is a challenging task because it is a nonlinear problem. Therefore, many Neural Network (NN)-based ECG analysis approaches were proposed to analyzes ECG signal in time domain in recent years which can improve the...
This paper proposed a novel method to improve automatic age estimation from human faces. Three types of feature extraction algorithms are used, such as Extended Curvature Gabor Filter (ECG), Completed Local Binary Pattern (CLBP), and Local Directional Pattern (LDP). While the ECG is applied to the entire human face, CLBP and LDP are only applied to blocks with randomized scales, positions and orientations...
Emotions are directly related whit external or internal stimulation which a person is submitted, influencing these stimuli on their physical and mental health. These emotional changes produce variations in cardiovascular behavior and respiratory activity. This research proposes the characterization of cardiac system in healthy subject submitted to 3 different audiovisual stimuli, by means of the analysis...
Wearable and mobile medical devices provide efficient, comfortable, and economic health monitoring, having a wide range of applications from daily to clinical scenarios. Health data security becomes a critically important issue. Electrocardiogram (ECG) has proven to be a potential biometric in human recognition over the past decade. Unlike conventional authentication methods using passwords, fingerprints,...
Recently, with the obvious increasing number of cardiovascular disease, the automatic classification research of Electrocardiogram signals (ECG) has been playing a significantly important part in the clinical diagnosis of cardiovascular disease. In this paper, a 1D convolution neural network (CNN) based method is proposed to classify ECG signals. The proposed CNN model consists of five layers in addition...
In this paper, we investigate a new method to analyze electrocardiogram (ECG) signal, extract the features, for the real time human identification using single lead human electrocardiogram. The proposed system extracts special parts of the ECG signal starting from the P wave, the QRS complex and ending with the T wave for that we used the multiresolution wavelet analysis. Different features are selected...
Traditional password based authentication has been proven inadequate and the use of biometrics have provided multiple solutions through the past years. One of the most recent approaches to biometric authentication is using Electrocardiograms (ECG), as they are closely related to unique characteristics of the heart of each person. In this paper a framework for efficient and usable user authentication,...
In this work, the advantages of coupling biomedical signal compressors with clinical feature-based distortion measures are demonstrated. Such a coupling allow biomedical signal compressors to self-establish hard limits with regards to choices surrounding compression ratios, or ‘quality settings’, a compressor can safely choose from to guarantee that features of clinical significance are protected...
In this paper, we proposed a novel biometric identification system which can extract more distinguishable features from the ECG signal. Based on the reference point detection, piecewise correction method was used to solve the problem of heart rate variability (HRV). Besides, we used some details of parts of the wavelet coefficient structure to reconstruct more distinguishable signal. At last, the...
Analysis and evaluation of ECG (Electrocardiogram) signals is one of the important methods used in the determination of heart diseases. Since the interpretation of ECG signals is a time consuming and demanding process for physicians, detailed analysis and interpretation software that gives the same result as the diagnosis of the physician at high rates by interpreting these signals in the computer...
Analysis of electrocardiogram and heart rate provides useful information about health condition of a patient. The North Sea Bicycle Race is an annual competition in Norway. Examination of ECG recordings collected from participants of this race may allow defining and evaluating the relationship between physical endurance exercises and heart electrophysiology. Parameters reflecting potentially alarming...
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...
The paper presents and discusses a novel method of biometrie identification based on ECG data. The main idea of the study is to apply Deep Neural Networks (DNN) for human identification based on the raw ECG signal. To improve overall system accuracy various signal pre-processing and outlier detection techniques have been applied. Also, to make ECG identification approach more user friendly, three-finger...
ElectroCardioGram ECG biometrics has recently been identified as a promising technique to identify subjects. Meanwhile, as ECG related data can reveal other factors like medical disease, the protection of the ECG biometric template is mandatory. The challenge is to guaranty the privacy of the ECG data, while keeping adequate performance results in terms of false acceptance rate and false rejection...
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