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In this paper, we present an efficient lossless ECG compression method for real-time applications. The proposed method hybridizes ECG predictive coding algorithm based on R-wave identification and Takagi-Sugeno fuzzy neural network. The ECG signal is predicted and encoded by using the periodicity of the electrocardiogram, the correlation between the electrocardiogram signal and the adjacent heartbeat,...
This work proposes a biometrie identification system based on electrocardiographic signal, for online applications. To acquire the signal, the NI MyDAQ and the AD8232 Heart Rate Monitor module were used. Once the signal was digitized, filtering was applied and algorithms to process the signal were designed to detect the fiducial markers of the signal, such as the R peak, the Q wave, the ending of...
This paper proposes an algorithm for QRS-complex onset detection in single channel ECG signals based on the first differential of the ECG signal and an adaptive baseline estimation. The proposed algorithm was developed and tested using the Physionet QT Database. A 100% detection rate on the onset of the QRS complex, with a mean error±(standard deviation) of −0.48±(11.26) ms was achieved against the...
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...
In this paper authors have studied the current state of the art, noting the achievements and gaps existing in published works. Most of the gaps are related to the testing data used, and therefore the reliability of the results obtained. With this in mind, the paper not only covers such review of the literature, but also the efforts of the authors in developing a solution that could demonstrate the...
Common hospital monitoring set up includes nurses taking rounds throughout their assigned wards and checking the patient's vital signs. This process is very time consuming and prone to human errors, especially for large scale hospitals. As a solution, an Internet of Things (IoT) based pulmonary monitoring system was proposed. The system uses a single-lead heart rate sensor connected to a microcontroller...
Repetitive respiratory disturbance during sleep is called Sleep Apnea Hypopnea Syndrome and causes various diseases. Different features and classifiers have been used by different researchers to detect sleep apnea. This study is undertaken to identify the better performing blood oxygen saturation features subset using an Artificial Neural Network classifier for sleep Apnea detection. A database of...
We present an energy efficient QRS detector for real-time ECG signal processing implemented in ASIC. An adaptive thresholding scheme based on forward search interval (FSI) algorithm together with simple preprocessing is proposed to accurately detect QRS peaks. The Verilog HDL codes with improved hardware utilization efficiency are validated using FPGA, achieving 99.59% sensitivity (Se) and 99.63%...
The accurate detection of T-wave characteristic points is the basis of the morphology judging, which plays a great role in the diagnosis of heart diseases, especially in the arrhythmia diagnosis. Recently, the method based on the sliding area has received increasing interest due to its stable robustness. In this study, we aim to evaluate the performance of T-wave detection based on the sliding area...
The QRS complex detection methods have been extensively studied over the past several decades, and the current common QRS detection algorithms can achieve high detection accuracy on the open-access ECG database. Although massive of researches exist on the performance of QRS detectors, the effect of the ECG signal gain is usually ignored and did not attract researchers' attentions in the past studies...
At the Charles University, Faculty of Medicine, in Hradec Kralove, the Biophysics and Biostatics subject is implemented as a traditional combination, i.e. contact, educational method and e-learning. The e-learning is realized in the LMS Moodle in the “multicourses” called Biophysics and Biostatics — General Medicine and Biophysics and Biostatics — Dentistry. A process of teaching during the whole...
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...
In this paper, a new approach to remove noise present in ECG signal is proposed. Baseline wander and high frequency noise is eliminated by using computationally efficient linear phase filter ie. interpolated finite impulse response (IFIR) filter. The IFIR filter is designed by using Kaiser window function to achieve high stop band attenuation. As compared to other methods, the technique presented...
The analysis of the Variability of the Heart Rate (HRV) is coming as an important indicator for different clinical applications like the prediction of arrhythmias, sudden cardiac death, assessing cardiovascular and metabolic illness progression or in sports physiology. In this paper we have developed an algorithm to detect a supraventricular arrhythmia, by processing the heart rate variability (HRV)...
Security concerns increase as the technology for falsification advances and biometrics provides airtight security by identifying an individual based on the physiological and/or behavioral characteristics. Physiological hidden biometrics represented by ECG biomedical signal is highly confidential, sensitive, and hard to steal and replicate, and also hold great promise to provide a more secure biometric...
Heart electrical activity is measured on the body surface; this measure is known as electrocardiogram (ECG). The ECG signals are commonly accompanied by different types of noise, that can lead to a difficult and imprecise computational process to diagnose heart diseases. In this paper, we propose the Kernel Principal Component Analysis (KPCA) method, usually used in image denoising, for minimizing...
Ischemic heart disease is a leading cause of death both in developed and developing countries. In this paper, efforts to detect myocardial ischemia are focused on developing a screening system through vectorcardiogram (VCG). Existing systems manifest a low accuracy, as a result of concentrating more on ST segment and statistical features of VCG. In our work, several novel strategies have been introduced,...
This paper presents a development of a handheld device for single-channel ambulatory ECG measurements. Our ECG device is designed for easy-to-use. ECG signal will be recorded for 1 minute by placing both thumbs on two dry electrodes and we have used this device to collect ECG signals from 50 volunteers. An ECG database is created from the collected ECG signals with self-annotated locations of QRS...
The Cardiovascular disease (CVD) is one of the most serious diseases in the world. To monitor cardiac condition and diagnose cardiovascular disease conveniently, a mobile intelligent Electrocardiogram (ECG) monitoring system is developed. The system is consisted of signal acquisition module, Bluetooth transmission module, signal processing module and intelligent diagnosis module. The ECG signals collected...
Left ventricular hypertrophy (LVH) is the main manifestation of cardiovascular disease in patients with hypertension, and is an independent risk factor for multiple cardiovascular complications. So the medical researchers attach enough importance to it in aspect of clinical practice. Electrocardiograph (ECG) has the unique advantages of simple operation and low price, which support its widespread...
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