This paper presents a method to analyze electrocardiogram (ECG) signal, extract the features, for the real time human identification. Data were obtained from short-term Lead-I ECG records (only one lead) of forty students at Paris Est University (UPEC). Signal averaging was applied to generate ECG databases and templates for reducing the noise recorded with palm ECG signals. Time domain signal ...

Each individual heartbeat in the cardiac cycle of the recorded electrocardiogram (ECG) waveform shows the time evolution of the heart’s electrical activity, which is made of distinct electrical depolarization–repolarization patterns of the heart. Any disorder of heart rate or rhythm, or change in the morphological pattern is an indication of an arrhythmia, which could be detected by analysis of...

The aim of the study was to evaluate the immediate reproducibility of time domain parameters in the signal averaged electrocardiogram using a new method for endpoint determination in individual Frank leads. The method is based on a statistical model of the electrocardiogram (ECG) in which maximum likelihood (ML) estimation is employed. The reproducibility of the ML method was compared to that o...

For our project, we designed and implemented a cardiac arrhythmia electrocardiogram (ECG) monitoring system that can adaptively modify and change the components of its processing chain to carry out the best treatment on electrocardiogram signals. We investigated and provided a solution to a fundamental problem in the area of biomedical signal processing: accurate QRS complex detection for varyi...

In recent years, adaptive filtering has become one of the effective and popular approaches for the processing and analysis of the signals with noise especially of the biomedical signals. Adaptive filters permit to detect time varying potentials and to track the dynamic variations of the signals. Besides, they modify their behavior according to the input signal. Therefore, they can detect shape ...

ECGSYN, a dynamical model that faithfully reproduces the main features of the human electrocardiogram (ECG), including heart rate variability, RR intervals and QT intervals is presented. Details of the underlying algorithm and an open-source software implementation in Matlab, C and Java are described. An example of how this model will facilitate comparisons of signal processing techniques is pr...

A prototype CAIS (cardiac arrhythmia interpretation syst/em) based on blackboard model is presented. The issue of automatic electrocardiogram analysis is addressed firstly. The idea ofbi-directional interaction is led into CAIS, which makes the system run in a kind ofway rather close to human mode of thinking. The process of PVC (premature ventricular contraction) diagnosis in CAIS is described...

Developing a mathematical model for the artificial generation of electrocardiogram (ECG) signals is a subject that has been widely investigated. One of its uses is for the assessment of diagnostic ECG signal processing devices. So the model should have the capability of producing a wide range of ECG signals, with all the nuances that reflect the sickness to which humans are prone, and this ...

In this study, a portable, low-cost system, Portable Motion Analyzer (PMA), is introduced to obtain data from daily physical motions, such as ECG, heart rate signals, as well as kinetic information of motion and freeliving gait. It can gather, process and analysis the signals from multiple input channels. To process these signals, digital filtering and wavelet analysis is used for quantitative ...

This study focuses on non-invasive fetal electrocardiogram extraction based a novel hybrid method, which combines the advantages of non-adaptive and adaptive approaches for morphological analysis. Besides estimating heart rate, is main parameter used in clinical practice, this provides ST segment analysis data from Abdominal Direct Fetal Electrocardiogram Database consisting simultaneous tradit...