Electrocardiogram (ECG) signals contain a great deal of essential information which can be utilized by physicians for the diagnosis of heart diseases. Unfortunately, ECG signals are inevitably corrupted by noise which will severely affect the accuracy of cardiovascular disease diagnosis. Existing ECG signal denoising methods based on wavelet shrinkage, empirical mode decomposition and nonlocal ...

This paper presents a novel method in driving distraction analysis: entropy analysis of ECG signals. ECG signals were recorded continuously while 15 drivers were driving with a simulator. Mental computation task was employed as driving distraction. Sample entropy and power spectrum entropy of drivers. ECG signals while they were driving with and without distraction were derived. The result indi...

ventricular arrhythmias are one of the most important causes of annual deaths in the world, which may lead to sudden cardiac deaths. accurate and early diagnosis of ventricular arrhythmias in heart diseases is essential for preventing mortality in cardiac patients. ventricular activity on the electrocardiogram (ecg) signal is in the interval from the beginning of qrs complex to t wave end. vari...

An Electrocardiogram signals change their statistical property over time and ECG signals are highly non-stationary signals. Growing of embedded technology has provided powerful tools to analysis of ECG. ECG is advanced recording method of bioelectric signal which is originated in the heart and it provides valuable information about the activity of human heart. Different types of features of the...

This paper proposes a human identification system via extracted electrocardiogram (ECG) signals. Two hierarchical classification structures based on global shape feature and local statistical feature is used to extract ECG signals. Global shape feature represents the outline information of ECG signals and local statistical feature extracts the information between signals in time domain. Genetic...

Electrocardiogram (ECG) signal processing aims basically 1) at artifact reduction to make the ECG signals cleaner and better interpretable by human or machine observers, 2) at revealing aspects not immediately observable in plain measured ECG signals even after artifact reduction, or 3) at diagnostics decision support and automated ECG signal interpretation, including classification of ECG sign...

In this paper the main objective is to quantify and compare the instantaneous value of heart rate for normal breathing patterns during Meditation and Non Meditation conditions. This paper involves Analysis of Variance (ANOVA) technique for the analysis of the heart rate variability patterns during the meditative and non meditative states. The analysis is divided into three stages i.e. data acqu...

In this paper the need for employing a data reduction algorithm in using digital graphic systems to display biomedical signals is firstly addressed and then, some such algorithms are compared from different points of view (such as complexity, real time feasibility, etc.). Subsequently, it is concluded that Turning Point algorithm can be a suitable one for real time implementation on a microproc...