An approach based on the consideration that electrocardiogram (ECG) signals are chaotic signals was presented for automated diagnosis of electrocardiographic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Recurrent neural network (RNN) was implemented and used as basis for detection of variabilities of ECG sign...

There are a limited number of studies on heart rate variability (HRV) dynamics immediately after exercise. The electrocardiogram (ECG) may be used to measure HRV, however acquiring ECG signals from subjects undergoing exercise is not convenient. Many researchers have demonstrated that photoplethysmogram (PPG) signals offer an alternative method to measure HRV when ECG and PPG signals are simult...

Noise in ECG signals will affect the result of post-processing if left untreated. Since ECG is highly subjective, the linear denoising method with a specific threshold working well on one subject could fail on another. Therefore, in this Letter, sparse-based method, which represents every segment of signal using different linear combinations of atoms from a dictionary, is used to denoise ECG si...

This work describes a Radial Basis Function (RBF) neural network method used to analyze ECG signals for diagnosing cardiac arrhythmias effectively. The proposed method can accurately classify and differentiate normal (Normal) and abnormal heartbeats. Abnormal heartbeats include left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contractions (APC) and premature v...

Electrocardiograms are widely used in biomedical signal processing to diagnose abnormal heart functioning. Many algorithms have been constructed to analyse, measure and compress these signals. These methods are hard to test because real ECG signals are distorted by several types of noise. In this paper we present an algorithm which generates realistic synthetic ECG signals. This algorithm, amon...

The Electrocardiogram signals are a very valuable source of data for physicians in diagnosing heart abnormalities. In this paper, we present an efficient technique for compression of electrocardiogram (ECG) signals. A new thresholding method based on the three level of quantization is proposed for encoding samples using an Embedded Zero-tree Wavelet (EZW) and Huffman algorithms. The modified en...

OBJECTIVE In the past few decades, several studies have reported the physiological effects of listening to music. The physiological effects of different music types on different people are different. In the present study, we aimed to examine the effects of listening to traditional Persian music on electrocardiogram (ECG) signals in young women. METHODS Twenty-two healthy females participated ...

A method applying independent component analysis (ICA) to detect the electrocardiogram of a prenatal cattle foetus is described. Three channels of signal, one is from chest lead and two are from abdominal leads, are picked up noninvasively by attaching disposable cutaneous electrodes on the body surface of a maternal cow. Measured signals are the mixtures of components including maternal ECG, f...

Detection of cardiac arrhythmias, particularly ventricular fibrillation (VF), and ventricular tachycardia (VT) have been highly regarded and has done several works in this field. In this study, a method based on the Takagi-Sugeno-Kang (TSK) fuzzy system for ECG arrhythmia detection and classification of normal sinus rhythm (NSR), ventricular fibrillation (VF) and ventricular tachycardia (VT) ha...