Electrocardiogram (ECG) is a valuable technique that has been in use for over a century. The analysis of fetal ECG signal has always been an interesting topic in the field of signal processing. The presence of noises in the ECG signal causes distortion in the signal morphology. While analyzing the fetal ECG this distortion of the signal is much more severe as the fetal ECG is much weaker than t...

The processing of electrocardiogram signals (ECG) using Hidden Markov Models (HMM) methodology is presented. The proposed framework enables complex ECG signal processing with all the necessary steps resolved by HMM approach. Firstly, general description and comprehensive survey of actual HMM state of art is carried out. Secondly, the ECG processing methods as noise removal, characteristic point...

The methods of Computational Intelligence (CI) including a framework of Granular Computing, open promising research avenues in the realm of processing, analysis and interpretation of biomedical signals. Similarly, they augment the existing plethora of “classic” techniques of signal processing. CI comes as a highly synergistic environment in which learning abilities, knowledge representation, an...

ECG signals are non-stationary, pseudo periodic in nature and whose behavior changes with time. The proper processing of ECG signal and its accurate detection is very much essential since it determines the condition of the heart. The analysis of ECG signal requires the information both in time and frequency, for clinical diagnosis. Hence the wavelet transforms becomes handy for analyzing these ...

This paper present a study of signal processing in Blind Source Separation (BSS) application for medical field, especially during medical data recording. There are two main techniques that will be investigated; Natural Gradient Method (NGM) and Self-Organized Neural Network (SONN). The main source signals are Electrocardiograph (ECG), and Electroencephalograph (EEG) that linearly mixed with noi...

Electrocardiography (ECG) is an ubiquitous vital sign health monitoring method used in the modern healthcare systems. Different designs of ECG system have been developed as alternatives to the common twelve-lead ECG systems. The recent approaches of the ECG system are focusing on the signal quality, portability and power consumption. In this work, an ECG sensing system is developed with capacit...

The Electrocardiogram (ECG) signal is one of the recognizing approaches to discover heart disease. One of the major difficulties in biomedical data processing like electrocardiography is the separation of the original signal from noises affected by body movement and respiration, electromagnetic field, power line and high frequency interference. Various methods of digital filters are exploited t...

Abstract. Empirical Mode Decomposition (EMD) is widely used in biomedical field for biomedical signal processing and especially for electrocardiogram (ECG) processing. Removal of artifacts that corrupt ECG, is carried out by proper selection of Intrinsic Mode Functions (IMF) for the partial signal reconstruction. In this paper a study of the influence of White Gaussian Noise in synthetic electr...

In this paper we introduce an effective ECG compression algorithm based on two dimensional multiwavelet transform. Multi-wavelets offer simultaneous orthogonality, symmetry and short support, which is not possible with scalar two-channel wavelet systems. These features are known to be important in signal processing. Thus multiwavelet offers the possibility of superior performance for image proc...

The electrocardiogram (ECG) signal can be derived from different sources. These include systems for surface ECG, Holter monitoring, ergometric stress tests, and telemetry systems and bedside monitoring of vital parameters, which are useful for rhythm and ST-segment analysis and ECG screening of electrical sudden cardiac death predictors. A precise ECG diagnosis is based upon correct recording, ...