ECG (Electrocardiogram) signals originating from heart muscles, generate massive volume of digital data. They need to be compressed or approximated for efficient transmission and storage. ECG signal compression is traditionally performed in three ways: direct, transform and parameter extraction. Polynomial approximation which is a form of parameter extraction method, is employed here. This pape...

The extraction and identification of ECG (electrocardiogram) signal characteristic parameters are the basis of ECG analysis and diagnosis. The fast and precise detection of QRS complexes is very important in ECG signal analysis, for it is the precondition of the correlative parameters calculation and diagnosis. In this paper, the modulus maxima of wavelet transform was applied to the QRS comple...

It is well known that biomedical signals carry important information about the behavior of the living systems under study. With the analysis of the Electrocardiogram (ECG) signal it may be possible to predict heart problems or monitor patient recovery after a heart intervention. A proper processing of these signals enhances their physiological and clinical information. The quality of biomedical...

The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through ...

In the modern world of automation, biological signals, especially Electroencephalogram (EEG) and Electrocardiogram (ECG), are gaining wide attention as a source of biometric information. Earlier studies have shown that EEG and ECG show versatility with individuals and every individual has distinct EEG and ECG spectrum. EEG (which can be recorded from the scalp due to the effect of millions of n...

A noiseless ECG identification technology is an emerging new biometric modality. Different techniques for de-noising of ECG signal are prevalent in recent literatures such as Particle Filter (PF), wavelet transforms (WT), Empirical Mode Decomposition (EMD) & Ensemble-EMD Method. In view of the fact that Analysis of ECG signals becomes difficult to inspect the cardiac activity in the presence of...

The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Good quality of ECG is utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts. Noise severely limits the utility of the recorded ECG and thus need to be removed, for better clinical evaluation....

Electrocardiogram is commonly used to detect abnormal heart rhythm. An electrocardiogram (ECG) records the electrical activity of the heart. ECG signal is corrupted by noise signal which restricts the correct diagnose of heart beats. Adaptive filters are used to reduce the noise signal from the ECG signal. In this paper an Adaptive filter has been designed to eliminate the noise from ECG signal...

Noise reduction in ECG signal is an important task of biomedical science.Adaptive filtering algorithms are evolving rapidly in biomedical science to remove the noise by an appreciable amount.In this paper, noise of ECG signal has been reduced by using adaptive filter based on RLS algorithm.The RLS algorithm performs well irrespective of the nature of signal and noise and its convergence rate is...

12-lead ECG signals fusion is crucial for further ECG signal processing. In this paper, a novel fusion data algorithm is proposed. In the method, 12-lead ECG signals are appropriately converted to a single-lead physiological signal via the idea of the local weighted linear prediction algorithm. For effectively inheriting the quality characteristics of the 12-lead ECG signals, the fuzzy inferenc...