The ECG finds its importance in the detection of cardiac abnormalities. Noise reduction in ECG signal is an important task of biomedical science. ECG signals are very low frequency signals of about 0.5Hz-100Hz and digital filters are very efficient for noise removal of such low frequency signals. In this Paper an adaptive filter for high resolution ECG Signal is presented which estimate the det...

The electrocardiogram (ECG) signal is one of the new trends techniques for human identification and authentication. So detecting all waves that formed this signal is very important for human identification and authentication. This research work proposes a new algorithm for detecting most of ECG signals like the signals that have positive R-peaks, the signals that have negative R-peaks and the s...

Cardiovascular disease (CVD) is a major public concern and socioeconomic problem across the globe. The popular high-end cardiac health monitoring systems such as magnetic resonance imaging (MRI), computerized tomography scan (CT scan), and echocardiography (Echo) are highly expensive and do not support long-term continuous monitoring of patients without disrupting their activities of daily livi...

Cardiac arrhythmias are common heart diseases. Electrocardiography (ECG) is an important measure for diagnosing arrhythmias. Researchers use the ECG signals in order to train artificial neural networks (ANN). In previous studies the ECG signals of males and females were analysed together. We know that there are some differences between male and female ECG signals. This paper suggests that class...

In today’s era Heart Rate Variability becomes an important characteristic to determine the condition of heart. That’s why the calculation of HRV and classification to generate rules is necessary. Human Heart Generates the electrical signal. ECG is used to detect the heart beat. ECG signal contains lots of noise. To classify the signals first to decompose the signals using wavelet transform. Man...

ECG signals have been proven a very versatile tool for detection of cardiovascular diseases. But during recording of these signals, the ECG data gets contaminated by various noise signals caused by power line interference, base line wander, electrode movement, muscle movement (EMG) etc. These noise signals are known as artifacts. These artifacts mislead the diagnosis of heart which is not desir...

Electrocardiographic (ECG) signals are emerging as a recent trend in the field of biometrics. In this paper, we propose a novel ECG biometric system that combines clustering and classification methodologies. Our approach is based on dominant-set clustering, and provides a framework for outlier removal and template selection. It enhances the typical workflows, by making them better suited to new...

The cardiovascular disease is number one killer of the world for both men and women. The ECG signal is mainly used to monitoring this type disease. In this paper proposes the compression of ECG signals and provide the encryption for that signals. The encryption technique of ECG signal is used to provide the security of the patient health information from the public media. This paper proposes th...

Adaptive processing and classification of electrocardiogram (ECG) signals are important in eliminating the strenuous process of manually annotating ECG recordings for clinical use. Such algorithms require robust models whose parameters can adequately describe the ECG signals. Although different dynamic statistical models describing ECG signals currently exist, they depend considerably on a prio...

Among many of the electrode designs used in electrocardiography (ECG), concentric ring electrodes (CREs) are one of the most promising due to their enhanced spatial resolution. Their development has undergone a great push due to their use in recent years; however, they are not yet widely used in clinical practice. CRE implementation in textiles will lead to a low cost, flexible, comfortable, an...