The paper shows that analytical dynamic models coupled with the available signal processing methods could be used for describing the self-organization and chaos degree in the heartbeats propagation and pressure pulses in ventricular at ejection phase. We proposed a unit analytical approach that could be associated with real ECG and pressure pulses signal processing. Our findings confirm that th...

This paper considers a system design for processing a multidimensional biomedical signal formed by EEG, ECG, EOG and motion recorded from a newborn, for the purpose of detection of epileptic seizures in newborns as an extension of the method reported in [1, 8]. We describe the proposed design, and discuss how the signals will be analysed and fused to detect the occurrence of seizure. We also di...

introduction automatic human emotion recognition is one of the most interesting topics in the field of affective computing. however, development of a reliable approach with a reasonable recognition rate is a challenging task. the main objective of the present study was to propose a robust method for discrimination of emotional responses thorough examination of heart rate variability (hrv). in t...

One of the common types arrhythmia is Atrial Fibrillation (AF), it may cause death to patients. Correct diagnosing heart problem through examining Electrocardiogram (ECG) signal will lead prescribe right treatment for a patient. This study proposes system that distinguishes between normal and AF ECG signals. First, this work provides novel algorithm segmenting extracting single heartbeat. The u...

over the years, the feasibility of using electrocardiogram (ecg) signal for human identification issue has been investigated, andsome methods have been suggested. in this research, a new effective intelligent feature selection method from ecg signals hasbeen proposed. this method is developed in such a way that it is able to select important features that are necessary for identificationusing a...

R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-or...

In this work, a simple and efficient artifact cancellation in ambulatory ECG using adaptive filter is designed for the detection of different cardiac diseases like bradycardia, tachycardia, left ventricular hypertrophy and right ventricular hypertrophy. Our work is focused on extraction of noise free ECG signal and the real-time implementation of artifacts removal techniques. As ECG signal is v...

Electrocardiogram (ECG) reflects activity of the central of the blood circulatory system, i.e. the heart. An ECG signal can provide us with a great deal of information on the normal and pathological physiology of heart activity. Thus, ECG is an important non-invasive clinical tool for the diagnosis of heart diseases. According to the medical definition the most important information in the ECG ...

Suppression of noise and artifacts is a necessary step in biomedical data processing. Adaptive filtering is known as useful method to overcome this problem. Among various contaminants, there are some situations such as electrical activities of muscles contribute to impulsive noise. This paper deals with modeling real-life muscle noise with α-stable probability distribution and adaptive filterin...

Life-threatening cardiovascular diseases require early detection or diagnosis. A standard procedure, long-term ECG monitoring of cardiac patients is currently the best way to reduce the number of heart failures. Dry and washable textile electrodes embedded in comfortable garment or in a wearable chest belt have been proven very effective for a long-term ECG monitoring in comparison to the conve...