Anomaly detection is one of the basic issues in data processing that addresses different problems healthcare sensory data. Technology has made it easier to collect large and highly variant time series data; however, complex predictive analysis models are required ensure consistency reliability. With rise size dimensionality collected data, deep learning techniques, such as autoencoder (AE), rec...

The electrocardiogram (ECG) is an essential tool in the field of cardiology, as it enables electrical activity heart to be measured. It involves placing electrodes on patient's skin, facilitating measurement and analysis cardiac rhythms. This non-invasive painless test provides information about heart's function helps diagnosing various conditions. classification ECG signals using deep learning...

Developing a mathematical model for the artificial generation of electrocardiogram (ECG) signals is a subject that has been widely investigated. One of the challenges is to generate ECG signals with a wide range of waveforms, power spectra and variations in heart rate variability (HRV)--all of which are important indexes of human heart functions. In this paper we present a comprehensive model f...

BACKGROUND Multi-channel recording of surface electromyographyic (EMG) signals is very likely to be contaminated by electrocardiographic (ECG) interference, specifically when the surface electrode is placed on muscles close to the heart. METHODS A novel fast independent component analysis (FastICA) based peel-off method is presented to remove ECG interference contaminating multi-channel surfa...

BACKGROUND The Electrocardiogram ECG is one of the most important non-invasive tools for cardiac diseases diagnosis. Taking advantage of the developed telecommunication infrastructure, several approaches that address the development of telemetry cardiac devices were introduced recently. Telemetry ECG devices allow easy and fast ECG monitoring of patients with suspected cardiac issues. Choosing ...

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...