Nowadays, heart diseases cause the maximum death in the world. Also, due to the noticeable increase of heart diseases, studying this field is one of the important matters in medical community. Therefore, this study tries to benefit using information in data base of cardiac arrhythmia and employ arterial intelligent and neural network, in order to…

The aim of this article was the identification of cardiovascular diseases, after applying Katz and Higuchi fractal algorithms on 4 databases of ECG signals downloaded from the Physionet website: heart failure (HF), hypertension (H), ischemic heart disease (IHD) and normal sinus rhythm (NSR). For this purpose, initially the ECG signals passed through a filtering stage…

In this work, our contribution will intervene to reduce the impact of noises on the ECG signals. Various ECG denoising approaches were tested to see how efficient they were in removing dominant noises that add to pure ECG signals. Due to different causes such as interference, muscular noise, body movement related to breathing, and so…

Effective Fetal Electrocardiogram (FECG) Extraction provides medical workers with precise knowledge for monitoring fetal health condition during gestational age. However, Fetal ECG Extraction still remains a challenge as the signal is weak and contaminated with noises of different kinds, more significantly maternal ECG. In this work, a new Moth Flame optimization algorithm (MFO)-based adaptive filter…

In this paper we propose the application of supervised learning techniques to recognize stress situations in drivers by analyzing their Skin Potential Response (SPR) and the Electrocardiogram (ECG). A sensing device is used to acquire the SPR from both hands of the drivers, and the ECG from their chest. We also consider a motion artifact…