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This paper presents a system which estimates blood glucose level (BGL) by non-invasive method using Photoplethysmography (PPG). Previous studies have shown better estimation of blood glucose level using an optical sensor. An optical sensor based data acquisition system is built and the PPG signal of the subjects is recorded. The main contribution of this paper is exploring various features of a PPG...
Cardiac event detection is one of the essential steps in cardiac signal processing, analysis and disease diagnosis. Complete morphology of cardiac waves (P–QRS–T) extracted from the location of R-peak is helpful for feature extraction of many applications related to cardiac diseases classification. Therefore cardiac event detection is a prerequisite for reliable cardiac disease diagnosis, and hence...
ECG(Electrocardiogram) signals get corrupted due to different noises and artifacts such as power line interference, baseline wander, motion artifacts, contact noise that hide lot of required information. This information is required for detecting various cardiac diseases. Hence before processing ECG signal denoising plays important role to obtain significant features of ECG signal. For denoising,...
Electrical activity in the heart is given by electrocardiogram (ECG) signal. Manual analysis of ECG beat is very time consuming task as it may contain hundreds of thousands of beats for 24 hours of ECG signal. This study gives a robust classification model for ECG using Rough Set Theory (RST). RST generates rules which are simple and more apprehensible for the user causing the extraction of more accurate...
In this paper, QRS morphological features and the artificial neural network method was used for Electrocardiogram (ECG) pattern classification. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, premature ventricular contraction, atrial premature beat and left bundle branch block beat. Authors propose a set of six ECG morphological features...
Electrocardiogram (ECG) is non-stationary signal as it contains the vital information about the heartbeat. Any problem associated with the heart is visible in the ECG as distortion or noise. By only ECG we can detect the arrhythmia. In arrhythmias the ECG signals become complicated and it is not easy to be understood as it contains number of heart beats, for analyzing it requires more time. So the...
The Electrocardiogram (ECG) is most widely used techniques to detect cardiac diseases. In this paper we propose ECG signal analysis and classification method using wavelet energy histogram method and support vector machine (SVM). The classification of cardiac arrhythmia in the ECG signal consists of three stages including ECG signal preprocessing, feature extraction and heartbeats classification....
Soft computing techniques have emerged as a highly synergistic, computationally appealing, and conceptually unified framework supporting intelligent system design and analysis. The key contributing technologies of soft computing are neural network computing, fuzzy inference system, genetic algorithm or fusion of these techniques. This work proposes a method of analyzing cardiac signal (ECG) to diagnose...
Arrhythmia can be detected by carefully studying the electrocardiogram (ECG) and the distortions in the QRS complex. Since the appearance of the distorted beats, the indicators of arrhythmia, may occur randomly with respect to time and span over a large time interval, an automated classification mechanism may reduce the tedium in identifying and isolating these beats. This paper proposes an arrhythmia...
Detection and delineation of Electrocardiogram has played a vital role in cardiovascular monitoring systems. The enormous database of heart beats which characterize the heart disease, uncertainity, randomness in occurrence of these beats necessitate the use of Rough set theory. Over the years Rough set theory has been effectively used for removal of uncertainties and reduction of dataset. This paper...
Electrocardiogram (ECG) is the P, QRS, T wave indicating the electrical activity of the heart. Electrocardiogram is the most easily accessible bioelectric signal that provides the doctors with reasonably accurate data regarding the patient heart condition. Many of the cardiac problems are visible as distortions in the electrocardiogram (ECG). Normally ECG related diagnoses are carried out manually...
Electrocardiogram is the most easily accessible bio- electric signal that provides the doctors with reasonably accurate data regarding the patient heart condition. Many of the cardiac problems are visible as distortions in the electrocardiogram (ECG). Normally ECG related diagnoses are carried out by the medical practitioners manually. The major task in diagnosing the heart condition is analyzing...
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