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Electrocardiogram (ECG) diagnosis is a widely-used clinical approach because it has been proven as an efficient way to diagnose cardiac disease. However, to get an accurate ECG diagnosis is a challenging task because it is a nonlinear problem. Therefore, many Neural Network (NN)-based ECG analysis approaches were proposed to analyzes ECG signal in time domain in recent years which can improve the...
Wearable and mobile medical devices provide efficient, comfortable, and economic health monitoring, having a wide range of applications from daily to clinical scenarios. Health data security becomes a critically important issue. Electrocardiogram (ECG) has proven to be a potential biometric in human recognition over the past decade. Unlike conventional authentication methods using passwords, fingerprints,...
The analysis of the Variability of the Heart Rate (HRV) is coming as an important indicator for different clinical applications like the prediction of arrhythmias, sudden cardiac death, assessing cardiovascular and metabolic illness progression or in sports physiology. In this paper we have developed an algorithm to detect a supraventricular arrhythmia, by processing the heart rate variability (HRV)...
A cardiac circumstance affected through irregular electrical action of the heart is called an arrhythmia. A noninvasive method called Electrocardiogram (ECG) is used to diagnosis arrhythmias or irregularities of the heart. The difficulty encountered by doctors in the analysis of heartbeat irregularities id due to the non-stationary of ECG signal, the existence of noise and the abnormality of the heartbeat...
In this study, heart beats are classified as normal, right branch block, left branch block, and paced rhythm using electro cardiographic (ECG) signals obtained from the MIT-BIH cardiac arrhythmia database. Average, standard deviation, energy and entropy of discrete wavelet transform (DWT) coefficients are proposed as the features for the classification. The classification was performed by selecting...
Electrocardiography (ECG) is a widely used noninvasive clinical tool for the diagnosis of cardiovascular disease. However, the accuracy of ECG analysis significantly affect the diagnostic error rate of cardiovascular diseases. Therefore, in recent year, many Neural Network (NN)-based approaches were proposed to automatically analyze the ECG signal. However, these methods suffer from long computing...
The purpose of this paper is to propose a modular integrating algorithm. This algorithm can let the program detect multiple arrhythmias and is very easy to add more diseases detection algorithm. Also, it can save the repeated calculations in multiple algorithms. By a real test of the program, the result is that the computing time of the integrating algorithm is 46.86% less than the sum of the computing...
Electrocardiogram (ECG) is nearly a periodic signal widely used for the detection and diagnosis of cardiac abnormalities. Recently with the inception of computer based techniques, automated analysis of shape and pattern of ECG waveform has facilitated physician to obtain fast and accurate diagnosis of cardiac disorders. Abnormalities related to sinus rhythms can be detected by using ECG signal beat...
This paper introduces the use of ECG signals from multiple leads to improve the accuracy of ECG signal classification with Artificial Neural Networks (ANN). The current methods commonly proposed rely on advanced signal processing or statistical analysis of the main lead II (MLII) in order to extract features that serve as a description of the signal. MLII, while being the most easily obtained ECG...
Electrocardiogram (ECG) is used as one of the important diagnostic tool for the detection of the health of a heart. Growing number of heart patients has necessitated development of automatic detection techniques for detecting various abnormalities or arrhythmias of the heart to reduce pressure on physicians and share their load. The present work will help in developing a computer based system that...
Arrhythmia is an abnormal rhythm of heart. Correct detection or interpretation is much more important. In this paper, we aim at detecting 5 types of ECG beats: (1) Normal beat (2) Right Bundle Branch Block, (3) Left Bundle Branch Block, (4) Premature Ventricular Contraction (5) Paced beat. We compared the performances of two different classifier using DWT feature extraction technique. The DWT technique...
According to the World Health Organization, cardiovascular diseases (CVD) are the main cause of death worldwide. An estimated 17.5 million people died from CVD in 2012, representing 31% of all global deaths. The electrocardiogram (ECG) is a central tool for the pre-diagnosis of heart diseases. Many advances on ECG arrhythmia classification have been developed in the last century; however, there is...
This paper proposes the system to predict eight cardiac arrhythmias using the radial basis function neural network (RBFN). In our study of neural network for heart rate time series, the prediction of Left bundle branch block (LBBB), Atrial fibrillation (AFIB), Normal Sinus Rhythm (NSR), Right bundle branch block (RBBB), Sinus bradycardia (SBR), Atrial flutter (AFL), Premature Ventricular Contraction...
We have conducted a study of detection system for premature ventricular contraction (PVC) developed in an android mobile phone. The system utilizes artificial neural network (ANN) with electrocardiographic (ECG) features of RR interval and QRS width. RR Interval and QRS width is Interval in ECG waveform. The algorithms of the detection are implemented using JAVA Eclipse Juno. The system is examined...
This work introduces a method for detection of premature ventricular contractions (PVCs) in photoplethysmogram (PPG). The method relies on 6 features, characterising PPG pulse power, and peak-to-peak intervals. A sliding window approach is applied to extract the features, which are later normalized with respect to an estimated heart rate. Artificial neural network with either linear and non-linear...
The objective of this paper is to develop an easy, efficient and robust algorithm for the analysis of electrocardiogram signals. The technique used in this algorithm is based on the use of Moving Average Filters and Adaptive Thresholding for QRS complex detection. Several established ECG databases published on PhysioNet with sampling frequency ranging from 128Hz–1KHz, were used for analyzing the technique...
ECG refers to non-invasive bioelectrical recording of the heart. Under the clinical settings, the ECG is interpreted by cardiologists via conventional inspection techniques. The methods however are exposed to visual error which leads to inaccurate diagnosis of the heart condition. Hence, as an attempt towards an automated diagnostic system, the paper elaborates on arrhythmia modelling based on ECG...
This paper introduces an electrocardiogram beat classification method based on deep belief networks. This method includes two parts: feature extraction and classification. In the feature extraction part, features are extracted from the original electrocardiogram signal: including features extracted by deep belief networks and timing interval features. Several classifiers are selected to classify the...
The purpose of this study was the development and investigation of the automatic Premature Ventricular Contraction (PVC) detection and classification method using Photoplethysmographic (PPG) signals. The main issue of using PPG for arrhythmia detection are the artefacts which may be falsely detected as an arrhythmic pulses. The method is based on 6 PPG features, obtained in 12 s analysis frame. The...
The sleep apnea is a sleep disorder characterized by cessation of respiratory flow (apnea) or a reduction in the flow (hypopnea). This disorder is often invalidating and may in some cases lead to death. During the night, symptoms can include nocturnal choking, heavy snoring, sweating, restless sleep, impotence, and witnessed apnea. As the sleep centers for apnea detection are usually overloaded and...
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