Cardiac Arrhythmias shows a condition of abnor-mal electrical activity in the heart which is a threat to humans. This paper presents a method to analyze electrocardiogram (ECG) signal, extract the fea-tures, for the c...Cardiac Arrhythmias shows a condition of abnor-mal electrical activity in the heart which is a threat to humans. This paper presents a method to analyze electrocardiogram (ECG) signal, extract the fea-tures, for the classification of heart beats according to different arrhythmias. Data were obtained from 40 records of the MIT-BIH arrhythmia database (only one lead). Cardiac arrhythmias which are found are Tachycardia, Bradycardia, Supraventricular Tachycardia, Incomplete Bundle Branch Block, Bundle Branch Block, Ventricular Tachycardia. A learning dataset for the neural network was obtained from a twenty records set which were manually classified using MIT-BIH Arrhythmia Database Directory and docu- mentation, taking advantage of the professional experience of a cardiologist. Fast Fourier transforms are used to identify the peaks in the ECG signal and then Neural Networks are applied to identify the diseases. Levenberg Marquardt Back-Propagation algorithm is used to train the network. The results obtained have better efficiency then the previously proposed methods.展开更多
FECG (Fetal ECG) signal contains potentially precise information that could assist clinicians in making more appro-priate and timely decisions during pregnancy and labor. The extraction and detection of the FECG signa...FECG (Fetal ECG) signal contains potentially precise information that could assist clinicians in making more appro-priate and timely decisions during pregnancy and labor. The extraction and detection of the FECG signal from com-posite maternal abdominal signals with powerful and advance methodologies is becoming a very important requirement in fetal monitoring. The purpose of this paper is to illustrate the developed algorithms on FECG signal extraction from the abdominal ECG signal using Neural Network approach to provide efficient and effective ways of separating and understanding the FECG signal and its nature. The FECG signal was isolated from the abdominal signal by neural network approach with different learning constant value and momentum as well so that acceptable signal can be con-sidered. According to the output it can be said that the algorithm is working satisfactory on high learning rate and low momentum value. The method appears to be exceedingly robust, correctly isolate the FECG signal from abdominal ECG.展开更多
The attributes of the ECG signal signifying the unique electrical properties of the heart offer the opportunity to expand the realm of biometrics, which pertains the identification of an individual based on physical c...The attributes of the ECG signal signifying the unique electrical properties of the heart offer the opportunity to expand the realm of biometrics, which pertains the identification of an individual based on physical characteristics. The temporal organization of the ECG signal offers a basis for composing a machine learning feature set. The four attributes of the feature set are derived through software automation enabled by Python. These four attributes are the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum and the Q wave minimum and S wave minimum relative to the R wave maximum. The multilayer perceptron neural network was applied and evaluated in terms of classification accuracy and time to develop the model. Superior performance was achieved with respect to a reduced feature set considering only the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum by comparison to all four attributes applied to the feature set and the temporal differential of the Q wave minimum and S wave minimum relative to the R wave maximum. With these preliminary findings and the advent of portable and wearable devices for the acquisition of the ECG signal, the temporal organization of the ECG signal offers robust potential for the field of biometrics.展开更多
The PQRST segment which include the major information in a heart beatis detected and used as the input pattern to cluster by ART2 model. The parametersof pacemaker which consist of pulse, QRS characteristics, clusteri...The PQRST segment which include the major information in a heart beatis detected and used as the input pattern to cluster by ART2 model. The parametersof pacemaker which consist of pulse, QRS characteristics, clustering results andprogrammed parameters are combined in analyzing paced ECG (PECG) synthetically.展开更多
文摘Cardiac Arrhythmias shows a condition of abnor-mal electrical activity in the heart which is a threat to humans. This paper presents a method to analyze electrocardiogram (ECG) signal, extract the fea-tures, for the classification of heart beats according to different arrhythmias. Data were obtained from 40 records of the MIT-BIH arrhythmia database (only one lead). Cardiac arrhythmias which are found are Tachycardia, Bradycardia, Supraventricular Tachycardia, Incomplete Bundle Branch Block, Bundle Branch Block, Ventricular Tachycardia. A learning dataset for the neural network was obtained from a twenty records set which were manually classified using MIT-BIH Arrhythmia Database Directory and docu- mentation, taking advantage of the professional experience of a cardiologist. Fast Fourier transforms are used to identify the peaks in the ECG signal and then Neural Networks are applied to identify the diseases. Levenberg Marquardt Back-Propagation algorithm is used to train the network. The results obtained have better efficiency then the previously proposed methods.
文摘FECG (Fetal ECG) signal contains potentially precise information that could assist clinicians in making more appro-priate and timely decisions during pregnancy and labor. The extraction and detection of the FECG signal from com-posite maternal abdominal signals with powerful and advance methodologies is becoming a very important requirement in fetal monitoring. The purpose of this paper is to illustrate the developed algorithms on FECG signal extraction from the abdominal ECG signal using Neural Network approach to provide efficient and effective ways of separating and understanding the FECG signal and its nature. The FECG signal was isolated from the abdominal signal by neural network approach with different learning constant value and momentum as well so that acceptable signal can be con-sidered. According to the output it can be said that the algorithm is working satisfactory on high learning rate and low momentum value. The method appears to be exceedingly robust, correctly isolate the FECG signal from abdominal ECG.
文摘The attributes of the ECG signal signifying the unique electrical properties of the heart offer the opportunity to expand the realm of biometrics, which pertains the identification of an individual based on physical characteristics. The temporal organization of the ECG signal offers a basis for composing a machine learning feature set. The four attributes of the feature set are derived through software automation enabled by Python. These four attributes are the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum and the Q wave minimum and S wave minimum relative to the R wave maximum. The multilayer perceptron neural network was applied and evaluated in terms of classification accuracy and time to develop the model. Superior performance was achieved with respect to a reduced feature set considering only the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum by comparison to all four attributes applied to the feature set and the temporal differential of the Q wave minimum and S wave minimum relative to the R wave maximum. With these preliminary findings and the advent of portable and wearable devices for the acquisition of the ECG signal, the temporal organization of the ECG signal offers robust potential for the field of biometrics.
文摘The PQRST segment which include the major information in a heart beatis detected and used as the input pattern to cluster by ART2 model. The parametersof pacemaker which consist of pulse, QRS characteristics, clustering results andprogrammed parameters are combined in analyzing paced ECG (PECG) synthetically.
