• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 55
Next 10 →

A Patient-Adaptive Profiling Scheme for ECG Beat Classification

by Miad Faezipour, Student Member, Adnan Saeed, Suma Ch, Rika Bulusu, Mehrdad Nourani, Senior Member, Hlaing Minn, Senior Member
"... Abstract—Recent trends in clinical and telemedicine applications highly demand automation in electrocardiogram (ECG) signal processing and heart beat classification. A patient-adaptive cardiac profiling scheme using repetition-detection concept is proposed in this paper. We first employ an efficient ..."
Abstract - Cited by 5 (1 self) - Add to MetaCart
Abstract—Recent trends in clinical and telemedicine applications highly demand automation in electrocardiogram (ECG) signal processing and heart beat classification. A patient-adaptive cardiac profiling scheme using repetition-detection concept is proposed in this paper. We first employ

using ECG Signals

by Pooja Bhardwaj, Rahul R Choudhary, Ravindra Dayama
"... ECG is a graphical record of the electrical tension of heart and has established as one the most important bio-signal used by cardiologists for diagnostic purposes and further to adopt an appropriate course of treatment. The difficulties faced in interpretation of ECG signals forced researchers to s ..."
Abstract - Add to MetaCart
to study about automatic detection of cardiac arrhythmia disorders. The data analysis techniques using specific computer software could easily interpret complex ECG signals, predict presence or absence of cardiac arrhythmia. This provides real time analysis and further facilitates for timely diagnosis

ECG Beat Diagnosis Approach for ECG Printout Based on Expert System

by Muzhir Shaban Al-ani, Atiaf Ayal Rawi
"... Abstract — An Electrocardiogram (ECG) is a bioelectrical signal which records the heart’s electrical activity versus time. It is a method used to measure the rate and regularity of heartbeats. This paper introduces a way of automating the diagnosis of cardiac disorders using an expert system designe ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
Abstract — An Electrocardiogram (ECG) is a bioelectrical signal which records the heart’s electrical activity versus time. It is a method used to measure the rate and regularity of heartbeats. This paper introduces a way of automating the diagnosis of cardiac disorders using an expert system

Analyzing ECG Signals and Detection of Cardiac Arrhythmia Using Back Propagation Neural Network- Part I: Model Development

by Akinlolu A. Ponnle, Oludare Y. Ogundepo
"... Electrocardiogram (ECG) is a graphic recording of the electrical activity produced by the heart. The accuracy of any electrocardiogram waveform extraction plays a vital role in helping a better diagnosis of any heart related illnesses. We present a computer-aided application model for detection of c ..."
Abstract - Add to MetaCart
of cardiac arrhythmia in ECG signal, which consists of signal pre-processing and detection of the ECG signal components adapting Pan-Tompkins and Hamilton-Tompkins algorithms; feature extraction from the detected QRS complexes, and classification of the beats extracted from QRS complexes using Back

A RULE-BASED EXPERT SYSTEM FOR AUTOMATED ECG DIAGNOSIS

by Muzhir Shaban Al-ani, Atiaf Ayal Rawi
"... This paper presents the development of a rule-based expert system that emulates the ECG interpretation skills of an expert cardiologist for introducing way of automating the diagnosis of cardiac disorders. The knowledge of an expert is confined to him and is not freely available for decision-making. ..."
Abstract - Add to MetaCart
beats (N), Sinus Bradycardia beat, Sinus Tachycardia beat and Sinus Arrhythmia beat. The ECG image from ECG simulator is processed by some image processing techniques such as red grid removing, noise rejection, and image thinning firstly, then, combining detection component of ECG signal(P,QRS,T) based

Automatic Arrhythmia Detection Based on Heart Beat Interval Series Recorded Through Bed Sensors During Sleep

by Matteo Migliorini, Sergio Cerutti, Luca T Mainardi, Juha M Kortelainen, Anna M Bianchi
"... A high frequency of cardiac arrhythmias has been reported in sleep disordered patients. In order to detect the presence of arrhythmia during sleep, cardiac activity needs to be monitored. Several devices exist able to provide reliable Heart Rate Variability (HRV) measures in a minimally-intrusive wa ..."
Abstract - Add to MetaCart
-intrusive way. Hence, there is the need for the development of robust methods for arrhythmia detection based on HRV measures. In the present study a method for automatic arrhythmia detection based on the analysis of an inter-beat series was developed and validated on recordings coming from the MIT

A Point Process Local Likelihood Algorithm for Robust and Automated Heart Beat Detection and Correction

by Luca Citi , Emery N Brown , Riccardo Barbieri , 2011
"... Abstract Robust and automated classification and correction of ECG-derived heart beats are a necessary prerequisite for an accurate real-time estimation of measures of heart rate variability and cardiovascular control. In particular, the low quality of the signal, as well as the presence of recurri ..."
Abstract - Add to MetaCart
Abstract Robust and automated classification and correction of ECG-derived heart beats are a necessary prerequisite for an accurate real-time estimation of measures of heart rate variability and cardiovascular control. In particular, the low quality of the signal, as well as the presence

Combination of Different Classifiers for Cardiac Arrhythmia Recognition

by M. R. Homaeinezhad, E. Tavakkoli, M. Habibi, S. A. Atyabi, A. Ghaffari
"... Abstract—This paper describes a new supervised fusion (hybrid) electrocardiogram (ECG) classification solution consisting of a new QRS complex geometrical feature extraction as well as a new version of the learning vector quantization (LVQ) classification algorithm aimed for overcoming the stability ..."
Abstract - Add to MetaCart
the stability-plasticity dilemma. Toward this objective, after detection and delineation of the major events of ECG signal via an appropriate algorithm, each QRS region and also its corresponding discrete wavelet transform (DWT) are supposed as virtual images and each of them is divided into eight polar sectors

A Generic and Robust System for Automated Patient-specific Classification of Electrocardiogram Signals

by Turker Ince, Serkan Kiranyaz, Moncef Gabbouj, Senior Member - IEEE Transactions on Biomedical Engineering , 2009
"... Abstract—This paper presents a generic and patient-specific classification system designed for robust and accurate detection of ECG heartbeat patterns. The proposed feature extraction process utilizes morphological wavelet transform features, which are projected onto a lower dimensional feature spac ..."
Abstract - Cited by 33 (5 self) - Add to MetaCart
Abstract—This paper presents a generic and patient-specific classification system designed for robust and accurate detection of ECG heartbeat patterns. The proposed feature extraction process utilizes morphological wavelet transform features, which are projected onto a lower dimensional feature

SPLINE ACTIVATED NEURAL NETWORK FOR CLASSIFYING CARDIAC ARRHYTHMIA

by Ganesh Kumar, Y. S. Kumaraswamy
"... Electro Cardiogram’s (ECG) biomedical signals characterizing cardiac anomalies are used for identifying cardiac arrhythmia. Irregular heartbeat-Arrhythmia-affects heart rate causing problems. Many methods, trying to simplify arrhythmia monitoring through automated detection, were developed over the ..."
Abstract - Add to MetaCart
Electro Cardiogram’s (ECG) biomedical signals characterizing cardiac anomalies are used for identifying cardiac arrhythmia. Irregular heartbeat-Arrhythmia-affects heart rate causing problems. Many methods, trying to simplify arrhythmia monitoring through automated detection, were developed over
Next 10 →
Results 1 - 10 of 55
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University