Computer-aided morphological analysis of Holter ECG recordings based on support vector learning system

Jankowski, S.; Oręziak, Artur; Skorupski, A.; Kowalski, H.; Szymański, Z; Piątkowska-Janko, Ewa

doi:10.1109/cic.2003.1291226

Cited by 9 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In case of ECG classi¯cation into two classes namely, Normal (healthy) and Abnormal (a®ected), various classi¯ers are used such as: arti¯cial neural networks (Probabilistic NN, Feed-Forward NN, multi-layer Perceptron NN (MLP)) 13 and linear quantization vector classi¯cation algorithm LQV. 14 Support vector machines SVMs are also applied for ECG signal analysis and arrhythmia classi¯cation, [15][16][17][18][19][20][21][22] where QRS detection is achieved by using some other techniques. The classi¯cation performances depend highly on how the combination of the involved methods is performed.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Time and Frequency Methods for Cardiac Anomalies Recognition

Slimane

Talha

Zairi

et al. 2016

Biomed. Eng. Appl. Basis Commun.

View full text Add to dashboard Cite

The main aim of this paper is a contribution to the design of an Intelligent Diagnosis System for Cardiac anomalies detection in acquired ECG (Electrocardiogram) signals. To attain this goal, the authors have developed two approaches to extract the most relevant and signi¯cant parameters that can best classify ECGs into two classes namely, Normal and Abnormal from real Electrocardiogram signals. The¯rst approach is a time method which consists in modeling ECGs using RBF (Radial Basis Function) neural network algorithm, whereas, the second approach is a frequency based method which relies on the DWT (Discrete Wavelet Transform) that projects the signal into a timescale (time-frequency) plane. In both approaches, the resulting data are submitted to the same SVM (Support Machine Vector) Classi¯er. The results obtained have shown a good adjustment of relevant parameters for each approach and have revealed the most e±cient combined processing-classi¯cation algorithm that has achieved classi¯cation rates up to 100 % allowing at a time a reduced number of parameters. This fact had considerably simpli¯ed the hardware for a real time implementation. Finally, the authors compared both approaches and have identi¯ed some implementation constraints, such as the sampling frequency for the frequency based approach and preprocessing and cutting for the time approach.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparison of Time and Frequency Methods for Cardiac Anomalies Recognition

Slimane

Talha

Zairi

et al. 2016

Biomed. Eng. Appl. Basis Commun.

View full text Add to dashboard Cite

show abstract

“…Both innovations can be formulated in a quadratic programming framework whose Biomedical Signal Processing and Control 3 (2008) 341-349 optimum solution is obtained in a computation time of a polynomial order. SVMs are applied for ECG signal analysis and arrhythmia classification [15][16][17][18][19][20][21], where in component wave detection is accomplished by using some other technique.…”

Section: Introductionmentioning

confidence: 99%

Development of SVM based classification techniques for the delineation of wave components in 12-lead electrocardiogram

Mehta

Lingayat

2008

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

“…Chu et al (2005) applied SVMs for cancer diagnosis based on microarray gene expression data and protein secondary structure prediction. SVMs have been applied for ECG signal analysis and arrhythmia classification (Roig et al , 2000; Jankowski & Oreziak, 2003; Jankowski et al , 2003; Osowski et al , 2004; Acir, 2005, 2006; Song et al , 2005), where component wave detection is accomplished by using some other technique. SVMs are also implemented successfully for the detection of QRS complexes in simultaneously recorded 12‐lead ECGs using different criteria for the generation of feature signals (Mehta & Lingayat, 2007a, 2007b, 2009).…”

Section: Introductionmentioning

confidence: 99%

Detection and delineation of P and T waves in 12‐lead electrocardiograms

Mehta¹,

Lingayat²,

Sanghvi

2009

Expert Systems

View full text Add to dashboard Cite

This paper presents an efficient method for the detection and delineation of P and T waves in 12-lead electrocardiograms (ECGs) using a support vector machine (SVM). Digital filtering techniques are used to remove power line interference and baseline wander. An SVM is used as a classifier for the detection and delineation of P and T waves. The performance of the algorithm is validated using original simultaneously recorded 12-lead ECG recordings from the standard CSE (Common Standards for Quantitative Electrocardiography) ECG multi-lead measurement library. A significant detection rate of 95.43% is achieved for P wave detection and 96.89% for T wave detection. Delineation performance of the algorithm is validated by calculating the mean and standard deviation of the differences between automatic and manual annotations by the referee cardiologists. The proposed method not only detects all kinds of morphologies of QRS complexes, P and T waves but also delineates them accurately. The onsets and offsets of the detected P and T waves are found to be within the tolerance limits given in the CSE library.

show abstract

Computer-aided morphological analysis of Holter ECG recordings based on support vector learning system

Cited by 9 publications

References 5 publications

Comparison of Time and Frequency Methods for Cardiac Anomalies Recognition

Comparison of Time and Frequency Methods for Cardiac Anomalies Recognition

Development of SVM based classification techniques for the delineation of wave components in 12-lead electrocardiogram

Detection and delineation of P and T waves in 12‐lead electrocardiograms

Contact Info

Product

Resources

About