ECG Characterization of paroxysmal atrial fibrillation: parameter extraction and automatic diagnosis algorithm

Ros, Eduardo; Mota, Sonia; Fernandez, Francisco Javier Ramírez; Toro, Francisco de; Bernier, José Luis

doi:10.1016/j.compbiomed.2003.10.002

Cited by 27 publications

(18 citation statements)

References 11 publications

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“…The evolutionary algorithms perform multipath searching and widely explore the input space. In addition, in the previous approach [19], [20], local minima could not be avoided. Furthermore, this paper illustrates how the use of evolutionary algorithms allows multiobjective and multimodal optimization, which is interesting in the context of diagnostic systems.…”

Section: Results Of Different Classification Systems For Paf Diamentioning

confidence: 99%

“…The extracted characteristics were those considered important in mapping an ECG trace for PAF diagnosis from the study carried out in [18]- [20]. We have studied combinatorial searching schemes for the optimization of the classification system [19], [20].…”

Section: Results Of Different Classification Systems For Paf Diamentioning

confidence: 99%

“…In this context the presented optimization scheme becomes interesting. To illustrate this we have extracted diverse characteristics mainly based on the morphologicalfeaturesofthe -wave [18]- [20]andtheheartrate. We have then applied the presented optimization methodology and evaluated the performance of different approaches.…”

Section: Results Of Different Classification Systems For Paf Diamentioning

confidence: 99%

“…More detailed results from characteristic scanning procedures can be found in [19] and [20], which shows single path and single objective searching techniques. The optimization strategies presented in this paper represent a development on results previously achieved.…”

Section: Comparison Between the Different Approachesmentioning

confidence: 99%

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Evolutionary Algorithms for Multiobjective and Multimodal Optimization of Diagnostic Schemes

Toro¹,

Ros

Mota

et al. 2006

IEEE Trans. Biomed. Eng.

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Abstract-This paper addresses the optimization of noninvasive diagnostic schemes using evolutionary algorithms in medical applications based on the interpretation of biosignals. A general diagnostic methodology using a set of definable characteristics extracted from the biosignal source followed by the specific diagnostic scheme is presented. In this framework, multiobjective evolutionary algorithms are used to meet not only classification accuracy but also other objectives of medical interest, which can be conflicting. Furthermore, the use of both multimodal and multiobjective evolutionary optimization algorithms provides the medical specialist with different alternatives for configuring the diagnostic scheme. Some application examples of this methodology are described in the diagnosis of a specific cardiac disorder-paroxysmal atrial fibrillation.Index Terms-Evolutionary algorithms, multiobjective and multimodal optimization, noninvasive medical diagnosis, paroxysmal atrial fibrillation.

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Section: Results Of Different Classification Systems For Paf Diamentioning

confidence: 99%

Section: Results Of Different Classification Systems For Paf Diamentioning

confidence: 99%

Section: Results Of Different Classification Systems For Paf Diamentioning

confidence: 99%

Section: Comparison Between the Different Approachesmentioning

confidence: 99%

See 2 more Smart Citations

Evolutionary Algorithms for Multiobjective and Multimodal Optimization of Diagnostic Schemes

Toro¹,

Ros

Mota

et al. 2006

IEEE Trans. Biomed. Eng.

Self Cite

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show abstract

“…Numerous approaches have been adopted for detecting and classifying cardiac rhythms (Madeiro et al, 2007;Paoletti and Marchesi, 2006;Ravier et al, 2007;Ubeyli, 2007). Some techniques have applied artificial neural networks (Osowski and Nghia, 2002;Ravier et al, 2007;Tsipouras et al, 2005), support vector machines (Acir, 2005;Ubeyli, 2007), Fou-rier and wavelet analysis (Chan et al, 2008;Chen et al, 2006;Medeiro et al, 2007;Osowski and Nghia, 2002), time-frequency analysis (Christov et al, 2006), the statistical classifier model (Dubois et al, 2007;De Chazal et al, 2004;Paoletti and Marchesi, 2006), and pattern recognition (Ros et al, 2004;Sternickel, 2002). However, large variations in ECG waveforms as noise continue to present challenges for these algorithms, and thus the problem persists.…”

Section: Introductionmentioning

confidence: 99%

An SPC-Based Forward-Backward Algorithm for Arrhythmic Beat Detection and Classification

Jiang¹,

Yang²,

Yang³

2013

Industrial Engineering and Management Systems

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Large variation in electrocardiogram (ECG) waveforms continues to present challenges in defining R-wave locations in ECG signals. This research presents a procedure to extract the R-wave locations by forward-backward (FB) algorithm and classify the arrhythmic beat conditions by using RR intervals. The FB algorithm shows forward and backward searching rules from QRS onset and eliminates lower-amplitude signals near the baseline using a statistical process control concept. The proposed algorithm was trained the optimal parameters by using MIT-BIH arrhythmia database (MITDB), and it was verified by actual Holter ECG signals from a local hospital. The signals are classified into normal (N) and three arrhythmia beat types including premature ventricular contraction (PVC), ventricular flutter/fibrillation (VF), and second-degree heart block (BII) beat. This work produces 98.54% accuracy in the detection of R-wave location; 98.68% for N beats; 91.17% for PVC beats; and 87.2% for VF beats in the collected Holter ECG signals, and the results are better than what are reported in literature.

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Using Fuzzy Clustering Technique for Function Approximation to Approximate ECG Signals

Guillén

Rojas

Ros

et al. 2005

Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach

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ECG Characterization of paroxysmal atrial fibrillation: parameter extraction and automatic diagnosis algorithm

Cited by 27 publications

References 11 publications

Evolutionary Algorithms for Multiobjective and Multimodal Optimization of Diagnostic Schemes

Evolutionary Algorithms for Multiobjective and Multimodal Optimization of Diagnostic Schemes

An SPC-Based Forward-Backward Algorithm for Arrhythmic Beat Detection and Classification

Using Fuzzy Clustering Technique for Function Approximation to Approximate ECG Signals

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