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Advanced Methods and Tools for ECG Data Analysis - Part 3 docx

Advanced Methods and Tools for ECG Data Analysis - Part 3 docx

Advanced Methods and Tools for ECG Data Analysis - Part 3 docx

... pp. 433 – 436 . [34 ] Maglaveras, N., et al., ECG Pattern Recognition and Classification Using Non-LinearTransformations and Neural Networks: A Review,” International Journal of MedicalInformatics, ... Press,2000.[1 03] Ivanov, P. C., et al., “Scaling Behaviour of Heartbeat Intervals Obtained by Wavelet-Based Time-Series Analysis, ” Nature, Vol. 38 3, September 1996, pp. 32 3 32 7.[104] Turcott, R. G., and ... sin(ωtj)2 (3. 6)If A = B =2N12, (3. 5) and (3. 6) reduce to the classical definitions [ (3. 3) and (3. 4)] For even sampling (t = constant) FTXreduces to the DFT and in the limitt...
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Advanced Methods and Tools for ECG Data Analysis - Part 6 docx

Advanced Methods and Tools for ECG Data Analysis - Part 6 docx

... 1965, pp. 32 1 33 3.[19] Abarbanel, H. D. I., R. Brown, and M. B. Kennel, “Local Lyapunov Exponents Computedfrom Observed Data, ” Journal of Nonlinear Science, Vol. 2, No. 3, 1992, pp. 34 3 36 5.[20] ... classified TWA de-tection into preprocessing, data reduction, and analysis stages. This section focusesupon the strengths and limitations of TWA analysis methods, broadly compris-ing short-term Fourier ... each QRS-detection fidu-cial point, an idealistic (zero-noise) representation of each beat’s morphology may bederived. This leads to a method for filtering and segmenting the ECG and thereforeaccurately...
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Advanced Methods and Tools for ECG Data Analysis - Part 8 docx

Advanced Methods and Tools for ECG Data Analysis - Part 8 docx

... incorporate time-domain analysis [3 5], the KLT approach [7], a combination of time-domain analysis and KLT approach [37 ], a neural network approach [17], and a combination of theKLT transform and a neural ... with standard algorithms. For the analysis of 10-second12-lead ECG signals, this is rarely an issue. However, when very large amountsof ECG data must be processed rapidly (e.g., for the analysis ... N., Tandon, and R. K. P., Bhatt, “Wavelet Based ST-Segment Analysis, ”Medical & Biological Engineering & Computing, Vol. 36 , 1998, pp. 568–572. [30 ] Andreao, R.V., et al., “ST-Segment Analysis...
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Advanced Methods and Tools for ECG Data Analysis - Part 1 ppt

Advanced Methods and Tools for ECG Data Analysis - Part 1 ppt

... Clustering 34 2 13. 3.2 k-Means Clustering 34 3 13. 3 .3 SOM 34 3 13. 3.4 Application of Unsupervised Learning in ECG Classification 34 6 13. 3.5 Advances in Clustering-Based Techniques 34 7 13. 3.6 Evaluation ... Learning for ECG Classification 33 9 13. 1 Introduction 33 9 13. 2 Basic Concepts and Methodologies 33 9 13. 3 Unsupervised Learning Techniques and Their Applications in ECG Classification 34 1 13. 3.1 Hierarchical ... Expansion 32 112.2.2 HOS Features of the ECG 32 312 .3 Supervised Neural Classifiers 32 412 .3. 1 Multilayer Perceptron 32 512 .3. 2 Hybrid Fuzzy Network 32 612 .3. 3 TSK Neuro-Fuzzy Network 32 712 .3. 4 Support...
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Advanced Methods and Tools for ECG Data Analysis - Part 2 doc

Advanced Methods and Tools for ECG Data Analysis - Part 2 doc

... for Generating and Managing ecgML-Based Infor-mation,” Computers in Cardiology, Vol. 31 , 2004, pp. 5 73 576. [33 ] Schneider, R., libRASCH, http://www.librasch.org/. [34 ] ANSI/AAMI-EC38, Ambulatory ... 11 :39 Chan-Horizon Azuaje˙Book 3. 3 Standard Clinical ECG Features 61Figure 3. 6 Standard fiducial points in the ECG (P, Q, R, S, T, and U) together with clinical features(listed in Table 3. 1).either ... electroencephalograms (and moreincreasingly is becoming the standard for ECGs); HL7 [26, 27] (an XML-basedformat for the exchange of data in hospitals); and WaveForm DataBase (WFDB),a set of libraries developed...
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Advanced Methods and Tools for ECG Data Analysis - Part 4 ppsx

Advanced Methods and Tools for ECG Data Analysis - Part 4 ppsx

... 108], compres-sion and filtering [119, 1 23] , and classification [124]. However, the required com-plexity for realistic models (particularly for ECG generation) has limited the devel-opment of ... Relationship Between π In-terval and Systolic Blood Pressure Fluctuations: A Frequency-Dependent Phenomenon,”Cardiovascular Research, Vol. 38 , 1998, pp. 33 2 33 9.[22] Mukkamala, R., and R. J. Cohen, ... observation, and short transientsources of noise. Joint time-frequency analysis (JTFA) is then essentially a transfor-mation of an N-point M-dimensional signal (usually where M = 1 for the ECG) into...
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Advanced Methods and Tools for ECG Data Analysis - Part 5 pdf

Advanced Methods and Tools for ECG Data Analysis - Part 5 pdf

... Filtering Methods Figure 5.8 Layout of a D-p-D auto-associative neural network.inputs and outputs; the target data vector is simply the input data vector. There-fore, no labeling of training data ... H., and H. Heinrich, Analysis of ECG Late Potentials Using Time-Frequency Methods, ” in M. Akay, (ed.), Time Frequency and Wavelets in Biomedical Signal Process-ing, Chapter 4, New York: Wiley-IEEE ... Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis, ” Proc. R. Soc. Lond. A, Vol. 454,1998, pp. 9 03 995.[14] Huang, N. E., and S. S. Shen, The Hilbert-Huang...
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Advanced Methods and Tools for ECG Data Analysis - Part 7 ppt

Advanced Methods and Tools for ECG Data Analysis - Part 7 ppt

... Prin-cipal Component Analysis, ” Journal of Ambulatory Monitoring, Vol. 5, No. 2 3, 19 93, pp. 167–1 73. [18] Garc´ia, J., et al., “Comparative Study of Local and Karhunen-Lo`eve-Based ST-T ... which are visually informative and accessible for analysis at any time instant.Therefore, a technique which converts such point event series into a form suitable for standard analysis techniques ... time se-ries contains scatter due to beat-by-beat measurement jitter and due to erroneousmeasurements which manifest as outliers. Figure 9.6 shows the time-domain and KLT-based diagnostic and...
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Advanced Methods and Tools for ECG Data Analysis - Part 9 ppsx

Advanced Methods and Tools for ECG Data Analysis - Part 9 ppsx

... 200 3 1.5%F 37 1 3 0.81% 37 0 10 2.70%j 117 3 2.56% 105 10 9.52%J 40 3 7.50% 39 4 10.25%S 512 2 0 .39 % 512 3 0.58%Total 6,690 74 1. 83% 6,095 159 4.09%P1: ShashiAugust 24, 2006 11:56 Chan-Horizon ... across whole waveform features for a range of ECGs.P1: ShashiAugust 24, 2006 11:55 Chan-Horizon Azuaje˙Book 33 6 Supervised Learning Methods for ECG Classification/Neural Networks and SVM ApproachesTable ... algorithms have been proposed for ECG classification. Rel-evant methods include Bayesian approaches [2], statistical methods [3] , expert 33 9P1: ShashiAugust 24, 2006 11: 53 Chan-Horizon Azuaje˙Book11.9...
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Advanced Methods and Tools for ECG Data Analysis - Part 10 pps

Advanced Methods and Tools for ECG Data Analysis - Part 10 pps

... discovery, 35 2–59grid structure, 35 3learning algorithm illustration, 35 4learning dynamics, 35 3learning process, 35 2maps, 35 5, 35 9performance, 35 3properties, 35 3–54shapes, 35 8spread amount, 35 3visualization ... 140, 31 1Weighted voting approach, 33 1WFDB, 36 annotation file, 40database analysis, 37 –41developments, 36 37 libraries, 39 tools, 38 , 40Wiener filtering, 136 –40application of, 137 , 138 39 defined, ... modelsDempster-Shafer formalism, 33 0Dendrograms, 34 2, 34 3Denoising, 144–48Design considerations data collection issues, 34 35 data collection location/length, 29 ECG- related signals, 33 electrode...
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