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

... η] † (6. 28) P1: Shashi August 24, 20 06 11: 46 Chan-Horizon Azuaje˙Book 6. 5 Model-Based Filtering 187 Figure 6. 6 Demonstration of ICA noise reduction for γ = 10: (a) original noise-free ECG signal, ... 6. 6. While both the χ-optimized cleaned signal [Figure 6. 6(b)] and the ρ-optimized cleaned signal [Figure 6. 6(d)] are similar to the original noise- free signal [Figure 6....

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... → LF HF LF HF γ Actual Value ↓ 0 .64 0 .64 0.39 0 .61 † 0 .64 0 .60 0.37 0 .62 0.8 0 .64 0.34 0. 26 0.74 0.7 0 .64 0.32 0.25 0. 76 0 .6 0 .64 0.47 0.32 0 .68 0.8 ‡ † indicates no ectopy is present. ‡ indicates ... Estima- tion Using the Karhunen-Lo ` eve Transform,” Computers in Cardiology, Vol. 16, 1989, pp. 269 –272. [29] Mark, R. G., and G. B. Moody, ECG Arrhythmia Analysis: Design...

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... domain, KLT [g] 77.2 86. 3 67 .5 69 .2 <– 79 .6 78.3 68 .4 67 .3 [a] 81.3 89.2 77 .6 68.9 <– 78.9 80.7 73.1 74.9 [17], Neural net [g] 85.0 68 .7 73.0 69 .5 –––– [a] 88 .6 78.4 72.2 67 .5 –––– [18], Neural ... 20 06 11:52 Chan-Horizon Azuaje˙Book 284 ST Analysis The open-source tool EVAL ST [45] provides first (record-by-record) and second- order (aggregate gross and ave...

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... Library. ISBN-10: 1-5 805 3-9 6 6- 1 ISBN-13: 97 8-1 -5 805 3-9 6 6- 1 Cover design by Michael Moretti © 20 06 ARTECH HOUSE, INC. 68 5 Canton Street Norwood, MA 02 062 All rights reserved. Printed and bound in ... 20 06 11:32 Chan-Horizon Azuaje˙Book Advanced Methods and Tools for ECG Data Analysis Gari D. Clifford Francisco Azuaje Patrick E. McSharry Ed...

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... body (and heart). P1: Shashi August 24, 20 06 11: 36 Chan-Horizon Azuaje˙Book 2.5 ECG Acquisition Hardware 49 also forms the basis for standards in many other countries including UL 60 60 1-1 for the ... electroencephalograms (and more increasingly is becoming the standard for ECGs); HL7 [ 26, 27] (an XML-based format for the exchange of data in hospitals); and WaveForm D...

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... observation, and short transient sources 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 ... 108], compres- sion and filtering [119, 123], and classification [124]. However, the required com- plexity for realistic models (particularly for ECG generation) has limite...

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... transform or the Karhunen-Lo ` eve transform (KLT). P1: Shashi August 24, 20 06 11: 46 Chan-Horizon Azuaje˙Book CHAPTER 6 Nonlinear Filtering Techniques Patrick E. McSharry and Gari D. Clifford 6. 1 ... 269 –272. [17] Mark, R. G., and G. B. Moody, ECG Arrhythmia Analysis: Design and Evaluation Strate- gies,” Chapter 18 in I. Gath and G. F. Inbar, (eds.), Advances in Processing...

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... time se- ries contains scatter due to beat-by-beat measurement jitter and due to erroneous measurements which manifest as outliers. Figure 9 .6 shows the time-domain and KLT-based diagnostic and ... not found, the procedure searches forwards in each ECG lead for up to T J = 68 ms for a part of the waveform which “starts to flatten” (i.e., a part of waveform where slope becam...

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... automated ECG interval analysis systems are unable to differentiate be- tween normal ECG waveforms (for which the automated annotations are generally reliable) and abnormal or unusual ECG waveforms (for ... 24, 20 06 11:55 Chan-Horizon Azuaje˙Book 3 36 Supervised Learning Methods for ECG Classification/Neural Networks and SVM Approaches Table 12 .6 The Comparison of DUV...

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... 1.02 V 54 10.37 9 .63 8.39 12 .60 13.07 7 .61 V 61 0.14 0 .60 0. 06 0 .62 0. 76 0.72 V 62 0.58 2.03 0.19 1 .60 2.15 2.14 V 63 0.40 1.43 0.13 1.07 1.48 1.49 V 64 5.80 18.87 1.94 18.11 18.50 16. 01 Average 4.11 ... 83 Nonstationarities, 64 67 activity-related changes, 86 89 arrhythmias, 66 67 dealing with, 83–92 heart rate hysteresis, 65 66 HRV metrics, 84– 86 manifestation,...

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