Thesis - Instituto de TelecomunicaÃ§Ãµes

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86 CHAPTER 4. SIGNAL PROCESSING AND FEATURE EXTRACTION11.5511.5011.45SC (uS)11.4011.3511.3011.25SC’01 2 3 4SC’’01 2 3 40 1 2 3 4 5Time (s)Figure 4.11: Small amplitude SCR event. First and second order derivatives are presented.11.45SC (uS)11.4011.3511.3011.25SC’02 4 6 8SC’’02 4 6 80 2 4 6 8 10Time (s)Figure 4.12: Pair of SCR’s events that overlap in the increasing zone of the first event. Firstand second order derivatives are presented.
4.3. ELECTROCARDIOGRAM 87to the cardiovascular system, with normal (non-pathologic) ECG signals.The easiest event to detect on a ECG signal is the R wave component, given that it isthe higher amplitude component in the signal. The simplest way to detect a R wave is todefine a threshold and detect the time instants where the signal passes this threshold.To automate the process of detection of the PQRST waves (the names of the waveshave been introduced in the previous chapter and are depicted in figure 3.9), the first stepis the definition of a threshold for the detection of the R waves.In the simplest ECGdetection algorithms [62] the threshold is established by finding the maximum value overall the signal. The threshold is set to a ratio of this value ( 1 2 to 1 4of the maximum value).This threshold is used to detect the R waves, marking all time instants where the wave hasa local maximum higher than the threshold. In stable ECG recordings, this method worksrelatively well, without missing many beats and without incorrectly detecting beats.However this approach is susceptible to fail in some conditions: (a) noisy signals canhave spurious events with higher amplitude than the threshold. The problem exists whensome of the P or T waves are high enough to pass the established threshold; (b) electromiographysignal added to the ECG, creating noise in the signal (associated to muscularactivity); (c) electrode displacement due to the subject’s movements, creating bad contactand noise in the form of high amplitude spikes in the signal. This polluted signal has an increasedcomplexity for detection of the interesting events, leading to failure of the previouslydescribed method.We developed an algorithm to compute the position of the R waves, to overcome some ofthese difficulties. Our ECG processing approach is a modified version of the Multiplicationof Backward Differences (MOBD) algorithm [137]. Our implementation follows the stepsbelow:(a) Normalizing an ECG signal,(b) Detecting the R wave via the derivative of the ECG signal,(c) Creating restrictions to detected beats of the ECG signal.We explain each of these steps in more detail in the following paragraphs.
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UNIVERSIDADE TÉCNICA DE LISBOAINST
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iior gave some support to the devel
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ResumoO comportamento humano tem si
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viii2.3.2 Voice . . . . . . . . . .
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x6.2.2 Feature Analysis . . . . . .
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xii4.2 The x-y representation of th
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xviiiFCT Fundação para a Ciência
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Chapter 1Introduction1.1 The Proble
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1.1. THE PROBLEM 3Figure 1.2: ECG s
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1.2. MOTIVATION 5authentication sit
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1.3. CONTRIBUTIONS 7resource in the
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1.4. THESIS STRUCTURE 9The present
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Chapter 2State of the ArtIn this ch
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2.1. BIOMETRICS CONCEPTS 13to class
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2.1. BIOMETRICS CONCEPTS 15by the s
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2.2. BIOMETRICS TECHNOLOGIES 17Figu
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2.3. BEHAVIORAL BIOMETRICS 19Septem
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2.3. BEHAVIORAL BIOMETRICS 21and co
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2.3. BEHAVIORAL BIOMETRICS 232. ove
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2.3. BEHAVIORAL BIOMETRICS 25Figure
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2.3. BEHAVIORAL BIOMETRICS 27situat
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2.3. BEHAVIORAL BIOMETRICS 29of sig
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2.4. MULTIMODAL BIOMETRICS 312.4 Mu
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2.6. DISCUSSION AND CONCLUSION 33al
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Page 75 and 76: 3.4. MATERIALS AND METHODS 51Figure
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Chapter 7ConclusionsIn this chapter
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7.2. APPLICATION SCENARIOS 139adequ
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7.3. FUTURE WORK 141the user clicks
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Appendix AToolsThe work underlying
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A.2. SCIENTIFIC COMPUTATION 145side
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Appendix BTestsThe set of tests dev
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B.1. INTELLIGENCE TEST 149Figure B.
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B.2. MEMORY TEST 151# 2 3 3# 2 3 3#
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B.2. MEMORY TEST 153Level. In this
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B.3. ASSOCIATION TEST 155Guide. Ini
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B.5. CONCENTRATION TEST 157Descript
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B.5. CONCENTRATION TEST 159Test Gen
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B.6. TEST’S SEQUENCE 161Orderpage
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B.7. RELATED DOCUMENTS 163Figure B.
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Bibliography[1] A. Acharya, M. Cutt
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BIBLIOGRAPHY 171[18] B. Bimbaum. US
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BIBLIOGRAPHY 173[42] H. D. Critchle
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BIBLIOGRAPHY 175[63] R. Fernandez a
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BIBLIOGRAPHY 177[84] H. Gamboa, A.
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BIBLIOGRAPHY 179[102] S. Hocquet, J
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BIBLIOGRAPHY 181[124] R. M. Kaplan
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BIBLIOGRAPHY 183[145] C. L. Lim, E.
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BIBLIOGRAPHY 185[169] NBSP. Interna
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BIBLIOGRAPHY 187[191] T. Pixley. Do
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BIBLIOGRAPHY 189[214] T. Shen and W
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BIBLIOGRAPHY 191[236] M. Trauring.
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