A rhythm analysis method for exercise electrocardiograms

Ankara : The Department of Electrical and Electronics Engineering and Institute of Engineering and Sciences of Bilkent University, 1996.Thesis (Master's) -- Bilkent University, 1996.Includes bibliographical references leaves 44-46Exercise electrocardiography (the exercise ECG test or the stress EGG test) is one of the most popular and the most important non-invasive diagnostic tests in the field of cardiovascular disease. Arrhythmia analysis is an important part of the exercise ECG. A new approach to arrhythmia analysis is proposed in this thesis. 12 lead ECG signal is first reduced into three orthogonal channels which contain all the power of ECG. The orthogonalization process, an online Singular Value Decomposition (SVD) algorithm, maintains that these channels are free from both baseline wander and EMG noise. The third output channel has very low power with respect to first two. Making use of the orthogonality of these new channels. Total Power Signal (TPS) is calculated by summing the squares of orthogonalized channels. Employing the first two channels in TPS yields 92-99% of ECG power contained in all channels. Any arrhythmic behaviour during exercise test effects the TPS. In order to obtain the fiducial points of QRS complex first derivative of TPS is calculated. The method is compared with an algorithm proposed previously. The method and the algorithm are tested on 22 complete stress ECG test each with a duration between 9.5 to 26.5 minutes.Çağlar, B KeremM.S

Characteristics of a detail preserving nonlinear filter.

by Lai Wai Kuen.Thesis (M.Phil.)--Chinese University of Hong Kong, 1993.Includes bibliographical references (leaves [119-125]).Abstract --- p.iAcknowledgement --- p.iiTable of Contents --- p.iiiChapter Chapter 1 --- IntroductionChapter 1.1 --- Background - The Need for Nonlinear Filtering --- p.1.1Chapter 1.2 --- Nonlinear Filtering --- p.1.2Chapter 1.3 --- Goal of the Work --- p.1.4Chapter 1.4 --- Organization of the Thesis --- p.1.5Chapter Chapter 2 --- An Overview of Robust Estimator Based Filters Morphological FiltersChapter 2.1 --- Introduction --- p.2.1Chapter 2.2 --- Signal Representation by Sets --- p.2.2Chapter 2.3 --- Robust Estimator Based Filters --- p.2.4Chapter 2.3.1 --- Filters based on the L-estimators --- p.2.4Chapter 2.3.1.1 --- The Median Filter and its Derivations --- p.2.5Chapter 2.3.1.2 --- Rank Order Filters and Derivations --- p.2.9Chapter 2.3.2 --- Filters based on the M-estimators (M-Filters) --- p.2.11Chapter 2.3.3 --- Filter based on the R-estimators --- p.2.13Chapter 2.4 --- Filters based on Mathematical Morphology --- p.2.14Chapter 2.4.1 --- Basic Morphological Operators --- p.2.14Chapter 2.4.2 --- Morphological Filters --- p.2.18Chapter 2.5 --- Chapter Summary --- p.2.20Chapter Chapter 3 --- Multi-Structuring Element Erosion FilterChapter 3.1 --- Introduction --- p.3.1Chapter 3.2 --- Problem Formulation --- p.3.1Chapter 3.3 --- Description of Multi-Structuring Element Erosion Filter --- p.3.3Chapter 3.3.1 --- Definition of Structuring Element for Multi-Structuring Element Erosion Filter --- p.3.4Chapter 3.3.2 --- Binary multi-Structuring Element Erosion Filter --- p.3.9Chapter 3.3.3 --- Selective Threshold Decomposition --- p.3.10Chapter 3.3.4 --- Multilevel Multi-Structuring Element Erosion Filter --- p.3.15Chapter 3.3.5 --- A Combination of Multilevel Multi-Structuring Element Erosion Filter and its Dual --- p.3.21Chapter 3.4 --- Chapter Summary --- p.3.21Chapter Chapter 4 --- Properties of Multi-Structuring Element Erosion FilterChapter 4.1 --- Introduction --- p.4.1Chapter 4.2 --- Deterministic Properties --- p.4.2Chapter 4.2.1 --- Shape of Invariant Signal --- p.4.3Chapter 4.2.1.1 --- Binary Multi-Structuring Element Erosion Filter --- p.4.5Chapter 4.2.1.2 --- Multilevel Multi-Structuring Element Erosion Filter --- p.4.16Chapter 4.2.2 --- Rate of Convergence of Multi-Structuring Element Erosion Filter --- p.4.25Chapter 4.2.2.1 --- Convergent Rate of Binary Multi-Structuring Element Erosion Filter --- p.4.25Chapter 4.2.2.2 --- Convergent Rate of Multilevel Multi-Structuring Element Erosion Filter --- p.4.28Chapter 4.3 --- Statistical Properties --- p.4.30Chapter 4.3.1 --- Output Distribution of Multi-Structuring Element Erosion Filter --- p.4.30Chapter 4.3.1.1 --- One-Dimensional Statistical Analysis of Multilevel Multi-Structuring Element Erosion Filter --- p.4.31Chapter 4.3.1.2 --- Two-Dimensional Statistical Analysis of Multilevel Multi-Structuring Element Erosion Filter --- p.4.32Chapter 4.3.2 --- Discussions on Statistical Properties --- p.4.36Chapter 4.4 --- Chapter Summary --- p.4.40Chapter Chapter 5 --- Performance EvaluationChapter 5.1 --- Introduction --- p.5.1Chapter 5.2 --- Performance Criteria --- p.5.2Chapter 5.2.1 --- Noise Suppression --- p.5.5Chapter 5.2.2 --- Subjective Criterion --- p.5.16Chapter 5.2.3 --- Computational Requirement --- p.5.20Chapter 5.3 --- Chapter Summary --- p.5.23Chapter Chapter 6 --- Recapitulation and Suggestions for Further WorkChapter 6.1 --- Recapitulation --- p.6.1Chapter 6.2 --- Suggestions for Further Work --- p.6.4Chapter 6.2.1 --- Probability Measure Function for the Two-Dimensional Filter --- p.6.4Chapter 6.2.2 --- Hardware Implementation --- p.6.5ReferencesAppendice

ESTIMATION-BASED SOLUTIONS TO INCOMPLETE INFORMATION PURSUIT-EVASION GAMES

Differential games are a useful tool both for modeling conflict between autonomous systems and for synthesizing robust control solutions. The traditional study of games has assumed decision agents possess complete information about one another’s strategies and numerical weights. This dissertation relaxes this assumption. Instead, uncertainty in the opponent’s strategy is treated as a symptom of the inevitable gap between modeling assumptions and applications. By combining nonlinear estimation approaches with problem domain knowledge, procedures are developed for acting under uncertainty using established methods that are suitable for applications on embedded systems. The dissertation begins by using nonlinear estimation to account for parametric uncertainty in an opponent’s strategy. A solution is proposed for engagements in which both players use this approach simultaneously. This method is demonstrated on a numerical example of an orbital pursuit-evasion game, and the findings motivate additional developments. First, the solutions of the governing Riccati differential equations are approximated, using automatic differentiation to obtain high-degree Taylor series approximations. Second, constrained estimation is introduced to prevent estimator failures in near-singular engagements. Numerical conditions for nonsingularity are approximated using Chebyshev polynomial basis functions, and applied as constraints to a state estimate. Third and finally, multiple model estimation is suggested as a practical solution for time-critical engagements in which the form of the opponent’s strategy is uncertain. Deceptive opponent strategies are identified as a candidate approach to use against an adaptive player, and a procedure for designing such strategies is proposed. The new developments are demonstrated in a missile interception pursuit-evasion game in which the evader selects from a set of candidate strategies with unknown weights

Multiresolution image modelling and estimation

Multiresolution representations make explicit the notion of scale in images, and facilitate the combination of information from different scales. To date, however, image modelling and estimation schemes have not exploited such representations and tend rather to be derived from two- dimensional extensions of traditional one-dimensional signal processing techniques. In the causal case, autoregressive (AR) and ARMA models lead to minimum mean square error (MMSE) estimators which are two-dimensional variants of the well-established Kalman filter. Noncausal approaches tend to be transform-based and the MMSE estimator is the two- dimensional Wiener filter. However, images contain profound nonstationarities such as edges, which are beyond the descriptive capacity of such signal models, and defects such as blurring (and streaking in the causal case) are apparent in the results obtained by the associated estimators. This thesis introduces a new multiresolution image model, defined on the quadtree data structure. The model is a one-dimensional, first-order gaussian martingale process causal in the scale dimension. The generated image, however, is noncausal and exhibits correlations at all scales unlike those generated by traditional models. The model is capable of nonstationary behaviour in all three dimensions (two position and one scale) and behaves isomorphically but independently at each scale, in keeping with the notion of scale invariance in natural images. The optimal (MMSE) estimator is derived for the case of corruption by additive white gaussian noise (AWGN). The estimator is a one-dimensional, first-order linear recursive filter with a computational burden far lower than that of traditional estimators. However, the simple quadtree data structure leads to aliasing and 'block' artifacts in the estimated images. This could be overcome by spatial filtering, but a faster method is introduced which requires no additional multiplications but involves the insertion of some extra nodes into the quadtree. Nonstationarity is introduced by a fast, scale-invariant activity detector defined on the quadtree. Activity at all scales is combined in order to achieve noise rejection. The estimator is modified at each scale and position by the detector output such that less smoothing is applied near edges and more in smooth regions. Results demonstrate performance superior to that of existing methods, and at drastically lower computational cost. The estimation scheme is further extended to include anisotropic processing, which has produced good results in image restoration. An orientation estimator controls anisotropic filtering, the output of which is made available to the image estimator

Digital Filters and Signal Processing

Digital filters, together with signal processing, are being employed in the new technologies and information systems, and are implemented in different areas and applications. Digital filters and signal processing are used with no costs and they can be adapted to different cases with great flexibility and reliability. This book presents advanced developments in digital filters and signal process methods covering different cases studies. They present the main essence of the subject, with the principal approaches to the most recent mathematical models that are being employed worldwide

Proposed automobile steering wheel test method for vibration

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis proposes a test method for evaluating the perceived vibration which occurs at the driver's hand in automotive steering wheel interface. The objective of the research was to develop frequency weightings for quantifying the human perception of steering wheel hand-arm vibration. Family of frequency weightings were developed from equal sensation curves obtained from the psychophysical laboratory experimental tests. The previous literature suggests that the only internationally standardised frequency weighting Wh is not accurate to predict human perception of steering wheel hand-arm vibration (Amman et. al, 2005) because Wh was developed originally for health effects, not for the human perception. In addition, most of the data in hand-arm vibration are based upon responses from male subjects (Neely and Burström, 2006) and previous studies based only on sinusoidal stimuli. Further, it has been continuously suggested by researchers (Gnanasekarna et al., 2006; Morioka and Griffin, 2006; Ajovalasit and Giacomin, 2009) that only one weighting is not optimal to estimate the human perception at all vibrational magnitudes. In order to address these problems, the investigation of the effect of gender, body mass and the signal type on the equal sensation curves has been performed by means of psychophysical laboratory experimental tests. The test participants were seated on a steering wheel simulator which consists of a rigid frame, a rigid steering wheel, an automobile seat, an electrodynamic shaker unit, a power amplifier and a signal generator. The category-ratio Borg CR10 scale procedure was used to quantify the perceived vibration intensity. A same test protocol was used for each test and for each test subject. The first experiment was conducted to investigate the effect of gender using sinusoidal vibration with 40 test participants (20 males and 20 females). The results suggested that the male participants provided generally lower subjective ratings than the female participants. The second experiment was conducted using band-limited random vibration to investigate the effect of signal type between sinusoidal and band-limited random vibration with 30 test participants (15 males and 15 females). The results suggested that the equal sensation curves obtained using random vibration were generally steeper and deeper in the shape of the curves than those obtained using sinusoidal vibration. These differences may be due to the characteristics of random vibration which produce generally higher crest factors than sinusoidal vibration. The third experiment was conducted to investigate the effect of physical body mass with 40 test participants (20 light and 20 heavy participants) using sinusoidal vibration. The results suggested that the light participants produced generally higher subjective ratings than the heavy participants. From the results it can be suggested that the equal sensation curves for steering wheel rotational vibration differ mainly due to differences of body size rather than differences of gender. The final experiments was conducted using real road signals to quantify the human subjective response to representative driving condition and to use the results to define the selection method for choosing the adequate frequency weightings for the road signals by means of correlation analysis. The final experiment was performed with 40 test participants (20 light and 20 heavy participants) using 21 real road signals obtained from the road tests. From the results the hypothesis was established that different amplitude groups may require different frequency weightings. Three amplitude groups were defined and the frequency weightings were selected for each amplitude group. The following findings can be drawn from the research: • the equal sensation curves suggest a nonlinear dependency on both the frequency and the amplitude. • the subjective responses obtained from band-limited random stimuli were steeper and the deeper in the shape of the equal sensation curves than those obtained using sinusoidal vibration stimuli. • females provided higher perceived intensity values than the males for the same physical stimulus at most frequencies. • light test participants provided higher perceived intensity than the heavy test participants for the same physical stimulus at most frequencies. • the equal sensation curves for steering wheel rotational vibration differ mainly due to differences in body size, rather than differences of gender. • at least three frequency weightings may be necessary to estimate the subjective intensity for road surface stimuli

Computationally Efficient QRS Detection Analysis In Electrocardiogram Based On Dual-Slope Method

A dramatic growth of interest for wearable technology has been fostered by recent technological advances in sensors, low-power integrated circuits and wireless communications. This interest originates from the need of monitoring a patient over extensive period of time. For cardiac patients, wearable heart monitoring sensors have already become a life-saving intervention ensuring continuous monitoring during daily life. Therefore, it is essential for an accurate monitoring and diagnosis of heart patients. Patients can be equipped with wireless, miniature and lightweight sensors. The sensors temporarily store physiological data and then periodically upload the data to a database server. These recorded data sets are then analyzed to predict any possibility of worsening patient\u27s situation or explored to assess the effect of clinical intervention. To obtain accurate response with less computational complexity as well as long battery life time, there is a demand of developing fast and accurate algorithm and prototypes for wearable heart monitoring sensors. A computationally efficient QRS detection algorithm is indispensable for low power operation on electrocardiogram (ECG) signal. In need of detecting QRS complex, most of the early works were proposed based on derivatives of ECG signal. They can be easily implemented with high computational speed. But owing to the inherent variability in ECG, these methods are highly affected by large derivatives of baseline noises. Algorithms based on neural network (NN) showed relatively robust performance against noise but requires exhaustive training and estimation of model parameter. On the other hand, wavelet based methods have the choice problem of mother wavelet. Hence, none of these methods is suitable for giving a long battery performance in wearable devices with high accuracy. Recently, Wang et al. proposed a novel dual slope QRS detection algorithm which has less computational complexity as well as high accuracy. Considering that the width of the QRS complex is relatively fixed, this algorithm is based on the fact that the largest change of slope usually happens at the peak of QRS complex. The hardware requirement is also low. However, the method has a set of time consuming slope calculations on both sides of each sample. To avoid such time consuming slope calculation, only one sample on each side can be highlighted. In addition, the multiplication of the left and right hand side slope should give us a very high value in QRS complex. The goal of this thesis is to develop a new computationally efficient method to detect QRS complexes and compare with the other renowned QRS detection algorithms. MIT-BIH arrhythmia database based on patients of different heart diseases and database containing ECG from healthy subjects are used. To analyze the performance, false negative (FN) and false positive (FP) are evaluated. A false negative (FN) occurs when algorithm fails to detect an actual QRS complex quoted in the corresponding annotation file of the database record and a false positive (FP) means a false beat detection. Error rate (ER) , Sensitivity (Se) and Specificity (Sp) are calculated using FP and FN