No description available.

Dysfunction in the central nervous system of the neonate is often first identified through seizures. The diffculty in detecting clinical seizures, which involves the observation of physical manifestations characteristic to newborn seizure, has placed greater emphasis on the detection of newborn electroencephalographic (EEG) seizure. The high incidence of newborn seizure has resulted in considerable mortality and morbidity rates in the neonate. Accurate and rapid diagnosis of neonatal seizure is essential for proper treatment and therapy. This has impelled researchers to investigate possible methods for the automatic detection of newborn EEG seizure. This thesis is focused on the development of algorithms for the automatic detection of newborn EEG seizure using adaptive time-frequency signal processing. The assessment of newborn EEG seizure detection algorithms requires large datasets of nonseizure and seizure EEG which are not always readily available and often hard to acquire. This has led to the proposition of realistic models of newborn EEG which can be used to create large datasets for the evaluation and comparison of newborn EEG seizure detection algorithms. In this thesis, we develop two simulation methods which produce synthetic newborn EEG background and seizure. The simulation methods use nonlinear and time-frequency signal processing techniques to allow for the demonstrated nonlinear and nonstationary characteristics of the newborn EEG. Atomic decomposition techniques incorporating redundant time-frequency dictionaries are exciting new signal processing methods which deliver adaptive signal representations or approximations. In this thesis we have investigated two prominent atomic decomposition techniques, matching pursuit and basis pursuit, for their possible use in an automatic seizure detection algorithm. In our investigation, it was shown that matching pursuit generally provided the sparsest (i.e. most compact) approximation for various real and synthetic signals over a wide range of signal approximation levels. For this reason, we chose MP as our preferred atomic decomposition technique for this thesis. A new measure, referred to as structural complexity, which quantifes the level or degree of correlation between signal structures and the decomposition dictionary was proposed. Using the change in structural complexity, a generic method of detecting changes in signal structure was proposed. This detection methodology was then applied to the newborn EEG for the detection of state transition (i.e. nonseizure to seizure state) in the EEG signal. To optimize the seizure detection process, we developed a time-frequency dictionary that is coherent with the newborn EEG seizure state based on the time-frequency analysis of the newborn EEG seizure. It was shown that using the new coherent time-frequency dictionary and the change in structural complexity, we can detect the transition from nonseizure to seizure states in synthetic and real newborn EEG. Repetitive spiking in the EEG is a classic feature of newborn EEG seizure. Therefore, the automatic detection of spikes can be fundamental in the detection of newborn EEG seizure. The capacity of two adaptive time-frequency signal processing techniques to detect spikes was investigated. It was shown that a relationship between the EEG epoch length and the number of repetitive spikes governs the ability of both matching pursuit and adaptive spectrogram in detecting repetitive spikes. However, it was demonstrated that the law was less restrictive forth eadaptive spectrogram and it was shown to outperform matching pursuit in detecting repetitive spikes. The method of adapting the window length associated with the adaptive spectrogram used in this thesis was the maximum correlation criterion. It was observed that for the time instants where signal spikes occurred, the optimal window lengths selected by the maximum correlation criterion were small. Therefore, spike detection directly from the adaptive window optimization method was demonstrated and also shown to outperform matching pursuit. An automatic newborn EEG seizure detection algorithm was proposed based on the detection of repetitive spikes using the adaptive window optimization method. The algorithm shows excellent performance with real EEG data. A comparison of the proposed algorithm with four well documented newborn EEG seizure detection algorithms is provided. The results of the comparison show that the proposed algorithm has significantly better performance than the existing algorithms (i.e. Our proposed algorithm achieved a good detection rate (GDR) of 94% and false detection rate (FDR) of 2.3% compared with the leading algorithm which only produced a GDR of 62% and FDR of 16%). In summary, the novel contribution of this thesis to the fields of time-frequency signal processing and biomedical engineering is the successful development and application of sophisticated algorithms based on adaptive time-frequency signal processing techniques to the solution of automatic newborn EEG seizure detection.

Aerospace structures comprising of metals and composites are exposed to extreme loading and environmental conditions which necessitates regular inspection and maintenance to verify and monitor overall structural integrity. The timely and accurate detection, characterization and monitoring of structural cracking, corrosion, delaminating, material degradation and other types of damage are of major concern in the operational environment. Along with these, stringent requirements of safety and operational reliability have lead to evolutionary methods for evaluation of structural integrity. As a result, conventional nondestructive evaluation methods have moved towards a new concept, Structural Health Monitoring (SHM). SHM provides in-situ information a bout the occurrence of damage if any, location and severity of damage and residual life of the structure and also helps in improving the safety, reliability and confidence levels of critical engineering structures. While the concepts underlying SHM are well understood, development of methods is still in a nascent stage which requires extensive research that is challenging and has been the main motivating factor for undertaking the work reported in the thesis. Under the scope of the investigations carried out in this thesis, an integrated approach using Ultrasonic (active) and Acoustic Emission (passive) methods has been explored for SHM of metallic and composite plate structures using a distributed array of surface bonded circular piezoelectric wafer active sensors(PWAS). In ultrasonic method, PWAS is used for actuation and reception of Lamb waves in plate structures. The damage detection is based on the interaction of waves with defects resulting in reflection, mode conversion and scattering. In acoustic emission (AE) technique, the same sensor is used to pick up the stress waves generated by initiation or growth of defects or damage. Thus, both the active and passive damage detection methods are used in this work for detection, location and characterization of defects and damage in metallic and composite plates with complex geometries and structural discontinuities. And, thus the strategy adopted is to use time-frequency analysis and time reversal technique to extract the information from Lamb wave signals for damage detection and a geodesic based Lamb wave approach for location of the damage in the structure. To start with experiments were conducted on aluminum plates to study the interaction of Lamb waves with cracks oriented at different angles and on a titanium turbine blade of complex geometry with a fine surface crack. Further, the interaction of Lamb wave modes with multiple layer delaminations in glass fiber epoxy composite laminates was studied. The data acquired from these experiments yielded complex sets of signals which were not easily discern able for obtaining the information required regarding the defects and damage. So, to obtain a basic understanding of the wave patterns, Spectral finite element method has been employed for simulation of wave propagation in composite beams with damages like delamination and material degradation. Following this, time-frequency analysis of a number of simulated and experimental signals due to elastic wave scattering from defects and damage were performed using wavelet transform (WT) and Hilbert-Huang transform(HHT).And, a comparison of their performances in the context of quantifying the damages has given detailed insight into the problem of identifying localized damages, dispersion of multi-frequency non-stationary signals after their interaction with different types of defects and damage, finally leading to quantification. Conventional Lamb wave based damage detection methods look for the presence of defects and damage in a structure by comparing the signal obtained with the baseline signal acquired under healthy conditions. The environmental conditions like change in temperature can alter the Lamb wave signals and when compared with baseline signals may lead to false damage prediction. So, in order to make Lamb wave based damage detection baseline free, in the present work, the time reversal technique has been utilized. And, experiments were conducted on metallic and composite plates to study the time reversal behavior ofA0 andS0Lamb wave modes. Damage in the form of a notch was introduced in an aluminum plate to study the changes in the characteristics of the time reversed Lamb wave modes experimentally. This experimental study showed that there is no change in the shape of the time reversed Lamb wave in the presence of defect implying no breakage of time reversibility. Time reversal experiments were further carried out on a carbon/epoxy composite T-pull specimen representing a typical structure. And, the specimen was subjected to a tensile loading in a Universal testing machine. PWAS sensor measurements were carried out at no load as also during different stages of delamination due to tensile loading. Application of time reversed A 0 and S0 modes for both healthy and delaminated specimens and studying the change in shape of the time reversed Lamb wave signals has resulted in successful detection of the presence of delamination. The aim of this study has been to show the effectiveness of Lamb wave time reversal technique for damage detection in health monitoring applications. The next step in SHM is to identify the damage location after the confirmation of presence of damage in the structure. Wave based acoustic damage detection methods (UT and AE) employing triangulation technique are not suitable for locating damage in a structure which has complicated geometry and contains structural discontinuities. And, the problem further gets compounded if the material of the structure is anisotropic warranting complex analytical velocity models. In this work, a novel geodesic approach using Lamb waves is proposed to locate the AE source/damage in plate like structures. The approach is based on the fact that the wave takes minimum energy path to travel from the source to any other point in the connected domain. The geodesics are computed numerically on the meshed surface of the structure using Dijkstra’s algorithm. By propagating the waves in reverse virtually from these sensors along the geodesic path and by locating the first inter section point of these waves, one can get the AE source/damage location. Experiments have been conducted on metallic and composite plate specimens of simple and complex geometry to validate this approach. And, the results obtained using this approach has demonstrated the advantages for a practicable source location solution with arbitrary surfaces containing finite discontinuities. The drawback of Dijkstra’s algorithm is that the geodesics are allowed to travel along the edges of the triangular mesh and not inside them. To overcome this limitation, the simpler Dijkstra’s algorithm has been replaced by a Fast Marching Method (FMM) which allows geodesic path to travel inside the triangular domain. The results obtained using FMM showed that one can accurately compute the geodesic path taken by the elastic waves in composite plates from the AE source/damage to the sensor array, thus obtaining a more accurate damage location. Finally, a new triangulation technique based on geodesic concept is proposed to locate damage in metallic and composite plates. The performances of triangulaton technique are then compared with the geodesic approach in terms of damage location results and their suitability to health monitoring applications is studied.