Automated Species Classification Methods for Passive Acoustic Monitoring of Beaked Whales

The Littoral Acoustic Demonstration Center has collected passive acoustic monitoring data in the northern Gulf of Mexico since 2001. Recordings were made in 2007 near the Deepwater Horizon oil spill that provide a baseline for an extensive study of regional marine mammal populations in response to the disaster. Animal density estimates can be derived from detections of echolocation signals in the acoustic data. Beaked whales are of particular interest as they remain one of the least understood groups of marine mammals, and relatively few abundance estimates exist. Efficient methods for classifying detected echolocation transients are essential for mining long-term passive acoustic data. In this study, three data clustering routines using k-means, self-organizing maps, and spectral clustering were tested with various features of detected echolocation transients. Several methods effectively isolated the echolocation signals of regional beaked whales at the species level. Feedforward neural network classifiers were also evaluated, and performed with high accuracy under various noise conditions. The waveform fractal dimension was tested as a feature for marine biosonar classification and improved the accuracy of the classifiers. [This research was made possible by a grant from The Gulf of Mexico Research Initiative. Data are publicly available through the Gulf of Mexico Research Initiative Information & Data Cooperative (GRIIDC) at https://data.gulfresearchinitiative.org.] [DOIs: 10.7266/N7W094CG, 10.7266/N7QF8R9K

A Hidden Markov Factor Analysis Framework for Seizure Detection in Epilepsy Patients

Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. Detection of seizure from the recorded EEG is a laborious, time consuming and expensive task. In this study, we propose an automated seizure detection framework to assist electroencephalographers and physicians with identification of seizures in recorded EEG signals. In addition, an automated seizure detection algorithm can be used for treatment through automatic intervention during the seizure activity and on time triggering of the injection of a radiotracer to localize the seizure activity. In this study, we developed and tested a hidden Markov factor analysis (HMFA) framework for automated seizure detection based on different features such as total effective inflow which is calculated based on connectivity measures between different sites of the brain. The algorithm was tested on long-term (2.4-7.66 days) continuous sEEG recordings from three patients and a total of 16 seizures, producing a mean sensitivity of 96.3% across all seizures, a mean specificity of 3.47 false positives per hour, and a mean latency of 3.7 seconds form the actual seizure onset. The latency was negative for a few of the seizures which implies the proposed method detects the seizure prior to its onset. This is an indication that with some extension the proposed method is capable of seizure prediction

A Review of EMG Techniques for Detection of Gait Disorders

Electromyography (EMG) is a commonly used technique to record myoelectric signals, i.e., motor neuron signals that originate from the central nervous system (CNS) and synergistically activate groups of muscles resulting in movement. EMG patterns underlying movement, recorded using surface or needle electrodes, can be used to detect movement and gait abnormalities. In this review article, we examine EMG signal processing techniques that have been applied for diagnosing gait disorders. These techniques span from traditional statistical tests to complex machine learning algorithms. We particularly emphasize those techniques are promising for clinical applications. This study is pertinent to both medical and engineering research communities and is potentially helpful in advancing diagnostics and designing rehabilitation devices

Unsupervised Classification of Uterine Contractions Recorded Using Electrohysterography

Pregnancy still poses health risks that are not attended to by current clinical practice motorization procedures. Electrohysterography (EHG) record signals are analyzed in the course of this thesis as a contribution and effort to evaluate their suitability for pregnancy monitoring. The presented work is a contributes with an unsupervised classification solution for uterine contractile segments to FCT’s Uterine Explorer (UEX) project, which explores analysis procedures for EHG records. In a first part, applied processing procedures are presented and a brief exploration of the best practices for these. The procedures include those to elevate the representation of uterine events relevant characteristics, ease further computation requirements, extraction of contractile segments and spectral estimation. More detail is put into the study of which characteristics should be chosen to represent uterine events in the classification process and feature selection methods. To such end, it is presented the application of a principal component analysis (PCA) to three sets: interpolated contractile events, contractions power spectral densities, and to a number of computed features that attempt evidencing time, spectral and non-linear characteristics usually used in EHG related studies. Subsequently, a wrapper model approach is presented as a mean to optimize the feature set through cyclically attempting the removal and re-addition of features based on clustering results. This approach takes advantage of the fact that one class is known beforehand to use its classification accuracy as the criteria that defines whether the modification made to the feature set was ominous. Furthermore, this work also includes the implementation of a visualization tool that allows inspecting the effect of each processing procedure, the uterine events detected by different methods and clusters they were associated to by the final iteration of the wrapper model

A wide area system for power transmission security enhancement using a process systems approach

Imperial Users onl

Towards Amyotrophic Lateral Sclerosis Interpretable Diagnosis Using Surface Electromyography

Amyotrophic Lateral Sclerosis (ALS) is a fast-progressing disease with no cure. It is diagnosed through the assessment of clinical exams, such as needle electromyography, which measures themuscles’ electrical activity by inserting a needle into themuscle tissue. Nevertheless, surface electromyography (SEMG) is emerging as a more practical and less painful alternative. Even though these exams provide relevant information regarding the electric structures conducted in the muscles, ALS symptoms are similar to those of other neurological disorders, preventing a faster detection of the disease. This dissertation focuses on implementing and analyzing innovative SEMG features related to the morphology of the functional structures present in the signal. To assess the efficiency of these features, a framework is proposed, aiming to distinguish healthy from pathological signals through the use of a classification algorithm. The classification task was performed using SEMG signals acquired from the upper limb muscles of healthy and ALS subjects. The results show the utility of employing the proposed set of features for ALS diagnosis, with an F1 Score higher than 80% in most experimental conditions. The novel features improved the model’s overall performance when combined with other state-of-art SEMG features and also demonstrated efficiency when used individually. These outcomes are of significant importance in supporting the use of SEMG as a complementary diagnosis exam. The proposed features demonstrate promising contributions for better and faster detection of ALS and increased classification interpretabilityA Esclerose Lateral Amiotrófica (ELA) é uma doença incurável de progressão rápida. O seu diagnóstico é feito através da avaliação de exames clínicos como a eletromiografia de profundidade, que mede a atividade elétrica muscular com agulhas inseridas no músculo. No entanto, a eletromiografia de superfície (SEMG) surge como uma alternativa mais prática e menos dolorosa. Embora ambos os exames forneçam informações relevantes sobre as estruturas elétricas conduzidas nos músculos, os sintomas da ELA são semelhantes aos de outras doenças neurológicas, impedindo uma identificação mais precoce da doença. Esta dissertação foca-se na implementação e análise de atributos inovadores de SEMG relacionados com a morfologia das estruturas funcionais presentes no sinal. Para avaliar a eficiência destes atributos, é proposto um framework, com o objetivo de distinguir sinais saudáveis e sinais patológicos através de um algoritmo de classificação. A tarefa de classificação foi realizada utilizando sinais de SEMG adquiridos dos músculos dos membros superiores de indivíduos saudáveis e com ELA. Os resultados demonstram a utilidade do conjunto de atributos proposto para o diagnóstico de ELA, com uma métrica de classificação F1 superior a 80% na maioria das condições experimentais. Os novos atributos melhoraram o desempenho geral do modelo quando combinados com outros atributos de SEMG do estado da arte, e também se comprovaram eficientes quando aplicados individualmente. Estes resultados são de grande importância na justificação da aplicabilidade da SEMG como um exame complementar de diagnóstico da ELA. Os atributos apresentados demonstram ser promissores para um melhor e mais rápido diagnóstico, e facilitam a explicação dos resultados da classificação devido à sua interpretabilidade

Acoustic anomaly detection using robust statistical energy processing

An anomaly is the specific event that causes the violation of a process observer's expectations about the process under observation. In this work, the problem of spatially locating an acoustic anomaly is addressed. Once reduced to a problem in robust statistics, an automated observer is designed to detect when high energy sources are introduced into an acoustic scene. Accounting for potential energy from signal amplitude, and kinetic energy from signal frequency in wavelet-filtered sub-bands, an outlier a robust statistical characterization scheme was developed using the Teager energy operator. With a statistical expectation of energy content in sub-bands, a methodology is designed to detect signal energies that violate the statistical expectation. These minor anomalies provide some sense that a fundamental change in energy has occurred in the sub-band. By examining how the signal is changing across all sub-bands, a detector is designed that is able to determine when a fundamental change occurs in the sub-band signal trends. Minor anomalies occurring during such changes are labeled as major anomalies. Using established localization methods, position estimates are obtained for the major anomalies in each sub-band. Accounting for the possibility of a source with spatiotemporal properties, the median of sub-band position estimates provides the final spatial information about the source

Diagnosis of low-speed bearings via vibration-based entropy indicators and acoustic emissions

Tesi del Pla de doctorat industrial de la Generalitat de Catalunya. Tesi en modalitat compendi de publicacions, amb diferents seccions retallades per drets dels editorsWind energy is one ofthe main renewable energies to replace fossil fuels in the generation of electricityworldwide. To enhance and accelerate its implementation at a large scale, it is vital to reduce the costs associated with maintenance. As com ponent breakages force the turbine to stop for long repair times, the wind industry m ust switch from the old-fashioned preventive or corrective maintenance to condition-based maintenance (also called predictive maintenance). The condition­based maintenance of pitch bearings is especiallychallenging, as the operating conditions include high mechanical stress and low rotational speed. Since these operating conditions im pact negatively on the results of the standard methods and techniques applied in current condition-based monitoring systems, the condition-based maintenance of pitch bearings is still a challenge. Therefore, this thes is is focused on the research of novel methods and techniques that obtain reliable information on the state of pitch bearings for condition-based maintenance. lnitially, the acknowledgment ofthe state ofthe art is performed to recognize the methods and signals. This step endorses the decision to analyze the vibration signals and acoustic emissions throughout this thesis. Due to the particular operating conditions of pitch bearings, this research states the need to create data sets to replicate the particular operating conditions in a controlled laboratory experiment. As a res ult, a datas et based on vibrations, and a second datas et based on acoustic emissions are generated. The vibration datas et allows the validation of a novel algorithm for the low-speed bearing diagnosis, which is based on the concept of entropy by the definition of Shannon and Rényi. In com parison to the classical methods found in the literature, the diagnosis of low-speed bearings based on entropy-based indicators can extract more reliable information. Moreover, the research of the com bination of several indicators to improve the diagnosis revea Is that the entropy-based indicators can extract more information than regular indicators used in academia. The datas et of acoustic emissions from low-speed bearings helps to contribute to the development of methods for diagnosis. In this research, the analysis of the energyfrom the signals reveals a dependencyon the intensityand the presence of damage. In addition, a relation between the waveform ofthe analyzed energy and the existence of damage is em phas ized.La energía eólica es una de las principales energías renovables consideradas para reemplazar los combustibles fósiles en la generación de electricidad a nivel mundial. Para mejorar y acelerar su implementación a gran escala, es vital reducir los costes asociados con el mantenimiento. Como las roturas de los componentes obligan a la turbina a detenerse durante largos períodos de reparación, la industria eólica necesita cambiar del anticuado mantenimiento preventiv o correctivo al mantenimiento basado en la condición (también llamado mantenimiento predictivo). El mantenimiento basado en la condición de los rodamientos pitch es especialmente desafiante, porque las condiciones de operación incluyen un alto estrés mecánico y bajas velocidades de rotación. Debido a que estas condiciones de operación impactan negativamente en los resultados de los métodos y técnicas estándar aplicados en los sistemas actuales de monitoreo basados en el estado, el mantenimiento basado en el estado de los rodamientos pitch sigue siendo un desafío. Por tanto, esta tesis se centra en la investigación de métodos y técnicas novedosas que obtengan información fiable sobre el estado de los rodamientos pitch para el mantenimiento basado en la condición. Inicialmente, se realiza el reconocimiento del estado del arte para reconocer los métodos y señales utilizados. Este paso avala la decisión de analizar las señales de vibración y las emisiones acústicas a lo largo de esta tesis. Debido a las condiciones de funcionamiento particulares de los rodamientos pitch, esta investigación reconoce la necesidad de crear un conjunto de datos para replicar las condiciones de funcionamiento particulares del rodamiento pitch en una experiencia de laboratorio controlado. Como resultado, se genera un conjunto de datos basado en vibraciones y un segundo conjunto de datos basado en emisiones acústicas. El conjunto de datos de vibraciones permite la validación de un algoritmo novedoso para el diagnóstico de rodamientos de baja velocidad, el cual se basa en el concepto de la entropía según la definición de Shannon y Rényi. En comparación con los métodos clásicos que se encuentran en la literatura, el diagnóstico de rodamientos de baja velocidad basado en indicadores basados en la entropía puede extraer información más confiable. Además, la investigación de la combinación de varios indicadores para mejorar el diagnóstico revela que los indicadores basados en la entropía pueden extraer más información que los indicadores habituales utilizados en la academia. El conjunto de datos de las emisiones acústicas de los rodamientos de baja velocidad ayuda a contribuir al desarrollo de métodos de diagnóstico. En esta investigación, el análisis de la energía de las señales revela una dependencia de la intensidad y la presencia de daño. Además, se enfatiza una relación entre la forma de onda de la energía analizada y la existencia de daño.L'energia eòlica és una de les principals energies renovables considerades per reemplaçar els combustibles fòssils en la generació d'electricitat a nivell mundial. Per millorar i accelerar la seva implementació a gran escala, és vital reduir els costos associats amb el manteniment. Com els trencaments dels components obliguen a la turbina a aturar-se durant llargs períodes de reparació, la industria eòlica necessita canviar de l'antiquat manteniment preventiu o correctiu al manteniment basat en la condició (també anomenat manteniment predictiu). El manteniment basat en la condició dels rodaments de pas és especialment desafiant, perquè les condicions d’operació inclouen un alt estrès mecànic i baixes velocitats de rotació. A causa de que aquestes condicions d’operació impacten negativament en els resultats dels mètodes i tècniques estàndard aplicats en els sistemes actuals de monitorització basats en l'estat, el manteniment basat en l'estat dels rodaments de pas segueix sent un desafiament. Per tant, aquesta tesi se centra en la investigació de mètodes i tècniques noves que obtinguin informació fiable sobre l'estat dels rodaments de pas per al manteniment basat en la condició. Inicialment, es realitza el reconeixement de l'estat de l'art per reconèixer els mètodes i senyals utilitzats. Aquest pas avala la decisió d'analitzar els senyals de vibració i les emissions acústiques al llarg d'aquesta tesi. A causa de les condicions de funcionament particulars dels rodaments de pas, aquesta investigació reconeix la necessitat de crear un conjunt de dades per replicar les condicions de funcionament particulars del rodament de pas en un experiment de laboratori controlat. Com a resultat, es genera un conjunt de dades basat en vibracions i un segon conjunt de dades basat en emissions acústiques. El conjunt de dades de vibracions permet la validació d'un algoritme nou per al diagnòstic de rodaments de baixa velocitat, el qual es basa en el concepte de l'entropia segons la definició de Shannon i Renyi. En comparació amb els mètodes clàssics que es troben a la literatura, el diagnòstic de rodaments de baixa velocitat basat en indicadors basats en l'entropia pot extreure informació més fiable. A més, la investigació de la combinació de diversos indicadors per millorar el diagnòstic revela que els indicadors basats en l'entropia poden extreure més informació que els indicadors habituals utilitzats en la literatura. El conjunt de dades de les emissions acústiques dels rodaments de baixa velocitat ajuda a contribuir al desenvolupament de mètodes de diagnòstic. En aquesta investigació, l’anàlisi de l'energia de les senyals revela una dependència de la intensitat i la presència de dany. A més, s'emfatitza una relació entre la forma d'ona de l'energia analitzada i l’existència de dany.Energia eolikoa mundu mailan elektrizitatea sortu eta erregai fosilak ordezkatzeko energia berriztagarri nagusietako bat da. Eskala handiko ezarpena hobetu eta bizkortzeko, ezinbestekoa da mantentze-lanekin lotutako kostuak murriztea. Osagaien hausturek turbina konponketa-aldi luzeetan gelditzera behartzen dutenez, industria eolikoak mantentze-lan prebentibo edo zuzentzaile zaharkitutik egoeran oinarritutako mantentzelanetara aldatu behar du (mantentze-lan prediktiboa ere esaten zaio). Pitch errodamenduen egoeran oinarritutako mantentzea bereziki desa atzailea da, tentsio mekaniko handiak jasaten baitituzte eta errotazio-abiadura txikietan egoten baitira abian. Operaziobaldintza horiek eragin negatiboa dutenez egoeran oinarritutako egungo monitorizazio sistemetan erabiltzen diren metodo eta teknika estandarren emaitzetan, pitch errodamenduen egoeran oinarritutako mantentze-lanak erronka bat izaten jarraitzen du. Tesi hau egoeran oinarritutako mantenurako pitch errodamenduen egoerari buruzko informazio dagarria lortzen duten metodo eta teknika berritzaileen ikerketan oinarritzen da. Hasieran, teknologiaren egungo egoera aztertzen da, erabilitako metodoak eta seinaleak ezagutzeko. Urrats honek tesi honetan zehar bibrazio-seinaleak eta emisio akustikoak aztertzeko erabakia bermatzen du. Pitch errodamenduen funtzionamendu baldintza bereziak direla eta, ikerketa honek adierazten du beharrezkoa dela datu multzo bat sortzea pitch errodamenduaren funtzionamendu baldintza partikularrak erreplikatzeko laborategi kontrolatuko testuinguru batean. Ondorioz, bibrazioetan oinarritutako datu-multzo bat eta emisio akustikoetan oinarritutako bigarren datu-multzo bat sortzen dira. Bibrazioen datu-multzoak abiadura txikiko errodamenduen diagnostikorako algoritmo berritzaile bat baliozkotzea ahalbidetzen du, zeina entropiaren kontzeptuan oinarritzen baita Shannon eta R enyiren de nizioaren arabera. Literaturan dauden metodo klasikoekin alderatuta, entropian oinarritutako adierazleek abiadura txikiko errodamenduen diagnostikorako informazio dagarriagoa atera dezakete. Gainera, diagnostikoa hobetzeko hainbat adierazleren konbinazioaren ikerketak agerian uzten du entropian oinarritutako adierazleek akademian erabiltzen diren ohiko adierazleek baino informazio gehiago atera dezaketela. Abiadura txikiko errodamenduen emisio akustikoen datu multzoak diagnostiko metodoak garatzen laguntzen du. Ikerketa lan honetan, seinaleen energiaren azterketak intentsitatearekiko eta kaltearen presentziarekiko dependentzia adierazten du. Gainera, aztertutako energiaren uhin-formaren eta kaltearen arteko erlazioa nabarmentzen da.Postprint (published version