Feature extraction and similarity of movement detection during sleep, based on higher order spectra and entropy of the actigraphy signal: Results of the Hispanic Community Health Study/Study of Latinos

[EN] The aim of this work was to develop a new unsupervised exploratory method of characterizing feature extraction and detecting similarity of movement during sleep through actigraphy signals. We here propose some algorithms, based on signal bispectrum and bispectral entropy, to determine the unique features of independent actigraphy signals. Experiments were carried out on 20 randomly chosen actigraphy samples of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) database, with no information other than their aperiodicity. The Pearson correlation coefficient matrix and the histogram correlation matrix were computed to study the similarity of movements during sleep. The results obtained allowed us to explore the connections between certain sleep actigraphy patterns and certain pathologies.Funding for this study was provided by the authors' departments. J.A.C. acknowledges support from the Ministerio de Economia, Industria y Competitividad, Grant MTM2016-75963-P. J.M.G.-G. y C.S. Ministerio de Ciencia Tecnologia y Telecomunicaciones, Grant DPI2016-80054-R. J.A.C., J.M.G.-G. and C.S. acknowledge support from the European Commission, CrowdHealth project (H2020-SC1-2016-CNECT No. 727560).Iglesias-Martinez, ME.; Garcia-Gomez, JM.; Sáez Silvestre, C.; Fernández De Córdoba, P.; Conejero, JA. (2018). Feature extraction and similarity of movement detection during sleep, based on higher order spectra and entropy of the actigraphy signal: Results of the Hispanic Community Health Study/Study of Latinos. Sensors. 18(12):4310-1-4310-17. https://doi.org/10.3390/s18124310S4310-14310-17181

Development of algorithms of statistical signal processing for the detection and pattern recognitionin time series. Application to the diagnosis of electrical machines and to the features extraction in Actigraphy signals

Tesis por compendio[ES] En la actualidad, el desarrollo y aplicación de algoritmos para el reconocimiento de patrones que mejoren los niveles de rendimiento, detección y procesamiento de datos en diferentes áreas del conocimiento resulta un tema de gran interés. En este contexto, y específicamente en relación con la aplicación de estos algoritmos en el monitoreo y diagnóstico de máquinas eléctricas, el uso de señales de flujo es una alternativa muy interesante para detectar las diferentes fallas. Asimismo, y en relación con el uso de señales biomédicas, es de gran interés extraer características relevantes en las señales de actigrafía para la identificación de patrones que pueden estar asociados con una patología específica. En esta tesis, se han desarrollado y aplicado algoritmos basados en el procesamiento estadístico y espectral de señales, para la detección y diagnóstico de fallas en máquinas eléctricas, así como su aplicación al tratamiento de señales de actigrafía. Con el desarrollo de los algoritmos propuestos, se pretende tener un sistema dinámico de indicación e identificación para detectar la falla o la patología asociada que no depende de parámetros o información externa que pueda condicionar los resultados, sólo de la información primaria que inicialmente presenta la señal a tratar (como la periodicidad, amplitud, frecuencia y fase de la muestra). A partir del uso de los algoritmos desarrollados para la detección y diagnóstico de fallas en máquinas eléctricas, basados en el procesamiento estadístico y espectral de señales, se pretende avanzar, en relación con los modelos actualmente existentes, en la identificación de fallas mediante el uso de señales de flujo. Además, y por otro lado, mediante el uso de estadísticas de orden superior, para la extracción de anomalías en las señales de actigrafía, se han encontrado parámetros alternativos para la identificación de procesos que pueden estar relacionados con patologías específicas.[CA] En l'actualitat, el desenvolupament i aplicació d'algoritmes per al reconeixement de patrons que milloren els nivells de rendiment, detecció i processament de dades en diferents àrees del coneixement és un tema de gran interés. En aquest context, i específicament en relació amb l'aplicació d'aquests algoritmes a la monitorització i diagnòstic de màquines elèctriques, l'ús de senyals de flux és una alternativa molt interessant per tal de detectar les diferents avaries. Així mateix, i en relació amb l'ús de senyals biomèdics, és de gran interés extraure característiques rellevants en els senyals d'actigrafia per a la identificació de patrons que poden estar associats amb una patologia específica. En aquesta tesi, s'han desenvolupat i aplicat algoritmes basats en el processament estadístic i espectral de senyals per a la detecció i diagnòstic d'avaries en màquines elèctriques, així com la seua aplicació al tractament de senyals d'actigrafia. Amb el desenvolupament dels algoritmes proposats, es pretén obtindre un sistema dinàmic d'indicació i identificació per a detectar l'avaria o la patologia associada, el qual no depenga de paràmetres o informació externa que puga condicionar els resultats, només de la informació primària que inicialment presenta el senyal a tractar (com la periodicitat, amplitud, freqüència i fase de la mostra). A partir de l'ús dels algoritmes desenvolupats per a la detecció i diagnòstic d'avaries en màquines elèctriques, basats en el processament estadístic i espectral de senyals, es pretén avançar, en relació amb els models actualment existents, en la identificació de avaries mitjançant l'ús de senyals de flux. A més, i d'altra banda, mitjançant l'ús d'estadístics d'ordre superior, per a l'extracció d'anomalies en els senyals d'actigrafía, s'han trobat paràmetres alternatius per a la identificació de processos que poden estar relacionats amb patologies específiques.[EN] Nowadays, the development and application of algorithms for pattern recognition that improve the levels of performance, detection and data processing in different areas of knowledge is a topic of great interest. In this context, and specifically in relation to the application of these algorithms to the monitoring and diagnosis of electrical machines, the use of stray flux signals is a very interesting alternative to detect the different faults. Likewise, and in relation to the use of biomedical signals, it is of great interest to extract relevant features in actigraphy signals for the identification of patterns that may be associated with a specific pathology. In this thesis, algorithms based on statistical and spectral signal processing have been developed and applied to the detection and diagnosis of failures in electrical machines, as well as to the treatment of actigraphy signals. With the development of the proposed algorithms, it is intended to have a dynamic indication and identification system for detecting the failure or associated pathology that does not depend on parameters or external information that may condition the results, but only rely on the primary information that initially presents the signal to be treated (such as the periodicity, amplitude, frequency and phase of the sample). From the use of the algorithms developed for the detection and diagnosis of failures in electrical machines, based on the statistical and spectral signal processing, it is intended to advance, in relation to the models currently existing, in the identification of failures through the use of stray flux signals. In addition, and on the other hand, through the use of higher order statistics for the extraction of anomalies in actigraphy signals, alternative parameters have been found for the identification of processes that may be related to specific pathologies.Iglesias Martínez, ME. (2020). Development of algorithms of statistical signal processing for the detection and pattern recognitionin time series. Application to the diagnosis of electrical machines and to the features extraction in Actigraphy signals [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/145603TESISCompendi

Signal Detection Techniques for Diagnostic Monitoring of Space Shuttle Main Engine Turbomachinery

An investigation to develop, implement, and evaluate signal analysis techniques for the detection and classification of incipient mechanical failures in turbomachinery is reviewed. A brief description of the Space Shuttle Main Engine (SSME) test/measurement program is presented. Signal analysis techniques available to describe dynamic measurement characteristics are reviewed. Time domain and spectral methods are described, and statistical classification in terms of moments is discussed. Several of these waveform analysis techniques have been implemented on a computer and applied to dynamc signals. A laboratory evaluation of the methods with respect to signal detection capability is described. A unique coherence function (the hyper-coherence) was developed through the course of this investigation, which appears promising as a diagnostic tool. This technique and several other non-linear methods of signal analysis are presented and illustrated by application. Software for application of these techniques has been installed on the signal processing system at the NASA/MSFC Systems Dynamics Laboratory

Aeronautical Engineering: A continuing bibliography with indexes (supplement 207)

This bibliography lists 484 reports, articles and other documents introduced into the NASA scientific and technical information system in November 1986

수면 호흡음을 이용한 폐쇄성 수면 무호흡 중증도 분류

학위논문 (박사)-- 서울대학교 융합과학기술대학원 융합과학부, 2017. 8. 이교구.Obstructive sleep apnea (OSA) is a common sleep disorder. The symptom has a high prevalence and increases mortality as a risk factor for hypertension and stroke. Sleep disorders occur during sleep, making it difficult for patients to self-perceive themselves, and the actual diagnosis rate is low. Despite the existence of a standard sleep study called a polysomnography (PSG), it is difficult to diagnose the sleep disorders due to complicated test procedures and high medical cost burdens. Therefore, there is an increasing demand for an effective and rational screening test that can determine whether or not to undergo a PSG. In this thesis, we conducted three studies to classify the snoring sounds and OSA severity using only breathing sounds during sleep without additional biosensors. We first identified the classification possibility of snoring sounds related to sleep disorders using the features based on the cyclostationary analysis. Then, we classified the patients OSA severity with the features extracted using temporal and cyclostationary analysis from long-term sleep breathing sounds. Finally, the partial sleep sound extraction, and feature learning process using a convolutional neural network (CNN, or ConvNet) were applied to improve the efficiency and performance of previous snoring sound and OSA severity classification tasks. The sleep breathing sound analysis method using a CNN showed superior classification accuracy of more than 80% (average area under curve > 0.8) in multiclass snoring sounds and OSA severity classification tasks. The proposed analysis and classification method is expected to be used as a screening tool for improving the efficiency of PSG in the future customized healthcare service.Chapter 1. Introduction ................................ .......................1 1.1 Personal healthcare in sleep ................................ ..............1 1.2 Existing approaches and limitations ....................................... 9 1.3 Clinical information related to SRBD ................................ .. ..12 1.4 Study objectives ................................ .........................16 Chapter 2. Overview of Sleep Research using Sleep Breathing Sounds ........... 23 2.1 Previous goals of studies ................................ ................23 2.2 Recording environments and related configurations ........................ 24 2.3 Sleep breathing sound analysis ................................ ...........27 2.4 Sleep breathing sound classification ..................................... 35 2.5 Current limitations ................................ ......................36 Chapter 3. Multiple SRDB-related Snoring Sound Classification .................39 3.1 Introduction ................................ .............................39 3.2 System architecture ................................ ......................41 3.3 Evaluation ................................ ...............................52 3.4 Results ................................ ..................................55 3.5 Discussion ................................ ...............................59 3.6 Summary ................................ ..................................63 Chapter 4. Patients OSA Severity Classification .............................65 4.1 Introduction ................................ .............................65 4.2 Existing Approaches ................................ ......................69 4.3 System Architecture ................................ ......................70 4.4 Evaluation ................................ ...............................85 4.5 Results ................................ ..................................87 4.6 Discussion ................................ ...............................94 4.7 Summary ................................ ..................................97 Chapter 5. Patient OSA Severity Prediction using Deep Learning Techniques .....99 5.1 Introduction ................................ .............................99 5.2 Methods ................................ ..................................101 5.3 Results ................................ ..................................109 5.4 Discussion ................................ ...............................115 5.5 Summary ................................ ..................................118 Chapter 6. Conclusions and Future Work ........................................120 6.1 Conclusions ................................ ..............................120 6.2 Future work ................................ ..............................127Docto

Modelling, Simulation and Data Analysis in Acoustical Problems

Modelling and simulation in acoustics is currently gaining importance. In fact, with the development and improvement of innovative computational techniques and with the growing need for predictive models, an impressive boost has been observed in several research and application areas, such as noise control, indoor acoustics, and industrial applications. This led us to the proposal of a special issue about “Modelling, Simulation and Data Analysis in Acoustical Problems”, as we believe in the importance of these topics in modern acoustics’ studies. In total, 81 papers were submitted and 33 of them were published, with an acceptance rate of 37.5%. According to the number of papers submitted, it can be affirmed that this is a trending topic in the scientific and academic community and this special issue will try to provide a future reference for the research that will be developed in coming years