35 research outputs found
The Usage of Data Augmentation Strategies on the Detection of Murmur Waves in a Pcg Signal
Cardiac auscultation is a key screening tool used for cardiovascular evaluation. When used properly, it speeds up treatment and thus improving the patient’s life quality. However, the analysis and interpretation of the heart sound signals is subjective and dependent of the physician’s experience and domain knowledge. A computer assistant decision (CAD) system that automatically analyse heart sound signals, can not only support physicians in their clinical decisions but also release human resources to other tasks. In this paper, and to the best of our knowledge, for the first time a SMOTE strategy is used to boost a Convolutional Neural Network performance on the detection of murmur waves. Using the SMOTE strategy, a CNN achieved an overall of 88.43%.info:eu-repo/semantics/publishedVersio
Synthesis of normal and abnormal heart sounds using Generative Adversarial Networks
En esta tesis doctoral se presentan diferentes métodos propuestos para el análisis y sÃntesis de sonidos cardÃacos normales y anormales, logrando los siguientes aportes al estado del arte: i) Se implementó un algoritmo basado en la transformada wavelet empÃrica (EWT) y la energÃa promedio normalizada de Shannon (NASE) para mejorar la etapa de segmentación automática de los sonidos cardÃacos; ii) Se implementaron diferentes técnicas de extracción de caracterÃsticas para las señales cardÃacas utilizando los coeficientes cepstrales de frecuencia Mel (MFCC), los coeficientes de predicción lineal (LPC) y los valores de potencia. Además, se probaron varios modelos de Machine Learning para la clasificación automática de sonidos cardÃacos normales y anormales; iii) Se diseñó un modelo basado en Redes Adversarias Generativas (GAN) para generar sonidos cardÃacos sintéticos normales. Además, se implementa un algoritmo de eliminación de ruido utilizando EWT, lo que permite una disminución en la cantidad de épocas y el costo computacional que requiere el modelo GAN; iv) Finalmente, se propone un modelo basado en la arquitectura GAN, que consiste en refinar señales cardÃacas sintéticas obtenidas por un modelo matemático con caracterÃsticas de señales cardÃacas reales. Este modelo se ha denominado FeaturesGAN y no requiere una gran base de datos para generar diferentes tipos de sonidos cardÃacos. Cada uno de estos aportes fueron validados con diferentes métodos objetivos y comparados con trabajos publicados en el estado del arte, obteniendo resultados favorables.DoctoradoDoctor en IngenierÃa Eléctrica y Electrónic
An audio processing pipeline for acquiring diagnostic quality heart sounds via mobile phone
Recently, heart sound signals captured using mobile phones have been employed to develop data-driven heart disease detection systems. Such signals are generally captured in person by trained clinicians who can determine if the recorded heart sounds are of diagnosable quality. However, mobile phones have the potential to support heart health diagnostics, even where access to trained medical professionals is limited. To adopt mobile phones as self-diagnostic tools for the masses, we would need to have a mechanism to automatically establish that heart sounds recorded by non-expert users in uncontrolled conditions have the required quality for diagnostic purposes. This paper proposes a quality assessment and enhancement pipeline for heart sounds captured using mobile phones. The pipeline analyzes a heart sound and determines if it has the required quality for diagnostic tasks. Also, in cases where the quality of the captured signal is below the required threshold, the pipeline can improve the quality by applying quality enhancement algorithms. Using this pipeline, we can also provide feedback to users regarding the cause of low-quality signal capture and guide them towards a successful one. We conducted a survey of a group of thirteen clinicians with auscultation skills and experience. The results of this survey were used to inform and validate the proposed quality assessment and enhancement pipeline. We observed a high level of agreement between the survey results and fundamental design decisions within the proposed pipeline. Also, the results indicate that the proposed pipeline can reduce our dependency on trained clinicians for capture of diagnosable heart sounds
Signal processing and machine learning techniques for Doppler ultrasound haemodynamic measurements
Haemodynamic monitoring is an invaluable tool for evaluating, diagnosing and treating
the cardiovascular system, and is an integral component of intensive care units, obstetrics
wards and other medical units. Doppler ultrasound provides a non-invasive, cost-effective
and fast means of haemodynamic monitoring, which traditionally necessitates highly invasive
methods such as Pulmonary artery catheter or transoesophageal echocardiography.
However, Doppler ultrasound scan acquisition requires a highly experienced operator and
can be very challenging. Machine learning solutions that quantify and guide the scanning
process in an automatic and intelligent manner could overcome these limitations and lead
to routine monitoring. Development of such methods is the primary goal of the presented
work.
In response to this goal, this thesis proposes a suite of signal processing and machine
learning techniques. Among these is a new and real-time method of maximum frequency
envelope estimation. This method, which is based on image-processing techniques and is
highly adaptive to varying signal quality, was developed to facilitate automatic and consistent
extraction of features from Doppler ultrasound measurements. Through a thorough
evaluation, this method was demonstrated to be accurate and more stable than alternative
state-of-art methods.
Two novel real-time methods of beat segmentation, which operate using the maximum
frequency envelope, were developed to enable systematic feature extraction from individual
cardiac cycles. These methods do not require any additional hardware, such as an electrocardiogram
machine, and are fully automatic, real-time and highly resilient to noise.
These qualities are not available in existing methods. Extensive evaluation demonstrated
the methods to be highly successful.
A host of machine learning solutions were analysed, designed and evaluated. This led to a set of novel features being proposed for Doppler ultrasound analysis. In addition, a state of-
the-art image recognition classification method, hitherto undocumented for Doppler
ultrasound analysis, was shown to be superior to more traditional modelling approaches.
These contributions facilitated the design of two innovative types of feedback. To reflect
beneficial probe movements, which are otherwise difficult to distinguish, a regression model
to quantitatively score ultrasound measurements was proposed. This feedback was shown
to be highly correlated with an ideal response.
The second type of feedback explicitly predicted beneficial probe movements. This was
achieved using classification models with up to five categories, giving a more challenging
scenario than those addressed in prior disease classification work. Evaluation of these, for
the first time, demonstrated that Doppler scan information can be used to automatically
indicate probe position.
Overall, the presented work includes significant contributions for Doppler ultrasound
analysis, it proposes valuable new machine learning techniques, and with continued work,
could lead to solutions that unlock the full potential of Doppler ultrasound haemodynamic
monitoring
Contributions à la sonification d’image et à la classification de sons
L’objectif de cette thèse est d’étudier d’une part le problème de sonification d’image
et de le solutionner à travers de nouveaux modèles de correspondance entre domaines
visuel et sonore. D’autre part d’étudier le problème de la classification de son et de le résoudre
avec des méthodes ayant fait leurs preuves dans le domaine de la reconnaissance
d’image.
La sonification d’image est la traduction de données d’image (forme, couleur, texture,
objet) en sons. Il est utilisé dans les domaines de l’assistance visuelle et de l’accessibilité
des images pour les personnes malvoyantes. En raison de sa complexité, un
système de sonification d’image qui traduit correctement les données d’image en son de
manière intuitive n’est pas facile à concevoir.
Notre première contribution est de proposer un nouveau système de sonification
d’image de bas-niveau qui utilise une approche hiérarchique basée sur les caractéristiques
visuelles. Il traduit, à l’aide de notes musicales, la plupart des propriétés d’une
image (couleur, gradient, contour, texture, région) vers le domaine audio, de manière
très prévisible et donc est facilement ensuite décodable par l’être humain.
Notre deuxième contribution est une application Android de sonification de haut
niveau qui est complémentaire à notre première contribution car elle implémente la traduction
des objets et du contenu sémantique de l’image. Il propose également une base
de données pour la sonification d’image.
Finalement dans le domaine de l’audio, notre dernière contribution généralise le motif
binaire local (LBP) Ã 1D et le combine avec des descripteurs audio pour faire de
la classification de sons environnementaux. La méthode proposée surpasse les résultats
des méthodes qui utilisent des algorithmes d’apprentissage automatique classiques et
est plus rapide que toutes les méthodes de réseau neuronal convolutif. Il représente un
meilleur choix lorsqu’il y a une rareté des données ou une puissance de calcul minimale.The objective of this thesis is to study on the one hand the problem of image sonification
and to solve it through new models of mapping between visual and sound domains.
On the other hand, to study the problem of sound classification and to solve it with
methods which have proven track record in the field of image recognition.
Image sonification is the translation of image data (shape, color, texture, objects)
into sounds. It is used in vision assistance and image accessibility domains for visual
impaired people. Due to its complexity, an image sonification system that properly conveys
the image data to sound in an intuitive way is not easy to design.
Our first contribution is to propose a new low-level image sonification system which
uses an hierarchical visual feature-based approach to translate, usingmusical notes, most
of the properties of an image (color, gradient, edge, texture, region) to the audio domain,
in a very predictable way in which is then easily decodable by the human being.
Our second contribution is a high-level sonification Android application which is
complementary to our first contribution because it implements the translation to the audio
domain of the objects and the semantic content of an image. It also proposes a dataset
for an image sonification.
Finally, in the audio domain, our third contribution generalizes the Local Binary
Pattern (LBP) to 1D and combines it with audio features for an environmental sound
classification task. The proposed method outperforms the results of methods that uses
handcrafted features with classical machine learning algorithms and is faster than any
convolutional neural network methods. It represents a better choice when there is data
scarcity or minimal computing power
Models and Analysis of Vocal Emissions for Biomedical Applications
The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies
A computational framework for sound segregation in music signals
Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200