Audio-Based Classification of Respiratory Diseases using Advanced Signal Processing and Machine Learning for Assistive Diagnosis Support

In global healthcare, respiratory diseases are a leading cause of mortality, underscoring the need for rapid and accurate diagnostics. To advance rapid screening techniques via auscultation, our research focuses on employing one of the largest publicly available medical database of respiratory sounds to train multiple machine learning models able to classify different health conditions. Our method combines Empirical Mode Decomposition (EMD) and spectral analysis to extract physiologically relevant biosignals from acoustic data, closely tied to cardiovascular and respiratory patterns, making our approach apart in its departure from conventional audio feature extraction practices. We use Power Spectral Density analysis and filtering techniques to select Intrinsic Mode Functions (IMFs) strongly correlated with underlying physiological phenomena. These biosignals undergo a comprehensive feature extraction process for predictive modeling. Initially, we deploy a binary classification model that demonstrates a balanced accuracy of 87% in distinguishing between healthy and diseased individuals. Subsequently, we employ a six-class classification model that achieves a balanced accuracy of 72% in diagnosing specific respiratory conditions like pneumonia and chronic obstructive pulmonary disease (COPD). For the first time, we also introduce regression models that estimate age and body mass index (BMI) based solely on acoustic data, as well as a model for gender classification. Our findings underscore the potential of this approach to significantly enhance assistive and remote diagnostic capabilities.Comment: 5 pages, 2 figures, 3 tables, Conference pape

Look-alike humans identified by facial recognition algorithms show genetic similarities

We thank François Brunelle for providing the look-alike images. We thank CERCA Programme/Generalitat de Catalunya and the Josep Carreras Foundation for institutional support. This work was funded by the governments of Catalonia (2017SGR1080) and Spain (RTI2018-094049-B-I00, SAF2014-55000, and TIN2017-90124-P) and the Cellex Foundation. M.E. conceived and designed the study; R.S.J. M.R. C.A.G.-P. M.C.d.M. D.P. S.M. V.D. P.C. M.F.-B. I.O. C.L.-F. A.N. C.F.-T. D.A. F.M.S. X.B. A.V. and M.E. analyzed multiomics and questionnaire data; R.J. and M.E. wrote the manuscript with contributions and approval from all authors. M.E. is a consultant of Ferrer International and Quimatryx. S.M. is an employee of Ferrer International. C.F.-T. is chief technical officer of Herta Security.We thank François Brunelle for providing the look-alike images. We thank CERCA Programme/Generalitat de Catalunya and the Josep Carreras Foundation for institutional support. This work was funded by the governments of Catalonia (2017SGR1080) and Spain (RTI2018-094049-B-I00, SAF2014-55000, and TIN2017-90124-P) and the Cellex Foundation.The human face is one of the most visible features of our unique identity as individuals. Interestingly, monozygotic twins share almost identical facial traits and the same DNA sequence but could exhibit differences in other biometrical parameters. The expansion of the world wide web and the possibility to exchange pictures of humans across the planet has increased the number of people identified online as virtual twins or doubles that are not family related. Herein, we have characterized in detail a set of "look-alike" humans, defined by facial recognition algorithms, for their multiomics landscape. We report that these individuals share similar genotypes and differ in their DNA methylation and microbiome landscape. These results not only provide insights about the genetics that determine our face but also might have implications for the establishment of other human anthropometric properties and even personality characteristics

Unified Framework for Identity and Imagined Action Recognition from EEG patterns

We present a unified deep learning framework for the recognition of user identity and the recognition of imagined actions, based on electroencephalography (EEG) signals, for application as a brain-computer interface. Our solution exploits a novel shifted subsampling preprocessing step as a form of data augmentation, and a matrix representation to encode the inherent local spatial relationships of multi-electrode EEG signals. The resulting image-like data is then fed to a convolutional neural network to process the local spatial dependencies, and eventually analyzed through a bidirectional long-short term memory module to focus on temporal relationships. Our solution is compared against several methods in the state of the art, showing comparable or superior performance on different tasks. Specifically, we achieve accuracy levels above 90% both for action and user classification tasks. In terms of user identification, we reach 0.39% equal error rate in the case of known users and gestures, and 6.16% in the more challenging case of unknown users and gestures. Preliminary experiments are also conducted in order to direct future works towards everyday applications relying on a reduced set of EEG electrodes

Look-alike humans identified by facial recognition algorithms show genetic similarities

The human face is one of the most visible features of our unique identity as individuals. Interestingly, monozygotic twins share almost identical facial traits and the same DNA sequence but could exhibit differences in other biometrical parameters. The expansion of the world wide web and the possibility to exchange pictures of humans across the planet has increased the number of people identified online as virtual twins or doubles that are not family related. Herein, we have characterized in detail a set of “look-alike” humans, defined by facial recognition algorithms, for their multiomics landscape. We report that these individuals share similar genotypes and differ in their DNA methylation and microbiome landscape. These results not only provide insights about the genetics that determine our face but also might have implications for the establishment of other human anthropometric properties and even personality characteristics.This work was funded by the governments of Catalonia (2017SGR1080) and Spain (RTI2018-094049-B-I00, SAF2014-55000, and TIN2017-90124-P) and the Cellex Foundation

Automated dental identification: A micro-macro decision-making approach

Identification of deceased individuals based on dental characteristics is receiving increased attention, especially with the large volume of victims encountered in mass disasters. In this work we consider three important problems in automated dental identification beyond the basic approach of tooth-to-tooth matching.;The first problem is on automatic classification of teeth into incisors, canines, premolars and molars as part of creating a data structure that guides tooth-to-tooth matching, thus avoiding illogical comparisons that inefficiently consume the limited computational resources and may also mislead the decision-making. We tackle this problem using principal component analysis and string matching techniques. We reconstruct the segmented teeth using the eigenvectors of the image subspaces of the four teeth classes, and then call the teeth classes that achieve least energy-discrepancy between the novel teeth and their approximations. We exploit teeth neighborhood rules in validating teeth-classes and hence assign each tooth a number corresponding to its location in a dental chart. Our approach achieves 82% teeth labeling accuracy based on a large test dataset of bitewing films.;Because dental radiographic films capture projections of distinct teeth; and often multiple views for each of the distinct teeth, in the second problem we look for a scheme that exploits teeth multiplicity to achieve more reliable match decisions when we compare the dental records of a subject and a candidate match. Hence, we propose a hierarchical fusion scheme that utilizes both aspects of teeth multiplicity for improving teeth-level (micro) and case-level (macro) decision-making. We achieve a genuine accept rate in excess of 85%.;In the third problem we study the performance limits of dental identification due to features capabilities. We consider two types of features used in dental identification, namely teeth contours and appearance features. We propose a methodology for determining the number of degrees of freedom possessed by a feature set, as a figure of merit, based on modeling joint distributions using copulas under less stringent assumptions on the dependence between feature dimensions. We also offer workable approximations of this approach

Clinical decision support using machine learning: Parkinsonism and Fabry Disease

Dissertação de mestrado integrado em Industrial Electronics and Computers EngineeringParkinsonismo Vascular (PVa), doença de Parkinson Idiopática (DPI), duas doenças associadas com Parkinsonismo, e a doença de Fabry (DF) foram investigadas usando métodos estatísticos e de aprendizagem automática (AP). O diagnóstico destas doenças é atualmente um grande desafio devido à enorme variação fenotípica e à sobreposição de fenótipos. De facto, existe um atraso considerável entre as primeiras manifestações destas doenças e o correto diagnóstico clínico. A investigação de biomarcadores capazes de assistir o atempado e correto diagnóstico de DF, PVa e DPI é extremamente importante. O objetivo deste estudo é avaliar a aptidão de métodos de AP quando utilizados para diagnosticar estas doenças. Foram utilizados sensores vestíveis para obter dados da marcha de 15 pacientes com DPI, 14 pacientes com PVa, 36 pacientes com DF e 36 controlos. Foram também obtidos dados cardíacos e neu rológicos de 95 pacientes de DF. Com base nos dados da marcha várias tarefas de classificação binárias foram realizadas utilizando seis métodos supervisionados de AP: Support Vector Machines (SVMs), Random Forests (RFs), Multiple Layer Perceptrons (MLPs), Deep Belief Networks (DBNs), Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). Foram aplicados métodos de clustering para identificar subgrupos homogéneos de: 1) pacientes com Parkinsonismo (DPI e PVa) usando os dados de marcha e 2) pacientes com DF usando os dados cardíacos. Todas as tarefas de classificação baseadas nos dados da marcha obtiveram ótimos resultados, destacando-se as SVMs e CNNs. Estes métodos deram provas da sua eficácia e precisão como ferramentas auxiliares capazes de assistir no processo de diagnóstico destas doenças. A análise de subgrupos homogéneos de pacientes que sofrem de Parkinsonismo revelou que PVa apresenta os padrões de marcha mais afetados. A análise de subgrupos homogéneos de pacientes com FD identificou que pacientes com manifestações cardíacas severas são mais suscetíveis a manifestações neurológicas. Os padrões de marcha de pacientes com DF revelaram também um potencial desenvolvimento de Parkinsonismo. Esta dissertação contribuiu para o desenvolvimento de ferramentas capazes de auxiliar no processo diagnóstico e avaliação clínica de DVa, DPI e FD, estas ferramentas facilitam um prévio e correto diagnóstico destas doenças, possibilitam um melhor tratamento e uma melhor qualidade de vida aos pacientes.Vascular Parkinsonism (VaP), Idiopathic Parkinson’s Disease (IPD), which are two diseases associated with Parkinsonism, and Fabry Disease (FD) were investigated with the support of different statistical and machine learning methods. Diagnosis of these diseases remains a challenge mostly due to phenotipical variability and highly overlapping phenotypes, with considerable delay between onset and clinical diagnosis. Additionally, growing evidence linking Parkinsonism with FD has been reported recently. It is then of extreme importance to explore biomarkers capable of assisting the early and correct diagnosis of FD, VaP, and IPD. The aim of this study is to evaluate the effectiveness of machine learning strategies when applied to the diagnosis of these disorders. Wearable sensors positioned on both feet were used to acquire gait data from 15 IPD patients, 14 VaP patients, 36 FD patients, and 34 control subjects. Cardiac and neurological evaluations were also collected from 95 FD patients. Based on gait data, various binary comparative classification analysis were performed by applying six supervised machine learning algorithms: Support Vector Machines (SVMs), Random Forests (RFs), Multiple Layer Perceptrons (MLPs), Deep Belief Networks (DBNs), Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNNs). Clustering methods were applied to identify homogeneous subgroups of: 1) patients with Parkinsonism (IPD and VaP) based on gait data and 2) FD patients based on cardiac data. All classification analysis based on gait data achieved very good results, especially SVMs and CNNs. These classifiers have proven to be very reliable and accurate assistance tools for the diagnosis of these disorders. The Parkinsonism subgroup analysis revealed that the most impaired gait patterns are mainly displayed by VaP patients. The FD subgroup analysis identified that patients with severe cardiac manifes tations also display neurological impairments. The gait patterns of FD patients also revealed interesting results suggesting the potential development of Parkinsonism. This dissertation contributed to the development of clinical diagnostic and evaluation tools of FD, IPD, and VaP, to facilitate the early and correct diagnosis of these diseases, leading to better treatment and improvement of the quality of life of patients.Gostava também de agradecer imenso ao projeto onde este trabalho se integra, NORTE-01-0145- FEDER- 000026 (DeM-Deus Ex Machina) financiados por NORTE2020 e FEDER

Speaker Recognition Using Machine Learning Techniques

Speaker recognition is a technique of identifying the person talking to a machine using the voice features and acoustics. It has multiple applications ranging in the fields of Human Computer Interaction (HCI), biometrics, security, and Internet of Things (IoT). With the advancements in technology, hardware is getting powerful and software is becoming smarter. Subsequently, the utilization of devices to interact effectively with humans and performing complex calculations is also increasing. This is where speaker recognition is important as it facilitates a seamless communication between humans and computers. Additionally, the field of security has seen a rise in biometrics. At present, multiple biometric techniques co-exist with each other, for instance, iris, fingerprint, voice, facial, and more. Voice is one metric which apart from being natural to the users, provides comparable and sometimes even higher levels of security when compared to some traditional biometric approaches. Hence, it is a widely accepted form of biometric technique and is constantly being studied by scientists for further improvements. This study aims to evaluate different pre-processing, feature extraction, and machine learning techniques on audios recorded in unconstrained and natural environments to determine which combination of these works well for speaker recognition and classification. Thus, the report presents several methods of audio pre- processing like trimming, split and merge, noise reduction, and vocal enhancements to enhance the audios obtained from real-world situations. Additionally, a text-independent approach is used in this research which makes the model flexible to multiple languages. Mel Frequency Cepstral Coefficients (MFCC) are extracted for each audio, along with their differentials and accelerations to evaluate machine learning classification techniques such as kNN, Support Vector Machines, and Random Forest Classifiers. Lastly, the approaches are evaluated against existing research to study which techniques performs well on these sets of audio recordings