Rui Pedro Paiva

Rui Pedro Paiva
University of Coimbra | UC · Department of Informatics Engineering

Professor at the Dept. of Informatics Engineerings

About

140
Publications
188,585
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
1,724
Citations
Introduction
(More info at: http://rppaiva.dei.uc.pt/) Rui Pedro Paiva is Professor at the Department of Informatics Engineering of the University of Coimbra. He concluded his Doctoral, Master and Bachelor (Licenciatura - 5 years), all in Informatics Engineering at the University of Coimbra, in 2007, 1999 and 1996, respectively. His main research interests are in the areas of Music Data Mining (MDM), Music Information Retrieval (MIR) and Medical Informatics.
Additional affiliations
February 2007 - present
University of Coimbra
Position
  • Senior Researcher
February 2007 - present
University of Coimbra
Position
  • Professor
October 1997 - February 2007
University of Coimbra
Position
  • Research and Teaching Assistant

Publications

Publications (140)
Preprint
Full-text available
Wheezes are adventitious respiratory sounds commonly present in patients with respiratory conditions. The presence of wheezes and their time location are relevant for clinical reasons, such as understanding the degree of bronchial obstruction. Conventional auscultation is usually employed to analyze wheezes, but remote monitoring has become a press...
Preprint
Wheezes are adventitious respiratory sounds commonly present in patients with respiratory conditions. The presence of wheezes and their time location are relevant for clinical reasons, such as understanding the degree of bronchial obstruction. Conventional auscultation is usually employed to analyze wheezes, but remote monitoring has become a press...
Article
Full-text available
Respiratory diseases constitute one of the leading causes of death worldwide and directly affect the patient's quality of life. Early diagnosis and patient monitoring, which conventionally include lung auscultation, are essential for the efficient management of respiratory diseases. Manual lung sound interpretation is a subjective and time-consumin...
Chapter
This chapter gives an overview of medical imaging methods that are used for generation of pulmonary images and it assesses their suitability for implementation into wearable instruments. It focuses on the noninvasive radiation-free method of electrical impedance tomography (EIT) which exhibits a high potential for clinical use in a wearable design....
Chapter
Auscultation of respiratory sounds is a common tool used by physicians to diagnose and monitor respiratory conditions. However, some drawbacks hinder the effectiveness of conventional auscultation. Automated respiratory sound analysis could potentially overcome those limitations. Current methods are much more sophisticated than the techniques used...
Article
This work presents a contribution to advance current solutions for the problem of melanoma detection based on deep learning (DL) approaches. This is an active research field, which aims to aid on the detection and classification of melanoma (the most lethal type of skin cancer) with non-invasive solutions. By exploiting both 2D and 3D characteristi...
Conference Paper
Machine learning algorithms are progressively assuming important roles as computational tools to support clinical diagnosis, namely in the classification of pigmented skin lesions using RGB images. Most current classification methods rely on common 2D image features derived from shape, colour or texture, which does not always guarantee the best res...
Conference Paper
Full-text available
Mechanically ventilated patients typically exhibit abnormal respiratory sounds. Squawks are short inspiratory adventitious sounds that may occur in patients with pneumonia, such as COVID-19 patients. In this work we devised a method for squawk detection in mechanically ventilated patients by developing algorithms for respiratory cycle estimation, s...
Conference Paper
Patients suffering from pulmonary diseases typically exhibit pathological lung ventilation in terms of homogeneity. Electrical Impedance Tomography (EIT) is a non- invasive imaging method that allows to analyze and quantify the distribution of ventilation in the lungs. In this article, we present a new approach to promote the use of EIT data and th...
Article
Medical image classification through learning-based approaches has been increasingly used, namely in the discrimination of melanoma. However, for skin lesion classification in general, such methods commonly rely on dermoscopic or other 2D-macro RGB images. This work proposes to exploit beyond conventional 2D image characteristics, by considering a...
Preprint
Full-text available
Patients suffering from pulmonary diseases typically exhibit pathological lung ventilation in terms of homogeneity. Electrical Impedance Tomography (EIT) is a non-invasive imaging method that allows to analyze and quantify the distribution of ventilation in the lungs. In this article, we present a new approach to promote the use of EIT data and the...
Preprint
Full-text available
Mechanically ventilated patients typically exhibit abnormal respiratory sounds. Squawks are short inspiratory adventitious sounds that may occur in patients with pneumonia, such as COVID-19 patients. In this work we devised a method for squawk detection in mechanically ventilated patients by developing algorithms for respiratory cycle estimation, s...
Conference Paper
Full-text available
Features are arguably the key factor to any machine learning problem. Over the decades, myriads of audio features and recently feature-learning approaches have been tested in Music Emotion Recognition (MER) with scarce improvements. Here, we shed some light on the suitability of the audio features provided by the Spotify API, the leading music stre...
Article
Objective: In this paper, an automated stable tidal breathing period (STBP) identification method based on processing Electrical Impedance Tomography (EIT) waveforms is proposed and the possibility of detecting and identifying such periods using EIT waveforms is analyzed. In wearable chest EIT, patients breathe spontaneously, and therefore, their...
Conference Paper
Full-text available
Patients with respiratory conditions typically exhibit adventitious respiratory sounds, such as wheezes. Wheeze events have variable duration. In this work we studied the influence of event duration on wheeze classification, namely how the creation of the non-wheeze class affected the classifiers' performance. First, we evaluated several classifier...
Article
Full-text available
(1) Background: Patients with respiratory conditions typically exhibit adventitious respiratory sounds (ARS), such as wheezes and crackles. ARS events have variable duration. In this work we studied the influence of event duration on automatic ARS classification, namely, how the creation of the Other class (negative class) affected the classifiers’...
Preprint
Full-text available
Patients with respiratory conditions typically exhibit adventitious respiratory sounds, such as wheezes. Wheeze events have variable duration. In this work we studied the influence of event duration on wheeze classification, namely how the creation of the non-wheeze class affected the classifiers' performance. First, we evaluated several classifier...
Article
Full-text available
The design of meaningful audio features is a key need to advance the state-of-the-art in Music Emotion Recognition (MER). This work presents a survey on the existing emotionally-relevant computational audio features, supported by the music psychology literature on the relations between eight musical dimensions (melody, harmony, rhythm, dynamics, to...
Article
Full-text available
Lung sounds acquired by stethoscopes are extensively used in diagnosing and differentiating respiratory diseases. Although an extensive know-how has been built to interpret these sounds and identify diseases associated with certain patterns, its effective use is limited to individual experience of practitioners. This user-dependency manifests itsel...
Article
Full-text available
Accurate skin lesion segmentation is important for identification and classification through computational methods. However, when performed by dermatologists, the results of clinical segmentation are affected by a certain margin of inaccuracy (which exists since dermatologist do not delineate lesions for segmentation but for extraction) and also si...
Preprint
Full-text available
Segmentation is a key stage in dermoscopic image processing, where the accuracy of the border line that defines skin lesions is of utmost importance for subsequent algorithms (e.g., classification) and computer-aided early diagnosis of serious medical conditions. This paper proposes a novel segmentation method based on Local Binary Patterns (LBP),...
Article
Full-text available
Objective: Over the last few decades, there has been significant interest in the automatic analysis of respiratory sounds. However, currently there are no publicly available large databases with which new algorithms can be evaluated and compared. Further developments in the field are dependent on the creation of such databases. Approach: This pa...
Conference Paper
Full-text available
We present a set of novel emotionally-relevant audio features to help improving the classification of emotions in audio music. First, a review of the state-of-the-art regarding emotion and music was conducted, to understand how the various music concepts may influence human emotions. Next, well known audio frameworks were analyzed, assessing how th...
Conference Paper
Full-text available
With the emergence of Big Data, the scarcity of data scientists to analyse all the data being produced in different domains became evident. To train new data scientists faster, web applications providing data science practices without requiring programming skills can be a great help. However, some available web applications lack in providing good d...
Conference Paper
Full-text available
With the emergence of Big Data, the scarcity of data scientists to analyse all the data being produced in different domains became evident. Moreover, the processing of such amounts of data also is challenging due to current technologies in use. With this in mind, the Data-Science4NP aims to explore the use of visual programming paradigms to enable...
Conference Paper
Full-text available
With technology advances, trying to replace expensive devices with cheaper, but efficient, systems is a promising approach to pursuit in the future. The main objective of this paper is to compare the Littmann 3200 (state-of-the-art electronic stethoscope) with a prototype built for this purpose (with an electric microphone as the sensor, and an Ard...
Article
Full-text available
This work advances the music emotion recognition state-of-the-art by proposing novel emotionally-relevant audio features. We reviewed the existing audio features implemented in well-known frameworks and their relationships with the eight commonly defined musical concepts. This knowledge helped uncover musical concepts lacking computational extracto...
Conference Paper
Full-text available
We present a multi-feature approach to the detection of cough and adventitious respiratory sounds. After the removal of near-silent segments, a vector of event boundaries is obtained and a proposed set of 126 features is extracted for each event. Evaluation was performed on a data set comprised of internal audio recordings from 18 patients. The bes...
Conference Paper
Full-text available
The automatic analysis of respiratory sounds has been a field of great research interest during the last decades. Automated classification of respiratory sounds has the potential to detect abnormalities in the early stages of a respiratory dysfunction and thus enhance the effectiveness of decision making. However, the existence of a publically avai...
Conference Paper
Full-text available
In spite of their growing maturity, current web monitoring tools are unable to observe all operating conditions. For example, clients in different geographical locations might get very diverse latencies to the server; the network between client and server might be slow; or third-party servers with external page resources might underperform. Ultimat...
Conference Paper
Full-text available
We present a new method for the discrimination of explosive cough events, which is based on a combination of spectral content descriptors and pitch-related features. After the removal of near-silent segments, a vector of event boundaries is obtained and a proposed set of 9 features is extracted for each event. Two data sets, recorded using electron...
Conference Paper
Full-text available
Atrial Fibrillation (AF) is the most common arrhythmia and it is estimated to affect 33.5 million people worldwide. AF is associated with an increased risk of mortality and morbidity, such as heart failure and stroke and affects mostly older persons and persons with other conditions (e.g. heart failure and coronary artery disease). In order to prev...
Conference Paper
Full-text available
The analysis of the respiratory sounds is a valuable diagnostic tool for the detection and the follow-up of respiratory diseases such as Chronic Obstructive Pulmonary Disease (COPD). Adventitious sounds, such as wheezes, stridors, squawks and crackles, refer to additional respiratory sounds superimposed on breath sounds and are highly correlated to...
Conference Paper
Full-text available
The automatic detection of adventitious sounds, such as wheezes and crackles, is a valuable non-invasive tool to detect and follow-up respiratory diseases such as chronic obstructive pulmonary disease. Crackles are short explosive sounds that seem to result from an abrupt opening or closing of the airways. Several methods have been proposed for aut...
Conference Paper
Full-text available
This research addresses the role of audio and lyrics in the music emotion recognition. Each dimension (e.g., audio) was separately studied, as well as in a context of bimodal analysis. We perform classification by quadrant categories (4 classes). Our approach is based on several audio and lyrics state-of-the-art features, as well as novel lyric fea...
Conference Paper
Full-text available
Lung sound signal processing has proven to be a great improvement to the traditional acoustic interpretation of lung sounds. However, that analysis can be seriously hindered by the presence of different types of noise originated in the acquisition environment or caused by physiological processes. Consequently, the diagnostic accuracy of pulmonary d...
Conference Paper
Full-text available
The global inhomogeneity (GI) index is a electrical impedance tomography (EIT) parameter that quantifies the tidal volume distribution within the lung. In this work the global inhomogeneity index was computed for twenty subjects in order to evaluate his potential use in the detection and follow up of chronic obstructive pulmonary disease (COPD) pat...
Conference Paper
Full-text available
The automatic detection of adventitious lung sounds is a valuable tool to monitor respiratory diseases like chronic obstructive pulmonary disease. Crackles are adventitious and explosive respiratory sounds that are usually associated with the inflammation or infection of the small bronchi, bronchioles and alveoli. In this study a multi-feature appr...
Article
Electrical impedance tomography (EIT) is increasingly used in patients suffering from respiratory disorders during pulmonary function testing (PFT). The EIT chest examinations often take place simultaneously to conventional PFT during which the patients involuntarily move in order to facilitate their breathing. Since the influence of torso and arm...
Conference Paper
Full-text available
WELCOME is an ambitious EU FP7 project which aims to bring about a change in the reactive and integrated care nature of the management of chronic diseases and in particular the Chronic Obstructive Pulmonary Disease and its comorbidities. In order to achieve these goals, the Welcome solution will incorporate several bio-sensors to enable bio-analyti...
Conference Paper
Full-text available
Atrial Fibrillation (AF) is the most common arrhythmia and is associated with an increased risk of heart-related deaths and the development of conditions such as heart failure, dementia, and stroke. Affecting mostly elderly people, AF is associated with high comorbidity, increased mortality and is a major socio-economic impact in our society. There...
Conference Paper
Full-text available
During the acquisition of lung sounds, several sources of noise can interfere with the recordings. Therefore, the detection of noise present in lung sounds plays an important role in the correct diagnosis of several pulmonary disorders such as in chronic obstructive pulmonary diseases. Denoising tools reported so far focus mainly in the detection o...
Article
Full-text available
Monitoring of cardiovascular function on a beat-to-beat basis is fundamental for protecting patients in different settings including emergency medicine and interventional cardiology, but still faces technical challenges and several limitations. In the present study, we propose a new method for the extraction of cardiovascular performance surrogates...
Conference Paper
Full-text available
In this work thirty features were tested in order to identify the best feature set for the robust detection of wheezes. The features include the detection of the wheezes signature in the spectrogram space (WS-SS) and twenty-nine musical features usually used in the context of Music Information Retrieval. The method proposed to detect the signature...
Conference Paper
Full-text available
Systolic time intervals (STI) have significant diagnostic and prognostic value to assess the global cardiac function. Presently, STIs are regarded as a promising tool for long-term follow-up of patients with chronic cardiovascular diseases. Heart sound has proven to be a valuable approach for STI estimation, in particular for the Pre-Ejection Perio...
Conference Paper
Full-text available
We present a Matlab framework for heart sound processing and analysis. This framework includes algorithms developed for segmentation of the main heart sound components capable of handling situations with high-grade murmur, and for measuring systolic time intervals (STI). Methods for cardiac function parameter extraction based on STI are also includ...
Article
Full-text available
We propose a novel approach to music emotion recognition by combining standard and melodic features extracted directly from audio. To this end, a new audio dataset organized similarly to the one used in MIREX mood task comparison was created. From the data, 253 standard and 98 melodic features are extracted and used with several supervised learning...
Article
Full-text available
Neurally mediated syncope (NMS) patients suffer from sudden loss of consciousness, which is associated with a high rate of falls and hospitalization. NMS negatively impacts a subject's quality of life and is a growing cost issue in our aging society, as its incidence increases with age. In the present paper we present a solution for prediction of N...
Article
Full-text available
Two methodologies for neurally mediated syncope (NMS) prediction, based on the joint analysis of the electrocardiogram (ECG) and photoplethysmogram (PPG), are compared. Several features that characterize the variations in the inotropic, chronotropic, vascular tone and blood pressure surrogates were extracted and fed into two prediction models. The...
Article
Full-text available
The presence of motion artifacts in photoplethysmographic (PPG) signals is one of the major obstacles in the extraction of reliable cardiovascular parameters in continuous monitoring applications. In the current paper we present an algorithm for motion artifact detection based on the analysis of the variations in the time and the period domain char...