Rui Pedro Paiva

• Professor at the Dept. of Informatics Engineerings
• Professor at University of Coimbra

Purpose Sleep constitutes a third of human life, underscoring its importance in health-related and psychophysiological research. Monitoring sleep stage evolution is critical for understanding sleep-related issues and diagnosing disorders. This study aims to classify sleep stages using a Hidden Markov Model (HMM) based on spectral statistical measur...

The Music Emotion Recognition (MER) field has seen steady developments in recent years, with contributions from feature engineering, machine learning, and deep learning. The landscape has also shifted from audio-centric systems to bimodal ensembles that combine audio and lyrics. However, a severe lack of public and sizeable bimodal databases has ha...

Background The Covid-19 pandemic has caused immense pressure on Intensive Care Units (ICU). In patients with severe ARDS due to Covid-19, respiratory mechanics are important for determining the severity of lung damage. Lung auscultation could not be used during the pandemic despite its merit. The main objective of this study was to investigate asso...

Classical machine learning techniques have dominated Music Emotion Recognition. However, improvements have slowed down due to the complex and time-consuming task of handcrafting new emotionally relevant audio features. Deep learning methods have recently gained popularity in the field because of their ability to automatically learn relevant feature...

Music can convey basic emotions, such as joy and sadness, and more complex ones, such as tenderness or nostalgia. Its effects on emotion regulation and reward have attracted much attention in cognitive and affective neuroscience. Understanding the underlying neural mechanisms of music-evoked emotions could guide the development of novel technologic...

In recent years, computerized methods for analyzing respiratory function have gained increased attention within the scientific community. This study proposes a deep-learning model to estimate the dimensionless respiratory airflow using only respiratory sound without prior calibration. We developed hybrid deep learning models (CNN + LSTM) to extract...

Music conveys both basic emotions, like joy and sadness, and complex ones, such as tenderness and nostalgia. Its effects on emotion regulation and reward have attracted much research attention, as the neural correlates of music-evoked emotions may inform neurorehabilitation interventions. Here, we used fMRI to decode and examine the neural correlat...

Patients with respiratory conditions typically exhibit adventitious respiratory sounds (ARS), such as wheezes and crackles. In recent years, computerized methods for analyzing respiratory function, namely ARS, have gained increased attention within the scientific community. Such methods primarily aim to facilitate diagnosing and monitoring patients...

Background and objective: Respiratory diseases are among the most significant causes of morbidity and mortality worldwide, causing substantial strain on society and health systems. Over the last few decades, there has been increasing interest in the automatic analysis of respiratory sounds and electrical impedance tomography (EIT). Nevertheless, n...

Current wearable respiratory monitoring devices provide a basic assessment of the breathing pattern of the examined subjects. More complex monitoring is needed for healthcare applications in patients with lung diseases. A multi-sensor vest allowing continuous lung imaging by electrical impedance tomography (EIT) and auscultation at six chest locati...

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...

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...

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...

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...

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...

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....

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

(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’...

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...

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...

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...

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...

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),...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

This paper describes a method for melody detection in polyphonic musical signals. Our approach starts by ob-taining a set of pitch candidates for each time frame, with recourse to an auditory model. Trajectories of the most salient pitches are then constructed. Next, note candidates are obtained by trajectory segmentation (in terms of frequency and...

Cough can be defined as a forced expulsive onrush, normally against a closed glottis, producing a characteristic sound. It can be an indicator of many respiratory diseases, and its counting and classification is an important aspect. We propose a method on internal sound signal to automatically identify, count and (partly) qualify cough sounds. Our...

Neurally medicated 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 for the healthcare systems in particular since mainly elderly are at risk of NMS in our aging societies. In the present...

We propose WELCOME, an innovative integrated care platform using wearable sensors and smart cloud computing for Chronic Obstructive Pulmonary Disease (COPD) patients with co-morbidities. WELCOME aims to bring about a change in the reactive nature of the management of chronic diseases and its comorbidities, in particular through the development of a...

Crackles are adventitious and explosive respiratory sounds that can be classified as fine or coarse. These sounds are usually associated with cardiopulmonary diseases such as the chronic obstructive pulmonary disease. In this work seven different features were tested with the objective to identify the best subset of features that allows a robust de...

Integrated care of patients with COPD and comorbidities requires the ability to regard patient status as a complex system. It can benefit from technologies that extract multiparametric information and detect changes in status along different axes. This raises the need for generation of systems that can unobtrusively monitor, compute, and combine mu...

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 systolic time intervals (STI) measurement using heart sound. Methods for cardiac function parameter extraction based on STI...

The presence of motion artifacts in the photople-thysmographic (PPG) signals is one of the major obstacles in the extraction of reliable cardiovascular parameters in real time and continuous monitoring applications. In the current paper we present a comparison between two motion artifacts detection methodologies proposed by Couceiro et al. [1] and...