Vincenzo Randazzo

Politecnico di Torino | polito · DET - Department of Electronics and Telecommunications

The electrocardiogram (ECG) signal describes the heart’s electrical activity, allowing it to detect several health conditions, including cardiac system abnormalities and dysfunctions. Nowadays, most patient medical records are still paper-based, especially those made in past decades. The importance of collecting digitized ECGs is twofold: firstly,...

Objectives The purpose of this study was to externally validate algorithms (previously developed and trained in two United States populations) aimed at early detection of severe oliguric AKI (stage 2/3 KDIGO) in intensive care units patients. Methods The independent cohort was composed of 10'596 patients from the university hospital ICU of Amsterd...

Due to the current high availability of omics, data-driven biology has greatly expanded, and several papers have reviewed state-of-the-art technologies. Nowadays, two main types of investigation are available for a multi-omics dataset: extraction of relevant features for a meaningful biological interpretation and clustering of the samples. In the l...

Every year cardiovascular diseases kill the highest number of people worldwide. Among these, pathologies characterized by sporadic symptoms, such as atrial fibrillation, are difficult to be detected as state-of-the-art solutions, e.g., 12-leads electrocardiogram (ECG) or Holter devices, often fail to tackle these kinds of pathologies. Many portable...

Continuous vital signal monitoring is becoming more relevant in preventing diseases that afflict a large part of the world’s population; for this reason, healthcare equipment should be easy to wear and simple to use. Non-intrusive and non-invasive detection methods are a basic requirement for wearable medical devices, especially when these are used...

Topological learning is a wide research area aiming at uncovering the mutual spatial relationships between the elements of a set. Some of the most common and oldest approaches involve the use of unsupervised competitive neural networks. However, these methods are not based on gradient optimization which has been proven to provide striking results i...

Acute Kidney Injury (AKI), a complication of Intensive Care Units (ICU) patients, is associated with a high mortality rate. Early prediction of AKI is essential in order to trigger the use of preventative care-bundles.. A key indicator of AKI is a decrease of urine output which while having good sensitivity, currently suffers from low specificity...

Parkinson’s is a disease of the central nervous system characterized by neuronal necrosis. Patients at the time of diagnosis have already lost up to 70% of the neurons. It is essential to define early detection techniques to promptly intervene with an appropriate therapy. Handwriting analysis has been proven as a reliable method for Parkinson’s dis...

Automated electrocardiogram analysis and classification is nowadays a fundamental tool for monitoring patient heart activity and, consequently, his state of health. Indeed, the main interest is detecting the arise of cardiac pathologies such as arrhythmia. This paper presents a novel approach for automatic arrhythmia classification based on a 1D co...

Dealing with time-varying high dimensional data is a big problem for real time pattern recognition. Non-stationary topological representation can be addressed in two ways, according to the application: life-long modeling or by forgetting the past. The G-EXIN neural network addresses this problem by using life-long learning. It uses an anisotropic c...

Dealing with time-varying high dimensional data is a big problem forreal time pattern recognition. Non-stationary topological representation can be ad-dressed in two ways, according to the application: life-long modeling or by forget-ting the past. The G-EXIN neural network addresses this problem by using life-longlearning. It uses an anisotropic c...

Automated electrocardiogram analysis and classification isnowadays a fundamental tool for monitoring patient heart activity and,consequently, his state of health. Indeed, the main interest is detectingthe arise of cardiac pathologies such as arrhythmia.This paper presents a novel approach for automatic arrhythmia classifi-cation based on a 1D convo...

Parkinson’s is a disease of the central nervous system characterizedby neuronal necrosis. Patients at the time of diagnosis have already lost up to70% of the neurons. It is essential to define early detection techniques topromptly intervene with appropriate therapy. Handwriting analysis has beenproven as a reliable method for Parkinson’s diagnose a...

Detection of stator-based faults in Induction Machines (IMs) can be carried out in numerous ways. In particular, the shorted turns in stator windings of IM are among the most common faults in the industry. As a matter of fact, most IMs come with pre-installed current sensors for the purpose of control and protection. At this aim, using only the sta...

This paper presents a comparison between two recurrent neural networks (RNN) for arterial blood pressure (ABP) estimation. ABP is a parameter closely related to the cardiac activity, for this reason its monitoring implies decreasing the risk of heart disease. In order to predict the ABP values (both systolic and diastolic), electrocardiographic (EC...

Despite all the progress made in biomedical field, the Electrocardiogram (ECG) is still one of the most commonly used signal used in medical examinations. The problem of ECG classification has been approached in many different ways. Most of them rely on the extraction of features from the signal in the form of temporal or morphological characterist...

Applications such as surveillance, banking and healthcare deal with sensitive data whose confidentiality and integrity depends on accurate human recognition. In this sense, the crucial mechanism for performing an effective access control is authentication, which unequivocally yields user identity. In 2018, just in North America, around 445K identit...

In recent years, due to the high availability of omic data, data driven biology has greatly expanded. However, the analysis of different data sources is still an open challenge. A few multi-omic approaches have been proposed in literature. However, none of them take into consideration the intrinsic topology of each omic. In this work, an unsupervis...

Cardiovascular Diseases represent the leading cause of deaths in the world. Arterial Blood Pressure (ABP) is an important physiological parameter that should be properly monitored for the purposes of prevention. This work applies the neural network output-error (NNOE) model to ABP forecasting. Three input configurations are proposed based on ECG an...

Deep learning has been widely used for supervised learning and classification/regression problems. Recently, a novel area of research has applied this paradigm to unsupervised tasks; indeed, a gradient-based approach extracts, efficiently and autonomously, the relevant features for handling input data. However, state-of-the-art techniques focus mos...

Topological learning is a wide research area aiming at uncovering the mutual spatial relationships between the elements of a set. Some of the most common and oldest approaches involve the use of unsupervised competitive neural networks. However, these methods are not based on gradient optimization which has been proven to provide striking results i...

Arterial Blood Pressure (ABP) is an important physiological parameter that should be properly monitored for the purposes of prevention and detection of cardiovascular diseases, which represent one of the leading causes of death in the world. Currently, the most common adopted noninvasive blood pressure measurement system is sphygmomanometer, which...

Smart devices are more and more present in every aspect of everyday life. From smartphones, which are now like mini-computers, through systems for monitoring sleep or fatigue, to specific sensors for the recording of vital parameters. A particular class of the latter regards health monitoring. Indeed, through the use of such devices, several vital...

Fault diagnostics for electrical machines is a very difficult task because of the non-stationarity of the input information. Also, it is mandatory to recognize the pre-fault condition in order not to damage the machine. Only techniques like the principal component analysis (PCA) and its neural variants are used at this purpose, because of their sim...

Automated ECG analysis and classification are nowadays a fundamental tool for monitoring patient heart activity properly. The most important features used in literature are the raw data of a time window, the temporal attributes and the frequency information from the eigenvector techniques. This paper compares these approaches from a topological poi...

Automated ECG analysis and classification are nowadays a funda-mental tool for monitoring patient heart activity properly. The most important features used in literature are the raw data of a time window, the temporal at-tributes and the frequency information from the eigenvector techniques. This paper compares these approaches from a topological p...

Fault diagnostics for electrical machines is a very difficult task because of the non-stationarity of the input information. Also, it is mandatory to recognize the pre-fault condition in order not to damage the machine. Only techniques like the Principal Component Analysis (PCA) and its neural variants are used at this purpose, because of their sim...

Past years have seen a dramatic increase in the development and use of connected devices to improve life quality. The introduction of smart appliances aims at addressing everyday issues in a home environment, such as reduce power consumption and waste, and automate some recurring processes. One of the main places where this ubiquitous computing rev...

Hierarchical clustering is an important tool for extracting information from data in a multi-resolution way. It is more meaningful if driven by data, as in the case of divisive algorithms, which split data until no more division is allowed. However, they have the drawback of the splitting threshold setting. The neural networks can address this prob...

Cardiovascular diseases (CVD) remain the most common cause of death worldwide. Standard techniques such as 12-leads electrocardiogram (ECG) or Holter system are not sufficient to fully address some sporadic ECG anomalies like atrial fibrillation. Several low-cost wearable devices have already been proposed but, each of them misses some important fe...

Nowadays, the use of electrolyzers to cleanly and efficiently generate hydrogen from renewable energy sources is an attractive solution. Like fuel cell systems, DC/DC converters are needed to interface the DC bus with the electrolyzer. Classic buck converters are generally used for this purpose. However, these topologies must meet several requireme...

Dealing with time-varying high dimensional data is a big problem for real time pattern recognition. Only linear projections, like principal component analysis, are used in real time while nonlinear techniques need the whole database (offline). Their incremental variants do no work properly. The onCCA neural network addresses this problem; it is inc...

Non-stationary topological representation can be addressed in two ways, according to the application: life-long modeling or by forgetting the past. Life-long learning requires neural networks equipped with a tool for judging if a neuron has to be created for tracking the input distribution. It is always implemented as an isotropic criterion (a hype...

Big high dimensional data is becoming a challenging field of research. There exist a lot of techniques which infer information. However, because of the curse of dimensionality, a necessary step is the dimensionality reduction (DR) of the information. DR can be performed by linear and nonlinear algorithms. In general, linear algorithms are faster be...

After surgery, patients should remain under medical supervision to avoid possible complications. For this purpose, several patient vital parameters are monitored to ensure their health status. These checks generally require medical instruments, specialized personnel, and a hospital bed. If it would be possible to discharge the patient from hospital...

Dealing with time-varying high dimensional data is a big problem for real time pattern recognition. Only linear projections, like principal component analysis, are used in real time while nonlinear techniques need the whole data-base (offline). Their incremental variants do no work properly. The onCCA neural network addresses this problem; it is in...

Fault detection of shorted turns in the stator windings of Induction Motors (IMs) is possible in a variety of ways. As current sensors are usually installed together with the IMs for control and protection purposes, using stator current for fault detection has become a common practice nowadays, as it is much cheaper than installing additional senso...

Real time pattern recognition applications often deal with high dimensional data, which require a data reduction step which is only performed offline. However, this loses the possibility of adaption to a changing environment. This is also true for other applications different from pattern recognition, like data visualization for input inspection. O...