G. Pietro - Academia.edu (original) (raw)

Papers by G. Pietro

High quality Electrocardiogram (ECG) data is very important because this signal is generally used... more High quality Electrocardiogram (ECG) data is very important because this signal is generally used for the analysis of heart diseases. Wearable sensors are widely adopted for physical activity monitoring and for the provision of healthcare services, but noise always degrades the quality of these signals. In this paper, we propose a novel numerical scheme for ECG Signal denoising with low computational requirements. It is computationally cheap because it belongs to the class of Infinite Impulse Response (IIR) noise reduction algorithms. The main contribution of the proposed scheme is that it does not require a direct application of the Fast Fourier Transform. Moreover, it offers the possibility of implementation on mobile computing devices in an easy way. Experiments on real datasets have been carried out in order to test the algorithm.

Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, 2015

Wearable sensors are widely adopted for the provision of healthcare services. Unfortunately the n... more Wearable sensors are widely adopted for the provision of healthcare services. Unfortunately the noise always degrades the quality of the acquired signals. In this paper, we propose a framework for mobile ECG denoising, based on a novel numerical scheme with low computational requirements. The proposed system is able to store a signal from a wearable sensor and process it in a remote way or directly on the device.

Artificial Intelligence in Medicine, 2020

The rapid increase in the percentage of chronic disease patients along with the recent pandemic p... more The rapid increase in the percentage of chronic disease patients along with the recent pandemic pose immediate threats on healthcare expenditure and elevate causes of death. This calls for transforming healthcare systems away from one-on-one patient treatment into intelligent health systems, to improve services, access and scalability, while reducing costs. Reinforcement Learning (RL) has witnessed an intrinsic breakthrough in solving a variety of complex problems for diverse applications and services. Thus, we conduct in this paper a comprehensive survey of the recent models and techniques of RL that have been developed/used for supporting Intelligent-healthcare (I-health) systems. This paper can guide the readers to deeply understand the state-of-theart regarding the use of RL in the context of I-health. Specifically, we first present an overview for the I-health systems challenges, architecture, and how RL can benefit these systems. We then review the background and mathematical modeling of different RL, Deep RL (DRL), and multiagent RL models. After that, we provide a deep literature review for the applications of RL in I-health systems. In particular, three main areas have been tackled, i.e., edge intelligence, smart core network, and dynamic treatment regimes. Finally, we highlight emerging challenges and outline future research directions in driving the future success of RL in I-health systems, which opens the door for exploring some interesting and unsolved problems.

2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2015

Question Classification is one of the key tasks of Cognitive Systems based on the Question Answer... more Question Classification is one of the key tasks of Cognitive Systems based on the Question Answering paradigm. It aims at identifying the type of the possible answer for a question expressed in natural language. Machine learning techniques are typically employed for this task, and exploit a high number of features extracted from labelled questions of benchmark training sets in order to achieve good classification results. However, the high dimensionality of the feature space often limits the possibility of applying more efficient classification approaches, due to high training costs. In this work, more compact sets of lexical and syntactic features are proposed to distinguish question classes. In particular, the widely used unigrams are substituted with a smaller number of features, extracted by modifying typical Natural Language Processing procedures for question analysis. The accuracy values gained on a benchmark dataset by using these different sets of features are compared among them and with the state-of-the-art, taking into account the required complexity at the same time. The new sets of extracted features show a good trade-off between accuracy and complexity.

Advances in Intelligent Systems and Computing, 2016

Fuzzy logic has gained increasing importance in Decision Support Systems (DSSs), in particular in... more Fuzzy logic has gained increasing importance in Decision Support Systems (DSSs), in particular in medical field, since it allows to build a transparent and interpretable knowledge base. However, in order to obtain a general description of a system, probabilistic approaches undoubtedly offer the most significant information. Moreover, a good classifier to be used for medical scopes should be able to: (i) classify data items which are lacking of some input features; (ii) extract knowledge from incomplete datasets; (iii) consider categorical features; (iv) give responses in terms of a set of possible classes with respective degrees of plausibility. The approach here proposed pursues and achieve these objectives by approximating probabilistic information from incomplete datasets with an interpretable fuzzy system for classifying medical data. Resulting fuzzy sets can be interpreted as the terms of the involved linguistic variables, corresponding to numerical and/or categorical features, while weighted rules model probabilistic information. Rules are presented in two forms: the first is a set of one-dimensional models, which can be used if only one input feature is known; the second is a multidimensional combination of them, which can be used if more input features are known. As a proof of concept, the method has been applied for the detection of Multiple Sclerosis Lesions. The results show that this method is able to construct, for each one of the variables influencing the classification, an interpretable fuzzy partition, and very simple if-then rules. Moreover, multidimensional rule bases can be constructed, by means of which improved results are obtained, also with respect to naive Bayes classifier.

Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, 2015

ABSTRACT

2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), 2015

Continuous blood pressure (BP) measurement is an important issue in the medical field. The hypoth... more Continuous blood pressure (BP) measurement is an important issue in the medical field. The hypothesis of existence of a nonlinear relationship between plethysmography (PPG) and BP values has been investigated in this paper. If this hypothesis is true, then it is possible to indirectly measure patient's BP in a non-invasive way through the application of a wearable wireless PPG sensor to patient's finger and through the use of the results of a regression analysis aimed at linking PPG and BP values. To find the relationship between these two biomedical characteristics we have used here Genetic Programming (GP), because in a regression task it can evolve in an automatic way the structure of the most suitable explicit mathematical model. An analysis of the related scientific literature shows that this is the first attempt to mathematically relate PPG and BP values through GP. In this paper some preliminary experiments on the use of GP in facing this regression task have been carried out. As a result, for both systolic and diastolic BP values explicit mathematical models providing nonlinear relationship between PPG and BP values have been achieved, involving an approximation error of around 2 mmHg in both cases. A prototypal mobile-based system has been realized which is able to continuously estimate in real time the two BP values for any given patient by using only a plethysmography signal and the obtained mathematical models.

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015

Falls represent one of the most common causes of injury-related morbidity and mortality in later ... more Falls represent one of the most common causes of injury-related morbidity and mortality in later life. Subjects with cardiovascular disorders (e.g., related to autonomic dysfunctions and postural hypotension) are at higher risk of falling. Autonomic dysfunctions increasing the risk of falling in the short and mid-term could be assessed by Heart Rate Variability (HRV) extracted by electrocardiograph (ECG). We developed three trials for assessing the usefulness of ECG monitoring using wearable devices for: risk assessment of falling in the next few weeks; prevention of imminent falls due to standing hypotension; and fall detection. Statistical and data-mining methods are adopted to develop classification and regression models, validated with the cross-validation approach. The first classifier based on HRV features enabled to identify future fallers among hypertensive patients with an accuracy of 72% (sensitivity: 51.1%, specificity: 80.2%). The regression model to predict falls due to orthostatic dropdown from HRV recorded before standing achieved an overall accuracy of 80% (sensitivity: 92%, specificity: 90%). Finally, the classifier to detect simulated falls using ECG achieved an accuracy of 77.3% (sensitivity: 81.8%, specificity: 72.7%). The evidence from these three studies showed that ECG monitoring and processing could achieve satisfactory performances compared to other system for risk assessment, fall prevention and detection. This is interesting as differently from other technologies actually employed to prevent falls, ECG is recommended for many other pathologies of later life and is more accepted by senior citizens.

Procedia Computer Science, 2015

High quality Electrocardiogram (ECG) data is very important because this signal is generally used... more High quality Electrocardiogram (ECG) data is very important because this signal is generally used for the analysis of heart diseases. Wearable sensors are widely adopted for physical activity monitoring and for the provision of healthcare services, but noise always degrades the quality of these signals. This paper describes a new algorithm for ECG signal denoising, applicable in the contest of the real-time health monitoring using mobile devices, where the signal processing efficiency is a strict requirement. The proposed algorithm is computationally cheap because it belongs to the class of Infinite Impulse Response (IIR) noise reduction algorithms. The main contribution of the proposed scheme is that removes the noise's frequencies without the implementation of the Fast Fourier Transform that would require the use of special optimized libraries. It is composed by only few code lines and hence offers the possibility of implementation on mobile computing devices in an easy way. Moreover, the scheme allows the local denoising and hence a real time visualization of the denoised signal. Experiments on real datasets have been carried out in order to test the algorithm from accuracy and computational point of view.

International Journal of Data Mining and Bioinformatics, 2014

Patient context awareness is an important concept for application services in mHealth environment... more Patient context awareness is an important concept for application services in mHealth environments. In this paper we present a multi-sensors system that uses a rule-based DSS able to enhance the accuracy of potentially dangerous heart rate variability by taking into account patient context information. In addition the proposed system is able to detect also patient falls in real time. We have designed and implemented an intelligent, user-friendly, and context-aware system that allows receiving data from several sensors and provides the computational power for context recognition. We also show that the use of an intelligent approach relying on a rule-based DSS for the analysis of data and vital signs is better than approaches missing either DSS or context-awareness. Finally, the paper also describes a case study where the system has revealed important benefits for both patients and medical staff.

2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI), 2015

ABSTRACT

Advances in Intelligent and Soft Computing, 2012

This paper presents an advanced ubiquitous system for home health monitoring. The main goal of th... more This paper presents an advanced ubiquitous system for home health monitoring. The main goal of the research presented in this paper is to develop a user-friendly and context-aware system that uses a rule-based Decision Support System to elaborate the data captured by the sensors. The paper also describes a case study where important benefits for patients have been revealed thanks to the use of the proposed home health monitoring system.

Biosystems & Biorobotics, 2015

Dementia is one of the biggest global public health challenges facing our generation. Alzheimer's... more Dementia is one of the biggest global public health challenges facing our generation. Alzheimer's disease (AD) is the most frequent cause of dementia in elderly people over 65 years of age. The typical characteristic of AD is impairment of memory. As the disease progresses, other cognitive domains such as language, praxis, visuo-spatial and executive functions become involved, eventually resulting in global cognitive decline. Behavioral Psychological Symptoms of Dementia (BPSD) problems are constant in AD and have a highly negative impact on the quality of life of patients and their families. ALPHA project aims at developing an intelligent situation-aware system to collect and process information

2013 Sixth International Conference on Developments in eSystems Engineering, 2013

ABSTRACT Automatic fall detection is a major issue in taking care of the health of elderly people... more ABSTRACT Automatic fall detection is a major issue in taking care of the health of elderly people. In this task the capability of telling in real time falls from normal daily activities is crucial. To this aim, this paper proposes an approach based on the automatic extraction of knowledge expressed as a set of IF…THEN rules from a database of fall recordings. This set of rules, generated offline, can then be exploited in a real-time mobile monitoring system: data gathered by wearable sensors are processed in real time and, if their values activate some of the rules describing falls, an alarm message is automatically produced. The approach has been compared against other classifiers on a real-world fall database, and its discrimination ability is shown to be higher. Moreover, a test phase for the real-time mobile monitoring system is being carried out over real cases.

Lecture Notes in Computer Science, 2013

ABSTRACT Decision Support Systems (DSSs) based on fuzzy logic have gained increasing importance t... more ABSTRACT Decision Support Systems (DSSs) based on fuzzy logic have gained increasing importance to help clinical decisions, since they rely on a transparent and interpretable rule base. On the other hand, probabilistic models are undoubtedly the most effective way to reach high performances. In order to join positive features of both these two approaches, this work proposes a hybrid approach, consisting in transforming the functions describing posterior probabilities, into a combination of orthogonal fuzzy sets approximating them. The resulting fuzzy partition has double hopefulness: since it approximates posterior probabilities, it is able to model information extracted from a dataset in such a form that they can be used to run predictions, and since it is a set of normal, orthogonal and convex fuzzy sets, it can be interpreted as the set of terms of a linguistic variable. As a proof of concept, the method has been applied to a real-life application pertaining the classification of Multiple Sclerosis Lesions. The results show that this method is able to construct, for each one of the variables influencing the classification, interpretable if-then rules, with classification power comparable to that of a classical Bayesian model.

Lecture Notes in Electrical Engineering, 2012

The recent research on classification problems, in fields where vague concepts have to be conside... more The recent research on classification problems, in fields where vague concepts have to be considered, agree on the utility of fuzzy logic. An important step of inference engines preparation is the definition of fuzzy sets. When probability distributions of concerned variables are known, they can be used to define fuzzy sets, and different methods allow to perform this transformation. A method recently proposed by authors is compared here with other existing methods, in terms of assumptions and properties about the obtained fuzzy ...

2012 12th International Conference on Hybrid Intelligent Systems (HIS), 2012

Abstract Fuzzy-based Decision Support Systems (DSSs) have gained increasing importance in medicin... more Abstract Fuzzy-based Decision Support Systems (DSSs) have gained increasing importance in medicine, since they rely on a transparent and interpretable rule base. A very attractive feature for these systems is to present their results as a set of plausible conclusions, each of them associated with a degree of possibility. In order to face this need, this work proposes a novel approach consisting in hybridization of possibility theory and a classical fuzzy clustering method, based on a distance metric interpretable in a probabilistic framework ...

Knowledge-Based Systems, 2014

ABSTRACT Fuzzy logic has gained much importance for its applications in Decision Support Systems ... more ABSTRACT Fuzzy logic has gained much importance for its applications in Decision Support Systems (DSSs), especially in fields like medicine, where the final user has to handle uncertain data and vague concepts, and needs an intelligible system based on clear rule bases. In medical applications, physicians are often skilled to reason using statistical approaches, since this type of information is often known, or can be extracted from data. However, since decisions have to be applied to single patients, clinical data items have to be classified in order to obtain the plausibility of conclusions, rather than their probability. Therefore, statistical information can be used, in order to define fuzzy sets and rules for constructing DSSs based on fuzzy logic. While the transformation of probability distributions is well known in literature, here, an approach is presented for transforming likelihood functions into fuzzy sets, based on possibility theory, which is actually instanced into four different new methods for knowledge representation. A comparison among different methods is shown, as well as the analysis of transformation properties and resulting fuzzy sets characteristics are considered, by using synthetic and real data. Finally, some considerations about the most suitable method to be used in the context of clinical DSSs are given.

International Journal of Critical Computer-Based Systems, 2013

ABSTRACT The current trend in designing health information systems is to apply federated architec... more ABSTRACT The current trend in designing health information systems is to apply federated architectures to integrate existing systems. This exacerbates the security guarantees that such systems are required to satisfy and demands the introduction of advanced methods to deal with security. This paper aims at describing how federated health information systems can offer security properties by adopting proper mechanisms to protect the exchanged data and the provided functionalities from malicious manipulations. We have experimentally evaluated the costs in terms of performance penalty related to the introduction of security mechanisms within the proposed solution.

ABSTRACT BACKGROUND: The Italian Constitution delivers autonomy to each Region about healthcare, ... more ABSTRACT BACKGROUND: The Italian Constitution delivers autonomy to each Region about healthcare, thus fostering the proliferation of heterogeneous healthcare information systems. In this scenario, realizing interoperable regional EHR systems, and at the same time, satisfying all the complex requirements and constraints indicated by a recent Italian law, is very challenging. OBJECTIVES: This paper describes the process undertaken in Italy to implement a nationwide interoperable EHR system by supporting the development of homogeneous regional solutions in order to improve healthcare efficiency and reduce costs. METHODS: An architectural model has been designed i) by respecting a shared ISO/HL7 EHR-S FM-based functional model defined at the national level, ii) by specifying a topology both at the regional and national level able to ensure technical interoperability and security, and iii) by identifying solutions for an unambiguous exchange of clinical documents and data through HL7 CDA Rel. 2 and LOINC standards. RESULTS: A federated architectural model which aims at enabling both technical and semantic interoperability among various regional healthcare information systems has been devised. The model has been approved by the Agency for Digital Italy, the Ministry of Health, governmental institutions, Regions and Autonomous Provinces. CONCLUSIONS: This work represents an important first step into the process of digitalizing the Italian health record system. The proposed model is turning out to be successful for both Regions that have already started an e-health process and Regions that are still at the starting line. Further technical details are still to be defined along with the implementation process.

High quality Electrocardiogram (ECG) data is very important because this signal is generally used... more High quality Electrocardiogram (ECG) data is very important because this signal is generally used for the analysis of heart diseases. Wearable sensors are widely adopted for physical activity monitoring and for the provision of healthcare services, but noise always degrades the quality of these signals. In this paper, we propose a novel numerical scheme for ECG Signal denoising with low computational requirements. It is computationally cheap because it belongs to the class of Infinite Impulse Response (IIR) noise reduction algorithms. The main contribution of the proposed scheme is that it does not require a direct application of the Fast Fourier Transform. Moreover, it offers the possibility of implementation on mobile computing devices in an easy way. Experiments on real datasets have been carried out in order to test the algorithm.

Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, 2015

Wearable sensors are widely adopted for the provision of healthcare services. Unfortunately the n... more Wearable sensors are widely adopted for the provision of healthcare services. Unfortunately the noise always degrades the quality of the acquired signals. In this paper, we propose a framework for mobile ECG denoising, based on a novel numerical scheme with low computational requirements. The proposed system is able to store a signal from a wearable sensor and process it in a remote way or directly on the device.

Artificial Intelligence in Medicine, 2020

The rapid increase in the percentage of chronic disease patients along with the recent pandemic p... more The rapid increase in the percentage of chronic disease patients along with the recent pandemic pose immediate threats on healthcare expenditure and elevate causes of death. This calls for transforming healthcare systems away from one-on-one patient treatment into intelligent health systems, to improve services, access and scalability, while reducing costs. Reinforcement Learning (RL) has witnessed an intrinsic breakthrough in solving a variety of complex problems for diverse applications and services. Thus, we conduct in this paper a comprehensive survey of the recent models and techniques of RL that have been developed/used for supporting Intelligent-healthcare (I-health) systems. This paper can guide the readers to deeply understand the state-of-theart regarding the use of RL in the context of I-health. Specifically, we first present an overview for the I-health systems challenges, architecture, and how RL can benefit these systems. We then review the background and mathematical modeling of different RL, Deep RL (DRL), and multiagent RL models. After that, we provide a deep literature review for the applications of RL in I-health systems. In particular, three main areas have been tackled, i.e., edge intelligence, smart core network, and dynamic treatment regimes. Finally, we highlight emerging challenges and outline future research directions in driving the future success of RL in I-health systems, which opens the door for exploring some interesting and unsolved problems.

2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2015

Question Classification is one of the key tasks of Cognitive Systems based on the Question Answer... more Question Classification is one of the key tasks of Cognitive Systems based on the Question Answering paradigm. It aims at identifying the type of the possible answer for a question expressed in natural language. Machine learning techniques are typically employed for this task, and exploit a high number of features extracted from labelled questions of benchmark training sets in order to achieve good classification results. However, the high dimensionality of the feature space often limits the possibility of applying more efficient classification approaches, due to high training costs. In this work, more compact sets of lexical and syntactic features are proposed to distinguish question classes. In particular, the widely used unigrams are substituted with a smaller number of features, extracted by modifying typical Natural Language Processing procedures for question analysis. The accuracy values gained on a benchmark dataset by using these different sets of features are compared among them and with the state-of-the-art, taking into account the required complexity at the same time. The new sets of extracted features show a good trade-off between accuracy and complexity.

Advances in Intelligent Systems and Computing, 2016

Fuzzy logic has gained increasing importance in Decision Support Systems (DSSs), in particular in... more Fuzzy logic has gained increasing importance in Decision Support Systems (DSSs), in particular in medical field, since it allows to build a transparent and interpretable knowledge base. However, in order to obtain a general description of a system, probabilistic approaches undoubtedly offer the most significant information. Moreover, a good classifier to be used for medical scopes should be able to: (i) classify data items which are lacking of some input features; (ii) extract knowledge from incomplete datasets; (iii) consider categorical features; (iv) give responses in terms of a set of possible classes with respective degrees of plausibility. The approach here proposed pursues and achieve these objectives by approximating probabilistic information from incomplete datasets with an interpretable fuzzy system for classifying medical data. Resulting fuzzy sets can be interpreted as the terms of the involved linguistic variables, corresponding to numerical and/or categorical features, while weighted rules model probabilistic information. Rules are presented in two forms: the first is a set of one-dimensional models, which can be used if only one input feature is known; the second is a multidimensional combination of them, which can be used if more input features are known. As a proof of concept, the method has been applied for the detection of Multiple Sclerosis Lesions. The results show that this method is able to construct, for each one of the variables influencing the classification, an interpretable fuzzy partition, and very simple if-then rules. Moreover, multidimensional rule bases can be constructed, by means of which improved results are obtained, also with respect to naive Bayes classifier.

Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, 2015

ABSTRACT

2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), 2015

Continuous blood pressure (BP) measurement is an important issue in the medical field. The hypoth... more Continuous blood pressure (BP) measurement is an important issue in the medical field. The hypothesis of existence of a nonlinear relationship between plethysmography (PPG) and BP values has been investigated in this paper. If this hypothesis is true, then it is possible to indirectly measure patient's BP in a non-invasive way through the application of a wearable wireless PPG sensor to patient's finger and through the use of the results of a regression analysis aimed at linking PPG and BP values. To find the relationship between these two biomedical characteristics we have used here Genetic Programming (GP), because in a regression task it can evolve in an automatic way the structure of the most suitable explicit mathematical model. An analysis of the related scientific literature shows that this is the first attempt to mathematically relate PPG and BP values through GP. In this paper some preliminary experiments on the use of GP in facing this regression task have been carried out. As a result, for both systolic and diastolic BP values explicit mathematical models providing nonlinear relationship between PPG and BP values have been achieved, involving an approximation error of around 2 mmHg in both cases. A prototypal mobile-based system has been realized which is able to continuously estimate in real time the two BP values for any given patient by using only a plethysmography signal and the obtained mathematical models.

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015

Falls represent one of the most common causes of injury-related morbidity and mortality in later ... more Falls represent one of the most common causes of injury-related morbidity and mortality in later life. Subjects with cardiovascular disorders (e.g., related to autonomic dysfunctions and postural hypotension) are at higher risk of falling. Autonomic dysfunctions increasing the risk of falling in the short and mid-term could be assessed by Heart Rate Variability (HRV) extracted by electrocardiograph (ECG). We developed three trials for assessing the usefulness of ECG monitoring using wearable devices for: risk assessment of falling in the next few weeks; prevention of imminent falls due to standing hypotension; and fall detection. Statistical and data-mining methods are adopted to develop classification and regression models, validated with the cross-validation approach. The first classifier based on HRV features enabled to identify future fallers among hypertensive patients with an accuracy of 72% (sensitivity: 51.1%, specificity: 80.2%). The regression model to predict falls due to orthostatic dropdown from HRV recorded before standing achieved an overall accuracy of 80% (sensitivity: 92%, specificity: 90%). Finally, the classifier to detect simulated falls using ECG achieved an accuracy of 77.3% (sensitivity: 81.8%, specificity: 72.7%). The evidence from these three studies showed that ECG monitoring and processing could achieve satisfactory performances compared to other system for risk assessment, fall prevention and detection. This is interesting as differently from other technologies actually employed to prevent falls, ECG is recommended for many other pathologies of later life and is more accepted by senior citizens.

Procedia Computer Science, 2015

High quality Electrocardiogram (ECG) data is very important because this signal is generally used... more High quality Electrocardiogram (ECG) data is very important because this signal is generally used for the analysis of heart diseases. Wearable sensors are widely adopted for physical activity monitoring and for the provision of healthcare services, but noise always degrades the quality of these signals. This paper describes a new algorithm for ECG signal denoising, applicable in the contest of the real-time health monitoring using mobile devices, where the signal processing efficiency is a strict requirement. The proposed algorithm is computationally cheap because it belongs to the class of Infinite Impulse Response (IIR) noise reduction algorithms. The main contribution of the proposed scheme is that removes the noise's frequencies without the implementation of the Fast Fourier Transform that would require the use of special optimized libraries. It is composed by only few code lines and hence offers the possibility of implementation on mobile computing devices in an easy way. Moreover, the scheme allows the local denoising and hence a real time visualization of the denoised signal. Experiments on real datasets have been carried out in order to test the algorithm from accuracy and computational point of view.

International Journal of Data Mining and Bioinformatics, 2014

Patient context awareness is an important concept for application services in mHealth environment... more Patient context awareness is an important concept for application services in mHealth environments. In this paper we present a multi-sensors system that uses a rule-based DSS able to enhance the accuracy of potentially dangerous heart rate variability by taking into account patient context information. In addition the proposed system is able to detect also patient falls in real time. We have designed and implemented an intelligent, user-friendly, and context-aware system that allows receiving data from several sensors and provides the computational power for context recognition. We also show that the use of an intelligent approach relying on a rule-based DSS for the analysis of data and vital signs is better than approaches missing either DSS or context-awareness. Finally, the paper also describes a case study where the system has revealed important benefits for both patients and medical staff.

2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI), 2015

ABSTRACT

Advances in Intelligent and Soft Computing, 2012

This paper presents an advanced ubiquitous system for home health monitoring. The main goal of th... more This paper presents an advanced ubiquitous system for home health monitoring. The main goal of the research presented in this paper is to develop a user-friendly and context-aware system that uses a rule-based Decision Support System to elaborate the data captured by the sensors. The paper also describes a case study where important benefits for patients have been revealed thanks to the use of the proposed home health monitoring system.

Biosystems & Biorobotics, 2015

Dementia is one of the biggest global public health challenges facing our generation. Alzheimer's... more Dementia is one of the biggest global public health challenges facing our generation. Alzheimer's disease (AD) is the most frequent cause of dementia in elderly people over 65 years of age. The typical characteristic of AD is impairment of memory. As the disease progresses, other cognitive domains such as language, praxis, visuo-spatial and executive functions become involved, eventually resulting in global cognitive decline. Behavioral Psychological Symptoms of Dementia (BPSD) problems are constant in AD and have a highly negative impact on the quality of life of patients and their families. ALPHA project aims at developing an intelligent situation-aware system to collect and process information

2013 Sixth International Conference on Developments in eSystems Engineering, 2013

ABSTRACT Automatic fall detection is a major issue in taking care of the health of elderly people... more ABSTRACT Automatic fall detection is a major issue in taking care of the health of elderly people. In this task the capability of telling in real time falls from normal daily activities is crucial. To this aim, this paper proposes an approach based on the automatic extraction of knowledge expressed as a set of IF…THEN rules from a database of fall recordings. This set of rules, generated offline, can then be exploited in a real-time mobile monitoring system: data gathered by wearable sensors are processed in real time and, if their values activate some of the rules describing falls, an alarm message is automatically produced. The approach has been compared against other classifiers on a real-world fall database, and its discrimination ability is shown to be higher. Moreover, a test phase for the real-time mobile monitoring system is being carried out over real cases.

Lecture Notes in Computer Science, 2013

ABSTRACT Decision Support Systems (DSSs) based on fuzzy logic have gained increasing importance t... more ABSTRACT Decision Support Systems (DSSs) based on fuzzy logic have gained increasing importance to help clinical decisions, since they rely on a transparent and interpretable rule base. On the other hand, probabilistic models are undoubtedly the most effective way to reach high performances. In order to join positive features of both these two approaches, this work proposes a hybrid approach, consisting in transforming the functions describing posterior probabilities, into a combination of orthogonal fuzzy sets approximating them. The resulting fuzzy partition has double hopefulness: since it approximates posterior probabilities, it is able to model information extracted from a dataset in such a form that they can be used to run predictions, and since it is a set of normal, orthogonal and convex fuzzy sets, it can be interpreted as the set of terms of a linguistic variable. As a proof of concept, the method has been applied to a real-life application pertaining the classification of Multiple Sclerosis Lesions. The results show that this method is able to construct, for each one of the variables influencing the classification, interpretable if-then rules, with classification power comparable to that of a classical Bayesian model.

Lecture Notes in Electrical Engineering, 2012

The recent research on classification problems, in fields where vague concepts have to be conside... more The recent research on classification problems, in fields where vague concepts have to be considered, agree on the utility of fuzzy logic. An important step of inference engines preparation is the definition of fuzzy sets. When probability distributions of concerned variables are known, they can be used to define fuzzy sets, and different methods allow to perform this transformation. A method recently proposed by authors is compared here with other existing methods, in terms of assumptions and properties about the obtained fuzzy ...

2012 12th International Conference on Hybrid Intelligent Systems (HIS), 2012

Abstract Fuzzy-based Decision Support Systems (DSSs) have gained increasing importance in medicin... more Abstract Fuzzy-based Decision Support Systems (DSSs) have gained increasing importance in medicine, since they rely on a transparent and interpretable rule base. A very attractive feature for these systems is to present their results as a set of plausible conclusions, each of them associated with a degree of possibility. In order to face this need, this work proposes a novel approach consisting in hybridization of possibility theory and a classical fuzzy clustering method, based on a distance metric interpretable in a probabilistic framework ...

Knowledge-Based Systems, 2014

ABSTRACT Fuzzy logic has gained much importance for its applications in Decision Support Systems ... more ABSTRACT Fuzzy logic has gained much importance for its applications in Decision Support Systems (DSSs), especially in fields like medicine, where the final user has to handle uncertain data and vague concepts, and needs an intelligible system based on clear rule bases. In medical applications, physicians are often skilled to reason using statistical approaches, since this type of information is often known, or can be extracted from data. However, since decisions have to be applied to single patients, clinical data items have to be classified in order to obtain the plausibility of conclusions, rather than their probability. Therefore, statistical information can be used, in order to define fuzzy sets and rules for constructing DSSs based on fuzzy logic. While the transformation of probability distributions is well known in literature, here, an approach is presented for transforming likelihood functions into fuzzy sets, based on possibility theory, which is actually instanced into four different new methods for knowledge representation. A comparison among different methods is shown, as well as the analysis of transformation properties and resulting fuzzy sets characteristics are considered, by using synthetic and real data. Finally, some considerations about the most suitable method to be used in the context of clinical DSSs are given.

International Journal of Critical Computer-Based Systems, 2013

ABSTRACT The current trend in designing health information systems is to apply federated architec... more ABSTRACT The current trend in designing health information systems is to apply federated architectures to integrate existing systems. This exacerbates the security guarantees that such systems are required to satisfy and demands the introduction of advanced methods to deal with security. This paper aims at describing how federated health information systems can offer security properties by adopting proper mechanisms to protect the exchanged data and the provided functionalities from malicious manipulations. We have experimentally evaluated the costs in terms of performance penalty related to the introduction of security mechanisms within the proposed solution.

ABSTRACT BACKGROUND: The Italian Constitution delivers autonomy to each Region about healthcare, ... more ABSTRACT BACKGROUND: The Italian Constitution delivers autonomy to each Region about healthcare, thus fostering the proliferation of heterogeneous healthcare information systems. In this scenario, realizing interoperable regional EHR systems, and at the same time, satisfying all the complex requirements and constraints indicated by a recent Italian law, is very challenging. OBJECTIVES: This paper describes the process undertaken in Italy to implement a nationwide interoperable EHR system by supporting the development of homogeneous regional solutions in order to improve healthcare efficiency and reduce costs. METHODS: An architectural model has been designed i) by respecting a shared ISO/HL7 EHR-S FM-based functional model defined at the national level, ii) by specifying a topology both at the regional and national level able to ensure technical interoperability and security, and iii) by identifying solutions for an unambiguous exchange of clinical documents and data through HL7 CDA Rel. 2 and LOINC standards. RESULTS: A federated architectural model which aims at enabling both technical and semantic interoperability among various regional healthcare information systems has been devised. The model has been approved by the Agency for Digital Italy, the Ministry of Health, governmental institutions, Regions and Autonomous Provinces. CONCLUSIONS: This work represents an important first step into the process of digitalizing the Italian health record system. The proposed model is turning out to be successful for both Regions that have already started an e-health process and Regions that are still at the starting line. Further technical details are still to be defined along with the implementation process.

L. Gallo and G. De Pietro, “Input devices and interaction techniques for VR-enhanced medicine”, i... more L. Gallo and G. De Pietro, “Input devices and interaction techniques for VR-enhanced medicine”, in Multimedia Techniques for Device and Ambient Intelligence (E. Damiani and J. Jeong, eds.), pp. 115-134, Springer US, 2009. doi:10.1007/978-0-387-88777-7_5