Abstract Taking into account that heart auscultation remains the dominant method for heart examination in the small health centers of the rural areas and generally in primary healthcare set-ups, the ...enhancement of this technique would aid significantly in the diagnosis of heart diseases. In this context, the present paper initially surveys the research that has been conducted concerning the exploitation of heart sound signals for automated and semi-automated detection of pathological heart conditions. Then it proposes an automated diagnosis system for the identification of heart valve diseases based on the Support Vector Machines (SVM) classification of heart sounds. This system performs a highly difficult diagnostic task (even for experienced physicians), much more difficult than the basic diagnosis of the existence or not of a heart valve disease (i.e. the classification of a heart sound as ‘healthy’ or ‘having a heart valve disease’): it identifies the particular heart valve disease. The system was applied in a representative global dataset of 198 heart sound signals, which come both from healthy medical cases and from cases suffering from the four most usual heart valve diseases: aortic stenosis (AS), aortic regurgitation (AR), mitral stenosis (MS) and mitral regurgitation (MR). Initially the heart sounds were successfully categorized using a SVM classifier as normal or disease-related and then the corresponding murmurs in the unhealthy cases were classified as systolic or diastolic. For the heart sounds diagnosed as having systolic murmur we used a SVM classifier for performing a more detailed classification of them as having aortic stenosis or mitral regurgitation. Similarly for the heart sounds diagnosed as having diastolic murmur we used a SVM classifier for classifying them as having aortic regurgitation or mitral stenosis. Alternative classifiers have been applied to the same data for comparison (i.e. back-propagation neural networks, k-nearest-neighbour and naïve Bayes classifiers), however their performance for the same diagnostic problems was lower than the SVM classifiers proposed in this work.
New technologies like echocardiography, color Doppler, CT, and MRI provide more direct and accurate evidence of heart disease than heart auscultation. However, these modalities are costly, large in ...size and operationally complex and therefore are not suitable for use in rural areas, in homecare and generally in primary healthcare set-ups. Furthermore the majority of internal medicine and cardiology training programs underestimate the value of cardiac auscultation and junior clinicians are not adequately trained in this field. Therefore efficient decision support systems would be very useful for supporting clinicians to make better heart sound diagnosis. In this study a rule-based method, based on decision trees, has been developed for differential diagnosis between "clear" Aortic Stenosis (AS) and "clear" Mitral Regurgitation (MR) using heart sounds.
For the purposes of our experiment we used a collection of 84 heart sound signals including 41 heart sound signals with "clear" AS systolic murmur and 43 with "clear" MR systolic murmur. Signals were initially preprocessed to detect 1st and 2nd heart sounds. Next a total of 100 features were determined for every heart sound signal and relevance to the differentiation between AS and MR was estimated. The performance of fully expanded decision tree classifiers and Pruned decision tree classifiers were studied based on various training and test datasets. Similarly, pruned decision tree classifiers were used to examine their differentiation capabilities. In order to build a generalized decision support system for heart sound diagnosis, we have divided the problem into sub problems, dealing with either one morphological characteristic of the heart-sound waveform or with difficult to distinguish cases.
Relevance analysis on the different heart sound features demonstrated that the most relevant features are the frequency features and the morphological features that describe S1, S2 and the systolic murmur. The results are compatible with the physical understanding of the problem since AS and MR systolic murmurs have different frequency contents and different waveform shapes. On the contrary, in the diastolic phase there is no murmur in both diseases which results in the fact that the diastolic phase signals cannot contribute to the differentiation between AS and MR. We used a fully expanded decision tree classifier with a training set of 34 records and a test set of 50 records which resulted in a classification accuracy (total corrects/total tested) of 90% (45 correct/50 total records). Furthermore, the method proved to correctly classify both AS and MR cases since the partial AS and MR accuracies were 91.6% and 88.5% respectively. Similar accuracy was achieved using decision trees with a fraction of the 100 features (the most relevant). Pruned Differentiation decision trees did not significantly change the classification accuracy of the decision trees both in terms of partial classification and overall classification as well.
Present work has indicated that decision tree algorithms decision tree algorithms can be successfully used as a basis for a decision support system to assist young and inexperienced clinicians to make better heart sound diagnosis. Furthermore, Relevance Analysis can be used to determine a small critical subset, from the initial set of features, which contains most of the information required for the differentiation. Decision tree structures, if properly trained can increase their classification accuracy in new test data sets. The classification accuracy and the generalization capabilities of the Fully Expanded decision tree structures and the Pruned decision tree structures have not significant difference for this examined sub-problem. However, the generalization capabilities of the decision tree based methods were found to be satisfactory. Decision tree structures were tested on various training and test data set and the classification accuracy was found to be consistently high.
Interoperability (IOP) is the ability of a product or system – whose interfaces (APIs) are publicly documented – to connect to and operate with other products or systems, without restrictions. ...Interoperability further enables information and usable data to be properly exchanged and ensures the alignment of different business processes in critical sectors. In addition, is a prerequisite for transparent, domain-agnostic, and sustainable public sector digital services, where Public Administrations (PA) can efficiently interact across borders and domains by using common frameworks, standards, and processes for sharing information and data. The European Interoperability Framework (EIF) enables interoperability with guidelines for digital services. Therefore, the alignment with EIF becomes pivotal for the European Union (EU) countries since different regulations that facilitate and impose the implementation of European policies such as the Single Digital Gateway (SDG) regulation and the Once-Only Principle (OOP) consider the IOP a crucial technical and operational component for government digital services. This article proposes the update of the Greek NIF, with guidelines of EIF, OOP and other technological trends in conjunction with new legal and policy provisions. This proposed assessment methodology can be reused in other countries and can be further adapted for updating the EIF.
•Interoperability is in digital services and is a prerequisite for transparent, domain agnostic and sustainable public sector digital services.•This proposal contributes to digital transformation and interoperability knowledge by providing a systemic approach methodology for the identification of gaps and the proposal of concrete improvements and recommendations.•The methodology includes the conceptualized technological trends such as the Once Only Principle (OOP), the CEF Building Blocks (BB) and the Single Digital Gateway (SDG) regulation, for this proposal, in conjunction with new legal and policy provisions and can be reused in other countries and for the update of EIF.
Adapting national interoperability frameworks beyond EIF 3.0 Kalogirou, Victoria; Stasis, Antonis; Charalabidis, Yannis
Proceedings of the 13th International Conference on Theory and Practice of Electronic Governance,
09/2020
Conference Proceeding
Interoperability (IoP) is a crucial factor for public sector digital services. IoP facilitates public digital services to be transparent, domain agnostic and helps so they can interact efficiently ...across borders with the use of common frameworks, standards, and processes for sharing information, data, and processes. The European Interoperability Framework (EIF) provides guidelines to the Public Administrations, so they can supply interoperable, efficient and effective public services to citizens and business. EIF has a conceptual model, which depicts four levels of interoperability (Legal, Organisational, Semantic, and Technical) and sets forward principles and recommendations. EIF is a mature interoperability framework and by aligning the current Greek National Interoperability Framework (eGIF) with the current version of EIF and beyond it, can increase the broader impact of Greek digital government and digitization. The implementation of proposed changes in key sectors such as health or finance can boost the economy of the country. The basis of this paper will be the methodology used in EIF for national eGIF assessments in conjunction with new legal and policy provisions so that the Greek eGIF can be more sustainable and adaptive to changes national framework. The work in this paper also aims to give practical proposals for defining an interoperable Governance and Policy-Making. The paper concludes to concrete proposals for the update of the Greek eGif considering the complex environment of interoperability and e-government services.
The wide deployment of cross-border e-government services in EU has highlighted the interoperability problems caused by different solutions adopted by Member States. This paper focuses on one of ...those areas that form the interoperability puzzle: signed e-documents. It identifies interoperability issues related to secure e-document exchange, provides a survey of currently available e-documents and e-signatures standards and presents solutions proposed by various ongoing activities in EU, emphasizing on the one specified by the large scale pilot project SPOCS.
In this study, decision tree algorithms are used with promising results in a crucial and at the same time complicated classification problem concerning differential diagnosis of heart sounds. ...Decision tree structures are constructed, using data mining/distillation methods and then are used to classify heart sounds that were recorded from patients that have either aortic stenosis (AS) or mitral regurgitation (MR). Emphasis is given on the selection of the appropriate features that are adequately independent from the heart sound signal acquisition method. The differentiation capabilities and the classification performance of the fully expanded decision tree classifiers and the pruned decision tree classifiers are studied for this problem. For each constructed decision tree classifier the partial classification accuracies for the AS and MR auscultation findings are also estimated.
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