43 research outputs found

    Artificial Intelligence-Based Triage for Patients with Acute Abdominal Pain in emergency Department; a Diagnostic Accuracy Study

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    Introduction: Artificial intelligence (AI) is the development of computer systems which are capable of doing human intelligence tasks such as decision making and problem solving. AI-based tools have been used for predicting various factors in medicine including risk stratification, diagnosis and choice of treatment. AI can also be of considerable help in emergency departments, especially patients’ triage. Objective: This study was undertaken to evaluate the application of AI in patients presenting with acute abdominal pain to estimate emergency severity index version 4 (ESI-4) score without the estimate of the required resources. Methods: A mixed-model approach was used for predicting the ESI-4 score. Seventy percent of the patient cases were used for training the models and the remaining 30% for testing the accuracy of the models. During the training phase, patients were randomly selected and were given to systems for analysis. The output, which was the level of triage, was compared with the gold standard (emergency medicine physician). During the test phase of the study, another group of randomly selected patients were evaluated by the systems and the results were then compared with the gold standard. Results: Totally, 215 patients who were triaged by the emergency medicine specialist were enrolled in the study. Triage Levels 1 and 5 were omitted due to low number of cases. In triage Level 2, all systems showed fair level of prediction with Neural Network being the highest. In Level 3, all systems again showed fair level of prediction. However, in triage Level 4, decision tree was the only system with fair prediction. Conclusion: The application of AI in triage of patients with acute abdominal pain resulted in a model with acceptable level of accuracy. The model works with optimized number of input variables for quick assessment

    PORTABLE HEART ATTACK WARNING SYSTEM BY MONITORING THE ST SEGMENT VIA SMARTPHONE ELECTROCARDIOGRAM PROCESSING

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    Cardiovascular disease (CVD) is the single leading cause of death in both developed and developing countries. The most deadly CVD is heart attack, which 7,900,000 Americans suffer each year, and 16% of cases are fatal. The Electrocardiogram (ECG) is the most widely adopted clinical tool to diagnose and assess the risk of CVD. Early diagnosis of heart attacks, by detecting abnormal ST segments within one hour of the onset of symptoms, is necessary for successful treatment. In clinical settings, resting ECGs are used to monitor patients automatically. However, given the sporadic nature of heart attacks, it is unlikely that the patient will be in a clinical setting at the onset of a heart attack. While Holter-based portable monitoring solutions offer 24 to 48-hour ECG recording, they lack the capability of providing any real-time feedback for the thousands of heart beats they record, which must be tediously analyzed offline.Processing ECG signals on a smartphone-based platform would unite the portability of Holter monitors and the real-time processing capability of state-of-the-art resting ECG machines to provide an assistive diagnosis for early heart attack warning. Furthermore, smartphones serve as an ideal platform for telemedicine and alert systems and have a portable form factor. To detect heart attacks via ECG processing, a real-time, accurate, context aware ST segment monitoring algorithm, based on principal component analysis and a support vector machine classifier is proposed and evaluated. Real-time feedback is provided by implementing a state-of-the-art, multilevel warning system ranging from audible notifications to text messages to points of contacts with the GPS location of the user. The smartphone test bed makes use of a novel, real-time verification system using a streaming database to analyze the strain of heart attack detection system under normal phone operation. Furthermore, the entire system is prototyped and fully functional, running on a smartphone to demonstrate the real-time, portable functionality of the platform. Experimental results show that a classification accuracy of 96% for ST segment elevation of individual beats can be achieved and all ST episodes were correctly detected during testing with the European ST database

    Π8 – Τεχνική έκθεση (εφαρμογές σε πραγματικά δεδομένα μέτρων αξιολόγησης ευστάθειας σε προβλήματα ταξινόμησης)

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    Η χρήση αναλυτικών μοντέλων αποφάσεων μπορεί να βοηθήσει σημαντικά προς την λήψη αποφάσεων στην ιατρική. Έρευνες έχουν δείξει ότι η χρήση συστημάτων υποστήριξης αποφάσεων συμβάλει θετικά στην ιατρική. Στην έρευνα αυτή παρουσιάζεται μια καινοτόμα μεθοδολογία πολυκριτήριας ανάλυσης για την ανάπτυξη ενός μοντέλου αποφάσεων που υποστηρίξει την ανάλυση του κινδύνου υποτροπής της Κολπικής Μαρμαρυγής (KM, atrial fibrillation) που είναι η πιο συνηθισμένη μορφή καρδιακής αρρυθμίας

    Analysis of Ventricular Depolarisation and Repolarisation Using Registration and Machine Learning

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    Our understanding of cardiac diseases has greatly advanced since the advent of electrocardiography (ECG). With the increasing influx of available data in recent times, significant research efforts have been put forth to automate the study and detection of cardiac conditions. Naturally, the focus has progressed toward studying dynamic changes in ventricular depolarisation and repolarisation across serial recordings - as complex beat-to-beat changes in morphology manifest over time. Manual extraction of diagnostic and prognostic markers is a laborious task. Hence, automated and accurate methods are required to extract markers for the study of ventricular lability and detection of common diseases, such as myocardial ischemia and myocardial infarction. The aim of this thesis is to improve automated marker extraction and detection of diseases for the study of ventricular depolarisation and repolarisation lability in ECG. As such, two novel template adaptation methods capable of capturing complex beat-to-beat morphological changes are proposed for three-dimensional and two-dimensional data, respectively. The proposed three-dimensional template adaptation method provides an inhomogeneous method for transforming template vectorcardiogram (VCG) by exploiting registrationinspired parametrisation and an efficient kernel ridge regression formulation. Analysis across simulated data and clinical myocardial infarction data demonstrates state-of-the-art results. The two-dimensional template adaptation method draws from traditional registrationbased techniques and treats the ECG as a two-dimensional point set problem. Validation against previously employed simulated data and a gold-standard annotated clinical database demonstrate the highest level of performance. Subsequently, frameworks employing the proposed template adaptation techniques are developed for the automated detection of ischemic beats and myocardial infarction. Furthermore, a small study analysing ventricular repolarisation variability (VRV) in non-ischemic cardiomyopathy (CM) is considered, utilising markers of cardiac lability proposed in the development of the three-dimensional template adaptation system. In summary, this thesis highlights the necessity for custom template adaptation methods for the accurate measurement of beat-to-beat variability in cardiac data. Two novel stateof- the-art methods are proposed and extended to study myocardial ischemia, myocardial infarction and non-ischemic CM.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 202

    Consistency analysis in quality classification problems with multiple rank-ordered agents

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    A relatively diffused quality decision problem is that of classifying some objects of interest into predetermined nominal categories. This problem is particularly interesting in the case: (i) multiple agents perform local classifications of an object, to be fused into a global classification; (ii) there is more than one object to be classified; and (iii) agents may have different positions of power, expressed in the form of an importance rank-ordering. Due to the specificity of the problem, the scientific literature encompasses a relatively small number of data fusion techniques. For the fusion to be effective, the global classifications of the objects should be consistent with the agents’ local classifications and their importance rank-ordering, which represent the input data. The aim of this article is to propose a set of indicators, which allow to check the degree of consistency between the global classification and the input data, from several perspectives, e.g., that of individual agents, individual objects, agents’ importance rank-ordering, etc. These indicators are independent from the fusion technique in use and applicable to a wide variety of practical contexts, such as problems in which some of the local classifications are uncertain or incomplete. The proposed indicators are simple, intuitive, and practical for comparing the results obtained through different techniques. The description therein is supported by several practical examples

    Sur les signaux électrophysiologiques : réflexion et quelques perspectives ouvertes

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    Nous disposons de signaux physiologiques riches en information sur les objets observés. Lorsque leur association est possible, ils peuvent renseigner différentes facettes du fonctionnement d'une entité structurelle, d'un organe ou encore d'un système. L'exploitation des informations qu'ils véhiculent en lien avec les données cliniques à des finalités diagnostiques et thérapeutique, mais aussi pour améliorer l'état des connaissances dans les champs disciplinaires concernés, demande de fédérer des équipes de recherche autour de projet intégrant d'emblée les dimensions méthodologiques, cliniques et technologiques

    Advanced Signal Processing in Wearable Sensors for Health Monitoring

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    Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods
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