Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network

Cardio-vascular diseases are one of the foremost causes of mortality in today’s world. The prognosis for cardiovascular diseases is usually done by ECG signal, which is a simple 12-lead Electrocardiogram (ECG) that gives complete information about the function of the heart including the amplitude and time interval of P-QRST-U segment. This article recommends a novel approach to identify the location of thrombus in culprit artery using the Information Fuzzy Network (IFN). Information Fuzzy Network, being a supervised machine learning technique, takes known evidences based on rules to create a predicted classification model with thrombus location obtained from the vast input ECG data. These rules are well-defined procedures for selecting hypothesis that best fits a set of observations. Results illustrate that the recommended approach yields an accurateness of 92.30%. This novel approach is shown to be a viable ECG analysis approach for identifying the culprit artery and thus localizing the thrombus

Analysis of Ventricular Depolarisation and Repolarisation Using Registration and Machine Learning

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

Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging

Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET). Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools — by tracking the heart’s kinetic activity using micro-sized MEMS sensors — and novel computational approaches — by deploying signal processing and machine learning techniques—for detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations. Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes. Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien käyttö sydänkardiografiassa sekä lääketieteellisessä 4D-kuvantamisessa Tausta: Sydän- ja verisuonitaudit ovat yleisin kuolinsyy. Näistä kuolemantapauksista lähes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron häiriöistä. Moniulotteiset mikroelektromekaaniset järjestelmät (MEMS) mahdollistavat sydänlihaksen mekaanisen liikkeen mittaamisen, mikä puolestaan tarjoaa täysin uudenlaisen ja innovatiivisen ratkaisun sydämen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjärjestelmien käyttämisen sydämen toiminnan tutkimuksessa sekä lääketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa. Menetelmät: Tämä väitöskirjatyö esittelee uuden sydämen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien käyttöön. Uudet laskennalliset lähestymistavat, jotka perustuvat signaalinkäsittelyyn ja koneoppimiseen, mahdollistavat sydämen patologisten häiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. Tässä tutkimuksessa keskitytään erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). Näiden tekniikoiden avulla voidaan mitata kardiorespiratorisen järjestelmän mekaanisia ominaisuuksia. Tulokset: Kokeelliset analyysit osoittivat, että integroimalla usean sensorin dataa voidaan mitata syketiheyttä 99% (terveillä n=29) tarkkuudella, havaita sydämen rytmihäiriöt (n=435) 95-97%, tarkkuudella, sekä havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). Lisäksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydämen 4D PET-kuvan laatua, kun liikeepätarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillä, n=9) osoitti lupaavia tuloksia sydänsykkeen ajoituksen ja intervallien sekä sydänlihasmuutosten mittaamisessa. Päätelmä: Tämän tutkimuksen tulokset osoittavat, että kardiologisilla MEMS-liikeantureilla on kliinistä potentiaalia sydämen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistää eteisvärinän (AFib), sydäninfarktin (MI) ja CAD:n havaitsemista. Lisäksi MEMS-liiketunnistus parantaa sydämen PET-kuvantamisen luotettavuutta ja laatua

Acute lung injury in paediatric intensive care: course and outcome

Introduction: Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) carry a high morbidity and mortality (10-90%). ALI is characterised by non-cardiogenic pulmonary oedema and refractory hypoxaemia of multifactorial aetiology [1]. There is limited data about outcome particularly in children. Methods This retrospective cohort study of 85 randomly selected patients with respiratory failure recruited from a prospectively collected database represents 7.1% of 1187 admissions. They include those treated with High Frequency Oscillation Ventilation (HFOV). The patients were admitted between 1 November 1998 and 31 October 2000. Results: Of the 85, 49 developed acute lung injury and 47 had ARDS. There were 26 males and 23 females with a median age and weight of 7.7 months (range 1 day-12.8 years) and 8 kg (range 0.8-40 kg). There were 7 deaths giving a crude mortality of 14.3%, all of which fulfilled the Consensus I [1] criteria for ARDS. Pulmonary occlusion pressures were not routinely measured. The A-a gradient and PaO2/FiO2 ratio (median + [95% CI]) were 37.46 [31.82-43.1] kPa and 19.12 [15.26-22.98] kPa respectively. The non-survivors had a significantly lower PaO2/FiO2 ratio (13 [6.07-19.93] kPa) compared to survivors (23.85 [19.57-28.13] kPa) (P = 0.03) and had a higher A-a gradient (51.05 [35.68-66.42] kPa) compared to survivors (36.07 [30.2-41.94]) kPa though not significant (P = 0.06). Twenty-nine patients (59.2%) were oscillated (Sensormedics 3100A) including all 7 non-survivors. There was no difference in ventilation requirements for CMV prior to oscillation. Seventeen of the 49 (34.7%) were treated with Nitric Oxide including 5 out of 7 non-survivors (71.4%). The median (95% CI) number of failed organs was 3 (1.96-4.04) for non-survivors compared to 1 (0.62-1.62) for survivors (P = 0.03). There were 27 patients with isolated respiratory failure all of whom survived. Six (85.7%) of the non-survivors also required cardiovascular support.Conclusion: A crude mortality of 14.3% compares favourably to published data. The A-a gradient and PaO2/FiO2 ratio may be of help in morbidity scoring in paediatric ARDS. Use of Nitric Oxide and HFOV is associated with increased mortality, which probably relates to the severity of disease. Multiple organ failure particularly respiratory and cardiac disease is associated with increased mortality. ARDS with isolated respiratory failure carries a good prognosis in children

Chagas Disease

Despite being described for the first time more than 110 years ago, Chagas disease (caused by the parasite Trypanosoma cruzi) persists as one of the most neglected tropical diseases. This persistent neglect of those affected by this disease is evidenced by the ongoing scientific evidence gaps, the difficulty of incorporating new diagnostic and treatment technologies into the market, and public health failures to ensure timely access to diagnosis and treatment associated with the development of consistent surveillance and control actions. As a result, there is a high burden of morbidity and mortality, and poor quality of life, poverty, stigma, and a fear of death persist for affected people, both in endemic areas in Latin America, and in non-endemic areas. This book spans a variety of disciplines and contributes significantly to reducing the gap in scientific knowledge on Chagas disease, towards achieving the Sustainable Development Goals by 2030