A Novel Electrocardiogram Segmentation Algorithm Using a Multiple Model Adaptive Estimator

This thesis presents a novel electrocardiogram (ECG) processing algorithm design based on a Multiple Model Adaptive Estimator (MMAE) for a physiological monitoring system. Twenty ECG signals from the MIT ECG database were used to develop system models for the MMAE. The P-wave, QRS complex, and T-wave segments from the characteristic ECG waveform were used to develop hypothesis filter banks. By adding a threshold filter-switching algorithm to the conventional MMAE implementation, the device mimics the way a human analyzer searches the complex ECG signal for a useable temporal landmark and then branches out to find the other key wave components and their timing. The twenty signals and an additional signal from an animal exsanuinaiton experiment were then used to test the algorithm. Using a conditional hypothesis-testing algorithm, the MMAE correctly identified the ECG signal segments corresponding to the hypothesis models with a 96.8% accuracy-rate for the 11539 possible segments tested. The robust MMAE algorithm also detected any misalignments in the filter hypotheses and automatically restarted filters within the MMAE to synchronize the hypotheses with the incoming signal. Finally, the MMAE selects the optimal filter bank based on incoming ECG measurements. The algorithm also provides critical heart-related information such as heart rate, QT, and PR intervals from the ECG signal. This analyzer could be easily added as a software update to the standard physiological monitors universally used in emergency vehicles and treatment facilities and potentially saving thousands of lives and reducing the pain and suffering of the injured

Effects of pregnancy on electrocardiographic, vasovagal tonus index, and echocardiographic variables in horses

Background and Aim: Pregnancy affects maternal hemodynamics. The changes in autonomic nervous system activity for hemodynamics adaptation in pregnant horses are still unclear. Thus, this study aimed to examine the effect of pregnancy on electrocardiographic, vasovagal tonus index, and echocardiographic variables in horses. Materials and Methods: A total of 23 Thai native crossbred mares without any cardiac abnormalities were included in this study. The animals were assigned into two groups, a non-pregnant mare group (n =12) and a pregnant mare group (n = 11). Electrocardiogram recordings (paper speed = 25 mm/s and calibration = 10 mm/mV) were performed to obtain six limb leads (leads I, II, III, aVR, aVL, and aVF). The vasovagal tonus index (VVTI) was calculated to assess variability in heart rate over short periods using just 20 consecutive beats. Cardiac structure and function were evaluated by echocardiography. Results: Heart rate, P wave duration, PR interval, QRS duration, QT interval, and T wave duration were significantly different between non-pregnant and pregnant horses (p < 0.05). Pregnant horses had significantly lower VVTI than non-pregnant (p < 0.05). There were no significant differences in cardiac structures including % interventricular septum (IVS), % left ventricular posterior wall (LVPW), IVS in diastole, left ventricular internal diameter at end-diastole, LVPW thickness at end-diastole, IVS in systole, left ventricular internal diameter at end-systole, LVPW thickness at end-systole, and left atrium/aortic roots ratio between the two groups. However, the pregnant horses had a significantly higher cardiac output and % ejection fraction than non-pregnant horses (p < 0.05). Conclusion: This study provided the first evidence that hemodynamic adaptations during pregnancy modified cardiac conduction, vasovagal tonus index, and echocardiographic variables in horses

System for the diagnosis and monitoring of coronary artery disease, acute coronary syndromes, cardiomyopathy and other cardiac conditions

Cardiac electrical data are received from a patient, manipulated to determine various useful aspects of the ECG signal, and displayed and stored in a useful form using a computer. The computer monitor displays various useful information, and in particular graphically displays various permutations of reduced amplitude zones and kurtosis that increase the rapidity and accuracy of cardiac diagnoses. New criteria for reduced amplitude zones are defined that enhance the sensitivity and specificity for detecting cardiac abnormalities

ECG Classification with an Adaptive Neuro-Fuzzy Inference System

Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases can be made through an Adaptive Neuro-Fuzzy Inference System (ANFIS) preprocessed by subtractive clustering. Six types of heartbeats are classified: normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. The goal is to detect important characteristics of an ECG signal to determine if the patient’s heartbeat is normal or irregular. The results from three trials indicate an average accuracy of 98.10%, average sensitivity of 94.99%, and average specificity of 98.87%. These results are comparable to two artificial neural network (ANN) algorithms: gradient descent and Levenberg Marquardt, as well as the ANFIS preprocessed by grid partitioning

Computationally Efficient QRS Detection Analysis In Electrocardiogram Based On Dual-Slope Method

A dramatic growth of interest for wearable technology has been fostered by recent technological advances in sensors, low-power integrated circuits and wireless communications. This interest originates from the need of monitoring a patient over extensive period of time. For cardiac patients, wearable heart monitoring sensors have already become a life-saving intervention ensuring continuous monitoring during daily life. Therefore, it is essential for an accurate monitoring and diagnosis of heart patients. Patients can be equipped with wireless, miniature and lightweight sensors. The sensors temporarily store physiological data and then periodically upload the data to a database server. These recorded data sets are then analyzed to predict any possibility of worsening patient\u27s situation or explored to assess the effect of clinical intervention. To obtain accurate response with less computational complexity as well as long battery life time, there is a demand of developing fast and accurate algorithm and prototypes for wearable heart monitoring sensors. A computationally efficient QRS detection algorithm is indispensable for low power operation on electrocardiogram (ECG) signal. In need of detecting QRS complex, most of the early works were proposed based on derivatives of ECG signal. They can be easily implemented with high computational speed. But owing to the inherent variability in ECG, these methods are highly affected by large derivatives of baseline noises. Algorithms based on neural network (NN) showed relatively robust performance against noise but requires exhaustive training and estimation of model parameter. On the other hand, wavelet based methods have the choice problem of mother wavelet. Hence, none of these methods is suitable for giving a long battery performance in wearable devices with high accuracy. Recently, Wang et al. proposed a novel dual slope QRS detection algorithm which has less computational complexity as well as high accuracy. Considering that the width of the QRS complex is relatively fixed, this algorithm is based on the fact that the largest change of slope usually happens at the peak of QRS complex. The hardware requirement is also low. However, the method has a set of time consuming slope calculations on both sides of each sample. To avoid such time consuming slope calculation, only one sample on each side can be highlighted. In addition, the multiplication of the left and right hand side slope should give us a very high value in QRS complex. The goal of this thesis is to develop a new computationally efficient method to detect QRS complexes and compare with the other renowned QRS detection algorithms. MIT-BIH arrhythmia database based on patients of different heart diseases and database containing ECG from healthy subjects are used. To analyze the performance, false negative (FN) and false positive (FP) are evaluated. A false negative (FN) occurs when algorithm fails to detect an actual QRS complex quoted in the corresponding annotation file of the database record and a false positive (FP) means a false beat detection. Error rate (ER) , Sensitivity (Se) and Specificity (Sp) are calculated using FP and FN

The right ventricle in Adult Congenital Heart Disease

Heart failure (HF) and sudden cardiac death (SCD) in congenital heart disease (CHD) is prevalent and can relate to abnormal right ventricular (RV) physiology and abnormalities of QRS duration, and QRS, JT and QT dispersion (d). Characterising disease and identifying factors that may predict adverse outcome in those with either a subpulmonary or subsystemic RV, as well as investigating potential avenues to ameliorate abnormal RV physiology is necessary to improve outcomes in this young population. I undertook several studies during the course of this Thesis to examine and further understand these two separate physiological substrates: In the first I studied the effect of isolated percutaneous (PPVI) pulmonary valve implantation on surface ECG parameters. PPVI represents a pure model of RV mechanical and electrophysiological changes post replacement as compared to surgical replacement: Ninety nine PPVI procedures in patients with CHD (aged 23.1±10 yrs) were studied pre, post and 1-year following PPVI with serial ECG’s and echocardiography/ magnetic resonance imaging (CMR). 43% had pulmonary stenosis, 27% pulmonary regurgitation (PR) and 29% mixed lesions. In those with predominantly PR (n=26), QRS duration decreased significantly (135±27 to 128±29ms; p=0.007). However, in the total cohort no significant change in QRS duration at 1 year was observed (137±29 to 134±29ms). QTc, QRSd, QTd and JTd all significantly reduced at 1 yr (p≤0.001). RV EDV correlated with pre-procedure QRS duration (r=0.34; p104ms and QTc >406ms had a sensitivity/specificity for predicting death of 96%/66% and 96%/56% respectively. Two year mortality was 36% when QRS104ms (p<0.0001 for difference). Further, compared to those with uncomplicated surgery, patients with complex surgical history had higher NT-proBNP levels (55±26 vs 20±35pmol/L; P=0.002) and longer QRS duration (116±28ms vs 89±11ms; P=0.0004) whilst showing no difference in NYHA class and RV function. There was a significant relationship between diastolic and systolic RV volumes and both NT-proBNP levels (r=0.43, P=0.01; r=0.53, P=0.001 respectively) and QRS duration (r=0.47, P=0.004; r=0.53, P=0.001 respectively). These findings suggest that QRS width and corrected QTc interval on surface ECG are associated with increased risk of death in adults late after atrial switch repair of TGA. Given that a QRS of only 104ms defines a high risk population, careful examination of the ECG is desirable in all patients and therapy to reduce risk attempted. Further, together with these simple surface ECG parameters, circulating NT-proBNP levels constitute safe, cost effective and widely available surrogate markers of systemic RV function and provide additional information on heart failure status. Both measures hold promise as prognostic markers and their association with long-term outcome should be determined. Lastly, I examined the mechanisms of late RV failure and studied their relationship to subjective quality of life assessment as this are poorly characterised. Equilibrium Contrast CMR imaging was used to quantify extracellular volume (ECV) in the septum and RV free wall of adults presenting to a specialist clinic late after atrial redirection surgery for TGA. These were compared to age and sex matched healthy volunteers. Patients were also assessed with a standardised CMR protocol, NT-proBNP and surface ECG measurement, and cardiopulmonary exercise (CPEX) testing. Patients also completed a Minnesota Living With Heart Failure Questionnaire (MLHFQ) self assessment. I determined that mean septal ECV was significantly higher in patients than controls (0.254±0.036, vs 0.230±0.032; p=0.03). NT-proBNP positively related to septal ECV (p=0.04; r=0.55) but chronotropic index (CI) during CPEX testing negatively related to ECV (p=0.04; r=-0.58). No relationship was seen with other CMR or CPEX parameters. Median MLHFQ score was 6(2-19), median NT-pro BNP 24 (16-43) and mean peak VO2 24±7mL/kg/min. There was a significant positive correlation between MLHFQ score and NT-proBNP (p=0.001, r=0.34) and a significant negative correlation with peak VO2 (p=0.001, r=0.49. ). Septal interstitial expansion is seen in adults late after atrial redirection surgery for TGA. It correlates well with NT-proBNP and CI and may have a role in the development of RV systolic impairment. The MLHFQ correlates highly with NT-proBNP and exercise capacity in patients with systemic RV impairment. The ability of the MLHFQ in predicting HF events and prognosis in adults with CHD needs further evaluation

Murine Gap Junction Remodelling Induced By Subdiaphragmatic Pacing

This thesis aims to adapt and develop a mouse model for stable dyssynchronous pacing and systematic investigation of mechanisms of structural gap-junctional remodelling (GJR) and correlation to functional changes. GJR, an altered abundance or localisation of connexin proteins strongly correlates with arrhythmogenic substrates. Wild type Cx43+/+ and heterozygous Cx43 knockout Cx43+/− (66% mean reduction in Cx43) mice were paced in vivo subxiphisternally at 10-15% above anaesthetised sinus rates for one to six hours avoiding intubation, vascular access, thoracic or mediastinal disruption. In Cx43+/+ mice, pacing resulted in electrical and mechanical dyssynchrony. Echocardiographic (ECHO) and electrocardiogram (ECG) indices, ventricular effective refractory period (VERP) and arrhythmia inducibility were not significantly altered. Pacing attenuated transmural gradients of Cx43 immunosignal in the LV free wall. Significant reductions in Cx43 mRNA abundance at the LV free wall occurred. Cx43, its isoforms and interacting protein expression were unchanged. Fractionation studies of 6hr-paced hearts demonstrated reduced Cx43 in membrane fractions while cytosolic fractions increased significantly. Cx43 degradation studies demonstrated substantially increased ubiquitinated forms with pacing. Cx43 protein expression in paced and unpaced Cx43+/− mice hearts was unchanged. In contrast to Cx43+/+ cells, Cx43+/− mice demonstrated significantly shorter action potential durations (APD), increased steady-state (Iss) and inward rectifier (IK1) potassium currents. Pacing prolonged action potential duration (APD) at 50ms and 90ms increased VERP at 80ms/100ms and significantly reduced Iss in Cx43+/− vs. unpaced Cx43+/− hearts. Pacing induces electro-mechanical dyssynchrony in wildtype Cx43 (Cx43+/+) hearts, results in remodelling of the cardiac gap junctions without sustained measurable effects or increased arrhythmia inducibility. Transgenic hearts (Cx43+/−) respond quite differently to pacing, which may be relevant in cardiac disease, where Cx43 is focally reduced. Pacing could lead to the remodelling of repolarisation currents in regions of reduced Cx43, enhancing dispersion of refractoriness and potentially creating a substrate for arrhythmia re-entry.Open Acces