In previous studies on heartbeat series, it has been proposed that the healthy heartbeat pattern represents complex nonlinear dynamics, and such cardiac nonlinearity may be used as a clinical indicator for the diagnosis of certain types of heart disease. However, it is still not quite clear whether there is any difference among the heartbeat series of patients with congestive heart failure (CHF), or whether cardiac nonlinearity represents a severe heart disease situation. In the present study, parallel cascade identification (PCI), which frequently requires only short stretches of data to obtain highly promising results, is used to distinguish severe congestive heart failure, a clinical situation associated with a high-risk of sudden death, from low-risk CHF. Parallel cascade identification is an accurate and robust method for identifying dynamic nonlinear systems. The PCI algorithm combined with a specified statistical test may be used as a severe congestive heart failure marker by comparing a nonlinear model with a “linear” model (more precisely, a first-order Volterra series). In this thesis, PCI is applied to distinguish R-R wave intervals of CHF patients who died from those of patients who survived in a 5-year study. The detection accuracy of the PCI detector is evaluated over a first set of 49 patients, and then over a larger set of a further 352 patients, and consistent results are obtained between the two sets. Over the larger set, Matthews' correlation coefficient of nonlinearity with unfavorable outcome (death) is , sensitivity for predicting unfavorable outcome is , while the specificity is . The R-R wave interval exhibits nonlinearity in patients who died during the 5-year study. However, typically nonlinearity cannot be detected in patients who survived during the study. These findings show that for patients with congestive heart failure, nonlinearity is associated with unfavorable outcome (death), while patients for whom nonlinearity cannot be detected overwhelmingly have good outcomes. This is significant for clinical diagnosis and prognosis of severe congestive heart failure. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2007-09-28 11:54:57.695

An accurate determination of muscle force is desired in many applications in different fields such as ergonomics, sports medicine, prosthetics, human-robot interaction and medical rehabilitation. Since individual muscle forces cannot be directly measured, force estimation using recorded electromyographic (EMG) signals has been extensively studied. This usually involves interpretation and analysis of the recorded EMG to estimate the underlying neuromuscular activity which is related to the force produced by the muscle. Although invasive needle electrode EMG recordings have provided substantial information about neuromuscular activity at the motor unit (MU) level, there is a risk of discomfort, injury and infection. Thus, non-invasive methods are preferred and surface EMG (SEMG) recording is widely used. However, physiological and non-physiological factors, including phase cancelation, tissue filtering, cross-talk from other muscles and non-optimal electrode placement, affect the accuracy of SEMG-based force estimation. In addition, the relative movement of the muscle bulk and the innervation zone (IZ) with respect to the electrode attached to the skin are two major challenges to overcome in force estimation during dynamic contractions. The objective of this work is to improve the accuracy of SEMG-based force estimation under static conditions, and devise methods that can be applied to force estimation under dynamic conditions. To achieve this objective, a novel calibration technique is proposed, which corrects for variations in the SEMG with changing joint angle. In addition, a modeling technique, namely parallel cascade identification (PCI) that can deal with non-linearities and dynamics in the SEMG-force relationship is applied to the force estimation problem. Finally, a novel integrated sensor that senses both SEMG and surface muscle pressure (SMP) is developed and the two signal modalities are used as input to a force prediction model. The experimental results show significant improvement in force prediction using data calibrated with the proposed calibration method, compared to using non-calibrated data. Joint angle dependency and the sensitivity to the location of the sensor in the SEMG-force relationship is reduced with calibration. The SEMG-force estimation error, averaged over all subjects, is reduced by 44\% for PCI modeling compared to another modeling technique (fast orthogonal search) applied to the same dataset. Significantly improved force estimation results are also achieved for dynamic contractions when joint angle based calibration and PCI are combined. Using SMP in addition to SEMG leads to significantly better force estimation compared to using only SEMG signals. The proposed methods have the potential to be combined and used to obtain better force estimation in more complicated dynamic contractions and for applications such as improved control of remote robotic systems or powered prosthetic limbs. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2013-08-20 20:46:56.897

Background: Respiratory illnesses can have a substantial impact on population health and burden hospitals in terms of patient load. Advance warnings of the spread of such illness could inform public health interventions and help hospitals manage patient services. Previous research showed that calls for respiratory complaints to Telehealth Ontario are correlated up to two weeks in advance with emergency department visits for respiratory illness at the provincial level. Objectives: This thesis examined whether Telehealth Ontario calls for respiratory complaints could be used to accurately forecast the daily and weekly number of emergency department visits for respiratory illness at the health unit level for each of the 36 health units in Ontario up to 14 days in advance in the context of a real-time syndromic surveillance system. The forecasting abilities of three different time series modeling techniques were compared. Methods: The thesis used hospital emergency department visit data from the National Ambulatory Care Reporting System database and Telehealth Ontario call data and from June 1, 2004 to March 31, 2006. Parallel Cascade Identification (PCI), Fast Orthogonal Search (FOS), and Numerical Methods for Subspace State Space System Identification (N4SID) algorithms were used to create prediction models for the daily number of emergency department visits using Telehealth call counts and holiday/weekends as predictors. Prediction models were constructed using the first year of the study data and their accuracy was measured over the second year of data. Factors associated with prediction accuracy were examined. Results: Forecast error varied widely across health units. Prediction error increased with lead time and lower call-to-visits ratio. Compared with N4SID, PCI and FOS had significantly lower forecast error. Forecasts of the weekly aggregate number of visits showed little evidence of ability to accurately flag corresponding actual increases. However, when visits were aggregated over a four day period, increases could be flagged more accurately than chance in six of the 36 health units accounting for approximately half of the Ontario population. Conclusions: This thesis suggests that Telehealth Ontario data collected by a real-time syndromic surveillance system could play a role in forecasting health services demand for respiratory illness. / Thesis (Master, Community Health & Epidemiology) -- Queen's University, 2009-08-11 16:20:44.553