Ventricular fibrillation detection algorithm for automated external defibrillators

The automatic external defibrillator (AED) is a lifesaving device, which processes and analyzes the electrocardiogram (ECG) and prompts a defibrillation shock if ventricular fibrillation is determined. This project investigates the possibility of developing a Ventricular Fibrillation (VF) detection algorithm based on Autoregressive Modeling (AR Modeling) and dominant poles for the use in AEDs. In particular, the ECG segment is modeled using AR modeling and the dominant poles are extracted from the model transfer function. The dominant pole frequencies were then used in classification based on the distance measure. The potential use of this method to distinguish between VF and Normal sinus rhythm (NSR) is discussed. The method was tested with ECG records from the widely recognized databases of American Heart Association (AHA) and the Creighton University (CU). Sensitivity and specificity for the new VF detection method were calculated to be 66% and 94% respectively. The proposed method has some advantages over other existing VF detection algorithms; it has a high detection accuracy, it is computationally inexpensive and can be easily implemented in hardware.

Ventricular fibrillation detection algorithm for automated external defibrillators

The automatic external defibrillator (AED) is a lifesaving device, which processes and analyzes the electrocardiogram (ECG) and prompts a defibrillation shock if ventricular fibrillation is determined. This project investigates the possibility of developing a Ventricular Fibrillation (VF) detection algorithm based on Autoregressive Modeling (AR Modeling) and dominant poles for the use in AEDs. In particular, the ECG segment is modeled using AR modeling and the dominant poles are extracted from the model transfer function. The dominant pole frequencies were then used in classification based on the distance measure. The potential use of this method to distinguish between VF and Normal sinus rhythm (NSR) is discussed. The method was tested with ECG records from the widely recognized databases of American Heart Association (AHA) and the Creighton University (CU). Sensitivity and specificity for the new VF detection method were calculated to be 66% and 94% respectively. The proposed method has some advantages over other existing VF detection algorithms; it has a high detection accuracy, it is computationally inexpensive and can be easily implemented in hardware.

High Level FPGA Implementation Of Adaptive Signal Segmentation And Autoregressive Modeling Techniques

This thesis contains new FPGA implementations of adaptive signal segmentation and autoregressive modeling techniques. Both designs use Simulink-to-FPGA methodology and have been successfully implemented onto Xilinx Virtex II Pro device. The implementation of adaptive signal segmentation is based on the conventional RLSL algorithm using double-precision floating point arithmetic for internal computation and is programmable for users providing data length and order selection functions. The implemented RLSL design provides very good performance of obtaining accurate conversion factor values with a mean correlation of 99.93% and accurate boundary positions for both synthesized and biomedical signals. The implementation of autoregressive (AR) modeling is based on the Burg-lattice algorithm using fixed point arithmetic. The implemented Burg design with order of 3 provides good performance of calculating AR coefficients of input biomedical signals.

High Level FPGA Implementation Of Adaptive Signal Segmentation And Autoregressive Modeling Techniques

This thesis contains new FPGA implementations of adaptive signal segmentation and autoregressive modeling techniques. Both designs use Simulink-to-FPGA methodology and have been successfully implemented onto Xilinx Virtex II Pro device. The implementation of adaptive signal segmentation is based on the conventional RLSL algorithm using double-precision floating point arithmetic for internal computation and is programmable for users providing data length and order selection functions. The implemented RLSL design provides very good performance of obtaining accurate conversion factor values with a mean correlation of 99.93% and accurate boundary positions for both synthesized and biomedical signals. The implementation of autoregressive (AR) modeling is based on the Burg-lattice algorithm using fixed point arithmetic. The implemented Burg design with order of 3 provides good performance of calculating AR coefficients of input biomedical signals.

Characterizing gait pattern dynamics during symmetric and asymmetric walking using autoregressive modeling

Gait asymmetry is often observed in populations with varying degrees of neuromuscular control. While changes in vertical ground reaction force (vGRF) peak magnitude are associated with altered limb loading that can be observed during asymmetric gait, the challenge is identifying techniques with the sensitivity to detect these altered movement patterns. Autoregressive (AR) modeling has successfully delineated between healthy and pathological gait during running; but has been little explored in walking. Thus, AR modeling was implemented to assess differences in vGRF pattern dynamics during symmetric and asymmetric walking. We hypothesized that the AR model coefficients would better detect differences amongst the symmetric and asymmetric walking conditions than the vGRF peak magnitude mean. Seventeen healthy individuals performed a protocol that involved walking on a split-belt instrumented treadmill at different symmetric (0.75m/s, 1.0 m/s, 1.5 m/s) and asymmetric (Side 1: 0.75m/s-Side 2:1.0 m/s; Side 1:1.0m/s-Side 2:1.5 m/s) gait conditions. Vertical ground reaction force peaks extracted during the weight-acceptance and propulsive phase of each step were used to construct a vGRF peak time series. Then, a second order AR model was fit to the vGRF peak waveform data to determine the AR model coefficients. The resulting AR coefficients were plotted on a stationarity triangle and their distance from the triangle centroid was computed. Significant differences in vGRF patterns were detected amongst the symmetric and asymmetric conditions using the AR modeling coefficients (p = 0.01); however, no differences were found when comparing vGRF peak magnitude means. These findings suggest that AR modeling has the sensitivity to identify differences in gait asymmetry that could aid in monitoring rehabilitation progression.

Skew Generalized Normal Innovations for the AR(p) Process Endorsing Asymmetry

The assumption of symmetry is often incorrect in real-life statistical modeling due to asymmetric behavior in the data. This implies a departure from the well-known assumption of normality defined for innovations in time series processes. In this paper, the autoregressive (AR) process of order p (i.e., the AR(p) process) is of particular interest using the skew generalized normal (SGN) distribution for the innovations, referred to hereafter as the ARSGN(p) process, to accommodate asymmetric behavior. This behavior presents itself by investigating some properties of the SGN distribution, which is a fundamental element for AR modeling of real data that exhibits non-normal behavior. Simulation studies illustrate the asymmetry and statistical properties of the conditional maximum likelihood (ML) parameters for the ARSGN(p) model. It is concluded that the ARSGN(p) model accounts well for time series processes exhibiting asymmetry, kurtosis, and heavy tails. Real time series datasets are analyzed, and the results of the ARSGN(p) model are compared to previously proposed models. The findings here state the effectiveness and viability of relaxing the normal assumption and the value added for considering the candidacy of the SGN for AR time series processes.

ARFBF MORPHOLOGICAL ANALYSIS - APPLICATION TO THE DISCRIMINATION OF CATALYST ACTIVE PHASES

This paper addresses the characterization of spatial arrangements of fringes in catalysts imaged by High Resolution Transmission Electron Microscopy (HRTEM). It presents a statistical model-based approach for analyzing these fringes. The proposed approach involves Fractional Brownian Field (FBF) and 2-D AutoRegressive (AR) modeling, as well as morphological analysis. The originality of the approach consists in identifying the image background as an FBF, subtracting this background, modeling the residual by 2-D AR so as to capture fringe information and, finally, discriminating catalysts from fringe characterizations obtained by morphological analysis. The overall analysis is called ARFBF (Auto-Regressive Fractional Brownian Field) based morphology characterization.