Biomedical subjects

Leo K Cheng

Publications and source records attributed to Leo K Cheng.

4 recordsLinked to original sources

PubMed2006

An integrative software package for gastrointestinal biomagnetic data acquisition and analysis using SQUID magnetometers.

The study of bioelectric and biomagnetic activity in the human gastrointestinal (GI) tract is of great interest in clinical research due to the proven possibility to detect pathological conditions thereof from electric and magnetic field recordings. The magnetogastrogram (MGG) and magnetoenterogram (MENG) can be recorded using superconducting quantum interference device (SQUID) magnetometers, which are the most sensitive magnetic flux-to-voltage converters currently available. To address the urgent need for powerful acquisition and analysis software tools faced by many researchers and clinicians in this important area of investigation, an integrative and modular computer program was developed for the acquisition, processing and analysis of GI SQUID signals. In addition to a robust hardware implementation for efficient data acquisition, a number of signal processing and analysis modules were developed to serve in a variety of both clinical procedures and scientific investigations. Implemented software features include data processing and visualization, waterfall plots of signal frequency spectra as well as spatial maps of GI signal frequencies. Moreover, a software tool providing powerful 3D visualizations of GI signals was created using realistic models of the human torso and internal organs.

Gastrointestinal Diseases↗

PubMed2003

Cardiac electrical activity--from heart to body surface and back again.

We report here on our latest developments in the forward and inverse problems of electrocardiology. In the forward problem, a coupled cellular model of cardiac excitation-contraction is embedded within an anatomically realistic model of the cardiac ventricles, which is itself embedded within a torso model. This continuum modelling framework allows the effects of cellular-level activity on the surface electrocardiogram (ECG) to be carefully examined. Furthermore, the contributions of contraction and local ischemia on body surface recordings can also be elucidated. Such information can provide theoretical limits to the sensitivity and ultimately the detection capability of body surface ECG recordings. Despite being very useful, such detailed forward modelling is not directly applicable when seeking to use densely sampled ECG information to assess a patient in a clinical environment (the inverse problem). In such a situation patient specific models must be constructed and, due to the nature of the inverse problem, the level of detail that can be reliably reproduced is limited. Extensive simulation studies have shown that the accuracy with which the heart is localised within the torso is the primary limiting factor. To further identify the practical performance capabilities of the current inverse algorithms, high quality experimental data is urgently needed. We have been working towards such an objective with a number of groups, including our local hospital in Auckland. At that hospital, in patients undergoing catheter ablation surgery, up to 256 simultaneous body surface signals were recorded by using various catheter pacing protocols. The geometric information required to customize the heart and torso model was obtained using a combination of ultrasound and laser scanning technologies. Our initial results indicate that such geometric imaging modalities are sufficient to produce promising inversely-constructed activation profiles.

Algorithms↗

PubMed2003

Effects of experimental and modeling errors on electrocardiographic inverse formulations.

The inverse problem of electrocardiology aims to reconstruct the electrical activity occurring within the heart using information obtained noninvasively on the body surface. Potentials obtained on the torso surface can be used as input for the inverse problem and an electrical image of the heart obtained. There are a number of different inverse algorithms currently used to produce electrical images of the heart. The relative performances of these inverse algorithms at this stage is largely unknown. Although there have been many simulation studies investigating the accuracy of each of these algorithms, to date, there has been no comprehensive study which compares a wide variety of inverse methods. By performing a detailed simulation study, we compare the performances of epicardial potential [Tikhonov, Truncated singular value decomposition (TSVD), and Greensite] and myocardial activation-based (critical point) inverse simulations along with different methods of choosing the appropriate level of regularization (optimal, L-curve, composite residual and smoothing operator, zero-crossing) to apply to each of these inverse methods. We also examine the effects of a variety of signal error, material property error, geometric error and a combination of these errors on each of the electrocardiographic inverse algorithms. Results from the simulation study show that the activation-based method is able to produce solutions which are more accurate and stable than potential-based methods especially in the presence of correlated errors such as geometric uncertainty. In general, the Greensite-Tikhonov method produced the most realistic potential-based solutions while the zero-crossing and L-curve were the preferred method for determining the regularization parameter. The presence of signal or material property error has little effect on the inverse solutions when compared with the large errors which resulted from the presence of any geometric error. In the presence of combined Gaussian and correlated errors representing conditions which may be encountered in an experimental or clinical environment, there was less variability between potential-based solutions produced by each of the inverse algorithms.

Algorithms↗

PubMed2003

Comparison of potential- and activation-based formulations for the inverse problem of electrocardiology.

Two predominant source formulations for the inverse problem of electrocardiology currently exist. They involve the reconstruction of epicardial potentials or myocardial activation times from noninvasively recorded torso surface potentials. Each of these formulations have their advantages, however, they have not been systematically compared against each other. We present results from a simulation study which compared a number of epicardial potential (Tikhonov, Truncated singular value decomposition (TSVD), Greensite-Tikhonov and Greensite-TSVD), and a myocardial activation time formulation for the inverse problem of electrocardiology. A number of different methods were also used to determine the appropriate level of regularization (optimal, L-curve, zero-crossing, and composite residual and smoothing operator) to apply to each formulation. The simulation study was conducted using an anatomically based boundary element porcine model with a variety of cardiac sources. Varying levels of geometric error were introduced to the system and solutions were computed using each of the inverse algorithms. Results show that under pure Gaussian noise potential-based methods performed best at low noise levels while the activation-based method was less effected by higher noise levels. In the presence of correlated geometric error, the activation-based method out performed the potential methods, with the Greensite-Tikhonov method being the most favored potential-based method when using the L-curve or zero-crossing method to determine the regularization parameter.

Algorithms↗