Biomedical subjects

D A Coast

Publications and source records attributed to D A Coast.

5 recordsLinked to original sources

PubMed1998

Vessel boundary extraction based on a global and local deformable physical model with variable stiffness.

Reliable and efficient vessel cross-sectional boundary extraction is very important for many medical magnetic resonance (MR) image studies. General purpose edge detection algorithms often fail for medical MR images processing due to fuzzy boundaries, inconsistent image contrast, missing edge features, and the complicated background of MR images. In this regard, we present a vessel cross-sectional boundary extraction algorithm based on a global and local deformable model with variable stiffness. With the global model, the algorithm can handle relatively large vessel position shifts and size changes. The local deformation with variable stiffness parameters enable the model to stay right on edge points at the location where edge features are strong and at the same time, fit a smooth contour at the location where edge features are missing. Directional gradient information is used to help the model to pick correct edge segments. The algorithm was used to process MR cine phase-contrast images of the aorta from 20 volunteers (over 500 images) with excellent results.

Algorithms↗

PubMed1990

Detection of late potentials by adaptive filtering.

Recent studies have shown small deflections in the terminal portion of the QRS complex in postmyocardial infarction (MI) patients with episodes of potentially dangerous ventricular arrhythmias. These very small deflections, termed late potentials, are difficult to observe in ECGs acquired with electrodes on the chest surface owing to myoelectric noise from underlying muscle and other environmental noise. Most research into enhancement of late potentials has focused on signal averaging. Our interest is in a mathematical signal-processing technique called time sequenced adaptive filtering. In ECG signals processed with adaptive filtering, late potentials are discernable in individual beats. This processing technique may also allow visualization of late potentials in signals from patients with intraventricular conduction delays. Preliminary studies used 12 normal subjects and 5 patients scheduled for electrophysiology studies (EPs), with histories of cardiopulmonary arrest, symptoms of unexplained syncope, or documented malignant ventricular arrhythmias. Three of the five patients studied by EPs had inducible sustained ventricular tachycardia. Subsequently, when ECG signals were obtained from surface electrodes and process by adaptive filtering, these same three patients had prolonged QRS durations (greater than 120 ms) with occurrence of additional low voltage activity (greater than or equal to 5-10 mV) in the terminal portion of the QRS. Two of these patients had bundle branch blocks as determined by a 12-lead ECG. The remaining patients and the control subjects had normal QRS durations with no additional activity. Two subjects in the control group had some activity in the terminal portion of the QRS without prolongation of QRS duration.(ABSTRACT TRUNCATED AT 250 WORDS)

Cardiac Pacing, Artificial↗

PubMed1990

Enhancement of low-level ECG components in noise with time-sequenced adaptive filtering.

Observation of low amplitude components in the ECG motivated our interest in time-sequenced adaptive filtering. This technique is applicable to signals that are cyclic in nature. Two simultaneously acquired signals are used in the technique. It is assumed that the underlying signal is correlated and that noise is uncorrelated between the two channels. Instead of one enhancer that continuously adjusts its characteristics over the time course of the signal, each cycle is subdivided into intervals; a given enhancer is used only on the same corresponding interval in successive cycles. This minimizes the signal range over which the enhancer must adjust its characteristics. In the time-sequenced approach, it is important that the enhancers adapt at the same rate. Thus, each has its own feedback coefficient derived from an average of 10 consecutive ECG cycles and an estimate of noise in intervals where no signal is present. Each feedback coefficient is augmented over the first 10 beats. To improve adaptation, a means to update each filter on the preceding beat and the current beat was developed. Lastly, a weight averaging scheme was developed to circumvent weight stalling. The procedure has enabled observation of both His activity and late potentials in individual beats from signals acquired from the chest surface.

Electrocardiography↗

PubMed1990

Use of hidden Markov models for electrocardiographic signal analysis.

Hidden Markov modelling (HMM) is a powerful stochastic modelling technique that has been successfully applied to automatic speech recognition problems. We are currently investigating the application of HMM to electrocardiographic signal analysis with the goal of improving ambulatory ECG analysis. The HMM approach specifies a Markov chain to model a "hidden" sequence that in this case is the underlying state of the heart. Each state of the Markov chain has an associated output function that describes the statistical characteristics of measurement samples generated during that state. Given a measurement sequence and HMM parameter estimates, the most likely underlying state sequence can be determined and used to infer beat classification. Advantages of this approach include resistance to noise, ability to model low-amplitude waveforms such as the P wave, and availability of an algorithm for automatically estimating model parameters from training data. We have applied the HMM approach to QRS complex detection and to arrhythmia analysis with encouraging results.

Algorithms↗

PubMed1990

An approach to cardiac arrhythmia analysis using hidden Markov models.

This paper describes a new approach to ECG arrhythmia analysis based on "hidden Markov modeling" (HMM), a technique successfully used since the mid-1970's to model speech waveforms for automatic speech recognition. Many ventricular arrhythmias can be classified by detecting and analyzing QRS complexes and determining R-R intervals. Classification of supraventricular arrhythmias, however, often requires detection of the P wave in addition to the QRS complex. The hidden Markov modeling approach combines structural and statistical knowledge of the ECG signal in a single parametric model. Model parameters are estimated from training data using an iterative, maximum likelihood reestimation algorithm. Initial results suggest that this approach may provide improved supraventricular arrhythmia analysis through accurate representation of the entire beat including the P wave.

Arrhythmias, Cardiac↗