Age-associated changes in regional aortic pulse wave velocity.

OBJECTIVES: This study was designed to determine noninvasively the age-associated changes in regional mechanical properties in normals using phase-contrast magnetic resonance imaging (PCMRI). BACKGROUND: It has been well documented that there is a progressive increase in aortic pulse wave velocity (PWV) with age. Previously, PWV has been measured at a single aortic location, or has compared arterial waves between carotid and femoral points to determine PWV. METHODS: Applanation tonometry (TONO) and in-plane PCMR was performed in 24 volunteers (12 men) ranging in age from 21 to 72 years old. The PCMRI PWV was measured in three aortic segments. As validation, TONO was performed to determine PWV between the carotid and femoral artery. RESULTS: When PCMRI PWV was averaged over the three locations, it was not different from TONO (7.9 +/- 2.3 vs. 7.6 +/- 2.4 m/s, respectively). When the volunteers were divided into groups of < 55 and > or =55 years old, the younger group showed similar PWV at each aortic location. However, the older group displayed significantly increased PWV in the region spanning the ascending and proximal descending aorta compared with the mid-thoracic or abdominal segments (10.6 +/- 2.5 m/s, 9.2 +/- 2.8 m/s, and 7.1 +/- 1.7 m/s, respectively, p < 0.001, analysis of variance). CONCLUSIONS: In-plane PCMRI permits determination of PWV in multiple aortic locations in a single acquisition. Progressive fragmentation of elastic fibers and alterations in the regulation of vascular tone may result in an age-related, regional increase in PWV primarily affecting the proximal aorta.

Wavelet-based enhancement of signal-averaged electrocardiograms for late potential detection.

An optimal wavelet filter to improve the signal-to-noise ratio (SNR) of the signal-averaged electrocardiogram is described. As the averaging technique leads to the best unbiased estimator, the challenge is to attenuate the noise while preserving the low amplitude signals that are usually embedded in it. An optimal, in the mean-square sense, wavelet-based filter has been derived from the model of the signal. However, such a filter needs exact knowledge of the noise statistic and the noise-free signal. Hence, to implement such a filter, a method based on successive sub-averaging and wavelet filtering is proposed. Its performance was evaluated using simulated and real ECGs. An improvement in SNR of between 6 and 10 dB can be achieved compared to a classical averaging technique which uses an ensemble of 64 simulated ECG beats. Tests on real ECGs demonstrate the utility of the method as it has been shown that by using fewer beats in the filtered ensemble average, one can achieve the same noise reduction. Clinical use of this technique would reduce the ensemble needed for averaging while obtaining the same diagnostic result.

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.

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.

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)

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.

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.