Alan V. Sahakian

The coherence spectrum. A quantitative discriminator of fibrillatory and nonfibrillatory cardiac rhythms.

Previous work has suggested that a comparison of electrograms from two or more sites may best differentiate fibrillatory from nonfibrillatory rhythms. The coherence spectrum is a measure by which two signals may be compared quantitatively in the frequency domain. In the present study, the coherence spectrum was used to quantify the relation between spectral components of electrograms from two sites in either the atrium or ventricle during both fibrillatory and nonfibrillatory rhythms. Bipolar recordings of 35 rhythms from 20 patients were analyzed for coherence in the 1-59 Hz band. The 17 nonfibrillatory rhythms were sinus rhythm (six), paroxysmal supraventricular tachycardia (two), atrial flutter (four), and monomorphic ventricular tachycardia (five). The 18 fibrillatory rhythms were atrial fibrillation (12) and ventricular fibrillation (six). Nonfibrillatory rhythms exhibited moderate-to-high levels of coherence throughout the 1-59 Hz band, with peaks concentrated at the rhythms fundamental frequency and its harmonics. Fibrillatory rhythms exhibited little coherence throughout the 1-59 Hz band, and harmonics were not evident. The mean magnitude-squared coherence (scale of 0 to 1) for the 1-59 Hz band ranged from 0.22 to 0.86 (mean +/- SD, 0.52 +/- 0.19) for nonfibrillatory rhythms and from 0.042 to 0.12 (0.067 +/- 0.021) for fibrillatory rhythms. Separation of fibrillatory and nonfibrillatory rhythms was possible whether signals were recorded by floating or fixed-electrode configurations. These findings indicate that comparison of two electrograms with magnitude-squared coherence measurements differentiates fibrillatory from nonfibrillatory rhythms. A recognition algorithm based on coherence spectra may provide a major variations in lead configuration.(ABSTRACT TRUNCATED AT 250 WORDS)

Diagnosis of atrial fibrillation from surface electrocardiograms based on computer-detected atrial activity☆

A computerized method to detect atrial fibrillatory activity on the surface electrocardiogram is presented. After ventricular activity was canceled by creating a remainder electrocardiogram, significant differences were found in the percent power of the remainder electrocardiograms for a group of rhythms with atrial fibrillation (mean +/- SD; lead V1, 47.4 +/- 29.7%; lead II, 39.4 +/- 26.8%) and a control group (irregular rhythms or rhythms without readily detectable P waves; lead V1, 17.6 +/- 14.6%; lead II, 19.2 +/- 13.9%) for both leads (p less than 0.0001). A discrimination algorithm that classified a rhythm as atrial fibrillation if the percent power was greater than 32% and if noncoupled P waves were not present had a specificity of 90.0% and a sensitivity of 69.7% for the training set and a specificity of 87.8% and a sensitivity of 68.3% for the test set. In addition, the algorithm correctly detected all 66 of the 66 sinus rhythms tested. The algorithm produced good results that may be incorporated into arrhythmia interpretation systems to improve their specificity.

Evidence for transient linking of atrial excitation during atrial fibrillation in humans.

BackgroundAtrial fibrillation is usually thought of as a “random” pattern of circulating wavelets. However, local atrial activation should be influenced by the constant anatomy and receding tail of refractoriness from the previous activation. The general tendency for wave fronts to follow paths of previous excitation has been termed “linking.” We examined intra-atrial electrograms recorded during atrial fibrillation for evidence of linking. Methods and ResultsTwo minutes of atrial fibrillation were recorded in 15 patients with an orthogonal catheter. We have previously demonstrated that this catheter can be used to detect changes in the direction of local atrial activation. A mean vector was calculated for each electrogram. The similarity of the direction of the vectors from two consecutive electrograms can be quantified on a scale of 1 to −1 by calculating the cosine (cos) of the smallest angle (&thetas;) between them. Two vectors pointing in the same or opposite directions then have cos(&thetas;) = 1 or −1, respectively. For the entire group of patients, mean cos(&thetas;) was significantly greater than 0 (mean, 0.36; p < 0.001). In nine of 15 patients, there were groups of six or more consecutive beats (total, 44 groups; range, six to 14 beats per group) in which the direction of activation of each beat was within 30° of the previous beat. The likelihood of one group of six or 14 consecutive similar beats occurring by chance in any one patient in 1 minute is < 0.05 and < 0.0000001, respectively. There was a significant correlation (r = 0.90) between the amount of linking during the first and second minutes of atrial fibrillation in each patient. ConclusionsTransient similarities in the direction of wavelet propagation in the majority of patients with atrial fibrillation is consistent with the presence of transient linking. To our knowledge, this is the first direct evidence that atrial activation during atrial fibrillation in humans is not entirely random.

Irreversible Electroporation Therapy in the Liver: Longitudinal Efficacy Studies in a Rat Model of Hepatocellular Carcinoma

Irreversible electroporation (IRE) is an innovative local-regional therapy that involves delivery of intense electrical pulses to tissue to induce nanoscale cell membrane defects for tissue ablation. The purpose of this study was to investigate the feasibility of using IRE as a liver-directed ablation technique for the treatment of hepatocellular carcinoma (HCC). In the N1-S1 rodent model, hepatomas were grown in 30 Sprague-Dawley rats that were divided into treatment and control groups. For treatment groups, IRE electrodes were inserted and eight 100-mus 2,500-V pulses were applied to ablate the targeted tumor tissues. For both groups, magnetic resonance imaging scans were performed at baseline and 15-day follow-up intervals to determine tumor sizes (one-dimensional maximum diameter, D(max); estimated two-dimensional cross-sectional area, C(max)) as a tactic to assess longitudinal outcomes. Additional groups of treated animals were sacrificed at 1-, 3-, and 7-day intervals posttherapy for pathology assessment of treatment response. Magnetic resonance images showed significant tumor size reductions within 15 days posttherapy (32 +/- 31% D(max) and 52 +/- 39% C(max) decreases compared with 110 +/- 35% D(max) and 286 +/- 125% C(max) increases for untreated tumors). Pathology correlation studies documented progression from poorly differentiated viable HCC tissues before treatment to extensive tumor necrosis and full regression in 9 of 10 treated rats 7 to 15 days after treatment. Our findings suggest that IRE can be an effective strategy for targeted ablation of liver tumors, prompting its further evaluation for HCC therapy.

Use of Sample Entropy Approach to Study Heart Rate Variability in Obstructive Sleep Apnea Syndrome

Sample entropy, a nonlinear signal processing approach, was used as a measure of signal complexity to evaluate the cyclic behavior of heart rate variability (HRV) in obstructive sleep apnea syndrome (OSAS). In a group of 10 normal and 25 OSA subjects, the sample entropy measure showed that normal subjects have significantly more complex HRV pattern than the OSA subjects (p < 0.005). When compared with spectral analysis in a minute-by-minute classification, sample entropy had an accuracy of 70.3% (69.5% sensitivity, 70.8% specificity) while the spectral analysis had an accuracy of 70.4% (71.3% sensitivity, 69.9% specificity). The combination of the two methods improved the accuracy to 72.9% (72.2% sensitivity, 73.3% specificity). The sample entropy approach does not show major improvement over the existing methods. In fact, its accuracy in detecting sleep apnea is relatively low in the well classified data of the physionet. Its main achievement however, is the simplicity of computation. Sample entropy and other nonlinear methods might be useful tools to detect apnea episodes during sleep.

Atrial fibrillation and waveform characterization

The surface electrocardiogram (ECG) is a convenient, cost effective, and noninvasive tool for the study of atrial fibrillation (AF). It can be used to examine the hypothesized mechanisms of AF as well as to quantify and assess the effect of electrophysiological remodeling and the effectiveness of treatment on different types of AF. Time domain methods can be used to characterize the signal in the surface ECG. The authors described observations that can be obtained directly from the signal, such as the general characteristics of AF in the surface ECG and the ventricular response to AF. A discussion on commonly used methods to characterize atrial activity is also presented. These methods include cancellation techniques, vector analysis, and autocorrelation. Observations show that combining time and frequency domain methods provides a more thorough understanding of the characteristics of the atrial activity in the surface ECG. Whether the study of atrial activity in the surface ECG can be used to distinctively distinguish between different mechanisms of AF is not yet known, but further investigation can improve our understanding of these mechanisms and help with the management of this common arrhythmia

Toward Carbon-Nanotube-Based Theranostic Agents for Microwave Detection and Treatment of Breast Cancer: Enhanced Dielectric and Heating Response of Tissue-Mimicking Materials

The experimental results reported in this paper suggest that single-walled carbon nanotubes (SWCNTs) have the potential to enhance dielectric contrast between malignant and normal tissue for microwave detection of breast cancer and facilitate selective heating of malignant tissue for microwave hyperthermia treatment of breast cancer. In this study, we constructed tissue-mimicking materials with varying concentrations of SWCNTs and characterized their dielectric properties and heating response. At SWCNT concentrations of less than 0.5% by weight, we observed significant increases in the relative permittivity and effective conductivity. In microwave heating experiments, we observed significantly greater temperature increases in mixtures containing SWCNTs. These temperature increases scaled linearly with the effective conductivity of the mixtures. This work is a first step towards the development of functionalized, tumor-targeting SWCNTs as theranostic (integrated therapeutic and diagnostic) agents for microwave breast cancer detection and treatment.

Computer detection of atrioventricular dissociation from surface electrocardiograms during wide QRS complex tachycardias.

Differentiation of wide QRS complex tachycardias on surface electrocardiograms is difficult for physicians and computers due in part to their inability to identify atrial activity, specifically atrioventricular (AV) dissociation. We studied 20 examples of AV associated rhythms and 17 examples of AV dissociated ventricular tachycardia. We applied an algorithm consisting of subtraction of a mean beat from each individual beat in leads II and V1 to generate remainder electrocardiograms. The remainder electrocardiograms were visually inspected for the presence of P wave candidates and then autocorrelated. AV dissociated P wave candidates were evident on visual inspection of remainder electrocardiograms in none of 20 AV associated and 15 of 17 AV dissociated rhythms. Atrial cycle length and the presence of AV dissociation were automatically detected by applying a peak selection algorithm to the autocorrelation function. AV association was detected in all 20 AV associated rhythms and AV dissociation was detected for 11 of 17 AV dissociated rhythms (sensitivity 65%, specificity 100%, positive and negative predictive accuracy 100%, 77%). The correlation coefficient of detected vs true atrial cycle length for the 11 correctly detected AV dissociated rhythms was r = .98. Visual inspection of the remainder electrocardiograms along with the original electrocardiogram may increase the ease with which human readers can identify the presence of AV dissociation and thus diagnose ventricular tachycardia. Computer diagnosis of wide QRS complex tachycardias should be significantly improved by use of this algorithm.

Computer Discrimination of Atrial Fibrillation and Regular Atrial Rhythms from Intra‐Atrial Electrograms

Reliable detection of atrial fibrillation from intra‐atrial data is an important requirement for automatic implantable anti‐tachycardia devices. Simultaneous filtered and unfiltered intra‐atrial electrograms were recorded from patients in regular rhythms (12 sinus rhythms and six regular atrial tachycardias) and atrial fibrillation (nine rhythms). Each rhythm was broken down into consecutive 4‐second data segments for analysis by atrial rate calculation, power spectrum analysis and amplitude probability density function generation. Significant differences were found between regular rhythms and atrial fibrillation for atrial rate, for the percentage of the total power in the 4–9 hertz band and for amplitude probability density close to the isoelectric region. There was no overlap for any of these three parameters. For each method of analysis, algorithms were generated to discriminate individual data segments from regular rhythms and atrial fibrillation with high sensitivity and specificity. Comparable results were found when sinus rhythm was excluded from the analysis. Characteristics of intra‐atrial recordings during atrial fibrillation were remarkably similar to previously published reports of intra‐ventricular recordings during ventricular fibrillation. Each of the three methods of analysis may provide an algorithm for accurate detection of atrial fibrillation by anti‐tachycardia devices.

Detection of atrial activity from high-voltage leads of implantable ventricular defibrillators using a cancellation technique

The inability to detect atrial activity limits implantable ventricular cardioverter defibrillators (ICD) in discriminating tachycardias and can result in inappropriate therapy. This study attempted to detect atrial activity on the wide-spaced bipole signals formed by the high-voltage (HV) leads of the ICD during device implantation and to develop an algorithm for the detection of atrial fibrillation (AFib) from these signals. The authors used a method that cancelled ventricular and correlated atrial activity from the HV lead signals and measured frequency and amplitude distribution information to discriminate sinus rhythm (SR) and AFib segments. The authors analyzed 186 data segments from 21 patients (six AFib, 14 SR, one AFib and SR). For individual segments in this data set, the sensitivity of the algorithm was 78%, specificity 92.65%, positive and negative predictive values 79.59 and 91.97%, respectively. These results demonstrate that atrial activity is present in the HV lead signals, and AFib detection can be achieved in many, but not all cases, using information currently available to ICDs. Prior work from surface electrocardiograms suggests that this algorithm can function during ventricular tachycardias. However, specificity of the algorithm is not high enough for clinical use.