Classification of lung pathologies in neonates using dual-tree complex wavelet transform.

INTRODUCTION: Undiagnosed and untreated lung pathologies are among the leading causes of neonatal deaths in developing countries. Lung Ultrasound (LUS) has been widely accepted as a diagnostic tool for neonatal lung pathologies due to its affordability, portability, and safety. However, healthcare institutions in developing countries lack well-trained clinicians to interpret LUS images, which limits the use of LUS, especially in remote areas. An automated point-of-care tool that could screen and capture LUS morphologies associated with neonatal lung pathologies could aid in rapid and accurate diagnosis. METHODS: We propose a framework for classifying the six most common neonatal lung pathologies using spatially localized line and texture patterns extracted via 2D dual-tree complex wavelet transform (DTCWT). We acquired 1550 LUS images from 42 neonates with varying numbers of lung pathologies. Furthermore, we balanced our data set to avoid bias towards a pathology class. RESULTS: Using DTCWT and clinical features as inputs to a linear discriminant analysis (LDA), our approach achieved a per-image cross-validated classification accuracy of 74.39% for the imbalanced data set. Our classification accuracy improved to 92.78% after balancing our data set. Moreover, our proposed framework achieved a maximum per-subject cross-validated classification accuracy of 64.97% with an imbalanced data set while using a balanced data set improves its classification accuracy up to 81.53%. CONCLUSION: Our work could aid in automating the diagnosis of lung pathologies among neonates using LUS. Rapid and accurate diagnosis of lung pathologies could help to decrease neonatal deaths in healthcare institutions that lack well-trained clinicians, especially in developing countries.

Pro-arrhythmic role of adrenergic spatial densities in the human atria: An in-silico study.

Chronic stress among young patients (≤ 45 years old) could result in autonomic dysfunction. Autonomic dysfunction could be exhibited via sympathetic hyperactivity, sympathetic nerve sprouting, and diffuse adrenergic stimulation in the atria. Adrenergic spatial densities could alter atrial electrophysiology and increase arrhythmic susceptibility. Therefore, we examined the role of adrenergic spatial densities in creating arrhythmogenic substrates in silico. We simulated three 25 cm2 atrial sheets with varying adrenergic spatial densities (ASD), activation rates, and external transmembrane currents. We measured their effects on spatial and temporal heterogeneity of action potential durations (APD) at 50% and 20%. Increasing ASD shortens overall APD, and maximum spatial heterogeneity (31%) is achieved at 15% ASD. The addition of a few (5% to 10%) adrenergic elements decreases the excitation threshold, below 18 µA/cm2, while ASDs greater than 10% increase their excitation threshold up to 22 µA/cm2. Increase in ASD during rapid activation increases APD50 and APD20 by 21% and 41%, respectively. Activation times of captured beats during rapid activation could change by as much as 120 ms from the baseline cycle length. Rapidly activated atrial sheets with high ASDs significantly increase temporal heterogeneity of APD50 and APD20. Rapidly activated atrial sheets with 10% ASD have a high likelihood (0.7 ± 0.06) of fragmenting otherwise uniform wavefronts due to the transient inexcitability of adrenergically stimulated elements, producing an effective functional block. The likelihood of wave fragmentation due to ASD highly correlates with the spatial variations of APD20 (ρ = 0.90, p = 0.04). Our simulations provide a novel insight into the contributions of ASD to spatial and temporal heterogeneities of APDs, changes in excitation thresholds, and a potential explanation for wave fragmentation in the human atria due to sympathetic hyperactivity. Our work may aid in elucidating an electrophysiological link to arrhythmia initiation due to chronic stress among young patients.

Multi-Level Classification of Lung Pathologies in Neonates using Recurrence Features.

The use of Lung Ultrasound (LUS) as a tool to diagnose and monitor lung diseases in neonates has increased in urban hospitals. LUS's main advantages compared to chest CT or X-rays is that it is less expensive, more accessible, and does not expose the patient to radiation. Performing LUS on neonates and diagnosing the LUS images require highly trained medical professional and clinicians. While availability of such specialists in general is not an issue in urban areas, there is lack of such personnel in rural and remote communities. Hence, an automated computer-aided screening approach as a first level diagnosis assistance in such scenarios might be of significant value. Many of the image morphologies used by clinicians in diagnosing the LUS have strong recurrence characteristics. Building upon this knowledge, in this paper, we propose a feature extraction method designed to quantify such recurrent features for classification of LUS images into 6 common neonatal lung conditions. These conditions were normal lung, chronic lung disease (CLD), transient tachypnea of the newborn (TTN), pneumothorax (PTX), respiratory distress syndrome (RDS), and consolidation (CON) that could be pneumonia or atelectasis. The proposed method extracts virtual scanlines from the LUS images and converts them into signals. Then using recurrence quantification analysis (RQA), features were extracted and fed to pattern classifiers. Using a simple linear classifier the proposed features can achieve a classification accuracy of 69.3% without clinical features and 77.6% with clinical features. Clinical Relevance- Development of an automated computer-aided screening tool for first level diagnosis assistance in neonatal LUS pathologies. Such a tool will be of significant value in rural and remote medical communities.

An In-Silico model for evaluating the directional shock vectors in terminating and modulating rotors.

Out-of-hospital cardiac arrest (OHCA) accounts for a majority of mortality worldwide. Survivability from an OHCA highly depends on timely and effective defibrillation. Most of the OHCA cases are due to ventricular fibrillation (VF), a lethal form of cardiac arrhythmia. During VF, previous studies have shown the presence of spatiotemporally organized electrical activities called rotors and that terminating these rotor-like activities could modulate or terminate VF in an in-hospital or research setting. However, such an approach is not feasible for OHCA scenarios. In the case of an OHCA, external defibrillation remains the main therapeutic option despite the low survival rates. In this study, we evaluated whether defibrillation effectiveness in an OHCA scenario could be improved if a shock vector directly targets rotor-like, spatiotemporal electrical activities on the myocardium. Specifically, we hypothesized that the position of defibrillator pads with respect to a rotor's core axis and shock current density could influence the likelihood of rotor termination and thereby result in successful defibrillation. We created a bidomain cardiac model based on porcine heart data using Aliev-Panfilov bidomain equations. We simulated localized rotors, which we attempted to terminate using different defibrillation pad orientations relative to the rotor axis (i.e., perpendicular, parallel, and oblique). In addition, we gradually increased current densities for each defibrillation pad orientation from 4 to 12 A/m2. We repeated the above defibrillation procedure for rotors originating from four different locations on the ventricles. The shock parameters and the outcomes were analyzed using a Generalized Linear Mixed Model (GLMM) with Logistic Regression to link rotor termination with the defibrillation pad orientation and current density. Our results suggest the highest average likelihood of terminating rotors during VF is when defibrillator pads are placed perpendicular to the rotor axis (0.99 ± 0.03), with an average current density of 7.2 A/m2, compared to any other orientation (parallel: 0.76 ± 0.26 and oblique: 0.08 ± 0.12). Our simulations suggest that optimal defibrillator pad orientation, combined with sufficient current density magnitude, could improve the likelihood of rotor termination during VF and thereby improving defibrillation success in OHCA patients.

Audio texture analysis of COVID-19 cough, breath, and speech sounds.

The coronavirus disease (COVID-19) first appeared at the end of December 2019 and is still spreading in most countries. To diagnose COVID-19 using reverse transcription - Polymerase chain reaction (RT-PCR), one has to go to a dedicated center, which requires significant cost and human resources. Hence, there is a requirement for a remote monitoring tool that can perform the preliminary screening of COVID-19. In this paper, we propose that a detailed audio texture analysis of COVID-19 sounds may help in performing the initial screening of COVID-19. The texture analysis is done on three different signal modalities of COVID-19, i.e. cough, breath, and speech signals. In this work, we have used 1141 samples of cough signals, 392 samples of breath signals, and 893 samples of speech signals. To analyze the audio textural behavior of COVID-19 sounds, the local binary patterns LBP) and Haralick's features were extracted from the spectrogram of the signals. The textural analysis on cough and breath sounds was done on the following 5 classes for the first time: COVID-19 positive with cough, COVID-19 positive without cough, healthy person with cough, healthy person without cough, and an asthmatic cough. For speech sounds there were only two classes: COVID-19 positive, and COVID-19 negative. During experiments, 71.7% of the cough samples and 72.2% of breath samples were classified into 5 classes. Also, 79.7% of speech samples are classified into 2 classes. The highest accuracy rate of 98.9% was obtained when binary classification between COVID-19 cough and non-COVID-19 cough was done.

Effect of Shock Vector Orientation in Modulating and Terminating Rotors - a Simulation Study.

The main treatment option for Ventricular Fibrillation (VF), especially in out-of-hospital cardiac arrests (OHCA) is defibrillation. Typically, the survival-to-discharge rates are very poor for OHCA. Existing studies have shown that rotors may be the sources of arrhythmia and ablating them could modulate or terminate VF. However, tracking rotors and ablating them is not a feasible solution in a OHCA scenario. Hence, if the sources (or rotors) can be regionally localized non-invasively and this information can be used to direct the orientation of the shock vectors, it may aid the termination of rotors and defibrillation success. In this work, using computational modeling, we present our initial results on testing the effect of shock vector orientation on modulating (or) terminating rotors. A combination of Sovilj's and Aliev Panfilov's monodomain cardiac models were used in inducing rotors and testing the effect of shock vector magnitude and direction. Based on our simulation results on an average with four experimental trials, a shock vector directed in the perpendicular direction along the axis of the rotor terminated the rotor with 16% lesser magnitude than parallel direction and 38% lesser magnitude than in oblique direction.Clinical Relevance- A rotor localization dependent defibrillation strategy may aid the defibrillation protocol procedures to improve the survival rates. Based on the four experimental trials, the results indicate shock vectors oriented perpendicular to the axis of the rotors were efficient in modulating or terminating rotors with lower magnitude than other directions.

Empirical Mode Decomposition Based Hyperspectral Data Analysis for Brain Tumor Classification.

The capability of Hyperspectral Imaging (HSI) in rapidly acquiring abundant reflectance data in a non-invasive manner, makes it an ideal tool for obtaining diagnostic information about tissue pathology. Identifying wavelengths that provide the most discriminatory clues for specific pathologies will greatly assist in understanding their underlying biochemical characteristics. In this paper, we propose an efficient and computationally inexpensive method for determining the most relevant spectral bands for brain tumor classification. Empirical mode decomposition was used in combination with extrema analysis to extract the relevant bands based on the morphological characteristics of the spectra. The results of our experiments indicate that the proposed method outperforms the benchmark in reducing computational complexity while performing comparably with a 7-times reduction in the feature-set for classification on the test data.

Screening and analysis of specific language impairment in young children by analyzing the textures of speech signal.

A child having a delayed development in language skills without any reason is known to be suffering from specific language impairment (SLI). Unfortunately, almost 7% kindergarten children are reported with SLI in their childhood. The SLI could be treated if identified at an early stage, but diagnosing SLI at early stage is challenging. In this article, we propose a machine learning based system to screen the SLI speech by analyzing the texture of the speech utterances. The texture of speech signals is extracted from the popular time-frequency representation called spectrograms. These spectrogram acts like a texture image and the textural features to capture the change in audio quality such as Haralick's feature and local binary patterns (LBPs) are extracted from these textural images. The experiments are performed on 4214 utterances taken from 44 healthy and 54 SLI speakers. Experimental results with 10-fold cross validation, indicates that a very good accuracy up to 97.41% is obtained when only 14 dimensional Haralick's feature is used. The accuracy is slightly boosted up to 99% when the 59-dimensional LBPs are amalgamated with Haralick's features. The sensitivity and specificity of the whole system is up to 98.96% and 99.20% respectively. The proposed method is gender and speaker independent and invariant to examination conditions.

Analysis of electrooculography signals for the detection of Myasthenia Gravis.

OBJECTIVE: A precursor to more severe forms of Myasthenia Gravis (MG) is ocular MG (OMG) in which the MG symptoms are localized to the eyes. Current MG diagnostic methods are often invasive, painful, and not always specific. The objective of the proposed work was to extract quantifiable features from electrooculography (EOG) signals recorded around the eyes and develop an alternative non-invasive screening method for detecting MG. METHODS: EOG signals acquired from MG and Control subjects were analyzed for eye movement characteristics and quantified using time and wavelet domain signal processing techniques. The ability of the proposed approaches to classify MG vs. control subjects was evaluated using a linear discriminant analysis (LDA) based classifier. RESULTS: The range of overall classification accuracies achieved by the proposed time and wavelet domain approaches for different groupings were between 82.1-83.3% (Rise Rate feature: P < 0.01, AUC ≥ 0.87) and 82.1-87.2% (Mean Scale Band Energy feature: P < 0.01, AUC ≥ 0.89), respectively. CONCLUSION: Our results demonstrate that an EOG-based signal analysis is a potentially viable non-invasive alternative for MG screening. SIGNIFICANCE: The proposed approach could lead to early detection of MG and thereby improve clinical outcomes in this population.

The effect of left ventricular pacing on transmural activation delay in myopathic human hearts.

Aims: Left ventricular (LV) epicardial pacing (LVEpiP) in human myopathic hearts does not decrease global epicardial activation delay compared with right ventricular (RV) endocardial pacing (RVEndoP); however, the effect on transmural activation delay has not been evaluated. To characterize the transmural electrical activation delay in human myopathic hearts during RVEndoP and LVEpiP compared with global epicardial activation delay. Methods and results: Explanted hearts from seven patients (5 male, 46 ± 10 years) undergoing cardiac transplantation were Langendorff-perfused and mapped using an epicardial sock electrode array (112 electrodes) and 25 transmural plunge needles (four electrodes, 2 mm spacing), for a total of 100 unipolar transmural electrodes. Electrograms were recorded during LVEpiP and RVEndoP, and epicardial (sock) and transmural (needle) activation times, along with patterns of activation, were compared. There was no difference between the global epicardial activation times (LVEpiP 147 ± 8 ms vs. RVEndoP 156 ± 17 ms, P = 0.46). The mean LV transmural activation time during LVEpiP was significantly shorter than that during RVEndoP (125 ± 44 vs. 172 ± 43 ms, P < 0.001). During LVEpiP, of the transmural layers endo-, mid-myocardium and epicardium, LV endocardial layer was often the earliest compared with other transmural layers. Conclusion: In myopathic human hearts, LVEpiP did not decrease global epicardial activation delays compared with RVEndoP. LV epicardial pacing led to early activation of the LV endocardium, revealing the importance of the LV endocardium even when pacing from the LV epicardium.

Effect of spatial resolution and filtering on mapping cardiac fibrillation.

BACKGROUND: Endocardial mapping tools use variable interelectrode resolution, whereas body surface mapping tools use narrow bandpass filtering (BPF) to map fibrillatory mechanisms established by high-resolution optical imaging. OBJECTIVE: The purpose of this study was to study the effect of resolution and BPF on the underlying mechanism being mapped. METHODS: Hearts from 14 healthy New Zealand white rabbits were Langendorff perfused. We studied the effect of spatial resolution and BPF on the location and characterization of rotors by comparing phase singularities detected by high-resolution unfiltered optical maps and of fibrillating myocardium with decimated and filtered maps with simulated electrode spacing of 2, 5, and 8 mm. RESULTS: As we decimated the maps with 2-mm, 5-mm, and 8-mm interelectrode spacing, the mean ( ± SD) number of rotors detected decreased from 10.2 ± 9.6, 1.6 ± 3.2, and 0.2 ± 0.5, respectively. Lowering the resolution led to synthesized pseudo-rotors that may be inappropriately identified. Applying a BPF led to fewer mean phase singularities detected (248 ± 207 vs 333 ± 130; P<.01), giving the appearance of pseudo-spatial stability measured as translation index (with BPF 3.6 ± 0.4 mm vs 4.0 ± 0.5 mm without BPF; P<.01) and pseudo-temporal stability with longer duration (70.0 ± 17.6 ms in BPF maps vs 44.1 ± 6.6 ms in unfiltered maps; P<.001) than true underlying fibrillating myocardium mapped. CONCLUSION: Electrode resolution and BPF of electrograms can result in distortion of the underlying electrophysiology of fibrillation. Newer mapping techniques need to demonstrate sensitivity analysis to quantify the degree of distortion before clinical use to avoid inaccurate electrophysiologic interpretation.

Electrophysiological Cardiac Modeling: A Review.

Cardiac electrophysiological modeling in conjunction with experimental and clinical findings has contributed to better understanding of electrophysiological phenomena in various species. As our knowledge on underlying electrical, mechanical, and chemical processes has improved over time, mathematical models of the cardiac electrophysiology have become more realistic and detailed. These models have provided a testbed for various hypotheses and conditions that may not be easy to implement experimentally. In addition to the limitations in experimentally validating various scenarios implemented by the models, one of the major obstacles for these models is computational complexity. However, the ever-increasing computational power of supercomputers facilitates the clinical application of cardiac electrophysiological models. The potential clinical applications include testing and predicting effects of pharmaceutical agents and performing patient-specific ablation and defibrillation. A review of studies involving these models and their major findings are provided.

Feature-based MRI data fusion for cardiac arrhythmia studies.

Current practices in studying cardiac arrhythmias primarily use electrical or optical surface recordings of a heart, spatially limited transmural recordings, and mathematical models. However, given that such arrhythmias occur on a 3D myocardial tissue, information obtained from such practices lack in dimension, completeness, and are sometimes prone to oversimplification. The combination of complementary Magnetic-Resonance Imaging (MRI)-based techniques such as Current Density Imaging (CDI) and Diffusion Tensor Imaging (DTI) could provide more depth to current practices in assessing the cardiac arrhythmia dynamics in entire cross sections of myocardium. In this work, we present an approach utilizing feature-based data fusion methods to demonstrate that complimentary information obtained from electrical current distribution and structural properties within a heart could be quantified and enhanced. Twelve (12) pairs of CDI and DTI image data sets were gathered from porcine hearts perfused through a Langendorff setup. Images were fused together using feature-based data fusion techniques such as Joint Independent Component Analysis (jICA), Canonical Correlation Analysis (CCA), and their combination (CCA+jICA). The results suggest that the complimentary information of cardiac states from CDI and DTI are enhanced and are better classified with the use of data fusion methods. For each data set, an increase in mean correlations of fused images were observed with 38% increase from CCA+jICA compared to the original images while mean mutual information of the fused images from jICA and CCA+jICA increased by approximately three-fold. We conclude that MRI-based techniques present potential viable tools in furthering studies for cardiac arrhythmias especially Ventricular Fibrillation.

Noise distribution and denoising of current density images.

Current density imaging (CDI) is a magnetic resonance (MR) imaging technique that could be used to study current pathways inside the tissue. The current distribution is measured indirectly as phase changes. The inherent noise in the MR imaging technique degrades the accuracy of phase measurements leading to imprecise current variations. The outcome can be affected significantly, especially at a low signal-to-noise ratio (SNR). We have shown the residual noise distribution of the phase to be Gaussian-like and the noise in CDI images approximated as a Gaussian. This finding matches experimental results. We further investigated this finding by performing comparative analysis with denoising techniques, using two CDI datasets with two different currents (20 and 45 mA). We found that the block-matching and three-dimensional (BM3D) technique outperforms other techniques when applied on current density ([Formula: see text]). The minimum gain in noise power by BM3D applied to [Formula: see text] compared with the next best technique in the analysis was found to be around 2 dB per pixel. We characterize the noise profile in CDI images and provide insights on the performance of different denoising techniques when applied at two different stages of current density reconstruction.

Tracking rotors with minimal electrodes: modulation index-based strategy.

BACKGROUND: High-frequency periodic sources during cardiac fibrillation can be detected by phase mapping techniques. To enable practical therapeutic options for modulating periodic sources (existing techniques require high density multielectrode arrays and real time simultaneous mapping capability), a method to identify electrogram morphologies colocalizing to rotors that can be implemented on few electrograms needs to be devised. METHOD AND RESULTS: Multichannel ventricular fibrillation electrogram data from 7 isolated human hearts using Langendorff setup and intraoperative clinical data from 2 human hearts were included in the analysis. The spatial locations of rotors were identified using phase maps constructed from 112 electrograms. Electrograms were analyzed for repeating patterns and discriminating signal morphologies around the locations of rotors and nonrotors were identified and quantified. Features were extracted from the unipolar electrogram patterns, which corroborated well with the spatial location of rotors. The results suggest that using the proposed modulation index feature, and as low as 1 sample point in the vicinity of the rotors, an accuracy as high as 86% (P<0.001) was obtained in separating rotor locations versus nonrotor locations. The analysis of bipolar electrogram signatures in the vicinity of the rotor locations suggest that 62.5% of the rotors occur at locations where the bipolar electrogram demonstrates continuous activities during ventricular fibrillation. CONCLUSIONS: Unipolar electrogram extracted modulation index-based detection of rotors is feasible with few electrodes and has greater detection rate than bipolar approach. This strategy may be suitable for nonarray-based single mapping catheter enabled detection of rotors.

Pathological speech signal analysis using time-frequency approaches.

Acoustical measures of vocal function are important in the assessments of disordered voice, and for monitoring patients' progress over the course of voice therapy. In the last 2 decades, a variety of techniques for automatic pathological voice detection have been proposed, ranging from traditional temporal or spectral approaches to advanced time-frequency techniques. However, comparison of these methods is a difficult task because of the diversity of approaches. In this article, we explain a framework that holds the existing methods. In the light of this framework, the methodologic principles of disordered voice analysis schemes are compared and discussed. In addition, this article presents a comprehensive review to demonstrate the advantages of time-frequency approaches in analyzing and extracting pathological structures from speech signals. This information may have an important role in the development of new approaches to this problem.

Role of KATP channels in the maintenance of ventricular fibrillation in cardiomyopathic human hearts.

RATIONALE: Ventricular fibrillation (VF) leads to global ischemia. The modulation of ischemia-dependent pathways may alter the electrophysiological evolution of VF. OBJECTIVE: We addressed the hypotheses that there is regional disease-related expression of K(ATP) channels in human cardiomyopathic hearts and that K(ATP) channel blockade promotes spontaneous VF termination by attenuating spatiotemporal dispersion of refractoriness. METHODS AND RESULTS: In a human Langendorff model, electric mapping of 6 control and 9 treatment (10 µmol/L glibenclamide) isolated cardiomyopathic hearts was performed. Spontaneous defibrillation was studied and mean VF cycle length was compared regionally at VF onset and after 180 seconds between control and treatment groups. K(ATP) subunit gene expression was compared between LV endocardium versus epicardium in myopathic hearts. Spontaneous VF termination occurred in 1 of 6 control hearts and 7 of 8 glibenclamide-treated hearts (P=0.026). After 180 seconds of ischemia, a transmural dispersion in VF cycle length was observed between epicardium and endocardium (P=0.001), which was attenuated by glibenclamide. There was greater gene expression of all K(ATP) subunit on the endocardium compared with the epicardium (P<0.02). In an ischemic rat heart model, transmural dispersion of refractoriness (ΔERP(Transmural)=ERP(Epicardium)-ERP(Endocardium)) was verified with pacing protocols. ΔERP(Transmural) in control was 5 ± 2 ms and increased to 36 ± 5 ms with ischemia. This effect was greatly attenuated by glibenclamide (ΔERP(Transmural) for glibenclamide+ischemia=4.9 ± 4 ms, P=0.019 versus control ischemia). CONCLUSIONS: K(ATP) channel subunit gene expression is heterogeneously altered in the cardiomyopathic human heart. Blockade of K(ATP) channels promotes spontaneous defibrillation in cardiomyopathic human hearts by attenuating the ischemia-dependent spatiotemporal heterogeneity of refractoriness during early VF.

Intramural activation during early human ventricular fibrillation.

BACKGROUND: We investigated patterns of intramural activation in early human ventricular fibrillation (VF) and hypothesized that intramural reentry colocalizes to sites with increased intramural fibrosis. METHODS AND RESULTS: Thirteen human Langendorff hearts were used for this study. Twenty-five plunge needles (4 unipoles/needle) were used to map 100 intramural sites. For the global mapping component, 11 20-s episodes of early VF were studied in 6 hearts. Simultaneous activation of all 4 electrodes was the most common pattern observed in 48.7% of needles, followed by an endocardial-to-epicardial activation pattern (9.8% of needles) and epicardial-to-endocardial activation pattern (5.5% of needles); 19.3% of needles had nonuniform multidirectional patterns. In 2 orthogonal planes, 1 parallel and 1 perpendicular to the epicardium and endocardium, reentry was detected in 14.3% of beats at any 1 level, and 5.8% of these were transmural. Simultaneous mapping of the epicardium and endocardium in 5 hearts detected concurrently rotating rotors with similar chirality and cycle length, suggesting the presence of transmural scroll waves (n=6), which was confirmed by high-resolution fixed-space mapping in 2 of those hearts plus 1 additional heart. Transmural optical mapping in 1 additional heart confirmed simultaneous epicardial and endocardial activation. Histopathology revealed greater fibrosis at sites of reentry compared to areas without (53.3±11.9% versus 27.5±2.4%, P=0.02). CONCLUSIONS: Intramural activation patterns suggest that early human VF does not organize as multiple reentrant wavefronts but is best explained by transmural scroll wave activation. Intramural reentry localizes to regions of greater intramural fibrosis.

Real-time electrogram analysis for monitoring coronary blood flow during human ventricular fibrillation: implications for CPR.

BACKGROUND: Effective chest compressions during prolonged ventricular fibrillation (VF) have been shown to increase the chances of successful defibrillation to a rhythm associated with a sustainable cardiac output. There is currently no effective method of recording the degree of antegrade coronary artery flow during chest compression in VF. OBJECTIVE: This study sought to quantify the relationship between the antegrade coronary flow and the characteristics of human VF using near real-time wavelet-based electrocardiographic markers. METHODS: VF experiments were conducted in 8 isolated human hearts. The Langendorff perfusion enabled different flow rates (perfusion) during VF, which allowed for the simulation of chest compression with different efficacies. After the initiation of VF, the hearts were maintained in ischemia (no flow) for 3 minutes, followed by a 2-minute reperfusion and defibrillation. The experiments were repeated at flows of 0%, 30%, and 100% of baseline perfusion, and volume-conducted surface electrograms were recorded and analyzed using continuous wavelet transform in 5-second frames. RESULTS: Near real-time wavelet features were derived that demonstrated significant differences in the multicomponent nature of VF signals and predicted perfusion rate characteristics for different flow rates (i.e., 0%, 30%, and 100%; P < .0006). A pattern classifier was trained using the feature values from 5 hearts, and the flow rates for 3 additional hearts were predicted with an accuracy of 90%. CONCLUSION: VF electrogram characteristics as measured by wavelet analysis relate to antegrade coronary flow rate during VF. These findings suggest that chest compression efficacy of physiological importance could be monitored using near real-time wavelet analysis.