On-line two-dimensional evaluation of ultrasonic integrated backscatter

We describe an apparatus for the on-line evaluation of integrated backscatter from areas of tissue. The equipment is fully integrated into a B-mode ultrasonic system: there are therefore no new operating procedures to be learned. It provides a simultaneous display of conventional information, together with parameters of tissue characterization. The apparatus is fast and, over a broad diagnostic frequency range, may be used in conjunction with conventional equipment employing transducers.     

Ultrasonic Assessment of Myocardial Microstructure

Echocardiography is a widely accessible imaging modality that is commonly used to noninvasively characterize and quantify changes in cardiac structure and function. Ultrasonic assessments of cardiac tissue can include analyses of backscatter signal intensity within a given region of interest. Previously established techniques have relied predominantly on the integrated or mean value of backscatter signal intensities, which may be susceptible to variability from aliased data from low frame rates and time delays for algorithms based on cyclic variation. Herein, we describe an ultrasound-based imaging algorithm that extends from previous methods, can be applied to a single image frame and accounts for the full distribution of signal intensity values derived from a given myocardial sample. When applied to representative mouse and human imaging data, the algorithm distinguishes between subjects with and without exposure to chronic afterload resistance. The algorithm offers an enhanced surrogate measure of myocardial microstructure and can be performed using open-access image analysis software.     

High resolution electrocardiography in healthy dogs: time domain parameters and comparison of the non-stationary (Wigner distribution) versus standard stationary frequency domain analysis methods

Fractionated heart activation can be detected as late potentials from surface recordings of signal-averaged electrocardiograms (SA ECG) which are considered as a marker of sustained ventricular tachycardia. For animal studies, reference values in time and frequency domain analyses are essentially missing. In the present study, we have established reference values in SA ECG time domain analysis and time-frequency representation of heart activation in healthy dogs. A group of 25 healthy mongrel dogs (body weight 12-15 kg) was investigated. Wigner distribution and our modification of Fast Fourier transform (FFT), gliding window FFT, was applied in SA ECG frequency domain analysis. Reference values in time domain SA ECG were established. Time and voltage criteria were adapted to short duration of heart cycle and fast voltage decrement of the QRS complex in dogs. Wigner distribution and gliding window FFT were applied in order to describe mean heart activation in the frequency domain. Contribution of higher frequencies (30-80 Hz) was detected by both frequency analysis methods in the second third of ventricular activation in healthy animals. Presented results could offer a basis for further experimental arrhythmologic studies.     

Revisiting QRS Detection Methodologies for Portable,Wearable, Battery-Operated,and Wireless ECG Systems

Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.     

Chaotic based reconstructed phase space features for detecting ventricular fibrillation

Among the variety of cardiac arrhythmias, ventricular fibrillation (VF) and ventricular tachycardia (VT) are life-threatening; thus, accurate classification of these arrhythmias is a crucial task for cardiologists. Nevertheless, VT and VF signals are very similar in the time domain and accurate distinguishing these signals with naked eyes in some cases is impossible. In this paper, a novel self-similarity image-based scheme is introduced to classify the underlying information of VT, VF and normal electrocardiogram (ECG) signals. In this study, VT, VF and normal ECG signals are selected from CCU of the Royal Infirmary of Edinburgh and MIT-BIH datasets. According to the time delay method, signal samples can be assigned to state variables and a trajectory can be achieved. To extract the proposed self-similarity feature, first, two different trajectories from each signal trial are drawn according to two different delay time values. The two-dimensional state space of each trial trajectory is considered as an image. Therefore, two trajectory images are produced for each signal. Number of visited pixels in the first image is determined and is subtracted from that of the second image as the self-similarity feature of that signal. Moreover, another scheme is proposed to have a better estimation of self-similarity in which the logical AND operator is applied to both images (matrices) of each ECG trial. The third proposed criterion is similar to box counting method by this difference that each pixel is assigned a weight according to the trajectory density at that point and finally visited weighted pixels are counted. To classify VF from VT and normal ECG, a threshold is determined through the cross validation phase under the Receiver Operating Characteristic (ROC) criterion. To assess the proposed methods, the mentioned signals are classified using the-state-of-art chaotic features such as correlation dimension, the largest Lyapunov exponent and Approximate Entropy (ApEn). Experimental results indicate superiority of the proposed method in classifying the VT, VF and normal ECG signals compared to present traditional schemes. In addition, computational complexity of the introduced methods is very low and can be implemented in real-time applications.     

Identification of patients with atrial fibrillation using HRV parameters]

Atrial fibrillation is the most common sustained cardiac rhythm disturbance. One of the most drastic complications is embolism, particularly stroke. Patients with atrial fibrillation have to be identified. This can lead to early therapy and thus avoiding strokes. The algorithm presented here detects atrial fibrillation securely and reliably. It is based on a single-channel ECG, which takes 60 min. First, the R-peaks are detected from the ECG and the RR interval is calculated. To be independent from pulse variations, the RR interval is normalized to 60 bpm. A parameter of heart rate variability is calculated in time domain (SDSD) and the so-called Poincaré plot is generated. The image analysis of the figures of the Poincaré plot is made automatically. The results from analysis in time domain, as well as image analysis, yield a risk level, which indicates the probability for the occurrence of atrial fibrillation. Even if there is no atrial fibrillation in the ECG while analyzing, it is possible to identify patients with atrial fibrillation. The sensitivity depends on the burden of atrial fibrillation. Even if a burden of 0% is assumed, the results still prove satisfactory (sensitivity of nearly 83%).     

Exercise ECG changes in normal women

Submaximal and/or maximal exercise was carried out by 357 women without a history of cardiovascular disease, using a bicycle ergometer and/or treadmill while monitored by a bipolar ECG lead CM₅. In 40- to 60-year-old women the incidence of an ischemic ECG pattern during or after exercise ranged from 20 to 50%. Because clinical coronary disease can be expected in less than 10% of normal women followed for 16 years, most of these ECG changes were not considered to be due to occult coronary disease. At the present time exercise ECG changes in women cannot be used with any reliability as an aid in the diagnosis of chest pain or in screening normal female populations for coronary heart disease.     

3D virtual human atria: A computational platform for studying clinical atrial fibrillation

Despite a vast amount of experimental and clinical data on the underlying ionic, cellular and tissue substrates, the mechanisms of common atrial arrhythmias (such as atrial fibrillation, AF) arising from the functional interactions at the whole atria level remain unclear. Computational modelling provides a quantitative framework for integrating such multi-scale data and understanding the arrhythmogenic behaviour that emerges from the collective spatio-temporal dynamics in all parts of the heart. In this study, we have developed a multi-scale hierarchy of biophysically detailed computational models for the human atria - the 3D virtual human atria. Primarily, diffusion tensor MRI reconstruction of the tissue geometry and fibre orientation in the human sinoatrial node (SAN) and surrounding atrial muscle was integrated into the 3D model of the whole atria dissected from the Visible Human dataset. The anatomical models were combined with the heterogeneous atrial action potential (AP) models, and used to simulate the AP conduction in the human atria under various conditions: SAN pacemaking and atrial activation in the normal rhythm, break-down of regular AP wave-fronts during rapid atrial pacing, and the genesis of multiple re-entrant wavelets characteristic of AF. Contributions of different properties of the tissue to mechanisms of the normal rhythm and arrhythmogenesis were investigated. Primarily, the simulations showed that tissue heterogeneity caused the break-down of the normal AP wave-fronts at rapid pacing rates, which initiated a pair of re-entrant spiral waves; and tissue anisotropy resulted in a further break-down of the spiral waves into multiple meandering wavelets characteristic of AF. The 3D virtual atria model itself was incorporated into the torso model to simulate the body surface ECG patterns in the normal and arrhythmic conditions. Therefore, a state-of-the-art computational platform has been developed, which can be used for studying multi-scale electrical phenomena during atrial conduction and AF arrhythmogenesis. Results of such simulations can be directly compared with electrophysiological and endocardial mapping data, as well as clinical ECG recordings. The virtual human atria can provide in-depth insights into 3D excitation propagation processes within atrial walls of a whole heart in vivo, which is beyond the current technical capabilities of experimental or clinical set-ups.     

Mapping of phase response properties of monopolar ECG voltages

In the present investigation an attempt has been made to study the phase response properties of monopolar chest lead ECG voltages. Using a generator model of the heart an equivalent circuit of ECG network has been developed. The equivalent impedance between WCT and probe electrode has been determined by reduction techniques. From this equivalent impedance the phasor characteristics of monopolar ECG voltages have been analysed for change in probe electrode locations. The source of the generated voltage, i.e., the heart, will develop a different voltage for its different condition. There will also be a change in impedances. Thus for the normal subject the distribution of the phasor of the ECG voltages will be different from that of the abnormal one. A software tool has been developed to evaluate the relative phase response of ECG voltages. The data acquisition of monopolar ECG records of chest leads V1 to V6 from chart recorder has been done with the help of AutoCAD application package. The harmonic constituents of ECG voltages have been evaluated at each harmonic plane and the phase characteristics have been studied in polar coordinate for normal subjects as well as for a typical case. An interesting result has been observed in typical cases which are indicated in the paper.     

Automatic classification of electrocardiogram signals based on transfer learning and continuous wavelet transform

Classification and subsequent diagnosis of cardiac arrhythmias is an important research topic in clinical practice. Confirmation of the type of arrhythmia at an early stage is critical for reducing the risk and occurrence of cardiovascular events. Nevertheless, diagnoses must be confirmed by a combination of specialist experience and electrocardiogram (ECG) examination, which can lead to delays in diagnosis. To overcome such obstacles, this study proposes an automatic ECG classification algorithm based on transfer learning and continuous wavelet transform (CWT). The transfer learning method is able to transfer the domain knowledge and features of images to a EGG, which is a one-dimensional signal when a convolutional neural network (CNN) is used for classification. Meanwhile, CWT is used to convert a one-dimensional ECG signal into a two-dimensional signal map consisting of time-frequency components. Considering that morphological features can be helpful in arrhythmia classification, eight features related to the R peak of an ECG signal are proposed. These auxiliary features are integrated with the features extracted by the CNN and then fed into the fully linked arrhythmia classification layer. The CNN developed in this study can also be used for bird activity detection. The classification experiments were performed after converting the two types of audio files containing songbird sounds and those without songbird sounds from the NIPS4Bplus bird song dataset into the Mel spectrum. Compared to the most recent methods in the same field, the classification results improved accuracy and recognition by 11.67% and 11.57%, respectively.     

A Comparison of ECG Signal Pre-processing Using FrFT,FrWT and IPCA for Improved Analysis

ObjectiveElectrocardiogram (ECG) is a diagnostic tool for recording electrical activities of the human heart non-invasively. It is detected by electrodes placed on the surface of the skin in a conductive medium. In medical applications, ECG is used by cardiologists to observe heart anomalies (cardiovascular diseases) such as abnormal heart rhythms, heart attacks, effects of drug dosage on subject's heart and knowledge of previous heart attacks. Recorded ECG signal is generally corrupted by various types of noise/distortion such as cardiac (isoelectric interval, prolonged depolarization and atrial flutter) or extra cardiac (respiration, changes in electrode position, muscle contraction and power line noise). These factors hide the useful information and alter the signal characteristic due to low Signal-to-Noise Ratio (SNR). In such situations, any failure to judge the ECG signal correctly may result in a delay in the treatment and harm a subject (patient) health. Therefore, appropriate pre-processing technique is necessary to improve SNR to facilitate better treatment to the subject. Effects of different pre-processing techniques on ECG signal analysis (based on R-peaks detection) are compared using various Figures of Merit (FoM) such as sensitivity (Se), accuracy (Acc) and detection error rate (DER) along with SNR.MethodsIn this research article, a new fractional wavelet transform (FrWT) has been proposed as a pre-processing technique in order to overcome the disadvantages of other existing commonly used techniques viz. wavelet transform (WT) and the fractional Fourier transform (FrFT). The proposed FrWT technique possesses the properties of multiresolution analysis and represents signal in the fractional domain which consists of representation in terms of rotation of signals in the time–frequency plane. In the literature, ECG signal analysis has been improvised using statistical pre-processing techniques such as principal component analysis (PCA), and independent component analysis (ICA). However, both PCA and ICA are prone to suffer from slight alterations in either signal or noise, unless the basis functions are prepared with a worldwide set of ECG. Independent Principal Component Analysis (IPCA) has been used to overcome this shortcoming of PCA and ICA. Therefore, in this paper three techniques viz. FrFT, FrWT and IPCA are selected for comparison in pre-processing of ECG signals.ResultsThe selected methods have been evaluated on the basis of SNR, Se, Acc and DER of the detected ECG beats. FrWT yields the best results among all the methods considered in this paper; 34.37dB output SNR, 99.98% Se, 99.96% Acc, and 0.036% DER. These results indicate the quality of biology-related information retained from the pre-processed ECG signals for identifying different heart abnormalities.ConclusionCorrect analysis of the acquired ECG signal is the main challenge for cardiologist due to involvement of various types of noises (high and low frequency). Twenty two real time ECG records have been evaluated based on various FoM such as SNR, Se, Acc and DER for the proposed FrWT and existing FrFT and IPCA preprocessing techniques. Acquired real-time ECG database in normal and disease situations is used for the purpose. The values of FoMs indicate high SNR and better detection of R-peaks in a ECG signal which is important for the diagnosis of cardiovascular disease. The proposed FrWT outperforms all other techniques and holds both analytical attributes of the actual ECG signal and alterations in the amplitudes of various ECG waveforms adequately. It also provides signal portrayals in the time-fractional-frequency plane with low computational complexity enabling their use practically for versatile applications.     

Distribution of soluble epoxide hydrolase, cytochrome P450 2C8, 2C9 and 2J2 in human malignant neoplasms

Summary Soluble epoxide hydrolase (sEH) is a bifunctional enzyme with a C-terminal epoxide hydrolase activity and an N-terminal phosphatase activity. Arachidonic acid epoxides, previously suggested to be involved in apoptosis, oncogenesis and cell proliferation, are generated by cytochrome P450 epoxygenases and are good substrates of the sEH C-terminal domain. In addition, the N-terminal phosphatase domain hydrolyzes isoprenoid mono- and pyrophosphates, which are involved in cell signaling and apoptosis. Here we provide a comprehensive analysis of the distribution of sEH, CYP2C8, 2C9 and 2J2 in human neoplastic tissues using tissue micro-arrays. The human neoplastic tissue micro-arrays provide a well-controlled side by side analysis of a wide array of neoplastic tissues and their surrounding normal tissue controls. Many of the neoplastic tissues showed altered expression of these enzymes as compared to normal tissues. Altered expression was not limited to the neoplastic tissues but also found in the surrounding non-neoplastic tissues. For example, sEH expression in renal and hepatic malignant neoplasms and surrounding non-neoplastic tissues was found to be significantly decreased, whereas expression was found to be increased in seminoma as compared to normal tissues. Our study warrants further investigation of the role of altered expression of these enzymes in neoplastic tissues.     

Qualitative and quantitative description of myocardial ischemia by means of magnetocardiography.

In the framework of this study quantitative parameters are presented which, derived from magnetocardiographic maps, aid in making a conclusion about ischemia in the myocardium. The analysis is based on the examination of 86 patients with unstable angina, of which 53 exhibited myocardial ischemia with high probability (Group I: angiographically proven stenosis of at least 50% in a coronary artery of first or second order and positive troponin), while in the 33 other patients myocardial ischemia could be ruled out with high probability (Group II: angiographically clean coronary bed and normal troponin values). The negative predictive value (the probability that there is no myocardial ischemia when the magnetocardiogram (MCG) is negative) is 96.2%; the positive predictive value (the probability that there is actually coronary heart disease when the magnetocardiogram is positive) is 91.2%. A 12-lead ECG taken at the same time as the MCG achieved a positive predictive value of 92.8%, but a negative predictive value of 53.4%. Consequently, the boundary values of the parameters selected lead to a markedly distinct separation between patients with myocardial ischemia from those without. For ruling out coronary heart disease in patients with unstable angina the MCG is superior to 12-lead ECG.     

Prediction of signal recognition particle RNA genes

We describe a method for prediction of genes that encode the RNA component of the signal recognition particle (SRP). A heuristic search for the strongly conserved helix 8 motif of SRP RNA is combined with covariance models that are based on previously known SRP RNA sequences. By screening available genomic sequences we have identified a large number of novel SRP RNA genes and we can account for at least one gene in every genome that has been completely sequenced. Novel bacterial RNAs include that of Thermotoga maritima, which, unlike all other non-gram-positive eubacteria, is predicted to have an Alu domain. We have also found the RNAs of Lactococcus lactis and Staphylococcus to have an unusual UGAC tetraloop in helix 8 instead of the normal GNRA sequence. An investigation of yeast RNAs reveals conserved sequence elements of the Alu domain that aid in the analysis of these RNAs. Analysis of the human genome reveals only two likely genes, both on chromosome 14. Our method for SRP RNA gene prediction is the first convenient tool for this task and should be useful in genome annotation.     

Pulse Examination by an Ultrasonic Method

One hundred patients with occlusive arterial disease affecting the lower extremities and 25 normal adults were examined by ultrasonic flow velocity detector. This new and harmless method is simple and has proved to be a very useful aid in both the diagnosis and the management of arterial disease.     

A Powerful Algorithm to aid Cardiac Arrhythmia Diagnosis

Abstract

The Electrocardiogram (ECG), represents the electrical activity of the heart. It is characterised by a number of waves P, QRS, T which are correlated to the status of the heart activity. In this paper, the aim is to present a powerful algorithm to aid cardiac diagnosis. The approach used is based on a determinist method, that of the tree decision. However, the different waves of the ECG signal need to be identified and then measured following a signal to noise enhancement. Signal to noise enhancement is performed by a combiner linear adaptive filter whereas P, QRS, T wave identification and measurement are performed by a derivative approach. Results obtained on simulated and real ECG signals are shown to be highly, satisfactory in the aid of cardiac arrhythmia diagnosis, such as junctionnal escapes, blocks, etc.     

A Powerful Algorithm to aid Cardiac Arrhythmia Diagnosis

The Electrocardiogram (ECG), represents the electrical activity of the heart. It is characterised by a number of waves P, QRS, T which are correlated to the status of the heart activity. In this paper, the aim is to present a powerful algorithm to aid cardiac diagnosis. The approach used is based on a determinist method, that of the tree decision. However, the different waves of the ECG signal need to be identified and then measured following a signal to noise enhancement. Signal to noise enhancement is performed by a combiner linear adaptive filter whereas P, QRS, T wave identification and measurement are performed by a derivative approach. Results obtained on simulated and real ECG signals are shown to be highly, satisfactory in the aid of cardiac arrhythmia diagnosis, such as junctionnal escapes, blocks, etc.     

Quantitative analysis of variability of electrocardiograms typical for polymorphic arrhythmias

A new approach to the analysis of variability of electrocardiograms (ECGs) typical of polymorphic arrhythmias is developed. In these ECGs, separate QRS complexes can be often hardly identified. As a result, the mathematical methods that have been elaborated hitherto are not suitable for such arrhythmias. The approach presented here is based on the quantitative estimation of the variability of neighboring parts of the ECG. In this case, the necessity of the identification of separate QRS complexes ceases to be significant. Based on this approach, the analysis of normalized ECG variability is developed in the framework of which two indices that characterize the oscillation variability and its changes in time are related to a part of the ECG and/or the ECG as a whole. Variations of these indices allow both the polymorphism of a separate ECG to be estimated and different ECGs to be compared with each other. The method presented may be useful in studies of the mechanisms and in the diagnosis of polymorphic arrhythmias.     

Increase in Ultrasonic Intensity of Blood Speckle across Moderate Coronary Artery Stenosis Is an Independent Predictor of Functional Coronary Artery Stenosis Measured by Fractional Flow Reserve: Pilot Study

Background and Aims

The degree of coronary artery stenosis should be assessed both anatomically and functionally. We observed that the intensity of blood speckle (IBS) on intravascular ultrasound (IVUS) is low proximal to a coronary artery stenosis, and high distal to the stenosis. We defined step-up IBS as the distal minus the proximal IBS, and speculated that this new parameter could be used for the functional evaluation of stenosis on IVUS. The aims of this study were to assess the relationships between step-up IBS and factors that affect coronary blood flow, and between step-up IBS and fractional flow reserve (FFR).

Methods and Results

This study enrolled 36 consecutive patients with angina who had a single moderate stenosis in the left anterior descending artery. All patients were evaluated by integrated backscatter IVUS and intracoronary pressure measurements. FFR was calculated from measurements using a coronary pressure wire during hyperemia. Conventional gray-scale IVUS images were recorded, and integrated backscatter was measured in three cross-sectional slices proximal and distal to the stenosis. Step-up IBS was calculated as (mean distal integrated backscatter value) − (mean proximal integrated backscatter value). Stepwise multiple linear regression analysis showed that the heart rate (r = 0.45, P = 0.005), ejection fraction (r = −0.39, P = 0.01), and hemoglobin level (r = −0.32, P = 0.04) were independently correlated with step-up IBS, whereas proximal and distal IBS were not associated with these factors. There was a strong inverse correlation between step-up IBS and FFR (r = −0.84, P < 0.001), which remained significant on stepwise multiple linear regression analysis.

Conclusions

The newly defined parameter of step-up IBS is potentially useful for the functional assessment of coronary artery stenosis.