Diagnosis Of Various Cardiovascular Diseases Research Articles

Laser flowmetry of microcirculation of the finger depending on the external temperature and the limb position

Introduction. The use of laser Doppler flowmetry (LDF) makes it possible to assess the condition and disorders of blood microcirculation, improving the quality of diagnosis of various cardiovascular diseases. Additional functional tests with laser flowmetric measurements are used to assess the state of the mechanisms that regulate the tissue blood flow. Objective. To identify patterns of blood flow changes in the microcirculatory bed when combining a postural test and a change in air temperature. Materials and methods. The study was conducted on a group of 20 subjects, which included an equal number of girls and boys aged 21–23 years, who were considered healthy and did not have cardiovascular diseases. The experiment was carried out in two stages: first in a room with an air temperature of 201 °C, then at 301 °C. To assess the difference in data between arm positions within the same study phase at the same temperature, the Student’s t-test was used to compare quantitative variables in two dependent samples. The Wilcoxon test was used to assess statistical differences in data for the same hand position but at different air temperatures. Changes were considered statistically significant at p<0.05. The subjects were measured the microcirculation of peripheral blood vessels on the phalanx of the finger in three positions relative to the level of the heart using a portable laser Doppler flowmeter LAZMA PF (Russia). Results. It is shown that an increase in ambient temperature leads to an increase in the difference in microcirculation of peripheral vessels when the arm position changes relative to the level of the heart. When a person is in a room with a temperature of 301 °C, dilation of blood vessels and changes in their tone occur, which explains the increase in microcirculation values in relative perfusion units. Conclusions. The results of the study showed an increase in statistically significant differences according to the Student’s t-test between the different hand positions with increasing room temperature (p<0.0001). In a comparative sample between the first stage (at an air temperature of 20±1 °C) and the second stage of the study (at an air temperature of 30±1 °C), statistically significant results (p<0.001) were obtained according to the Wilcoxon test for the hand position at the heart level and the raised arm.

Continuous cuffless blood pressure monitoring using photoplethysmography-based PPG2BP-net for high intrasubject blood pressure variations

Continuous, comfortable, convenient (C3), and accurate blood pressure (BP) measurement and monitoring are needed for early diagnosis of various cardiovascular diseases. To supplement the limited C3 BP measurement of existing cuff-based BP technologies, though they may achieve reliable accuracy, cuffless BP measurement technologies, such as pulse transit/arrival time, pulse wave analysis, and image processing, have been studied to obtain C3 BP measurement. One of the recent cuffless BP measurement technologies, innovative machine-learning and artificial intelligence-based technologies that can estimate BP by extracting BP-related features from photoplethysmography (PPG)-based waveforms have attracted interdisciplinary attention of the medical and computer scientists owing to their handiness and effectiveness for both C3 and accurate, i.e., C3A, BP measurement. However, C3A BP measurement remains still unattainable because the accuracy of the existing PPG-based BP methods was not sufficiently justified for subject-independent and highly varying BP, which is a typical case in practice. To circumvent this issue, a novel convolutional neural network(CNN)- and calibration-based model (PPG2BP-Net) was designed by using a comparative paired one-dimensional CNN structure to estimate highly varying intrasubject BP. To this end, approximately 70%, 20%, and 10% of 4185 cleaned, independent subjects from 25,779 surgical cases were used for training, validating, and testing the proposed PPG2BP-Net, respectively and exclusively (i.e., subject-independent modelling). For quantifying the intrasubject BP variation from an initial calibration BP, a novel ‘standard deviation of subject-calibration centring (SDS)’ metric is proposed wherein high SDS represents high intrasubject BP variation from the calibration BP and vice versa. PPG2BP-Net achieved accurately estimated systolic and diastolic BP values despite high intrasubject variability. In 629-subject data acquired after 20 minutes following the A-line (arterial line) insertion, low error mean and standard deviation of 0.209pm 7.509 and 0.150pm 4.549;textrm{mmHg} for highly varying A-line systolic and diastolic BP values, respectively, where their SDSs are 15.375 and 8.745. This study moves one step forward in developing the C3A cuffless BP estimation devices that enable the push and agile pull services.

Toward Automated Right Ventricle Segmentation via Edge Feature-Induced Self-Attention Multiscale Feature Aggregation Full Convolution Network

In the field of cardiac magnetic resonance (MR) image analysis, the accurate segmentation of right ventricle (RV) regions plays an important role in the quantitative examination and medical diagnosis of various cardiovascular diseases. However, the automated RV segmentation in cardiac MR images is still challenging, due to its obscure and ill-defined boundaries, variably crescent-shaped structures, and extremely-unbalanced area ratio between the RV region and the background. In this work, an edge feature-induced self-attention multi-scale feature aggregation full convolutional neural network, called EFiSaMsFAFUnet, is proposed, to address the RV segmentation tasks. Specifically, EFiSaMsFAFUnet introduces an edge feature extraction module (EFEM) to mine the crescent-shaped boundary features of the RV area, and a kind of self-attention multi-scale feature expansion block (SaMsFEB) is proposed to mine the internal structure characteristics of the crescent-shaped RV internal regions. In addition, a kind of composite loss function is used to address the problem of model degradation caused by the proportion imbalance between RV region and background. The proposed EFiSaMsFAFUnet was evaluated on the MICCAI2017 Automatic Cardiac Diagnosis Challenge (ACDC) dataset. Extensive confirmatory and comparative experiments show that the EFiSaMsFAFUnet can achieve better segmentation results than the representatively state-of-the-art methods, and it can achieve comparable or the closest segmentation results to the manual segmentation of clinical specialist.

State-of-the-art myocardial strain by CMR feature tracking: clinical applications and future perspectives.

Based on conventional cine sequences of cardiac magnetic resonance (CMR), feature tracking (FT) is an emerging tissue tracking technique that evaluates myocardial motion and deformation quantitatively by strain, strain rate, torsion, and dyssynchrony. It has been widely accepted in modern literature that strain analysis can offer incremental information in addition to classic global and segmental functional analysis. Furthermore, CMR-FT facilitates measurement of all cardiac chambers, including the relatively thin-walled atria and the right ventricle, which has been a difficult measurement to obtain with the reference standard technique of myocardial tagging. CMR-FT objectively quantifies cardiovascular impairment and characterizes myocardial function in a novel way through direct assessment of myocardial fiber deformation. The purpose of this review is to discuss the current status of clinical applications of myocardial strain by CMR-FT in a variety of cardiovascular diseases. KEY POINTS: • CMR-FT is of great value for differential diagnosis and provides incremental value for evaluating the progression and severity of diseases. • CMR-FT guides the early diagnosis of various cardiovascular diseases and provides the possibility for the early detection of myocardial impairment and additional information regarding subclinical cardiac abnormalities. • Direct assessment of myocardial fiber deformation using CMR-FT has the potential to provide prognostic information incremental to common clinical and CMR risk factors.

Research on cholesterol solution sensor based on tilted fiber Bragg grating

Detecting cholesterol content is an important auxiliary method for clinical diagnosis of various cardiovascular diseases. In this work, we experimentally demonstrate a high-sensitivity tilted-fiber-Bragg-grating (TFBG) sensor for detecting cholesterol solution with a targeted concentration range of 0.2–4.0 mg/mL. The TFBG embedded in fiber core enables a strong coupling between multiple cladding modes and core mode, generating plenty of resonance peaks in the transmission spectrum for sensing the index change of external environment. The results show that the resonance peak exhibits a good linear relationship (~ 0.12 nm/(mg/mL)) with the concentration of cholesterol solution, and the resonance wavelength is red-shifted with the increase of the concentration. Through temperature-sensing experiments, it is proved that the thermal sensitivity of the core mode and the cladding mode is roughly similar with a value of ~ 8.0 pm/℃. Accordingly, the temperature-induced interference in the concentration-sensing process can also be eliminated in practical application. This study provides a new simple method for the high-efficiency measurement of cholesterol solution and has great potential in the detection of fat soluble solution.

Application of Deep Neural Network for Congestive Heart Failure Detection Using ECG Signals

Congestive heart failure (CHF) is a common serious heart disease that requires a number of clinical examinations to diagnose, which are costly and time-consuming. Electrocardiogram (ECG) is widely used in the diagnosis of various cardiovascular diseases due to its advantages of non-invasive, convenient and cheap, so the automatic CHF detection algorithm based on ECG signals can overcome the above shortcomings and has great application prospects. In this paper, inspired by the idea of DenseNet in computer version, we refined it to be applicable to CHF detection task, thus improving the diagnosis accuracy of the model. Secondly, to improve the robustness of the algorithm, we built a CHF database on PhysioBank, which contained more diverse data compared with similar studies, and conducted experiments on the built database. Finally, we presented an evaluation method based on the “inter-patient” pattern to evaluate the performance of the method more objectively. The results show that our algorithm can efficiently detect CHF with accuracy, sensitivity and specificity up to 94.97%, 89.38% and 99.50%, respectively. The algorithm proposed in this study can provide reliable references for doctors, and can be used in portable devices to realize real-time monitoring for patients.

Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks.

Cardiac magnetic resonance (CMR) imaging is used widely for morphological assessment and diagnosis of various cardiovascular diseases. Deep learning approaches based on 3D fully convolutional networks (FCNs), have improved state-of-the-art segmentation performance in CMR images. However, previous methods have employed several pre-processing steps and have focused primarily on segmenting low-resolutions images. A crucial step in any automatic segmentation approach is to first localize the cardiac structure of interest within the MRI volume, to reduce false positives and computational complexity. In this paper, we propose two strategies for localizing and segmenting the heart ventricles and myocardium, termed multi-stage and end-to-end, using a 3D convolutional neural network. Our method consists of an encoder–decoder network that is first trained to predict a coarse localized density map of the target structure at a low resolution. Subsequently, a second similar network employs this coarse density map to crop the image at a higher resolution, and consequently, segment the target structure. For the latter, the same two-stage architecture is trained end-to-end. The 3D U-Net with some architectural changes (referred to as 3D DR-UNet) was used as the base architecture in this framework for both the multi-stage and end-to-end strategies. Moreover, we investigate whether the incorporation of coarse features improves the segmentation. We evaluate the two proposed segmentation strategies on two cardiac MRI datasets, namely, the Automatic Cardiac Segmentation Challenge (ACDC) STACOM 2017, and Left Atrium Segmentation Challenge (LASC) STACOM 2018. Extensive experiments and comparisons with other state-of-the-art methods indicate that the proposed multi-stage framework consistently outperforms the rest in terms of several segmentation metrics. The experimental results highlight the robustness of the proposed approach, and its ability to generate accurate high-resolution segmentations, despite the presence of varying degrees of pathology-induced changes to cardiac morphology and image appearance, low contrast, and noise in the CMR volumes.

Reaction-Based Electrochemiluminescent Sensors for Selective Detection of Biochemical and Environmental Targets

Electrogenerated chemiluminescence (ECL) is a luminescent process which generates light through sequential electron transfer reactons on the electrode surface. ECL-based molecular sensors have several advantages over the conventional analytical techniques such as high sensitivity and low background signal. Additionally, the ECL provides the possibility of potential point-of-care-testing and field-monitoring with the simplicity of equipment and the method. However, ECL detection of small molecules is still a great challenge because most of ECL detection methods have been developed via specific biomacromolecular recognition such as antibody-antigen and aptamer-protein interactions. Herein, we report ECL molecular sensors for qualitative and quantitative detection of biologically important and environmentally toxic molecules.In the first part, a chemodosimetric approach will be introduced for one-step analysis of Hcy levels based on the ECL. A rationally designed cyclometalated iridium(III) complex possessing a phenylisoquinoline main ligand underwent a selective ring-formation reaction with Hcy to generate a binding adduct, which produced highly luminescent excited states, and yielded strong ECL signals on the surface of an electrode. The level of Hcy was successfully monitored by the ECL increment with a linear correlation between 0–40 µM in 99.9% aqueous media. The approach required neither sample preparation nor bulky instrument, suggesting the point-of-care testing of Hcy levels, and is potentially useful for routine, cost-effective, and precautionary diagnosis of various cardiovascular diseases. ECL sensors for other biologically important analytes (pyrophosphate, hydrogen sulfide, cysteine, H2O2) and environmentally toxic molecules (thiophenol, glyphosate, Hg(II)) will also be presented.In the second part, we report a totally different approach to discriminate a target among various interferences. This is called potential-dependent electrochemiluminescence. The iridium complex having a dicyanovinyl group on the main ligand cannot perfectly distinguish cyanide from sulfide and cysteine because not only cyanide but also sulfide and cysteine can attack the beta position of the dicyanovinyl group to increase the PL intensity. However, cyanide adduct is electrochemically oxidized at much less positive potential (~ 1.0 V) than those of sulfide and cysteine adducts, and this enables the discrimination of cyanide via ECL approach. This method enables complete removal of the interfering signals from sulfide and other thiols by controlling the potential range (0 – 1.4 V).

Dynamic pixel-wise weighting-based fully convolutional neural networks for left ventricle segmentation in short-axis MRI

Left ventricle (LV) segmentation in cardiac MRI is an essential procedure for quantitative diagnosis of various cardiovascular diseases. In this paper, we present a novel fully automatic left ventricle segmentation approach based on convolutional neural networks. The proposed network fully takes advantages of the hierarchical architecture and integrate the multi-scale feature together for segmenting the myocardial region of LV. Moreover, we put forward a dynamic pixel-wise weighting strategy, which can dynamically adjust the weight of each pixel according to the segmentation accuracy of upper layer and force the pixel classifier to take more attention on the misclassified ones. By this way, the LV segmentation performance of our method can be improved a lot especially for the apical and basal slices in cine MR images. The experiments on the CAP database demonstrate that our method achieves a substantial improvement compared with other well-know deep learning methods. Beside these, we discussed two major limitations in convolutional neural networks-based semantic segmentation methods for LV segmentation.

A Robust Automatic Ultrasound Spectral Envelope Estimation

Accurate estimation of ultrasound Doppler spectrogram envelope is essential for clinical pathological diagnosis of various cardiovascular diseases. However, due to intrinsic spectral broadening in the power spectrum and speckle noise existing in ultrasound images, it is difficult to obtain the accurate maximum velocity. Each of the standard existing methods has their own limitations and does not work well in complicated recordings. This paper proposes a robust automatic spectral envelope estimation method that is more accurate in phantom recordings and various in-vivo recordings than the currently used methods. Comparisons were performed on phantom recordings of the carotid artery with varying noise and additional in-vivo recordings. The accuracy of the proposed method was on average 8% greater than the existing methods. The experimental results demonstrate the wide applicability under different blood conditions and the robustness of the proposed algorithm.

Automated recognition of cardiac arrhythmias using sparse decomposition over composite dictionary

Background and objective: Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. Due to an increase in the rate of global mortalities, biopathological signal processing and evaluation are widely used in the ambulatory situations for healthcare applications. For decades, the processing of pathological electrocardiogram (ECG) signals for arrhythmia detection has been thoroughly studied for diagnosis of various cardiovascular diseases. Apart from these studies, efficient diagnosis of ECG signals remains a challenge in the clinical cardiovascular domain due to its non-stationary nature. The classical signal processing methods are widely employed to analyze the ECG signals, but they exhibit certain limitations and hence, are insufficient to achieve higher accuracy.Methods: This study presents a novel technique for an efficient representation of electrocardiogram (ECG) signals using sparse decomposition using composite dictionary (CD). The dictionary consists of the stockwell, sine and cosine analytical functions. The technique decomposes an input ECG signal into stationary and non-stationary components or atoms. For each of these atoms, five features i.e., permutation entropy, energy, RR-interval, standard deviation and kurtosis are extracted to determine the feature sets representing the heartbeats that are classified into different categories using the multi-class least-square twin support vector machines. The artificial bee colony (ABC) technique is used to determine the optimal classifier parameters. The proposed method is evaluated under category and personalized schemes and its validation is performed on MIT-BIH data.Results: The experimental results reported a higher overall accuracy of 99.21% and 90.08% in category and personalized schemes respectively than the existing techniques reported in the literature. Further a sensitivity, positive predictivity and F-score of 99.21% each in the category based scheme and 90.08% each in the personalized schemes respectively.Conclusions: The proposed methodology can be utilized in computerized decision support systems to monitor different classes of cardiac arrhythmias with higher accuracy for early detection and treatment of cardiovascular diseases.

ECG Measurements to Emphasize Atrial Potentials Using Modified Limb Lead System

With the advancement in the field of Electrocardiology (ECG), the acquisition of electrical activity of the heart plays an important role in the diagnosis of various cardiovascular diseases. In general, this activity of conduction is acquired using Standard 12 Lead ECG system. Recently, a Modified Limb Lead system has been developed to study the atrial electrical activity for the diagnosis of atrial arrhythmias. The present study is to derive the normal limits of female subjects using Modified Limb Lead (MLL) system. This study was evaluated in 59 female subjects 19.52 ± 1 (Mean ± S.D) and is primarily involved to study atrial potentials with the use of precordial and modified limb lead position. In sinus rhythm female subjects, the P wave amplitude was 33.06 ± 21.18 µV and the Ta wave amplitude was 20.72 ± 18.16 µV. The P wave duration was 94.22 ± 10.64 ms, the Ta wave duration was 85.51 ± 17.49 ms. The results show that modifications in the limb electrode placement have significant effect on amplitudes and PR segment which may be of great importance for the assessment of atrial potentials.

Electrochemiluminescent chemodosimeter based on iridium(III) complex for point-of-care detection of homocysteine levels

Elevated levels of plasma homocysteine (Hcy) are an independent risk factor for cardiovascular disease. Although a routine, rapid, and simple determination of Hcy levels is highly desired, the existing methods are practically limited because of complicated sample preparation and bulky instrumentation. Herein, we report a chemodosimetric approach for one-step analysis of Hcy levels based on the electrochemiluminescence (ECL). A rationally designed cyclometalated iridium(III) complex possessing a phenylisoquinoline main ligand underwent a selective ring-formation reaction with Hcy to generate a binding adduct, which enabled producing highly luminescent excited states, and yielded strong ECL signals on the surface of electrode without any use of enzymes or antibodies. The level of Hcy was successfully monitored by the ECL increment with a linear correlation between 0 and 40µM in 99.9% aqueous media. The approach required neither sample preparation nor bulky instrument, suggesting the point-of-care testing of Hcy levels, and is potentially useful for routine, cost-effective, and precautionary diagnosis of various cardiovascular diseases.

Tenascin-C in Cardiovascular Tissue Remodeling

Tenascin-C (TN-C) is a matricellular protein expressed during embryonic development, as well as in wound healing and cancer invasion in various tissues, and may regulate cell behavior and matrix organization during tissue remodeling. In the cardiovascular system, TN-C is transiently expressed at several important steps of embryonic development, playing important roles in the differentiation of cardiomyocytes and in coronary vasculo/angiogenesis. TN-C is sparse in normal adults, but upregulated under pathological conditions such as myocarditis, myocardial infarction, cardiac fibrosis, atherosclerosis, stenotic neointimal hyperplasia, and aneurysm, and is closely associated with tissue injury and inflammation. In view of its specific expression, TN-C could be a realistic and promising biomarker and a target for molecular imaging for the diagnosis of various cardiovascular diseases. TN-C also has diverse functions, including weakening of cell adhesion, up-regulating the expression and activity of matrix metalloproteinases, modulating inflammatory responses, promoting recruitment of myofibroblasts, and enhancing fibrosis. TN-C could exert both harmful and protective effects and might be a therapeutic target as a key molecule in the control of the balance of beneficial and undesirable cellular responses during tissue remodeling.

Antibody imaging in the evaluation of cardiovascular diseases.

Antimyosin antibody was originally developed for in vivo detection of acute myocardial infarction. However, its utility has expanded to include diagnosis of various cardiovascular diseases in which myocyte necrosis constitutes an obligatory component of the disease. Thus antimyosin has also been used clinically for noninvasive diagnosis of acute myocarditis, heart transplant rejection, drug-induced cardiotoxicity, and other cardiomyopathies. This first-generation monoclonal antibody, antimyosin, has opened the way for the second-generation monoclonal antibodies such as antifibrin and antiplatelet for in vivo diagnostic use in the detection of deep venous thrombosis and pulmonary embolism and antiatherosclerotic lesion-specific antibody for diagnosis of metabolically active lesions. Whether the third generation of antibodies will include ultrasmall antigen-binding units or negative charge-modified antibodies must await future studies.