Search

Perioperative risk assessment and stratification before craniotomy is necessary to identify and optimize modifiable risk factors. Due to the high costs of diagnostic testing and concerns for delaying surgery, some have questioned whether and when surgery delays are warranted and supported by the current body of literature. The objective of this scoping review was to evaluate the available evidence on the prognostic value of preoperative risk assessment before anesthesia for elective craniotomy. In this scoping review, we reviewed 156 papers that assess preoperative risk assessment before elective craniotomy, of which 27 papers were included in the final analysis. There is little high-quality evidence to suggest significant risk reduction when 4 common preexisting abnormalities are present: preoperative chronic aspirin therapy, cardiac arrhythmias, deep vein thrombosis, or hyperglycemia. The risk of delaying craniotomy should ultimately be weighed against the perceived risks associated the patient's comorbid conditions and should be considered on an individualized basis. [ABSTRACT FROM AUTHOR]

Respiratory interactions with the heart have remained a challenging physiological phenomenon since their discovery more than two hundred and fifty years ago. In the course of translating the seminal publications of Carl Ludwig and his disciple Pavel Petrovich Einbrodt into English, we became aware of some under-appreciated aspects of their work that contain useful insights into the history of the phenomenon now called respiratory arrhythmia. Ludwig observed arrhythmic effects of respiratory movements in experiments on dogs and horses and published his findings in 1847. He subsequently undertook further work on this problem, together with Einbrodt. Already in 1847 Ludwig had mentioned an exciting observation on the possible role of mechanical factors of the respiratory movements on the action of the heart in a dog in whom he had artificially induced bouts of coughing. Einbrodt decided to systematically develop methods to increase or decrease the pressure of the air the animal had to breathe. He observed that this procedure led to a greater or lesser degree of compression or decompression of all the organs in the thoracic cavity without apparently causing harmful consequences during the time of its application. How the mechanical influence of breathing affects cardiac activity during respiratory arrhythmia has been the subject of scientific discussions and controversies over a period of more than 150 years and is still unresolved. Recent publications suggest that cardiac mechano-electrical coupling plays an important role in the emergence of cardio-respiratory interdependence. [ABSTRACT FROM AUTHOR]

Fructus Choerospondiatis is the dried and mature fruit of Choerospondias axillaris (Roxb.) Burtt et Hill. It has been used for a long time in Tibetan and Mongolian medicine, first recorded in the ancient Tibetan medicine book "Medicine Diagnosis of the King of the Moon" in the early 8th century. Fructus Choerospondiatis shows multiple pharmacological activities, especially in treating cardiovascular diseases. This paper reviews the progress in research on the botanical characteristics, traditional uses, chemical constituents, pharmacological activity, clinical studies, and quality control of Fructus Choerospondiatis. This review aims to summarize current research and provide a reference for further development and utilization of Fructus Choerospondiatis resources. The sources for this review include the Pharmacopeia of the People's Republic of China (2020), theses, and peer-reviewed papers (in both English and Chinese). Theses and papers were downloaded from electronic databases including Web of Science, PubMed, SciFinder, Scholar, Springer, and China National Knowledge Infrastructure.The search terms used were " Choerospondias axillaris ", " C. axillaris ", " Choerospondias axillaris (Roxb.) Burtt et Hill", "Fructus choerospondiatis", "Guangzao", "Lapsi", and "Lupsi". Fructus Choerospondiatis contains polyphenols, organic acids, amino acids, fatty acids, polysaccharides, and other chemical components. These ingredients contribute to its diverse pharmacological activities such as antioxidant activity, protection against myocardial ischemia-reperfusion injury, anti-myocardial fibrosis, heart rhythm regulation, anti-tumor, liver protection, and immunity enhancement. It also affects the central nervous system, with the ability to repair damaged nerve cells. Fructus Choerospondiatis , with its various chemical compositions and pharmacological activities, is a promising medicinal resource. However, it remains under-researched, particularly in pharmacodynamic material basis and quality control. These areas require further exploration by researchers in the future. [Display omitted] • This is the first review systemically summarized Choerospondiatis Fructus (CF). • The cardiovascular effect of CF was reviewed, which accords to its traditional use. • The structure-activity relationship of chemical constituents in CF was summarized. • The clinical uses of major prescriptions containing CF were summarized. • The possible quality control components of CF were summarized for the first time. [ABSTRACT FROM AUTHOR]

In this paper, an analog current-mode circuit with low power consumption and low area is designed and implemented to detect the QRS complexes from the ECG signal. An efficient algorithm is proposed for this purpose which uses the energy operator. This algorithm is fully implemented in the analog domain with the transistors operating in the subthreshold region, resulting in ultra-low power consumption. The proposed method is evaluated with the data contained in the standard MIT-BIH arrhythmia database and reaches the acceptable values of 98.53% for the average accuracy, 99.41% for sensitivity and 99.13% for positive prediction. The proposed circuit is implemented in 0.18 μm CMOS technology with a power supply of 1.8 V. Using this circuit, there is no need to use an analog-to-digital converter in the detector system, which can lead to more reduction in the power consumption and area of the system. With power consumption of 15.77 nW and area of 0.078 mm2, this circuit is suitable for using in wearable and implantable applications. • This structure is a fully-analog QRS-detector realized by using current-mode circuits. • The circuit attains an ultra-low power and low area while has an acceptable accuracy. • The proposed circuit has an error rate of 1.47% and consumes 15.77 nW. • Analog-to-digital converter can be omitted for heart-rate monitoring in this design. • The circuit output can be used for efficient sampling, Transmitting and processing. [ABSTRACT FROM AUTHOR]

In the initial 3 papers in this Focus Seminar series, the fundamentals and key concepts of precision medicine were reviewed, followed by a focus on precision medicine in the context of vascular disease and cardiomyopathy. For the remaining 2 papers, we focus on precision medicine in the context of arrhythmias. Specifically, in this fourth paper we focus on long QT syndrome, Brugada syndrome, and atrial fibrillation. The final (fifth) paper will deal with catecholaminergic polymorphic ventricular tachycardia. These arrhythmias represent a spectrum of disease ranging from common to relatively rare, with very different genetic and environmental causative factors, and with differing clinical manifestations that range from almost no consequences to lethality in childhood or adolescence if untreated. Accordingly, the emerging precision medicine approaches to these arrhythmias vary significantly, but several common themes include increased use of genetic testing, avoidance of triggers, and personalized risk stratification to guide the use of arrhythmia-specific therapies. [ABSTRACT FROM AUTHOR]

The proportion of abnormal electrocardiogra (ECG) in patients with coronary artery fistula (CAF) is relatively high, but the correlation between CAF and arrhythmia is mostly reported in individual case studies. This paper analyzes the correlation between imaging features and ECG features. This paper aims to analyze the incidence and distribution characteristics of abnormal ECG in patients with CAF and further explore the difference in ECG characteristics between coronary-cameral fistula (CCF) and coronary-pulmonary artery fistula (CPAF). A total of 144,448 patients who underwent coronary computerized tomography angiography (CTA) examination from January 2016 to December 2022 were included in this study, and 284 patients with CAF (excluding coronary atherosclerosis) were selected for analysis of their ECG and image characteristics. And divided them into the CPAF (221 cases) and CCF (63 cases) groups, the differences in ECG between the two groups was compared. The changes in the ECG after the operation were analyzed. The incidence of abnormal ECG in patients with CAF was approximately 72.9%. There were significant differences in the proportion of ECG block, myocardial ischemia and structural ECG changes between the CPAF group and CCF group (P < 0.05). CCF was more likely to cause conduction block and ischemic and structural ECG changes. A total of 53 patients with CAF underwent surgical treatment, 28 patients with improved ECG (52%). CCF especially CCF patients often have abnormal ECG findings such as conduction block, myocardial ischemia, and structural changes, which can often be restored to normal through surgery. • Our Hospital is a specialized heart hospital with abundant and large number of disease sources. This is the first large-scale analysis of ECG characteristics in patients with coronary artery fistula. • Most patients with coronary fistula may have electrocardiogram abnormalities. Detection of abnormal electrocardiogram and timely imaging investigation is helpful for early diagnosis of coronary artery fistula. Coronary artery CTA has obvious advantage in the evaluation of coronary fistula. • Previous literature at home and abroad mainly preliminarily counted the proportion of abnormal ECG or various arrhythmias in patients with coronary artery fistula, but did not specifically analyze the correlation between the causes of arrhythmia and coronary artery fistula. The correlation between coronary artery fistula and arrhythmia is only a case report. • The purpose of this topic is to conduct a preliminary study on the ECG distribution characteristics of patients with coronary artery fistula, and further explore the correlation between the imaging characteristics and ECG characteristics of patients with coronary artery fistula. The gaps in the ECG characteristics of coronary fistulas may be filled, and more precise clinical guidelines may be needed for the surgical treatment of coronary fistulas, especially for small coronary fistulas. [ABSTRACT FROM AUTHOR]

Tebuconazole (TEB) is an important fungicide that belongs to the triazole family. It is widely used in agriculture and its use has experienced a tremendous increase in the last decade. The long-term exposure of humans to this pesticide is a real threat because it is stable in water and soil. The association between long-term exposure to TEB and damage of several biological systems, including hepatotoxicity and cardiotoxicity is evident, however, acute toxicological studies to reveal the toxicity of TEB are limited. This research paper addressed the acute exposure of TEB in murine hearts, cardiomyocytes, and human cardiomyocytes derived from an induced pluripotent stem cell (hiPSC-CMs), spelling out TEB's impact on electromechanical properties of the cardiac tissue. In ex vivo experiments, TEB dose dependently, caused significant electrocardiogram (ECG) remodeling with prolonged PR and QTc interval duration. The TEB was also able to change the action potential waveform in murine cardiomyocytes and hiPSC-CMs. These effects were associated with the ability of the compound to block the L-type calcium current (IC 50 = 33.2 ± 7.4 μmol.l−1) and total outward potassium current (IC 50 = 5.7 ± 1.5 μmol.l−1). TEB also increased the sodium/calcium exchanger current in its forward and reverse modes. Additionally, sarcomere shortening and calcium transient in isolated cardiomyocytes were enhanced when cells were exposed to TEB at 30 μmol.l−1. Combined, our results demonstrated that acute TEB exposure affects the cardiomyocyte's electro-contractile properties and triggers the appearance of ECG abnormalities. [ABSTRACT FROM AUTHOR]

Atrial fibrillation is the most common type of arrhythmia associated with a high mortality rate. Left atrium segmentation is crucial for the diagnosis and treatment of atrial fibrillation. Accurate left atrium segmentation with limited labeled data is a tricky problem. In this paper, a novel multi-level consistency semi-supervised learning method is proposed for left atrium segmentation from 3D magnetic resonance images. The proposed framework can efficiently utilize limited labeled data and large amounts of unlabeled data by performing consistency predictions under task level, data level, and feature level perturbations. For task consistency, the segmentation results and signed distance maps were used for both segmentation and distance estimation tasks. For data level perturbation, random flips (horizontal or vertical) were introduced for unlabeled data. Moreover, based on virtual adversarial training, we design a multi-layer feature perturbation in the structure of skipping connection. Our method is evaluated on the publicly available Left Atrium Segmentation Challenge dataset version 2018. For the model trained with a label rate of 20%, the evaluation metrics Dice, Jaccard, ASD, and 95HD are 91.69%, 84.71%, 1.43 voxel, and 5.44 voxel, respectively. The experimental results show that the proposed method outperforms other semi-supervised learning methods and even achieves better performance than the fully supervised V-Net. [ABSTRACT FROM AUTHOR]

Left atrial appendage (LAA) is the source of thrombi formation in more than 90% of strokes in patients with nonvalvular atrial fibrillation. Catheter-based LAA occlusion is being increasingly applied as a treatment strategy to prevent stroke. Anatomical complexity of LAA makes percutaneous occlusion commonly performed under transesophageal echocardiography (TEE) and X-ray (XR) guidance especially challenging. Image fusion techniques integrating 3D anatomical models derived from pre-procedural imaging into the live XR fluoroscopy can be applied to guide each step of the LAA closure. Cardiac magnetic resonance (CMR) imaging gains in importance for radiation-free evaluation of cardiac morphology as alternative to gold-standard TEE or computed tomography angiography (CTA). Manual delineation of cardiac structures from non-contrast enhanced CMR is, however, labor-intensive, tedious, and challenging due to the rather low contrast. Additionally, arrhythmia often impairs the image quality in ECG synchronized acquisitions causing blurring and motion artifacts. Thus, for cardiac segmentation in arrhythmic patients, there is a strong need for an automated image segmentation method. Deep learning-based methods have shown great promise in medical image analysis achieving superior performance in various imaging modalities and different clinical applications. Fully-convolutional neural networks (CNNs), especially U-Net, have become the method of choice for cardiac segmentation. In this paper, we propose an approach for automatic segmentation of cardiac structures from non-contrast enhanced CMR images of arrhythmic patients based on CNNs implemented in a multi-stage pipeline. Two-stage implementation allows subdividing the task into localization of the relevant cardiac structures and segmentation of these structures from the cropped sub-regions obtained from previous step leading to efficient and effective way of automated cardiac segmentation. • Deep learning approaches utilizing CNNs successfully achieved state-of-the-art for cardiac image segmentation. • Proposed two-stage CNN-based approach subdivides the segmentation task into localization and subsequent segmentation. • Introduced approach yields efficient method of cardiac image segmentation in arrhythmic patients with improved performance. [ABSTRACT FROM AUTHOR]

• Boiling of ammonia in a vertical dimpled tube bundle with round PVC nonconductive rods. • Study of wide range of experimental parameters. • Enhanced heat transfer coefficient compared to previous studies. This paper presents the experimental results of saturated two-phase flow ammonia in a vertical shell and tube heat exchanger comprising of seven dimpled enhanced tubes with solid PVC nonconductive round rods inserted in each tube creating an annular section. The source of heat was hot ethylene glycol/water solution flowing on the shell side. The range of saturation temperature for ammonia was –20 °C ≤ T sat ≤ –2 °C, heat flux range 5 kW m−2 ≤ q ˙ ≤ 45 kW m−2, exit vapor quality range 0.1 ≤ x ≤ 0.8 and mass flux 76.9 kg m−2 s−1≤ G ≤ 145.7 kg m−2 s−1. For each saturation temperature, the effects of heat flux and mass flux were studied while the exit vapor quality at each mass flux varied between 0.1 and 0.8. Two phase heat transfer coefficient increased with saturation temperature and heat flux. For all test runs, typical thermal performance was observed, i.e., initial increase and then a drop at the onset of dry out. Annular flow boiling showed better heat transfer coefficient compared to the dimple tube for quality x < 0.5. [ABSTRACT FROM AUTHOR]

Multi-label arrhythmia classification plays a crucial role in the prevention and diagnosis of cardiac diseases. Intelligent analysis of multi-lead electrocardiogram (ECG) signals is the main method of classification. Existing methods have shown that different lead ECG signals contribute differently to the classification network. In this paper, we propose a Lead Feature Guide Network (LFG-Net) to improve the feature representation capability of the network by focusing on the lead features with high contribution. Firstly, the lead features with high contributions are selected by the Shapley Additive exPlanations (SHAP) method. Secondly, the lead features are utilized to guide the classification model in bridging the gap between the selected features and the classification model by minimizing the difference in their losses. Finally, the network improves the accuracy of arrhythmia classification due to the extraction of more valid feature information. The results demonstrated that the F1-score reached 0.842 for classification on the China Physiological Signal Challenge (CPSC) 2018 dataset, which indicated the network outperformed other state-of-the-art methods. The parameters of the network during prediction are 1.02 M. Through experiments, it can be verified that the effectiveness of the proposed method on arrhythmia classification with multi-label, and it can also be demonstrated that the lead features with high contributions can improve the network classification accuracy. [ABSTRACT FROM AUTHOR]

• The histological quantification of large transmural biopsies in its entirety remains a bottleneck in the image analysis stage and usually relies on analysis of selected regions of interest only. • A structured protocol including coding scripts is provided with all key steps of histological quantification of cardiac innervation in large transmural biopsies. • The workflow, using freely available software, can be easily adjusted, performed and holds potential to be applied in many fields of histological research. The heart is under strict regulation of the autonomic nervous system, during which, in a healthy state, the effects of sympathetic and parasympathetic branches are balanced. In recent years, there has been increasing interest in pathological remodeling and outgrowth of cardiac autonomic nerves in relation to arrhythmogenesis. However, the small size of the cardiac nerves in relatively large tissues renders research using histological quantification of these nerves extremely challenging and usually relies on quantification of the nerve density in selected regions of interest only. Our aim was to develop a method to be able to quantify the histological nerve density in transmural tissue sections. Here we describe a novel workflow that enables visualization and quantification of variable innervation types and their heterogeneity within transmural myocardial tissue sections. A custom semiautomatic workflow for the quantification of cardiac nerves involving Python, MATLAB and ImageJ is provided and described in this protocol in a stepwise and detailed manner. The results of two example tissue sections are represented in this paper. An example tissue section taken from the infarction core with a high heterogeneity value of 0.20, 63.3% normal innervation, 12.2% hyperinnervation, 3.6% hypoinnervation and 21.0% denervation. The second example tissue section taken from an area of the left ventricle remote from the infarction showed a low heterogeneity value of 0.02, 95.3% normal innervation, 3.8% hyperinnervation, 0.5% hypoinnervation and 0.5% denervation. : This approach has the potential to be broadly applied to any research involving high-resolution imaging of nerves in large tissues. [ABSTRACT FROM AUTHOR]

As one of the tropical diseases, malaria is endemic in developing countries. Severe malaria, mainly caused by the Plasmodium falciparum parasite, can result in life-threatening complications. Traditionally, cardiac involvement has not been included as a frequent cause of morbidity and mortality. This could be due to under-reporting or underdiagnosing. Specific cardiovascular (CV) complications include electrocardiogram abnormalities, myocarditis, pericarditis, pericardial effusion, ischemic disease, and heart failure. According to the data analyzed, CV manifestations can lead to severe consequences. Possible theories related to the pathophysiological mechanisms related to CV compromise include an imbalanced pro-inflammatory cytokine response and/or erythrocyte sequestration by increased cytoadherence to endothelium. Although there is a paucity of data regarding cardiac manifestations of malaria, an algorithm for appropriate use of diagnostic tools to assess cardiac involvement has been developed in this paper. Furthermore, it is important to note that typical antimalarial treatment regimens can have fatal cardiac side-effects. [ABSTRACT FROM AUTHOR]

Takotsubo syndrome (TTS) has been a recognized clinical entity for 31 years, since its first description in 1990. TTS is now routinely diagnosed in patients who present with acute chest pain, electrocardiographic changes, troponin elevation, unobstructed coronary arteries, and a typical pattern of circumferential left ventricular wall motion abnormalities that usually involve the apical and midventricular myocardium. Increasing understanding of this intriguing syndrome stems from wider recognition, possible increasing frequency, and a rising number of publications focused on the pathophysiology in clinical and laboratory studies. A comprehensive understanding of TTS pathophysiology and evidence-based treatments are lacking, and specific and effective treatments are urgently required. This paper reviews the pathophysiology of this fascinating syndrome; what is known from both clinical and preclinical studies, including review of the evidence for microvascular dysfunction, myocardial beta-adrenergic signaling, inflammation, and electrophysiology; and where focused research needs to fill gaps in understanding TTS. [ABSTRACT FROM AUTHOR]

• An efficient algorithm for ECG heartbeats classification is proposed. The proposed algorithm termed ML-WCNN is based on Convolutional Neural Networks from the deep learning theory, and the Stationary Wavelet Transform (SWT). • The proposed End-to-End deep learning architectures can automatically learn effective discriminative features from the original ECG signal and its wavelets coefficients simultaneously. • The developed ML-WCNN methods can recognize effectively six ECG beat classes without requirement of any pre-processing or features extraction steps. • ECG signals form the standard database MIT-BIH Arrhythmia have been used for evaluating the proposed approach in comparison with others established Machine and Deep learning techniques. • The quantitative results show that the proposed method exhibits a higher or comparable performances for ECG classification on the against some state of the art algorithms with a maximum accuracy of 99.57%. This paper presents a new End-to-End Deep Learning method for heart diseases diagnosis from single channel ECG signal. Motivated by the great efficiency and popularity of deep learning algorithms for time series classification, the proposed work is mainly based on the One Dimensional Convolutional Neural Networks (1D-CNN). Unlike the traditional CNN models based classification, a new Multi-Level Wavelet Convolutional Neural Networks (ML-WCNN) is proposed to recognize automatically various types of cardiac arrhythmias. The proposed approach incorporates the 1D-CNN model and the Stationary Wavelet Transform (SWT) to extract discriminative features from different wavelet sub-bands and from the raw ECG signal simultaneously. The extracted features by the ML-WCNN model are then merged using different fusion strategies, especially by concatenation and maximization. This improves greatly the features learning process at different scales of the ECG signal, providing better diagnosis performances. The proposed ML-WCNN framework is evaluated on the Standard Database MIT-BIH Arrhythmia considering six classes of heart Beats: Normal (N), Premature Ventricular Contraction (PVC), Right Bundle Brunch Block (RBBB), Left Bundle Brunch Block (LBBB), Atrial Premature Contraction (APC) and Paced beats (PAC). The experimental results demonstrate the superiority of the proposed ML-WCNN, in comparison with the state-of-the-art heart diseases diagnosis based Machine/Deep learning algorithms, with a maximum Accuracy of 99.57 % using the 10-fold cross validation technique. [ABSTRACT FROM AUTHOR]

• In this paper, ECG arrhythmia classification was achieved without distinguishing subjects. Not only the classification within subjects, but also the classification among subjects. • This paper improved the ant colony clustering algorithm by introducing a correction mechanism to correct outlier points to improve model classification accuracy; introducing an increased flow rate ρ to improve the speed and stability of model convergence; and realising that the next transfer target is selected by a true self-adjusting transfer method where the transfer probability is dynamically adjusted according to pheromone concentration and path distance. • This paper compares different models with and without the correction mechanism, and experiments show that the number of classification errors is reduced by an order of magnitude (101). • Compared with the experimental models, the classification accuracy of the proposed method is improved by 0.2% ∼ 16.6%. Compared with other current studies, the classification accuracy of the proposed method is improved by 0.65% ∼ 7.5%. As a representative type of cardiovascular disease, persistent arrhythmias can often become life-threatening. In recent years, machine learning-based ECG arrhythmia classification aided methods have been effective in assisting physicians with their diagnosis, but these methods have problems such as complex model structures, poor feature perception ability, and low classification accuracy. In this paper, a self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism is proposed. This method does not distinguish between subjects when establishing the dataset in order to reduce the effect of differences in ECG signal features between individuals, thus improving the robustness of the model. When the classification is achieved, a correction mechanism is introduced to correct outliers caused by the accumulation of errors in the classification process in order to improve the classification accuracy of the model. According to the principle that the flow rate of gas can be increased under the convergence channel, a dynamically updated pheromone volatilization coefficient ρ, namely the increased flow rate ρ, is introduced to help the model converge more stably and faster. As the ants move, the next transfer target is selected by a truly self-adjusting transfer method, and the transfer probability is dynamically adjusted according to the pheromone concentration and the path distance. Based on the MIT-BIH arrhythmia dataset, the new algorithm achieved classification of five heart rhythm types, with an overall accuracy of 99.00%. Compared to other experimental models, the classification accuracy of the proposed method represents a 0.2% to 16.6% improvement, and compared to other current studies, the classification accuracy of the proposed method is 0.65% to 7.5% better. This paper addresses the shortcomings of ECG arrhythmia classification methods based on feature engineering, traditional machine learning and deep learning, and presents a self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism. Experiments demonstrate the superiority of the proposed method compared to basic models as well as those with improved partial structures. Furthermore, the proposed method achieves very high classification accuracy with a simple structure and fewer iterations than other current methods. [ABSTRACT FROM AUTHOR]

The aim of this paper is to describe an uncommon physiological EEG artifact in newborns caused by tongue movements (TM), mimicking anterior slow dysrhythmia (ASD). The subjects are two full-term newborns (39 weeks gestational age (GA)), admitted to the Neonatal Intensive Care Unit for respiratory distress. Both underwent polygraphic video-EEG recording in order to better characterize tremor-like movements of all four limbs that appeared 48 hours after birth. Multichannel video-EEG polygraphy was performed using the 10–20 electrode montage modified for neonates. Ninety minutes of EEG was recorded for each subject, capturing different behavioral states. Background EEG activity was normal for both subjects. During active sleep (AS), synchronous and symmetric slow activity was recorded over bifrontal head regions. For subject 1, bursts of monomorphic 2 Hz delta waves, with an amplitude between 50–100 μV lasting two seconds, were recorded and identified as anterior slow dysrhythmia. For subject 2, polymorphic 1-2 Hz delta waves, 50–100 μV in amplitude and lasting for 20 seconds, were recorded only during suction. After thorough analysis of simultaneous digital video recording synchronized with the EEG trace, this activity was thought to be compatible with glossokinetic artifact. Interpretation of neonatal EEG can be challenging; the background activity is frequently intermixed with physiological artifacts, such as ocular, muscle and movement artifacts, complicating the interpretation. Even continuous video-recording might not make the diagnosis immediately obvious. Therefore, when a rhythmic monomorphic pattern without evolution in amplitude or frequency is seen, we suggest that tongue movement artifact should be considered. [ABSTRACT FROM AUTHOR]

Arrhythmia is one of the most common cardiovascular diseases. The search for new drugs to suppress various types of cardiac arrhythmias has always been the focus of attention. In the past decade, the screening of antiarrhythmic active substances from plants has received extensive attention. These natural compounds have obvious antiarrhythmic effects, and chemical modifications based on natural compounds have greatly increased their pharmacological properties. The chemical modification of botanical antiarrhythmic drugs is closely related to the development of new and promising drugs. Therefore, the structural characteristics and action targets of natural compounds with antiarrhythmic effects are reviewed in this paper, so that pharmacologists can select antiarrhythmic lead compounds from natural compounds based on the disease target - chemical structural characteristics. [Display omitted] • The pharmacological targets of drugs against arrhythmia involve multiple aspects. • Natural products may be a potential anti-arrhythmic drug. • Summarizing the characteristics of antiarrhythmic natural products for further development. [ABSTRACT FROM AUTHOR]

In this paper, a novel multi-lead fusion approach for accurate detection of the QRS complex from 12-lead ECG (Electrocardiogram) signals is proposed. The framework is divided into two major stages. In the first stage, the single-lead QRS complex detection algorithm detects the QRS complex location of each lead. In the second stage, the multi-lead fusion method combines the QRS complex locations obtained in each of the 12-leads, thereby improving the performance of QRS complex detection in terms of sensitivity and positive predictivity by discarding the false positives detected in each lead. The performance of the proposed method is validated using the databases of the St. Petersburg Institute of Cardiological Technics (INCART) (Goldberger et al., 2000) and the CSE (Common Standards for Electrocardiography) (Willems et al., 1987). The proposed framework achieves 99.87% sensitivity and 99.96% positive predictive accuracy for the INCART database. Sensitivity and positive predictivity for the CSE database were found to be 100% and 99.13%, respectively. • The QRS complex detection is important because various cardiac disease detection algorithms depend on accurate detection of it. • Missed QRS complex in single-lead detector with arrhythmia can lead to inaccurate heart disease predictions. • Initially, a single lead QRS complex detection algorithm is used for the detection of QRS complex location on 12 lead ECG signals. • Then, a multi-lead fusion (MLF) algorithm is proposed to combine information of all leads for the accurate QRS complex detection. • The performance of the proposed algorithm is evaluated on the INCART and CSE databases. [ABSTRACT FROM AUTHOR]

• Fast permutation entropy is proposed for online analysis of PRV signals. • The feasibility of developing low-cost wearable devices to verify the proposed method. • The computational speed of the proposed method is demonstrated by simulated and measured data to be faster than the traditional permutation entropy. Pulse rate variability (PRV) signals are extracted from pulsation signal can be effectively used for cardiovascular disease monitoring in wearable devices. Permutation entropy (PE) algorithm is an effective index for the analysis of PRV signals. However, PE is computationally intensive and impractical for online PRV processing on wearable devices. Therefore, to overcome this challenge, a fast permutation entropy (FPE) algorithm is proposed based on the microprocessor data updating process in this paper, which can analyze PRV signals with single-sample recursive. The simulation data and PRV signals extracted from pulse signals in " Fantasia database" were utilized to verify the performance and accuracy of the improved methods. The results show that the speed of FPE is 211 times faster than PE and maintain the accuracy of algorithm (Root Mean Squared Error = 0) for simulation data with a length of 10,000 samples and embedded dimension m = 5, time delay τ = 5, buffer length Lw = 512. For the RRV signals with 3000∼5000 samples, the result show that the consumption of FPE is less than 0.2 s, which is 175 times faster than PE. This indicates that FPE has better application performance than PE. Furthermore, a low-cost wearable signal detection system is developed to verify the proposed method, the result show that the proposed method can calculate the FPE of PRV signal online with single-sample recursive calculation. Subsequently, entropy-based features are used to explore the performance of decision trees in identifying life-threatening arrhythmias, and the method resulted in a classification accuracy of 85.43%. It can therefore be inferred that the proposed method has great potential in cardiovascular disease. [ABSTRACT FROM AUTHOR]

• ECG-DCGAN model to solve the imbalance of ECG data. • Improved QRS wave detection algorithm to improve the quality of generated data. • The proposed CNN model has superior classification performance. • Experiments prove that this model can better assist doctors in diagnosis. Our blood vessels show signs of aging as we grow older, which leads to various cardiovascular diseases. Arrhythmia is usually the symptom of patients with early cardiovascular diseases. Early detection of arrhythmia is of great significance to the mortality of cardiovascular diseases. Applying deep learning to arrhythmia detection can help doctors discover cardiovascular diseases in time. At present, the performance of arrhythmia classification algorithms based on convolutional neural networks has far surpassed traditional methods. However, the imbalance of arrhythmia data will seriously affect the performance of the classification algorithm. To better apply the convolutional neural network to the arrhythmia classification, a large amount of labeled ECG data is required. Therefore, this article proposes ECG Deep Convolution Generative Adversarial Networks (ECG-DCGAN) to expand the scarce data in the arrhythmia dataset and solve the problem of arrhythmia data imbalance. In addition, the convolution neural network (CNN) model is used to automatically classify the ECG signals without artificial feature extraction. Experimental results show that the classification method proposed in this paper improves the accuracy of arrhythmia diagnosis to 98.7% and that the algorithm used in this paper has good recognition performance and high clinical application value. [ABSTRACT FROM AUTHOR]

Highlights • The hybrid intelligent model was built using artificial neural networks and fuzzy logic. • This hybrid model was tested with the MIT-BIH arrhythmia database. • A type-1 fuzzy system (type-1 FIS) and an interval type-2 fuzzy system (IT2FIS) were designed. • The best classification rate results are obtained using IT2FIS instead of type-1 FIS in the basic units. • A type-1 FIS is used to find a global classification for the 2 basic units in the hybrid model. Abstract This paper describes an approach using computational intelligence methods to form a hybrid model as a classification method for 2-lead cardiac arrhythmias. The hybridization of methods can increase the performance in a system and take advantage of the benefits offered by such techniques in solving complex problems. The interpretation of electrocardiograms is a useful task for physicians, but when it comes to reviewing more than 24 h of information, it becomes a laborious task for them. For this reason, the design a computational model that helps in such a task is very useful for the timely medical diagnosis. The hybrid model is build using artificial neural networks and fuzzy logic. Training and testing of the hybrid model was with the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) arrhythmia database. The heartbeats are preprocessed to improve results of classification. Ten different classes of normal and arrhythmia signals for building the hybrid model are considered. We used two electrode signals or leads included in the MIT-BIH arrhythmia database, MLII and V1, V2, or V3 as second electrode signal. The hybrid model is composed by two basic module units, as described below. A basic module unit to perform the classification for each signal lead is used. Each basic module unit is composed of three different classifiers based on the following models: fuzzy KNN algorithm, multilayer perceptron with gradient descent and momentum (MLP-GDM), and multilayer perceptron with scaled conjugate gradient backpropagation (MLP-SCG). The outputs from the classifiers are combined using a fuzzy system for integration of results. We designed two fuzzy systems, Mamdani type-1 fuzzy system (type-1 FIS) and an interval type-2 fuzzy system (IT2FIS). The reason is to perform a comparison between type-1 FIS and IT2FIS in the hybrid model. We have obtained best results in the classification rate using IT2FIS instead of type-1 FIS in the basic units. Finally, a type-1 FIS is used to determine the global classification for the 2 basic units in hybrid model. We obtained a good classification rate in each basic module unit, 92.90% and 92.70% of classification rate for basic modules unit 1 and unit 2 respectively. Finally, we obtained a 93.80% when used type-1 FIS and 94.20% of classification rate used IT2FIS combining both basic module units. In the results presented, we improve the global classification in proposed hybrid model combining neural networks and fuzzy logic used both signal lead included in MIT-BIH arrhythmia database. The proposed hybrid model maybe extended to use multi-lead arrhythmia classification using other databases that contain 12 leads to be able to make a complete medical diagnosis. [ABSTRACT FROM AUTHOR]

• A novel method to detect the QRS complexes from ECG signals is presented. • Tunable-Q wavelet transform and correntropy are employed. • System is validated using the MIT-BIH arrhythmia dataset. • Obtained accuracy is 99.88%. The patient monitoring and diagnosis using electrocardiogram (ECG) signal involve accurate QRS complex detection. However, the reliability of QRS detection is limited by the presence of variety of noises and morphologies. A high performance QRS complex detection scheme based on the tunable-Q wavelet transform (TQWT) is presented in this paper. The tunable parameters of TQWT can improve the localization of QRS when selected suitably. These parameters help to choose appropriate mother wavelet and proper sub-band of interest with fine separation from the undesired sub-bands. The method starts with TQWT based decomposition and reconstruction of the ECG signal with optimally selected input parameters and sub-bands. The correntropy envelope is computed from the resultant QRS enhanced signal which in turn assists the QRS localization. Our proposed method has yielded an average detection accuracy of 99.88%, sensitivity of 99.89% and positive productivity of 99.83% on the MIT-BIH arrhythmia database. [ABSTRACT FROM AUTHOR]

Abstract The electrocardiogram (ECG) denoising is of paramount importance for accurate disease diagnosis, but individual differences bring great difficulties for ECG denoising, especially for Dynamic Electrocardiography (DCG). In this paper, a multi-lead model-based ECG signal denoising method is proposed, in which a guided filter is inherently adapted to denoise ECG signal. For each person, a patient-specific statistical model will be constructed by sparse autoencoder (SAE) which can effectively preserve the detailed signal features. Thus, the guided signal producing by the statistical model can perform well in the guided filter. Especially, even the sudden morphological changes, the denoised ECG signals can still be conserved. The results on the 12-lead Arrhythmia Database and the MIT-BIH Arrhythmia Database demonstrate that the signal-to-noise ratio (SNR) improvement of the proposed method can reach as high as 21.54 dB, and the mean squared error (MSE) is less than 0.0401. Besides achievement of minimum signal distortion in comparisons with the major of the current denoising algorithms for complex noise environment, the proposed method demonstrate robustness in the complex interferences, especially in tracing the sudden morphological changes of ECG signals. Due to the remarkable superiority in preserving diagnostic and detail features of ECG signals, the proposed method can handle ECG signals with abnormal heart beats, and then can improve the accuracy detection of the disease. [ABSTRACT FROM AUTHOR]

Abstract This paper adopts network analysis to investigate the feasibility of recruiting nearby fishing vessels as the initial rescue force for search-and-rescue operations. Network analysis was conducted to analyze the proximity among fishing vessels according to the type of fishing. The results were used as input to an adjacency matrix and schematized by applying the network. Given that fish species tend to travel together, fishing vessels in pursuit of them also operate in the same waters, essentially forming a network. It is thus desirable to secure a search-and-rescue system among fishing vessels of the same fishing type for prompt rescue. Specifically, network analysis confirmed that fishing vessels of the same type within 10 nautical miles can become vigilant, and a system of prompt rescue support can thus be secured; this approach improves the overall speed of rescue operations. These findings can be used as baseline data for developing a practical search-and-rescue system through a network of fishing vessels of the same fishing type. Highlights • Network analysis to evaluate the proximity among fishing vessels by fishing type. • Study utilized the fishing vessel location data for a 1 month span. • Intensity of the connection between two vessels by relative weight. • Network analysis can be applied to developing a rescue system. [ABSTRACT FROM AUTHOR]

Abstract Magnetic resonance imaging of patients who find difficulty lying still or holding their breath can be challenging. Unresolved intra-frame motion yields blurring artifacts and limits spatial resolution. To correct for intra-frame non-rigid motion, such as in pediatric body imaging, this paper describes a multi-scale technique for joint estimation of the motion occurring during the acquisition and of the desired uncorrupted image. This technique regularizes the motion coefficients to enforce invertibility and minimize numerical instability. This multi-scale approach takes advantage of variable-density sampling patterns used in accelerated imaging to resolve large motion from a coarse scale. The resulting method improves image quality for a set of two-dimensional reconstructions from data simulated with independently generated deformations, with statistically significant increases in both peak signal to error ratio and structural similarity index. These improvements are consistent across varying undersampling factors and severities of motion and take advantage of the variable density sampling pattern. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) is a standard test to monitor the activity of the heart. Many cardiac abnormalities are manifested in the ECG including arrhythmia that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal heart beats, and their correct classification based on ECG morphology. This paper proposes a novel and robust approach for representation learning of ECG sequences using a LSTM autoencoder for anomaly detection. The encoder part encodes the ECG signal into a lower dimensional latent space representation and decoder part then tries to reconstruct the specified ECG signal. The model is trained only on normal (non-anomalous) ECG signals. Then reconstruction loss of test ECG signals are calculated. Next a reconstruction loss threshold value is determined from the frequency distribution of the reconstruction losses so that from the reconstruction loss value above a certain threshold is determined as anomaly, otherwise it will be treated as normal. Determination of threshold is done using manual and Kapur's automated thresholding procedures. The aforementioned model has been applied on publicly available ECG5000 dataset. From the experimental results it is observed that the proposed model achieved more than 98% accuracy having precision, recall and F1 values more than 0.94, 0.97, 0.96 respectively. The performance of the proposed method is also found to be superior in most of the cases as compared to the results of seven other recent counter-part methods reported in the literature. [ABSTRACT FROM AUTHOR]

This paper presents ArrhyMon , a self-attention-based LSTM-FCN model for arrhythmia classification from ECG signal inputs. ArrhyMon targets to detect and classify six different types of arrhythmia apart from normal ECG patterns. To the best of our knowledge, ArrhyMon is the first end-to-end classification model that successfully targets the classification of six detailed arrhythmia types and compared to previous work does not require additional preprocessing and/or feature extraction operations separate from the classification model. ArrhyMon 's deep learning model is designed to capture and exploit both global and local features embedded in ECG sequences by integrating fully convolutional network (FCN) layers and a self-attention-based long and short-term memory (LSTM) architecture. Moreover, to enhance its practicality, ArrhyMon incorporates a deep ensemble-based uncertainty model that generates a confidence-level measure for each classification result. We evaluate ArrhyMon 's effectiveness using three publicly available arrhythmia datasets (i.e., MIT-BIH, Physionet Cardiology Challenge 2017 and 2020/2021) to show that ArrhyMon achieves state-of-the-art classification performance (average accuracy 99.63%), and that confidence measures show close correlation with subjective diagnosis made from practitioners. • We propose a fully end-to-end arrhythmia classification system, ArrhyMon, based on a self-attention-based LSTM-FCN deep learning architecture. • ArrhyMon adopts a deep ensemble-based approach to self-quantify uncertainty levels for its classification results. As a result, ArrhyMon offers per-classification output confidence information to assist clinical decision-making processes. • Using representative ECG datasets, we show that ArrhyMon achieves state-of-the-art performance in classifying six arrhythmia types and distinguishing them from normal ECG patterns. Furthermore, we show that the epistemic uncertainty of ArrhyMon's classification results are reliable for clinical use [ABSTRACT FROM AUTHOR]

Introduction Patients surviving myocardial infarction (MI) can be divided into high and low arrhythmic risk groups. Distinguishing between these two groups is of crucial importance since the high-risk group has been shown to benefit from implantable cardioverter defibrillator insertion; a costly surgical procedure with potential complications and no proven advantages for the low-risk group. Currently, markers such as left ventricular ejection fraction and myocardial scar size are used to evaluate arrhythmic risk. Methods In this paper, we propose quantitative discriminative features extracted from late gadolinium enhanced cardiac magnetic resonance images of post-MI patients, to distinguish between 20 high-risk and 34 low-risk patients. These features include size, location, and textural information concerning the scarred myocardium. To evaluate the discriminative power of the proposed features, we used several built-in classification schemes from matrix laboratory (MATLAB) and Waikato environment for knowledge analysis (WEKA) software, including k-nearest neighbor (k-NN), support vector machine (SVM), decision tree, and random forest. Results In Experiment 1, the leave-one-out cross-validation scheme is implemented in MATLAB to classify high- and low-risk groups with a classification accuracy of 94.44%, and an AUC of 0.965 for a feature combination that captures size, location and heterogeneity of the scar. In Experiment 2 with the help of WEKA, nested cross-validation is performed with k-NN, SVM, adjusting decision tree and random forest classifiers to differentiate high-risk and low-risk patients. SVM classifier provided average accuracy of 92.6%, and AUC of 0.921 for a feature combination capturing location and heterogeneity of the scar. Experiment 1 and Experiment 2 show that textural features from the scar are important for classification and that localization features provide an additional benefit. Conclusion These promising results suggest that the discriminative features introduced in this paper can be used by medical professionals, or in automatic decision support systems, along with the recognized risk markers, to improve arrhythmic risk stratification in post-MI patients. [ABSTRACT FROM AUTHOR]

As per the report of the World Health Organization (WHO), the mortalities due to cardiovascular diseases (CVDs) have increased to 50 million worldwide. Therefore, it is essential to have an efficient diagnosis of CVDs to enhance the healthcare in the clinical cardiovascular domain. The ECG signal analysis of a patient is a very popular tool to perform diagnosis of CVDs. However, due to the non-stationary nature of ECG signal and higher computational burden of the existing signal processing methods, the automated and efficient diagnosis remains a challenge. This paper presents a new feature extraction method using the sparse representation technique to efficiently represent the different ECG signals for efficient analysis. The sparse method decomposes an ECG signal into elementary waves using an overcomplete gabor dictionary. Four features such as time delay, frequency, width parameter, and square of expansion coefficient are extracted from each of the significant atoms of the dictionary. These features are concatenated and analyzed to determine the optimal length of discriminative feature vector representing each of the ECG signal. These extracted features representing the ECG signals are further classified using machine learning techniques such as least-square twin SVM, k-NN, PNN, and RBFNN. Further, the learning parameters of the classifiers are optimized using ABC and PSO techniques. The experiments are carried out for the proposed methods (i.e. feature extraction along with all classifiers) using benchmark MIT-BIH data and evaluated under category and personalized analysis schemes. Experimental results show that the proposed ECG signal representation using sparse decomposition technique with PSO optimized least-square twin SVM (best classifier model among k-NN, PNN and RBFNN) reported higher classification accuracy of 99.11% in category and 89.93% in personalized schemes respectively than the existing methods to the state-of-art diagnosis. [ABSTRACT FROM AUTHOR]

Jervell and Lange-Nielsen syndrome (JLNS), a rare autosomal recessive congenital QT prolongation syndrome, is characterized by cardiac arrhythmias, syncopal episodes, and profound deafness. A cochlear implant (CI) for patients with JLNS is expected to result in hearing improvement. Sometimes, defibrillation is required if a patient experiences lethal arrhythmia. In this paper, we report a pediatric patient with JLNS who received defibrillation after CI surgery in his right ear at the age of 2 years. With intensive care, the post-operative course was uneventful, and the patient acquired satisfactory speech and hearing abilities. Five years after the surgery, he underwent defibrillation because of the incidence of syncopal attack. Thereafter, arrhythmic syncope recurred three times, which necessitated defibrillation therapy. To prevent recurrence of cardiac arrhythmia, he underwent ICD (implantable cardioverter-defibrillator) implantation at the age of 11 years. At present, CI works well and provides good hearing, while syncopal attack is prevented by ICD. From the experience of this case, electronic circuit of CI is thought to tolerate emergency countershock if the speech processor is removed. [ABSTRACT FROM AUTHOR]

Radiofrequency catheter ablation is one of the commonly available therapeutic methods for patients suffering from cardiac arrhythmias. The prerequisite of successful ablation is sufficient energy delivery at the target site. However, cardiac and respiratory motion, coupled with endocardial irregularities, can cause catheter drift and dispersion of the radiofrequency energy, thus prolonging procedure time, damaging adjacent tissue, and leading to electrical reconnection of temporarily ablated regions. Therefore, positional accuracy and stability of the catheter tip during energy delivery is of great importance for the outcome of the procedure. This paper presents an analytical scheme for assessing catheter tip stability, whereby a sequence of catheter tip motion recorded at sparse locations on the endocardium is decomposed. The spatial sliding component along the endocardial wall is extracted from the recording and maximal slippage and its associated probability are computed at each mapping point. Finally, a global map is generated, allowing the assessment of potential areas that are compromised by tip slippage. The proposed framework was applied to 40 retrospective studies of congenital heart disease patients and further validated on phantom data and simulations. The results show a good correlation with other intraoperative factors, such as catheter tip contact force amplitude and orientation, and with clinically documented anatomical areas of high catheter tip instability. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes an electrocardiogram (ECG) processor for premature ventricular contraction (PVC) detection in wearable monitoring. A novel feature called QRS areas ratio (QAR) is extracted from wavelet transform coefficients which can efficiently enhance accuracy of PVC beat classification. This feature along with other two beat interval features is introduced to a single neuron perceptron model based classifier. And the classifier achieves ultra-low complexity by linear processing and reduces power consumption by eliminating memory overhead. Finally, the proposed processor exhibits an average sensitivity of 98.7% and specificity of 98.9% in testing of MIT-BIH arrhythmia database. Implemented in 40 nm CMOS technology, it achieves 127 nW of power consumption at 0.5 V supply voltage. [ABSTRACT FROM AUTHOR]

Since 2005, when the International Conference on Harmonisation (ICH) E14 guideline was adopted, no drug has been withdrawn because of QTc prolongation or torsade de pointes arrhythmia. There are, however, costs associated with this success. In addition to the time and money invested, thorough QT (TQT) studies have limited the efficiency of the drug development pipeline. In this paper, we discuss the relevance of TQT trials as a tool for proarrhythmic risk prediction as a part of the debate regarding their usefulness. [ABSTRACT FROM AUTHOR]

• Proposes a weighted IForest algorithm to mark a small part of the data. Expert decision making rules and use logical regression algorithm to obtain the weight of features. • Improves the FDA function and uses it as the loss function of SGRU to improve the accuracy of the algorithm. Background and objective At present, many achievements have been made in anomaly detection of big data using deep neural network, However, in many practical application scenarios, there are still some problems, such as shortage of data, too large workload of manual data annotating and so on. Methods This paper proposes weighted iForest and Siamese GRU (WIF-SGRU) algorithm on small sample anomaly detection. In the data annotation stage, we propose a weighted IForest algorithm for automatic annotation of unlabeled data. In the training phase of anomaly detection model, the Siamese GRU is proposed to train the target data to obtain the anomaly model and detect the real-time anomaly of small sample data. Results The proposed algorithm is verified on six public datasets (Arrhythmia, Shuttle, Staellite, Sttimage-2, Lymphography, and WBC). The experimental results show that compared with the traditional data annotation and anomaly detection algorithm, the algorithm of weighted IForest and Siamese GRU improves the accuracy and real-time performance. Conclusions This paper proposes a weighted IForest and Siamese GRU algorithm architecture, which provides a more accurate and efficient method for outlier detection of data. Firstly, the framework uses the improved IForest algorithm to label the label-free data, Then the Siamese GRU is optimized by the improved F D A loss function,the optimized network is used to learn the distance between data for real-time and efficient anomaly detection. Experiments show that the framework has good potential. [ABSTRACT FROM AUTHOR]

• We propose a novel single-path hybrid model combined with dual attention mechanism for automatic arrhythmia classification under severe class imbalance. • The novel combination of local attention and global attention at various stages of the hybrid model significantly improves model performance. • Without any data-level augmentation methods, our approach achieves excellent accuracy and improves performance for the few sample classes. • The proposed model alleviates the negative impact of class imbalance in the ECG database and provides a new approach for applying the attention mechanism in arrhythmia classification. Arrhythmia classification based on electrocardiograms (ECG) can enhance clinical diagnostic efficiency. However, due to the significant differences in the number of different categories of heartbeats, the performance of classes with fewer samples in arrhythmia classification have not met expectations under the inter-patient paradigm. This paper aims to mitigate the adverse effects of category imbalance and improve arrhythmia classification performance. We constructed a novel dual attention hybrid network (DA-Net) for arrhythmia classification under sample imbalance, based on modified convolutional networks with channel attention (MCC-Net) and sequence-to-sequence network with global attention (Seq2Seq). The refined local features of the input heartbeat are first extracted by MCC-Net and then sent to Seq2Seq for further feature fusion. By applying local and global attention in the feature extraction and fusion parts, respectively, the method fully fuses low-level feature details and high-level context information and enhances the ability to extract discriminative features. Based on the MIT-BIH arrhythmia database, under the inter-patient paradigm without any data augmentation methods, the proposed method achieved 99.98% accuracy (ACC) for five categories. The various performance indicators are as follows: Class N: sensitivity (SEN) = 99.96%, specificity (SPEC) = 99.93%, positive predictive value (PPV) = 99.99%; Class S: SEN = 99.67%, SPEC = 99.98%, PPV = 99.56%; Class V: SEN = 100%, SPEC = 99.99%, PPV = 99.91%; Class F: SEN = 100%, PPV = 99.98%, SPEC = 97.17%. In further experiments simulating extreme cases, the model still achieved ACC of 99.54% and 98.91% in the three-category and five-category categories when the training sample size was much smaller than the test sample. Without any data augmentation methods, the proposed model not only alleviates the negative impact of class imbalance and achieves excellent performance in all categories but also provides a new approach for dealing with class imbalance in arrhythmia classification. Additionally, our method demonstrates potential in conditions with fewer samples. [ABSTRACT FROM AUTHOR]

The correct identification of individuals through different biometric traits is becoming increasingly important. Apart from traditional biomarkers (like fingerprints), many alternative measures have been proposed during the last two decades: electrocardiogram (ECG) and electroencephalogram (EEG) signals, iris or facial recognition, conductual traits, etc. Several works have shown that ECG-based recognition is a feasible alternative, either for stand-alone or multi-biometric recognition systems. In this paper, we propose a novel framework for ECG-based biometric identification, consisting of a simple and robust feature extraction approach and a clustering-based feature reduction method, that enables for an efficient and scalable biometric identification. The proposed feature reduction approach is a two phase method: it uses a clustering algorithm to group features according to their similarities first, and then clusters are represented in terms of a prototype vector and associated to the available subjects. On its side, the proposed time-domain feature extraction method is a semi-fiducial procedure, where the well-known Pan–Tompkins algorithm is first used to detect the R wave peaks of the QRS complexes, and then fixed-width time segments are selected for further dimensionality reduction and feature extraction. The resulting combined methods are efficient, robust, scalable and attain excellent results (with up-to 98.6% sensitivity) on all the subjects of the Physikalisch-Technische Bundesanstalt (PTB) database, regardless of their pathological or healthy status. Additionally, we also show how the existing Auto Correlation/Discrete Cosine Transform (AC/DCT)-based non-fiducial feature extraction method can be integrated within our framework, allowing us to attain up to 90.6% sensitivity on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Since this database is much noisier and has a much lower sampling rate (360 Hz instead of 1000 Hz), we claim that this is a very good result. • We propose a novel efficient framework for ECG-based biometric identification. • A clustering-based classifier is used to reduce the computational/storage cost. • Hierarchical agglomerative clustering (HAC) is used to build the clusters. • A novel semi-fiducial time-domain feature extraction method is proposed. • Statistical analysis of P-QRS-T complexes is performed on MIT-BIH arrhythmia and PTB. [ABSTRACT FROM AUTHOR]

• Presented an extensive overview on different stages of electrocardiogram analysis. • Summarized the most notable developments of smart health monitoring systems. • Discussed the latest hardware implementations of real-time ECG monitoring systems. • Identified challenges and limitations for various automatic ECG diagnosis systems. • Outlines the future vision of next generation ECG monitoring systems for healthcare. Over the last decade, cardiovascular diseases (CVD's) are the leading cause of death globally. Early prediction of CVD's can help in reducing the complications of high-risk patients. The electrocardiogram (ECG) is an efficient aiding tool, provides accurate information about various cardiac conditions of the human heart. Evaluation and interpretation of ECG signal has become the major goal in current research to identify and mitigate risky cardiovascular conditions. The ECG signal is efficiently analysed and classified using signal processing techniques, ranging from traditional to machine learning approaches and its subbranches, such as deep learning are used for the early detection and diagnosis of cardiac conditions and arrhythmias. The development of novel types of body sensors increases the need for automated, low-cost, real-time, and efficient ECG monitoring systems that can be used at home or in ambulatory settings. IoT in healthcare industry enables the doctors and specialists to diagnose the patient status remotely in a smart and efficient manner. Motivated with all the research done in this area for early diagnosis of cardiac abnormalities and save the life of high-risk patients, we surveyed numerous research works reported in the literature and provide a glance on several aspects of ECG signal monitoring systems. We first introduce the step-by-step framework of ECG signal analysis starting data acquisition to classification, describes each stage analysis of both traditional and advanced machine learning models that have been reported in the literature. We provide a deep discussion on ECG signal acquisition, pre-recorded clinical ECG data taken from the databases, signal processing and denoising, detection of feature based on feature engineering, and signal classification along with the comparative assessment amongst reviewed studies. This work also presents the detailed analysis of smart health care system that employs portable and wearable devices, as well as the Internet of Things (IoT) and wireless technologies, as an innovative medium for data transmission that allows for remote access to a patient's health status at lower costs and with greater efficiency. Moreover, this work provides an extensive knowledge on various hardware platforms that have been adopted for the development of biomedical processors including microcontrollers, FPGA and ASIC. Additionally, the challenges and limitations are discussed in this research field and directions for future work are suggested. This is the first review paper of its kind to present a comprehensive discussion on all aspects of cardiovascular disease monitoring systems in one place. [ABSTRACT FROM AUTHOR]

• WavelNet , a novel convolutional neural network architecture, is capable of wavelet transform-based spectral analysis of raw physiological signals. • Accurate, interpretable, and highly reproducible end-to-end arrhythmia classification can be accomplished by adopting WavelNet. • The MIT-BIH arrhythmia database and Google Colab Pro+ environment are adopted, allowing easy reproduction and application of this study. Automated detection of arrhythmias from electrocardiograms (ECGs) can be of considerable assistance to medical professionals in providing efficient treatment for patients with cardiovascular diseases. In recent times, convolutional neural network (CNN)-based arrhythmia classification models have been introduced, but their decision-making processes remain unclear and their performances are not reproducible. This paper proposes an accurate, interpretable, and reproducible end-to-end arrhythmia classification model based on a novel CNN architecture named WavelNet , which is interpretable and optimal for dealing with ECGs. Inspired by SincNet, which is capable of band-pass filtering-based spectral analysis, WavelNet was devised to achieve wavelet transform-based spectral analysis. WavelNet was trained using a subject-oriented five-class ECG arrhythmia dataset generated from the MIT-BIH Arrhythmia Database while following a benchmark scheme. By adopting various mother wavelets, multiple WavelNet -based arrhythmia classification models were implemented. To investigate whether our wavelet transform-based approach outperforms original end-to-end and band-pass filtering-based approaches, our proposed models were compared with vanilla CNN- and SincNet -based models. Model implementation and evaluation processes were repeated ten times in a Google Colab Pro+ environment. Furthermore, our most successful model was compared with state-of-the-art arrhythmia classification models for performance evaluation. The proposed WavelNet -based models showed excellent performance on classifying non-ectopic, supraventricular ectopic, and ventricular ectopic beats because of their ability to perform adaptive spectral analysis while preserving temporal ECG information compared with vanilla CNN- and SincNet -based models. In particular, a Symlet 4 wavelet-adopting WavelNet -based model achieved the best performance with nearly 90% overall accuracy as well as the highest levels of sensitivity in classifying each arrhythmia class: 91.4%, 49.3%, and 91.4% for non-ectopic, supraventricular ectopic, and ventricular ectopic beat classifications, respectively. These results were comparable to those of state-of-the-art models. In addition, the results are reproducible, which differentiates our study from previous studies. Our proposed WavelNet -based arrhythmia classification model achieved remarkable performance based on a reasonable decision-making process, in comparison with other models. As its noteworthy performance is clinically reasonable and reproducible, our proposed model can contribute toward implementing a real-world precision healthcare system for patients with cardiovascular diseases. [ABSTRACT FROM AUTHOR]

A classifier for patient adaptability is vital to overcome instances of inter and intra-patient variance using the automatic arrhythmia diagnosis approach is currently a high focus. The design of an automated cardiac arrhythmia diagnosis system to detect and classify the heartbeats is an active research topic. The paper describes an efficient heart monitoring system in remote areas where cardiologists are scarce and therapies are highly-priced. The proposed technique incorporates the pre-processing of the ECG (electrocardiogram) signals, detecting the Quick Reaction Strike (QRS) beats, characteristics extraction from ECG, identifying abnormality in ECG waveforms and categorizing the ECG beats with a profile curve. A mathematical procedure adopted to filter out unwanted sounds that adjust the window length depending on the proximity. The shifting window mean technique eradicates high-frequency noise that includes interference from a power line and noise from electromyography. The Fast Fourier Transform (FFT) eliminates the low-frequency noise baseline drifting and motion artefact. The most distinguishable waveforms noticed in ECG are QRS beats. Hence, the detection of the complexes in QRS is vital for the analysis of an ECG. The suggested work showcases high rate accuracy, minimal sensitivity, and high specificity. [ABSTRACT FROM AUTHOR]

When compared to the general population, patients with rheumatoid arthritis (RA) have an overall standard mortality ratio of approximately two, with more than 50% of premature deaths attributable to cardiovascular disease (CVD). Moreover, RA patients were twice as likely to experience sudden cardiac death (SCD) compared with non-RA subjects, as a putative consequence of an increased incidence of malignant arrhythmias. Accordingly, mounting data indicate that in patients affected with RA the risk of developing rhythm disturbances, particularly tachyarrhythmias, is high. Although a number of papers reviewing the problem of cardiovascular involvement in RA are currently available, the main focus is on the mechanisms of accelerated atherosclerosis and related ischemic consequences in the clinical setting. On the contrary, only little consideration has been specifically given to the arrhythmic risk so far. In the light of this concern, in the present paper we reviewed the topic with the aim to put together the apparently fragmentary existing information, with particular attention to the putative role of chronic systemic inflammation characterizing the disease. In fact, although the underlying mechanisms accounting the arrhythmogenic substrate in RA are probably intricate, the leading role seems to be played by inflammatory activation, able to promote arrhythmias either indirectly, by accelerating the development of structural CVD, and directly by affecting cardiac electrophysiology. In this view, lowering inflammatory burden through an increasingly tight control of disease activity may represent the most effective intervention to reduce arrhythmic risk and prevent life-threatening complications in these patients. [ABSTRACT FROM AUTHOR]

We observed 10.3% cases of ischemia in our patients during 5-FU infusion; in nearly half it was effort-induced (asymptomatic in 68%, and with severe arrhythmias in 15%). The only predictive factors were atypical symptoms during daily life. Silent and effort ischemia may be easily underdiagnosed. A practical screening flow-chart using stress-test is suggested.Background Effort-induced myocardial ischemia (EMI) has been seldom described. Aims of our study were (A) to evaluate the prevalence of EMI during long-lasting 5-FU infusion; (B) to identify possible risk factors of EMI during 5-FU infusion. Patients and methods For the purpose (A), we prospectively evaluated a group of patients undergoing in-hospital continuous infusion (c.i.) of 5-FU. Patients with rest ischemia were excluded. Among 358 consecutive patients, 21 (5.9%) had rest ischemia; 109 could not perform a stress test. The remaining 228 patients underwent a treadmill stress test (TST) after >46 h of 5-FU infusion. For the purpose (B), we compared the characteristics of patients with EMI (including 3 previously described in a 2001 paper) with those without EMI. Results Among 228 patients, 16 (6.9%) had EMI. These 16 had a second TST after stopping 5-FU: in 14, it was negative, 2 patients with coronary artery disease had milder ischemia. The whole group of 231 (including 3 described in a previous paper) patients undergoing TST included 148 males and 83 females, with mean age of 57.5. Cardiovascular risk factors were present in 178 of them. Eight patients had ischemic heart disease. Among 19 patients with EMI, 7 had angina, 12 silent ischemia. ST segment at ECG was elevated in 10 patients, depressed in 9. Comparing the group with toxicity and the one without, the only significant difference was the complaint of atypical symptoms at rest before the TST. No difference was observed as regards: chemotherapy schedule (chronic c.i. in 49, 5 days in 178, FOLFOX type in 12), coronary risk factors or heart disease. Conclusions EMI is as frequent as rest ischemia during 5-FU infusion. Patients undergoing 5-FU continuous infusions should be adviced to avoid unusual efforts, to refer any cardiac symptom, and should be investigated for EMI. [ABSTRACT FROM PUBLISHER]