Search

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]

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]

• The paper introduces an adaptive dynamic graph for multi-label classification of ECG signals. The dynamic graph is generated through adaptive capturing of the category relationships in the signals, thereby improving the correlations among different labels. • The paper proposes the ED-DGCN, which is mainly composed of three modules. The encoder block is utilized to extract signal feature information. The decoder block can define semantic regions and generate a categorical representation of content perception for each ECG signal. The DGCN mainly models category representation relationship learning. • The transfer learning method is adopted to pre-train the network model on a large dataset and to load the training weight onto the CPSC2018 dataset. The effectiveness of the proposed method for arrhythmia classification was verified by experiments on the CPSC2018 dataset. Cardiac arrhythmia is a condition characterized by abnormal heart electrical activity, posing a significant health risk to patients. Despite significant progress in existing arrhythmia classification methods, the accuracy of network-based classification still requires improvement. In the paper, we propose an effective classification algorithm called Encoder-Decoder Dynamic Graph Convolutional Network (ED-DGCN). In the encoder stage, a convolutional neural network(CNN) is utilized to extract feature information from electrocardiogram signals. The decoder stage employs a bidirectional long short-term memory network to define semantic regions and generate corresponding class relationships. A dynamic graph convolutional network module is constructed to capture label dependencies by obtaining a dynamic adjacency matrix. Additionally, transfer learning is employed by pre-training the proposed network on a large dataset to further enhance convergence performance and classification accuracy. The proposed method is verified on the China Physiological Signal Challenge 2018 (CPSC2018) dataset. Experimental results demonstrate that the average F1-score of our method achieved 0.843, which is 1.6 % improved over existing methods. The proposed method effectively enhances the classification performance of the network and provides an effective solution for multi-label classification tasks in cardiac arrhythmias. The method holds promising prospects for clinical applications. [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]

Cardiac arrhythmias that can lead to sudden cardiac death are common. Electrocardiograms (ECGs) offer valuable information about cardiac status and play a crucial role in evaluating patients with arrhythmia in clinical diagnosis. The development of machine learning technologies has made the computer-aided diagnosis of cardiac arrhythmias possible, which can improve the efficiency and quality of medical treatment. In this study, a new method for the automatic classification of heartbeats is developed. This paper presents a novel approach to detect arrhythmias in the inter-patient paradigm using a convolutional neural network (CNN) that integrates multiscale convolutional blocks, frequency convolutional block attention (FCBA) modules, and RR interval features. The proposed method also includes oversampling for the heartbeats of the minority class and the addition of random noise to increase training samples and alleviate the problem of data imbalance. We used the MIT-BIH-AR arrhythmia database , which is a globally recognized ECG database, to evaluate the classification performance of the proposed model in this paper. Based on the experiments, the sensitivities of the normal beats (N), supraventricular ectopic beats (SVEB), and ventricular ectopic beats (VEB) were 96.9%, 89.3%, and 93.3%, respectively. In the inter-patient heartbeat classification paradigm, the overall accuracy of the proposed method is 95.60% in classifying heartbeats. The study shows that the proposed scheme outperforms other published schemes in terms of classification results. • A heartbeat classification method combining deep neural networks and medical features is proposed. • The use of multiscale convolutional fusion allows the network to extract richer electrocardiogram (ECG) features. • A frequency convolutional block attention (FCBA) module is proposed to adjust the wight relationships. • Medical features, namely RR intervals, are embedded into our deep model to further improve the performance of heartbeat classification. • Accuracy of 95.6% was achieved under inter-patient scheme. [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 most common and clinically important cardiac arrhythmia is atrial fibrillation (AF), which has a large negative impact on public health due to higher fatalities, morbidity, and healthcare expenditure rates. This study aims to provide valuable insights into the effectiveness and outcomes of various treatment approaches and interventions for AF. Systematic review. The most pertinent published research (original papers and reviews) in the scientific literature were searched for and critically assessed using the online, internationally indexed databases PubMed, Medline, and Cochrane Reviews. These studies are summarised in this review. Keywords like "Atrial Fibrillation", "emerging therapies", "treatment", "catheter ablation", and "atrial appendage" were used to search the papers. The papers were researched and examined to be relevant to the topic. A lot of work has gone into enhancing AF management to deal with this expanding public health concern. Significant developments and advances in the treatment of AF during the past few years have aided clinicians in giving AF patients better care. The most recent treatments for AF include medication, catheter ablation, cryo-balloon ablation, and left atrial appendage closure. [ABSTRACT FROM AUTHOR]

To address the problems of the imbalanced datasets, limited computing power, and memory capability of wearable devices, this paper proposes a new hybrid method for imbalanced dataset mitigation and a novel lightweight Convolutional Neural Network (CNN) framework for electrocardiogram(ECG) signal classification. First, data enhancement method is used by changing the amplitude of a small number of samples and combining with the weighted loss function in the deep learning model. Then, One-Dimensional Convolutional Block Attention Module (1D_CBAM) mechanism autoencoder model is proposed to compress the ECG heartbeat signals and extract the features of the ECG heartbeats. A modified one-dimensional lightweight CNN model based on MobileNet architecture is eventually used to categorize various arrhythmias utilizing the above features. Using the MIT-BIH arrhythmia dataset, the proposed model is tested by dividing the MIT Database(MITDB) into Dataset1 and Dataset2, in conjunction with suggestions from AAMI and other heartbeat signal classification methods. The experimental results show that the proposed imbalanced dataset mitigation method has higher performance than other methods in this paper, the proposed model has the most stable convergence and the lowest reconstruction loss (approximately 0.1) in the feature extraction process under the same conditions. During the classification process, the lightweight CNN model has approximately 4x fewer trainable parameters(Params: 12,788) and its performance [Dataset1: F1-Score: 96.24% ACC: 98.21%; Dataset2: F1-Score: 98.73% ACC: 99.61%] is comparable to the traditional CNN model [Dataset1: F1-Score: 96.98% ACC: 99.17%; Dataset2: F1-Score: 99.18% ACC: 99.72%]. • We develop a new processing method for mitigating the imbalanced dataset. It contains the following two steps, the first step is data augmentation, and the second step is class weight optimization. • We propose a new data compression and coding model based on the autoencoder Algorithm. This method can be used to reconstruct the ECG signal while paying attention to the amplitude characteristic of the signal itself, as well as the correlations between the temporal and spatial position of ECG signals. • We design the lightweight CNN model based on MobileNet architecture to reduce the number of parameters and computations of the traditional CNN model. [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]

• This paper presents a novel deep wavelet convolutional neural network for end-to-end ECG signal denoising. • The proposed DW-CNN avoids QRS waveform detection, which can avoid iterative errors caused by QRS complex detection. Additionally, there is no need to perform any other normalization operation to simplify the preprocess process. • CNN is used as an automatic feature extractor to build an encoder and decoder, which can effectively automatically extract numerous deep features and achieve end-to-end automatic noise reduction. This end-to-end method reaches better denoising performance and generalization ability. • For overcoming the problem of simply discarding the input information caused by the pooling layer, this paper utilizes DWT and IDWT to build DWT-based pooling layer and IDWT-based up-sampling layer, which can retain all the effective information and improve the denoising performance. • Based on the dataset under the real noise conditions, compared with the existing methods, the proposed denoising model has better denoising performance, which reduces 0.194 RMSE and improves 5.99 SNR on average. Recently, more than 80% of sudden cardiac death is caused by arrhythmia, whose incidence has increased rapidly. In the actual wearable device acquisition process, ECG recordings are polluted by many different types of noise, mainly including baseline drift (BW), muscle artifact (MA) and electrode motion (EM). Therefore, the effective de-noising of ECG signals plays an important role in the subsequent accurate arrhythmia diagnosis. Nowadays, traditional filtering methods often only focus on one or two kinds of noise. When encountering different noises, different filtering algorithms need to be used, resulting in insufficient generalization ability of the algorithms. Aiming to effectively denoising ECG signals, this paper proposes a novel signal denoising method based on deep wavelet convolutional neural network. Inspired by the structure of the de-noising self-encoder, the convolution layers are used to replace the simple full-connected layers to build the encoder and decoder. Based on automatic feature extraction in convolution layer, the discrete wavelet transform is used to convert the signal into high-frequency and low-frequency components for replacing pooling layers to compress the input data and fully preserve the effective information. Finally, based on the dataset under the real noise conditions, compared with the existing methods, the proposed denoising model has better denoising performance, which reduces 0.194 RMSE and improves 5.99 SNR on average. Thus, the proposed denoising model has good potential in wearable devices. [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]

The electrocardiogram (ECG) is immensely beneficial for diagnosing the arrhythmias that may lead to serious complications in the heart health. In this paper, the continuous wavelet transform (CWT) was used for electrocardiogram arrhythmias detection. The natural arrhythmias were: Supra-ventricular arrhythmias (SV), atrioventricular (AV) and Normocardia (NC) were chosen for detection as well as for testing the proposed method The Natural signals were taken from MIT-BIH database to be used for testing. The continuous wavelet transform was connected to the standard deviation (SD) and Shannon entropy (SE) for feature extraction stage. For classification a safe threshold has been suggested to discriminate between the different arrhythmias. Several combinations of the CWT application were testing. The wavelet packet transform was used for comparison. All combinations have given reasonable results, but continuous wavelet transform with standard deviation taken for the third sub signal have given the superior results. The results of our study will open the door for choosing the continuous transform for detection that has been neglected by the researchers for this task. [ABSTRACT FROM AUTHOR]

• Combining 2D ECG beats with novel deep learning approach to identify arrhythmias. • 2D ECG beat images are provided from 1D ECG signals in the MIT-BIH database. • Outperforming method alleviating imbalanced data in arrhythmia classification. • The automatic cardiac arrhythmia detection method improves diagnostic efficiency. Electrocardiogram (ECG) is a method of recording the electrical activity of the heart and it provides a diagnostic means for heart-related diseases. Arrhythmia is any irregularity of the heartbeat that causes an abnormality in the heart rhythm. Early detection of arrhythmia has great importance to prevent many diseases. Manual analysis of ECG recordings is not practical for quickly identifying arrhythmias that may cause sudden deaths. Hence, many studies have been presented to develop computer-aided-diagnosis (CAD) systems to automatically identify arrhythmias. This paper proposes a novel deep learning approach to identify arrhythmias in ECG signals. The proposed approach identifies arrhythmia classes using Convolutional Neural Network (CNN) trained by two-dimensional (2D) ECG beat images. Firstly, ECG signals, which consist of 5 different arrhythmias, are segmented into heartbeats which are transformed into 2D grayscale images. Afterward, the images are used as input for training a new CNN architecture to classify heartbeats. The experimental results show that the classification performance of the proposed approach reaches an overall accuracy of 99.7%, sensitivity of 99.7%, and specificity of 99.22% in the classification of five different ECG arrhythmias. Further, the proposed CNN architecture is compared to other popular CNN architectures such as LeNet and ResNet-50 to evaluate the performance of the study. Test results demonstrate that the deep network trained by ECG images provides outstanding classification performance of arrhythmic ECG signals and outperforms similar network architectures. Moreover, the proposed method has lower computational costs compared to existing methods and is more suitable for mobile device-based diagnosis systems as it does not involve any complex preprocessing process. Hence, the proposed approach provides a simple and robust automatic cardiac arrhythmia detection scheme for the classification of ECG arrhythmias. [ABSTRACT FROM AUTHOR]

• End-to-end single-lead ECG diagnosis with high accuracy and efficiency. • Multi-scale convolutions and hybrid RNNS for multi-angle ECG insights. • Imbalanced clinical datasets: realistic data distribution. • Segmentation and recombination: for large-scale datasets with varying lengths. Arrhythmia is a prevalent cardiovascular disease that can unveil various heart health issues. In recent times, the rise of wearable devices has garnered attention towards the advantages of portable single-lead ECG devices in heart health monitoring. Achieving end-to-end ECG automatic diagnosis through deep learning is essential due to the time-consuming and labour-intensive characteristics of manual diagnosis of ECG records. This paper presents the design of a hybrid residual recurrent convolutional neural network (HRRCNN) for automatically diagnosing single-lead ECG with multiple labels. Firstly, the convolutional layers and residual networks extract the spatial and short-term temporal features. Subsequently, a hybrid recurrent neural network (RNN) comprised of a double-layer bidirectional gated recurrent unit (GRU) and a bidirectional long short-term memory (LSTM) is employed to capture long and short-term temporal features. Finally, an output layer, comprising an attention layer and a fully connected layer, converts the extracted features into classification results. To evaluate the performance of HRRCNN, a dataset from the Shanghai First People's Hospital Affiliated to Shanghai JiaoTong University(Shanghai General Hospital, SGH) is utilized. The experimental results demonstrate that HRRCNN achieves an accuracy of 95.08 %, F1-macro of 87.22 %, Sensitivity of 88.51 %, and a positive prediction rate of 86.13 %. Comparative analysis with existing methods underscores the superior performance of HRRCNN, thereby establishing its potential as an out-of-hospital heart health monitoring tool and an auxiliary tool in clinical settings. [ABSTRACT FROM AUTHOR]

Premature ventricular contractions (PVCs) are a common type of arrhythmia and can be life-threatening if deteriorated. There are several challenges remain to be resolved to the inter-patient paradigm of PVCs detection task by deep learning approaches. This research aims to overcome data imbalance, inaccurate ECG segments labeling and finding a generalized end-to-end model. In this paper, we present a deep learning network, PVCNet, to detect PVCs based on single-lead ECG. All ECG records are firstly split into fixed length segments based on the R-peak locations. To obtain more contextual information and improve the accuracy of segments labeling, we innovatively label the segment with sequence heartbeats label in the single heartbeat classification task, which was widely labeled with single heartbeat label in previous practices. Meanwhile, a loss function of redesigned weights is introduced to encourage the model to focus on detecting the specific heartbeat in the input segment. Additionally, a novel data augmentation method is conducted to alleviate the data imbalance problem. The proposed model is trained and validated on the MIT-BIH Arrhythmia Database (MITDB) with the inter-patient paradigm and tested on the St.Petersburg INCART Database (INCARTDB). It achieves a sensitivity of 89.91%, positive prediction value of 96.72%, F1-score of 93.19% and an overall accuracy of 98.50% on the testing database and outperforms existing approaches. The experimental results validate the effectiveness of the proposed approach and potentially can be applied to long-term ECG monitoring scenarios for the PVC detection task. [Display omitted] • We label the input ECG segment with a sequence of four heartbeats which decreases the possible label noise and provides more contextual information. • A loss function of redesigned weights is introduced to encourage the model to focus on detecting the specific heartbeat in the input segment. • Several comparisons and ablation studies results validate the effectiveness of the proposed PVC detection approach. [ABSTRACT FROM AUTHOR]

Abstract This paper presents an robust and efficient unsupervised method for ECG arrhythmia classification. The proposed method consists of two steps: feature selection and clustering. In proposed method, initially the input ECG data is fed into feature selection method to reduce the dimensionality. In this paper, three well known feature selection methods i.e., Principal Component Analysis, Linear Discriminant Analysis (LDA) and Regularized Locality Preserving Indexing (RLPI) are employed. Further, the reduced data is clustered using a Robust Spatial Kernel FCM (RSKFCM) method. RSKFCM is variant of FCM method which uses Gaussian kernel as distance metric and incorporates neighborhood information. To evaluate the proposed method the experimentation's are conducted on UCI arrhythmia dataset and compared the results with traditional FCM and Kernel Fuzzy C-Means (KFCM). Extensive experimental results show that, the proposed RSKFCM method with RLPI feature selection method outperforms other methods in comparison. [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]

• To pre-process the collected ECG signals using the Sequential Savitzky-Golay filtering model (SEQ-SG). • To present Attention Dilated U-Net Model (ADil-UNet) model for segmentation of converted multimodal images. • To extract the features using Swin-Patch-Transformer (Swin-PT) model, the classification process is performed using Coati Depth Wise Convolutional Deep Capsule Network (DeW-DCN). • To optimize the loss function, the Coati optimization algorithm (CoA) is used to optimize the hyperparameters of the network model. Arrhythmias are irregular changes in the normal heartbeat that take a long time to diagnose manually and depend on the doctor's experience. In everyday clinical practice, the electrocardiogram (ECG) is a critical diagnostic tool for predicting cardiac arrhythmias. However, in order to produce superior results, the feature learning ability and parameter consideration must be efficient. As a result, this paper proposes a novel automated deep-learning model for improving segmentation and classification performance. First, the collected ECG signals are pre-processed using the sequential Savitzky-Golay filter model (SEQ-SG) to remove noises and unwanted artifacts from the signal. Using the Markov Transition Field (MTF), Recurrence Plot (RP), and Gramian Angular Field (GAF), the pre-processed ECG data are turned into a multi-modal image. The converted images are segmented using the Attention Dilated U-Net Model (ADil-UNet) to identify the relevant regions in the multi-modal image. The appropriate features are extracted from the segmented images using the Swin-Patch Transformer (Swin-PT) model to reduce feature dimensionality issues. The extracted features can be merged using a weighted fusion method, and the Coati Depth Wise Convolutional Deep Capsule Network (DeW-DCN) is used to classify arrhythmias as normal or abnormal. To optimize the loss function, the network model's hyperparameters are tuned using the Coati optimization algorithm (CoA). Python software is used to implement the proposed method. The proposed method results in 99.31 % accuracy for the MIT-BIH arrhythmia dataset and 99.58 % accuracy using the UCI arrhythmia ECG dataset. [ABSTRACT FROM AUTHOR]

• Our proposed MMDN can extract features from ECG signals in multiple dimensions, which can improve arrhythmia classification performances effectively by fusing these features. • The proposed MSTA can extract multi-timescale features from ECG signals, leading to improve arrhythmia classification accuracy. • The auxiliary information was introduced into the classification network to enhance the arrhythmia classification performances through using the relationship between different types of arrhythmias and auxiliary information. Automatic arrhythmia classification plays an important role in preventing cardiac death. Due to the intricate multi-periodic patterns inherent in arrhythmias, how to improve the classification accuracy is a challenging problem. In this paper, a multi-scale multi-view dual-branch fusion network (MMDN) is proposed to implement accurate and interpretable arrhythmia classification by fusing features at different levels. The proposed MMDN method consists of three parts: a multi-view block, an additional information fusion block, and a feature fusion block. The multi-view block employs channel, spatial, and the proposed multi-scale temporal attention module to extract anomalous features in raw data from diverse perspectives. Subsequently, the output of the multi-view block is fed into an additional information fusion block, which enhances features by incorporating auxiliary information such as age and gender. The feature fusion block combines the output to produce recognition results using a multi-layer perceptron. Signal Challenge 2018 database (CPSC 2018 DB) is used to validate the classification performances of the proposed MMDN method. Experimental results demonstrate that MMDN outperforms current state-of-the-art methods for ECG classification tasks, with an accuracy of 0.861, a recall of 0.844, and an F1 score of 0.850. [ABSTRACT FROM AUTHOR]

In this paper, Recurrent Neural Networks (RNN) have been applied for classifying the normal and abnormal beats in an ECG. The primary aim of this paper was to enable automatic separation of regular and irregular beats. The MIT-BIH Arrhythmia database is being used to classify the beat classification performance. The methodology used is carried out using huge volume of standard data i.e. ECG time-series data as inputs to Long Short Term Memory Network. We divided the dataset as training and testing sub-data. The effectiveness, accuracy and capabilities of our methodology ECG arrhythmia detection is demonstrated and quantitative comparisons with different RNN models have also been carried out. [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]

Electrocardiogram (ECG) is the most frequent and routine diagnostic tool used for monitoring heart electrical signals and evaluating its functionality. The human heart can suffer from a variety of diseases, including cardiac arrhythmias. Arrhythmia is an irregular heart rhythm that in severe cases can lead to stroke and can be diagnosed via ECG recordings. Since early detection of cardiac arrhythmias is of great importance, computerized and automated classification and identification of these abnormal heart signals have received much attention for the past decades. This paper introduces a light Deep Learning (DL) approach for high accuracy detection of 8 different cardiac arrhythmias and normal rhythms. To employ DL techniques, the ECG signals were preprocessed using resampling and baseline wander removal techniques. The classification was performed using an 11-layer network employing a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). In order to evaluate the proposed technique, ECG signals are chosen from the two physionet databases, the MIT-BIH arrhythmia database and the long-term AF database. The proposed DL framework based on the combination of CNN and LSTM showed promising results than most of the state-of-the-art methods. The proposed method reaches the mean diagnostic accuracy of 98.24%. A trained model for arrhythmia classification using diverse ECG signals were successfully developed and tested. This study presents a lightweight classification technique with high diagnostic accuracy compared to other notable methods, making it a potential candidate for implementation in Holter monitor devices for arrhythmia detection. Finally, we used SHapley Additive exPlanations (SHAP), the most popular Explainable Artificial Intelligence (XAI) method to understand how our model make predictions. The results indicate that those features (ECG samples) that have contributed the most to a prediction are consonant with clinicians' decisions. Therefore, the use of interpretable models increases the trust of clinicians in AI and thus leads to decreasing the number of misdiagnoses of cardiovascular diseases. • LSTM along with CNN architectures used as input affects sequence-based prediction. • The proposed model detects and classifies 8 arrhythmias and normal rhythm. • Shapley values are used to show the influence of each ECG sample on the models prediction. [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]

• 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]

In order to improve the classification performance of the model for different kinds of arrhythmias, a parallel classification model of arrhythmia based on DenseNet-BiLSTM is researched and proposed. Firstly, the model adopts a parallel structure. After wavelet denoising and heartbeat segmentation of ECG signals, this model can simultaneously capture the waveform features of small-scale heartbeat and large-scale heartbeat containing RR interval; Then, based on deep learning, Densely connected convolutional network (DenseNet) is applied to improve the model's ability to extract local features of ECG signals, and bidirectional long short-term memory network (BiLSTM) is introduced to improve the performance of the model in extracting time series features of ECG signals; Finally, weighted cross entropy loss function is used to alleviate the class imbalance of arrhythmia, and Softmax function is applied to achieve 4 classifications of arrhythmia. Experiments based on MIT-BIH arrhythmia database show that under the intra-patient paradigm, training time for each epoch, overall accuracy, F 1 and specificity are 42 s, 99.44%, 95.89% and 99.32%, respectively; Under the inter-patient paradigm, training time for each epoch, overall accuracy, F 1 and specificity are 23 s, 92.37%, 63.49% and 94.51%, respectively. Compared with other classification models, the model proposed in this paper has a good classification effect and is expected to be used in clinical auxiliary diagnosis. [ABSTRACT FROM AUTHOR]

Proposed overall architecture. [Display omitted] • Heart failure occurs when heart fails to pump adequate amount of blood to the body. • This paper develops an automatic CHF prediction system to assist cardiologists. • To achieve this target, HBA-FRCNN is proposed for CHF prediction with high accuracy. • The HBA-FRCNN classifies the abnormal ECG signals from BIDMC and MIT-BIH database. • It is highly effective in terms of all performance metrics in CHF prediction. If the blood circulation of the heart is not adequate then it causes arrhythmias and Congestive Heart Failure (CHF) which requires immediate medical attention or else it leads to the loss of one's life. An Electrocardiogram (ECG) is a golden standard to diagnose the fatal complications in the heart caused by arrhythmias and it comprises a massive information related to the heartbeat rhythm. The main challenge focused in this paper is to extract the crucial information present in the ECG signal by visual analysis and classify the different abnormalities exhibited in the ECG signal. This paper presents a Honey Badger Algorithm optimized Faster Region-based Convolutional Neural Network (HBA-FRCNN) for CHF prediction with higher diagnostic accuracy. The noisy input ECG signals such as muscle contraction, electrode touch noise, and different noise artifacts are preprocessed using the Delayed Normalized Least Mean Square (DNLMS). The electrocardiographic complex (QRS complex) consisting of the Q, R, and S waves are extracted using the Discrete Cosine Transform (DCT) and fast Fourier transforms (FFT). The target detection box and the anchor parameter for the FRCNN model are tuned using the HBA algorithm to overcome the missed target detection, overfitting, and computational cost. The ECG signals for this study were obtained from Beth Israel Deaconess Medical Center (BIDMC) Congestive Heart Failure Database and the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Normal Sinus Rhythm Database. The proposed methodology offers an accuracy, positive predictive value, sensitivity, and specificity score of 98.65%, 97.81%, 98.5%, and 98.2% respectively when evaluated with the ECG signals of the two datasets. For the Cardiac Arrhythmias (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR) classes present in the MIT-BIH dataset, the proposed model offers an accuracy of 99%, 100%, and 98% respectively and for the classes such as CHF severe and CHF normal in the BIDMC dataset, it offers an accuracy of 98% and 97%. The study mainly demonstrates the effectiveness of the FRCNN technique in predicting arrhythmias and CHF in patients by taking the increased number of features in the ECG signal. It also serves as a promising solution for physicians for long-time surveillance of patients prone to CHF with abnormal heartbeat rhythm. [ABSTRACT FROM AUTHOR]

• Compression performance comparative analysis. • Compression ratio. • Data reconstruction without diagnostic information. Compression of an electrocardiogram (ECG) signal has given much consideration to the researchers since the computer-aided analysis of ECG has come into being. In some critical cases, viz. , astronauts, a person under cardiac surveillance, ambulatory patients and in Holter monitoring system, continuous ECG data recording and transmitting from one location to other location is required. However, the size of the recorded data becomes so voluminous, that its transmission of data becomes practically impossible. In this paper, ECG data compression using wavelet-based techniques are presented, such that: a) wavelet packet transform with run-length encoding (RLE), b) wavelet transform with Huffman encoding, c) wavelet transform with RLE and d) wavelet transform and Lempel ZivWelch (LZW). The results have been tested using MIT-BIH (Massachusetts Institute of Technology/Beth Israel Hospital) arrhythmia databases. The performances of these methodologies are examined in the quantitative and qualitative manner. From Tabular results, it can be observed that the methodology based on WT and LZW provides efficient results in terms of compression ratio (CR ∼ = 20 to 30) and peak root mean square difference (PRD ∼ = 0.01 to 1.8) both, hence the overall QS value is improved. [ABSTRACT FROM AUTHOR]

Single‑lead electrocardiograms (1 L-ECGs) are increasingly used in (pre)clinical settings for the detection and monitoring of a range of rhythm and conduction disorders. In this short communication paper, we aim to provide an overview of the usefulness and potential pitfalls when implementing 1 L-ECGs into everyday clinical practice. Moreover, we provide recommendations for improving signal quality, as well as a systematic approach to the interpretation of 1 L-ECGs, which is somewhat different from standard 12‑lead ECGs. Clinicians can use our illustrations and checklist as guidance when recording and interpreting 1 L-ECGs. [ABSTRACT FROM AUTHOR]

Accurate and early detection of cardiac arrhythmia present in an electrocardiogram (ECG) can prevent many premature deaths. Cardiac arrhythmia arises due to the improper conduction of electrical impulses throughout the heart. In this paper, we propose an improved RR interval-based cardiac arrhythmia classification approach. The Discrete Wavelet Transform (DWT) and median filters were used to remove high-frequency noise and baseline wander from the raw ECG. Next, the processed ECG was segmented after the determination of the QRS region. We extracted the primary feature RR interval and other statistical features from the beats to classify the Normal, Premature Ventricular Contraction (PVC), and Premature Atrial Contraction (PAC). The K-Nearest Neighbour (k-NN), Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), and Random Forest (RF) classifier were utilised for classification. Overall performance of SVM with Gaussian kernel achieved Se % = 99.28, Sp % = 99.63, +P % = 99.28, and Acc % = 99.51, which is better than the other classifiers used in this method. The obtained results of the proposed method are significantly better and more accurate. [ABSTRACT FROM AUTHOR]

Real-time electrocardiogram (ECG) monitoring through portable or wearable devices is critical for detecting lethal arrhythmias. Despite the accuracy of 12-lead ECGs in clinical image analysis, their integration into portable devices poses challenges. This paper introduces a novel method for nonmalignant arrhythmia classification, optimized for wearable and portable devices. We utilize ECG records from Shanghai First People's Hospital, proposing a lightweight neural network strategy involving benchmark network selection, model pruning, and learning rate decay-based finetuning. The proposed Random Horizontal Flip (RHF)-based classification method demonstrated superior performance, achieving a 94.8 % accuracy on a 12-lead clinical ECG test dataset. Utilizing the modified pruning method, the classification accuracy for five-class ECGs improved by 7.2 % over the benchmark network. The model size was reduced by 51.26 %, with parameters and FLOPs decreasing by 47.6 % and 49.1 %, respectively, compared to the benchmark, all under identical hardware conditions. Further experiments were conducted on the Hold-out Test Set (HTS), designed to include ECGs that present slight variations to the original conditions, yielding a slightly lower accuracy of 92.4 %, reflecting the dataset's complexity and clinical variability. Moreover, benchmarking tests using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database validated the method's effectiveness, achieving a 99.24 % accuracy and maintaining a lightweight model of 4366KB. Comparative analysis with existing methods confirmed the proposed method's superiority in accuracy and real-world applicability. This research presents a significant advancement in ECG analysis, offering a viable solution for efficient arrhythmia monitoring in portable healthcare devices. • 12-lead ECG data used for nonmalignant arrhythmia classification. • Model's generalization tested with noisy, varied hold-out dataset. • Lightweight neural network strategy merges selection, pruning, finetuning, and augmentation. • Method's accuracy and practicality confirmed with MIT-BIH database. • Approach tailored for wearable devices on monitoring arrhythmias. [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]

Cardiac arrhythmias, deviations from the normal rhythmic beating of the heart, are subtle yet critical indicators of potential cardiac challenges. Efficiently diagnosing them requires intricate understanding and representation of both spatial and temporal features present in Electrocardiogram (ECG) signals. This paper introduces ECGTransForm , a deep learning framework tailored for ECG arrhythmia classification. By embedding a novel Bidirectional Transformer (BiTrans) mechanism, our model comprehensively captures temporal dependencies from both antecedent and subsequent contexts. This is further augmented with Multi-scale Convolutions and a Channel Recalibration Module, ensuring a robust spatial feature extraction across various granularities. We also introduce a Context-Aware Loss (CAL) that addresses the class imbalance challenge inherent in ECG datasets by dynamically adjusting weights based on class representation. Extensive experiments reveal that ECGTransForm outperforms contemporary models, proving its efficacy in extracting meaningful features for arrhythmia diagnosis. Our work offers a significant step towards enhancing the accuracy and efficiency of automated ECG-based cardiac diagnoses, with potential implications for broader cardiac care applications. The source code is available at https://github.com/emadeldeen24/ECGTransForm. [ABSTRACT FROM AUTHOR]

In this paper, we present a new method based on deep learning for the detection of cardiac arrhythmias. The proposed method is based on three fundamental components: an inception-ResNet structure, bidirectional LSTM layers and a multi-scale signal analysis technique. To improve processing of the MIT-BIH arrhythmia dataset, we have developed a three-stage CBD filter that successively incorporates Chebyshev, Butterworth and Daubechies filters. This filter is designed meticulously to denoise the ECG signal while preserving its relevant features. By combining this filter with our deep learning model and multi-scale approach, we have significantly improved the quality of the ECG signal. Such integration not only enhances the accuracy and reliability of arrhythmia detection, but also contributes to the identification of complex patterns in various temporal and spatial dimensions. In terms of performance, our model is remarkably robust, with 98.94% accuracy and 97.20% precision. • Introduced a three-stage CBD filter for the MIT-BIH arrhythmia dataset. • Utilized inception-ResNet, bidirectional LSTM and multi-scale signal analysis. • Identified patterns in diverse temporal and spatial dimensions. [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]

• For the first time, we conducted a study on the automatic diagnosis of pediatric ECGs in high-altitude areas. • We developed a model that combines a GA and the stacked ensemble method, capable of differentiating between normal and abnormal ECGs and classify four types of rhythm. • We incorporated not only popular ML models but also the most successful deep learning models for tabular data tasks in recent years into the baseline models, enabling us to demonstrate the advantages of the developed model. • We conducted a characteristic importance analysis, revealing important factors contributing to ECG abnormalities in children living in high-altitude regions. Electrocardiogram (ECG) is a commonly used diagnostic tool in clinical practice that plays a vital role in the diagnosis and treatment of various heart diseases. Previous studies have indicated that the incidence of heart disease in children living in high-altitude areas is significantly higher than those in low-altitude areas, which increases the demand for ECG examinations in these regions. However, there is a lack of research and technology that focus on automatic ECG diagnosis for children in high-altitude areas. This study utilized electronic medical records of pediatric electrocardiograms (ECGs) from high-altitude areas in Yunnan Province, China as research data. An improved genetic algorithm (GA) was employed to find the best combination of base classifiers, and a stacked ensemble approach was utilized to develop a reliable model for automatic diagnosis of pediatric ECGs. The developed model is capable of identifying abnormal ECGs and classifying heart rhythm types into four categories: normal sinus rhythm, sinus tachycardia, sinus bradycardia, and other arrhythmias. The model was tested and compared with commonly used classification methods. The results show that the model developed in this paper exhibits better performance in terms of accuracy, recall, and F1 score. The model's classification effectiveness was further demonstrated by creating a receiver operating characteristic (ROC) curve. Finally, feature importance analysis highlighted the significance heart rate, developmental stage, and QRS complex axis deviation angle in the model. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) is a ubiquitous medical diagnostic tool employed to identify arrhythmias that are characterized by anomalous waveform morphology and erratic intervals. Current ECG analysis methods primarily rely on the feature extraction of single leads or scales, thereby overlooking the critical complementary data obtainable from multiple channels and scales. This paper introduces the Multi-Scale Grid Transformer (MSGformer) network, which extracts spatial features from limb and chest leads and employs a multi-scale grid attention mechanism to capture temporal features. The self-attention mechanism-based multi-lead feature fusion approach leverages diverse leads' perspectives to reflect each lead's heart's comprehensive state and extract unique essential features. Furthermore, MSGformer incorporates a multi-scale grid attention feature extraction strategy that employs multi-head and multi-scale attention mechanisms to extract multi-scale temporal features from two dimensions. The MSGformer network combines these feature extraction strategies, resulting in simultaneous capturing of morphological characteristics across different leads and temporal characteristics within the same lead in ECG. This integration facilitates the effective detection of morphological abnormalities and erratic intervals in cardiac electrical activity. Utilizing the publicly available 2018 China Physiological Signal Challenge (CPSC 2018) and MIT-BIH electrocardiogram datasets, the performance of MSGformer was evaluated and compared to existing ECG classification models. Experimental results demonstrate that MSGformer achieved an F 1 score of 0.86, while on the MIT-BIH dataset, it attained accuracy, sensitivity, and positive predictive value of 99.28%, 97.13%, and 97.87%, respectively, outperforming other current models. • A network with feature fusion and multi-scale attention for ECG arrhythmia detection. • Capturing local and global temporal features via multi-scale attention mechanism. • Enhancing detection via self-attention integration of limb and chest leads. • An impressive 0.86 F1 score is achieved compared to existing models. [ABSTRACT FROM AUTHOR]

• Discrete Wavelet Transformation (DWT) is used to appropriately classify ECG signals during the pre-processing stage by removing intrinsic noise. • To recognize the QRS complex, the location and size of R-peaks are to be determined. The retrieved features from the QRS complex are further enhanced using recently created variation of Equilibrium Optimization metaheuristic namely Opposition based Laplacian Equilibrium Optimizer (OB-LEO). • Deep neural network (DNN) with OB-LEO is used to accomplish signal categorization among sixteen categories using the MIT-BIH Arrhythmia ECG dataset with a 98.779 percent accuracy. The leading cause of death in the world is cardiovascular disease (CVD). It is required to detect, prevent and manage cardiovascular diseases to reduce their effect and improve the outcome. Interpretation of ECG signals may vary from one expert to another. That's why an automatic categorization of ECG recordings is needed. The proposed study attempted to address the above issues by developing an automated method for predicting arrhythmias based on ECG signal categorization. This method would offer more precise and enhanced interpretation of ECGs while reducing small biases brought on by manual interpretation. In this paper, a unique deep learning-based algorithm is used to efficiently and swiftly classify cardiac arrhythmias. We have used Discrete Wavelet Transformation (DWT) to appropriately classify ECG data into important arrhythmia categories during the pre-processing stage by removing intrinsic noise from ECG signals. To obtain optimal placement (equilibrium state) for better search space diversification, OB-L-EO uses Laplace distribution followed by opposition-based modelling to modify the intensity of search agents. A powerful type of machine learning technique called deep neural network (DNN) is used to accomplish signal categorization among sixteen categories using the MIT-BIH Arrhythmia ECG dataset. With the help of analysing classification technique including optimized feature vector and the OBLEO method with DNN, the heart rate has been detected with a 98.779% accuracy, 98.67% sensitivity, 98.87% specificity and execution time with a value of 1.34 ms across the entire dataset. The application of 10-fold cross validation provides the better accuracy i.e 99.79%. The achieved performance parameters have demonstrated that the suggested ECG signal classification model is capable of categorizing arrhythmia classes appropriately using ECG signals. [ABSTRACT FROM AUTHOR]

Growing evidence shows that certain acute exposures, especially alcohol, may trigger episodes of paroxysmal atrial fibrillation (AF). However, there is a lack of methods for assessing the relation between triggers and AF episodes in individual patients. The present paper proposes an approach to identifying AF triggers based on the assumption that the post-trigger AF burden is larger than the pre-trigger AF burden during the analysis time interval. For the purpose of identification, a measure of relational strength between pre- and post-trigger burden is introduced, accounting for the cumulative effect of the triggers contained in the observation interval. The proposed approach is explored for different types of AF episode pattern, generated using the alternating, bivariate Hawkes model, whose conditional intensity function is designed to account for the effect of alcohol. In total, 7200 different AF patterns were generated for different numbers of AF triggers and alcohol units. The simulation study demonstrates that, depending on the pattern type, the relational strength increases 3–6 times with alcohol consumption in comparison with no consumption. The proposed approach to identifying triggers in individual patients with paroxysmal AF should facilitate the implementation of longitudinal studies for the objective assessment of trigger effect on AF occurrence. • Certain acute exposures, e.g., alcohol, may trigger episodes of paroxysmal atrial fibrillation. • Survey-based studies for trigger identification suffer from biases. • Objective trigger identification is complex due to nonstationary binary data. • The objective approach to triggers in individual patients is introduced. • The approach should facilitate the implementation of longitudinal studies. [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]

In this paper, a new method based on the continuous wavelet transform is described in order to detect the QRS, P and T waves. QRS, P and T waves may be distinguished from noise, baseline drift or irregular heartbeats. The algorithm, described in this paper, has been evaluated using the Computers in Cardiology (CinC) Challenge 2011 database and also applied on the MIT-BIH Arrhythmia database (MITDB). The data from the CinC Challenge 2011 are standard 12 ECG leads recordings with full diagnostic bandwidth compared to the MITDB which only includes two leads for each ECG signal. Firstly, our algorithm is validated using fifty 12 leads ECG samples from the CinC collection. The samples have been chosen in the “acceptable records” list given by Physionet. The detection and the duration delineation of the QRS, P and T waves given by our method are compared to expert physician results. The algorithm shows a sensitivity equal to 0.9987 for the QRS complex, 0.9917 for the T wave and 0.9906 for the P wave. The accuracy and the Youden index values show that the method is reliable for the QRS, T and P waves detection and delineation. Secondly, our algorithm is applied to the MITDB in order to compare the detection of QRS wave to results of other some works in the literature. [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]

Arrhythmia is a cardiac condition generated by the abnormal electrical activity of the heart, and an electrocardiogram (ECG) is a tool utilized by the cardiologists for determining the arrhythmias or heart abnormalities. Owing to the existence of noise, the non-stationary nature of the ECG signal and the abnormality of the heartbeat, physicians face difficulties in the diagnosis of arrhythmias. Hence, there is a need for computer-aided diagnosis system which can achieve higher recognition accuracy. Since the existing methods of classification consumed huge time and less accurate in case of considerably similar ECG signal, this paper proposes an effective method termed as Genetic Bat optimization algorithm for training the Support Vector Neural Network (GB-SVNN) for arrhythmia classification using ECG signals. Initially, multi-resolution wavelet-based approach and the Gabor filters are used for extracting the wave interval features and other texture features from the ECG signal. Based on the features, the SVNN classifies the ECG signal as the affected arrhythmia or no arrhythmia signal. The SVNN is trained using the GB algorithm. The experimentation of the proposed method is done using MATLAB 2015.a, and the performance is evaluated with the existing methods, such as KNN, Neural Network (NN), Fuzzy Subtractive Clustering, and Support Vector Neural Network (SVNN) for accuracy, specificity, and sensitivity. From the results, it can be shown that the proposed GD-SVNN attains a maximum value of accuracy, sensitivity, and specificity at a rate of 0.9696, 0.99, and 0.9583 respectively. [ABSTRACT FROM AUTHOR]