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Purpose: This study introduces an algorithm specifically designed for processing unprocessed 12-lead electrocardiogram (ECG) data, with the primary aim of detecting cardiac abnormalities. Methods: The proposed model integrates Diagonal State Space Sequence (S4D) model into its architecture, leveraging its effectiveness in capturing dynamics within time-series data. The S4D model is designed with stacked S4D layers for processing raw input data and a simplified decoder using a dense layer for predicting abnormality types. Experimental optimization determines the optimal number of S4D layers, striking a balance between computational efficiency and predictive performance. This comprehensive approach ensures the model's suitability for real-time processing on hardware devices with limited capabilities, offering a streamlined yet effective solution for heart monitoring. Results: Among the notable features of this algorithm is its strong resilience to noise, enabling the algorithm to achieve an average F1-score of 81.2% and an AUROC of 95.5% in generalization. The model underwent testing specifically on the lead II ECG signal, exhibiting consistent performance with an F1-score of 79.5% and an AUROC of 95.7%. Conclusion: It is characterized by the elimination of pre-processing features and the availability of a low-complexity architecture that makes it suitable for implementation on numerous computing devices because it is easily implementable. Consequently, this algorithm exhibits considerable potential for practical applications in analyzing real-world ECG data. This model can be placed on the cloud for diagnosis. The model was also tested on lead II of the ECG alone and has demonstrated promising results, supporting its potential for on-device application. [ABSTRACT FROM AUTHOR]

Heart disease is a major issue, and the severity of its effects can be reduced through early detection and prevention. ECG is an effective diagnostic tool. Automating ECG analysis increases the possibility of timely analysis and prediction of heart conditions, improving patient outcomes. The extraction of heart-related features further enhances the accuracy of ECG-based classification models, paving the way for more effective and efficient online detection and prevention of heart diseases.The image-vectorization technique suggested in this study produces a vector representation that precisely captures the distinctive features of the heart signal. It involves image cropping, erasing the ECG grid lines, and assigning pixels to distinguish the heart signal from the background. Compared to the feature vector produced by VGG16, the extracted feature vector is 589 times shorter than the feature vector produced by VGG16, which significantly decreased the amount of memory required, increased algorithm convergence, and required less computing power. The feature vector extracted using image-vectorization is used to create the training dataset, which is used to train artificial neural networks (ANNs). The results demonstrate that using image-vectorization techniques improved the performance of machine learning algorithms compared to using conventional feature extraction algorithms like CNNs and VGG16.

Effective cardiovascular health monitoring relies on precise electrocardiogram (ECG) analysis for early diagnosis and treatment of heart conditions. Recent advancements in deep learning, particularly through Convolutional Neural Networks (CNNs), have significantly enhanced the automation, accuracy, and personalization of ECG analysis. This review targets both medical professionals and a broader audience interested in deep learning applications. Our work explores the evolution of deep learning techniques in ECG analysis, from early CNN applications to current innovations in real-time processing and privacy-preserving methods. The paper discusses various deep learning models, including hybrid models, Recurrent Neural Networks (RNNs), and attention mechanisms, and their impact on diagnostic accuracy for diseases like myocardial infarction. Additionally, our paper examines ECG-based authentication systems, addressing challenges related to security and privacy, and highlighting recent technological advancements. By providing a detailed overview of these developments, the review offers valuable insights into future directions for deep learning in cardiovascular health monitoring and ECG-based authentication.

The increasing prevalence of overweight and obesity is a worldwide problem, with several well-known consequences that might start to develop early in life during childhood. The present research based on data from children that have been followed since birth in a previously established cohort study (Generation XXI, Porto, Portugal), taking advantage of State-of-the-Art (SoA) data science techniques and methods, including Neural Architecture Search (NAS), explainable Artificial Intelligence (XAI), and Deep Learning (DL), aimed to explore the hidden value of data, namely on electrocardiogram (ECG) records performed during follow-up visits. The combination of these techniques allowed us to clarify subtle cardiovascular changes already present at 10 years of age, which are evident from ECG analysis and probably induced by the presence of obesity. The proposed novel combination of new methodologies and techniques is discussed, as well as their applicability in other health domains. [ABSTRACT FROM AUTHOR]

The left ventricular summit is a source of idiopathic ventricular arrhythmias and presents distinct challenges for mapping and ablation. These arrhythmias are typically targeted from the distal coronary venous system or most often from endocardial vantage points such as the left coronary cusp, basal left ventricle or septal right ventricular outflow tract. In this article, we review the electrocardiographic patterns that suggest a possible origin from the left ventricular summit and the features that may help to predict the most likely site of successful ablation. [ABSTRACT FROM AUTHOR]

Background: Disturbed heart dynamics in depression seriously increases mortality risk. Heart rate variability (HRV) is a rich source of information for studying this dynamics. This paper is a meta-analytic review with methodological commentary of the application of nonlinear analysis of HRV and its possibility to address cardiovascular diseases in depression. Objective: This paper aimed to appeal for the introduction of cardiological screening to patients with depression, because it is still far from established practice. The other (main) objective of the paper was to show that nonlinear methods in HRV analysis give better results than standard ones. Methods: We systematically searched on the web for papers on nonlinear analyses of HRV in depression, in line with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 framework recommendations. We scrutinized the chosen publications and performed random-effects meta-analysis, using the esci module in jamovi software where standardized effect sizes (ESs) are corrected to yield the proof of the practical utility of their results. Results: In all, 26 publications on the connection of nonlinear HRV measures and depression meeting our inclusion criteria were selected, examining a total of 1537 patients diagnosed with depression and 1041 healthy controls (N=2578). The overall ES (unbiased) was 1.03 (95% CI 0.703-1.35; diamond ratio 3.60). We performed 3 more meta-analytic comparisons, demonstrating the overall effectiveness of 3 groups of nonlinear analysis: detrended fluctuation analysis (overall ES 0.364, 95% CI 0.237-0.491), entropy-based measures (overall ES 1.05, 95% CI 0.572-1.52), and all other nonlinear measures (overall ES 0.702, 95% CI 0.422-0.982). The effectiveness of the applied methods of electrocardiogram analysis was compared and discussed in the light of detection and prevention of depression-related cardiovascular risk. Conclusions: We compared the ESs of nonlinear and conventional time and spectral methods (found in the literature) and demonstrated that those of the former are larger, which recommends their use for the early screening of cardiovascular abnormalities in patients with depression to prevent possible deleterious events. [ABSTRACT FROM AUTHOR]

A sharp deterioration of the patient’s condition against the backdrop of the development of life-threatening arrhythmias with symptoms of acute heart failure (AHF), multiple organ dysfunction syndrome (MODS) or cerebral edema (CE) can lead to the death of the patient. Since the known methods of automated diagnostics currently cannot accurately and promptly determine that the patient is in a life-threatening condition leading to the fatal outcome caused by AHF, MODS or CE, there is a need to develop appropriate methods. One of the ways to identify predictors of such a state is to apply machine learning methods to the collected datasets. In this article, we consider using data analysis methods to test the hypothesis that there is a predictor of death risk assessment, which can be derived from the previously obtained values of the ECG intervals, which gives a statistically significant difference for the ECG of the two groups of patients: those who suffered deterioration leading to the fatal outcome caused be MODS, AHF or CE, and those with favorable outcome. A method for unifying ECG data was proposed, which allow, based on the sequence of RR and QT intervals, to the construct of a number that is a characteristic of the patient's heart condition. Based on this characteristic, the patients are classified into groups: the main (patients with fatal outcome) and control (patients with favorable outcome). The resulting classification method lays the potential for the development of methods for identifying the patient's health condition, which will automate the detection of its deterioration. The novelty of the result lies in the confirmation of the hypothesis stated above, as well as the proposed classification criteria that allow solving the urgent problem of an automatic detection of the deterioration of the patient's condition.

The increasing prevalence of overweight and obesity is a worldwide problem, with several well-known consequences that might start to develop early in life during childhood. The present research based on data from children that have been followed since birth in a previously established cohort study (Generation XXI, Porto, Portugal), taking advantage of State-of-the-Art (SoA) data science techniques and methods, including Neural Architecture Search (NAS), explainable Artificial Intelligence (XAI), and Deep Learning (DL), aimed to explore the hidden value of data, namely on electrocardiogram (ECG) records performed during follow-up visits. The combination of these techniques allowed us to clarify subtle cardiovascular changes already present at 10 years of age, which are evident from ECG analysis and probably induced by the presence of obesity. The proposed novel combination of new methodologies and techniques is discussed, as well as their applicability in other health domains.

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Heart disease is a major issue, and the severity of its effects can be reduced through early detection and prevention. ECG is an effective diagnostic tool. Automating ECG analysis increases the possibility of timely analysis and prediction of heart conditions, improving patient outcomes. The extraction of heart-related features further enhances the accuracy of ECG-based classification models, paving the way for more effective and efficient online detection and prevention of heart diseases. The image-vectorization technique suggested in this study produces a vector representation that precisely captures the distinctive features of the heart signal. It involves image cropping, erasing the ECG grid lines, and assigning pixels to distinguish the heart signal from the background. Compared to the feature vector produced by VGG16, the extracted feature vector is 589 times shorter than the feature vector produced by VGG16, which significantly decreased the amount of memory required, increased algorithm convergence, and required less computing power. The feature vector extracted using image-vectorization is used to create the training dataset, which is used to train artificial neural networks (ANNs). The results demonstrate that using image-vectorization techniques improved the performance of machine learning algorithms compared to using conventional feature extraction algorithms like CNNs and VGG16., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

Electrocardiogram captures the electrical activity of the heart. The signal obtained can be used for various purposes such as emotion recognition, heart rate measuring and the main one, cardiac disease diagnosis. But ECG analysis and classification require experienced specialists once it presents high variability and suffers interferences from noises and artefacts. With the increase of data amount on long term records, it might lead to long term dependencies and the process become exhaustive and error prone. Automated systems associated with signal processing techniques aim to help on these tasks by improving the quality of data, extracting meaningful features, selecting the most suitable and training machine learning models to capture and generalize its behaviour. This review brings a brief stage sense of how data flows into these approaches and somewhat techniques are most used. It ends by presenting some of the countless applications that can be found in the research community.

Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases are considered the number one cause of death across the globe which can be primarily identified by the abnormal heart rhythms of the patients. By generating electrocardiogram (ECG) signals, wearable Internet of Things (IoT) devices can consistently track the patient’s heart rhythms. Although Cloud-based approaches for ECG analysis can achieve some levels of accuracy, they still have some limitations, such as high latency. Conversely, the Fog computing infrastructure is more powerful than edge devices but less capable than Cloud computing for executing compositionally intensive data analytic software. The Fog infrastructure can consist of Fog-based gateways directly connected with the wearable devices to offer many advanced benefits, including low latency and high quality of services. To address these issues, a modular one-dimensional convolution neural network (1D-CNN) approach is proposed in this work. The inference module of the proposed approach is deployable over the Fog infrastructure for analysing the ECG signals and initiating the emergency countermeasures within a minimum delay, whereas its training module is executable on the computationally enriched Cloud data centers. The proposed approach achieves the F1-measure score ≈1 on the MIT-BIH Arrhythmia database when applying GridSearch algorithm with the cross-validation method. This approach has also been implemented on a single-board computer and Google Colab-based hybrid Fog-Cloud infrastructure and embodied to a remote patient monitoring system that shows 25% improvement in the overall response time.

Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology.

The paper aims to consider the software methods used for detection of elements of high-resolution electrocardiographic (ECG) signals recorded using a nanosensor-based hardware and software complex. To achieve the aim, the study employed the following methods: correlation analysis, bi-directional filtering, first-order derivative, and correction algorithms. For cases when the automatic detection of elements was made with an error, an additional algorithm for manual correction of QRS elements has been developed.

Electrocardiogram (ECG) gives essential information about different cardiac conditions of the human heart. Its analysis has been the main objective among the research community to detect and prevent life threatening cardiac circumstances. Traditional signal processing methods, machine learning and its subbranches, such as deep learning, are popular techniques for analyzing and classifying the ECG signal and mainly to develop applications for early detection and treatment of cardiac conditions and arrhythmias. A detailed literature survey regarding ECG signal analysis is presented in this article. We first introduce a stages-based model for ECG signal analysis where a survey of ECG analysis related work is then presented in the form of this stage-based process model. The model describes both traditional time/frequency-domain and advanced machine learning techniques reported in the published literature at every stage of analysis, starting from ECG data acquisition to its classification for both simulations and real-time monitoring systems. We present a comprehensive literature review of real-time ECG signal acquisition, prerecorded clinical ECG data, ECG signal processing and denoising, detection of ECG fiducial points based on feature engineering and ECG signal classification along with comparative discussions among the reviewed studies. This study also presents a detailed literature review of ECG signal analysis and feature engineering for ECG-based body sensor networks in portable and wearable ECG devices for real-time cardiac status monitoring. Additionally, challenges and limitations are discussed and tools for research in this field as well as suggestions for future work are outlined.

Objective: This study involving automated external defibrillators (AEDs) in early treatment of refibrillation aims to evaluate the performance of a new shock advisory system (SAS) during chest compressions (CC) in out-of-hospital cardiac arrest (OHCA) patients., Methods: This work focuses on AED SAS performance as a secondary outcome of DEFI 2022 clinical prospective study, which included first-analysis shockable OHCA patients. SAS employs the Analyze Whilst Compressing (AWC) algorithm to interact with both cardiopulmonary resuscitation (CPR) and shock advice by conditional operation of two-stage ECG analysis in presence or absence of chest compressions. AWC is triggered by the first-shock recommendation. Then, after 1 min of CPR, ECG analysis during CC decides between two treatment scenarios. For patients with refibrillation, CPR is paused for immediate confirmation analysis and shock advice. For patients with non-shockable rhythms, CPR is continued for 2 min until standard analysis., Results: Clinical data from 285 OHCA patients with shock recommendation at the first-analysis by AEDs (DEFIGARD TOUCH7, Schiller Médical) consisted of 576 standard analyses, 2011 analyses during CC, 577 confirmation analyses in absence of CC. Global AED SAS performance meets the standard recommendations for arrhythmia analysis sensitivity (94.9%) and specificity (>99.3%). AWC provided innovative treatment of shockable rhythms by stopping CPR earlier than 2 min in most ventricular fibrillations (92.9%), while most non-shockable patients (86.5-95.2%) benefitted from continuous CPR for at least 2 min., Conclusion: This study provides positive evidence for routine use of AEDs with AWC-integrated algorithm for ECG analysis during CPR by first-responders in early OHCA treatment. Clinical Trial Registration: Registration number: NCT04691089, trial register: ClinicalTrials.gov., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: “The authors VK, IJ, BF, DJ, CD and VH declare no competing interests. JPD and SM are employees of SCHILLER Médical, Wissembourg, France. JPD holds the patents for the Analyze Whilst Compressing algorithm under evaluation.”., (© 2024 The Author(s).)

Heart rate variability (HRV) is commonly used to assess the function of the autonomic nervous system, which is linked to diseases such as cardiovascular disease, diabetes, hypertension, respiratory diseases, and stress. Many studies of the relationship between these diseases and HRV indices have been reported. Generally, the computation of HRV indices is relatively complicated. Moreover, recent researches regarding HRV have employed increasing numbers of electrocardiogram records. Thus, the computation and data processing required are even more complex. Therefore, we propose computer programs for visualizing and analyzing HRV. The proposed programs are developed under MATLAB GUIDE and are available as open source software tools for researchers to develop or modify. We evaluate the programs with MIT-BIH database. The results show that the proposed software tools facilitates the computation of HRV in batch processing mode and the visualization of all of the details, as well as the properties and trends, of HRV indices over long successive epochs. Especially, the software allows us to divide signals into groups for comparing HRV indices. Therefore, the tools are useful for researchers who deal with large cohort ECG signals. [ABSTRACT FROM AUTHOR]

Background: Phospholamban (PLN) p.Arg14del mutation carriers are known to develop dilated and/or arrhythmogenic cardiomyopathy, and typical electrocardiographic (ECG) features have been identified for diagnosis. Machine learning is a powerful tool used in ECG analysis and has shown to outperform cardiologists.Objectives: We aimed to develop machine learning and deep learning models to diagnose PLN p.Arg14del cardiomyopathy using ECGs and evaluate their accuracy compared to an expert cardiologist.Methods: We included 155 adult PLN mutation carriers and 155 age- and sex-matched control subjects. Twenty-one PLN mutation carriers (13.4%) were classified as symptomatic (symptoms of heart failure or malignant ventricular arrhythmias). The data set was split into training and testing sets using 4-fold cross-validation. Multiple models were developed to discriminate between PLN mutation carriers and control subjects. For comparison, expert cardiologists classified the same data set. The best performing models were validated using an external PLN p.Arg14del mutation carrier data set from Murcia, Spain (n = 50). We applied occlusion maps to visualize the most contributing ECG regions.Results: In terms of specificity, expert cardiologists (0.99) outperformed all models (range 0.53-0.81). In terms of accuracy and sensitivity, experts (0.28 and 0.64) were outperformed by all models (sensitivity range 0.65-0.81). T-wave morphology was most important for classification of PLN p.Arg14del carriers. External validation showed comparable results, with the best model outperforming experts.Conclusion: This study shows that machine learning can outperform experienced cardiologists in the diagnosis of PLN p.Arg14del cardiomyopathy and suggests that the shape of the T wave is of added importance to this diagnosis. [ABSTRACT FROM AUTHOR]

Aims: Current deep learning algorithms for automatic ECG analysis have shown notable accuracy but are typically narrowly focused on singular diagnostic conditions. This exploratory study aims to investigate the capability of a single deep learning model to predict a diverse range of both cardiac and non-cardiac discharge diagnoses based on a single ECG collected in the emergency department., Methods and Results: In this study, we assess the performance of a model trained to predict a broad spectrum of diagnoses. We find that the model can reliably predict 253 ICD codes (81 cardiac and 172 non-cardiac) in the sense of exceeding an AUROC score of 0.8 in a statistically significant manner., Conclusion: The model demonstrates proficiency in handling a wide array of cardiac and non-cardiac diagnostic scenarios, indicating its potential as a comprehensive screening tool for diverse medical encounters., Competing Interests: Conflict of interest: none declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

In this article, we propose a novel ECG classification framework for atrial fibrillation (AF) detection using spectro-temporal representation (i.e., time varying spectrum) and deep convolutional networks. In the first step we use a Bayesian spectro-temporal representation based on the estimation of time-varying coefficients of Fourier series using Kalman filter and smoother. Next, we derive an alternative model based on a stochastic oscillator differential equation to accelerate the estimation of the spectro-temporal representation in lengthy signals. Finally, after comparative evaluations of different convolutional architectures, we propose an efficient deep convolutional neural network to classify the 2D spectro-temporal ECG data. The ECG spectro-temporal data are classified into four different classes: AF, non-AF normal rhythm (Normal), non-AF abnormal rhythm (Other), and noisy segments (Noisy). The performance of the proposed methods is evaluated and scored with the PhysioNet/Computing in Cardiology (CinC) 2017 dataset. The experimental results show that the proposed method achieves the overall F1 score of 80.2%, which is in line with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Heartbeat detection for ambulatory cardiac monitoring is more challenging as the level of noise and artefacts induced by daily-life activities are considerably higher than monitoring in a hospital setting. It is valuable to understand the relationship between the characteristics of electrocardiogram (ECG) noises and the beat detection performance in the cardiac monitoring system. For this purpose, three well-known algorithms for the beat detection process were re-implemented. The beat detection algorithms were validated using two types of ambulatory datasets, which were the ECG signal from the MIT-BIH Arrhythmia Database and the simulated noise-contaminated ECG signal with different intensities of baseline wander (BW), muscle artefact (MA) and electrode motion (EM) artefact from the MIT-BIH Noise Stress Test Database. The findings showed that signals contaminated with noise and artefacts decreased the potential of beat detection in ambulatory signal with the poorest performance noted for ECG signal affected by the EM artefacts. In conclusion, none of the algorithms was able to detect all QRS complexes without any false detection at the highest level of noise. The EM noise influenced the beat detection performance the most in comparison to the MA and BW noises that resulted in the highest number of misdetections and false detections. [ABSTRACT FROM AUTHOR]

Tiny embedded systems have not been an ideal outfit for high performance computing due to their constrained resources. Limitations in processing power, battery life, communication bandwidth, and memory constrain the applicability of existing complex medical analysis algorithms such as the Electrocardiogram ( ECG) analysis. Among various limitations, battery lifetime has been a major key technological constraint. In this paper, we address the issue of partitioning such a complex algorithm while the energy consumption due to wireless transmission is minimized. ECG analysis algorithms normally consist of preprocessing, pattern recognition, and classification. Considering the orientation of the ECG leads, we devise a technique to perform preprocessing and pattern recognition locally in small embedded systems attached to the leads. The features detected in the pattern recognition phase are considered for the classification. Ideally, if the features detected for each heartbeat reside in a single processing node, the transmission will be unnecessary. Otherwise, to perform classification, the features must be gathered on a local node and, thus, the communication is inevitable. We perform such a feature grouping by modeling the problem as a hypergraph and applying partitioning schemes which yield a significant power saving in wireless communications. Furthermore, we utilize dynamic reconfiguration by software module migration. This technique, with respect to partitioning, enhances the overall power saving in such systems. Moreover, it adaptively alters the system configuration in various environments and on different patients. We evaluate the effectiveness of our proposed techniques on MIT/BIH benchmarks and, on average, achieve 70 percent energy saving.

This work presents algorithms and software for the automated ECG processing and analysis in the mobile cardiac monitoring system based on smartphone. The key component of the system is portable ECG acquisition device realized in the form of smartphone case. The registered ECG is preprocessed by the smartphone software and is transferred further via Internet to the cloud service where additional computer analysis of the signal is implemented and the ECG and its analysis results are stored in the database. The data from the cloud service are available both to the patient and to his doctor. The following main functions of ECG analysis are realized by the algorithms: pacemaker spikes detection, heart rate control, cardiac arrhythmia recognition and heart rate variability analysis. Several modifications of the ECG acquisition device for iPhone series 5/5S, 6/6S are in batch production now in the frames of CardioQVARK project. At the same time the data from the system database are used permanently for the further enhancement of the algorithms.

• Nanosensors exhibit noise immunity and greater sensitivity to the signal. • The hardware and software complex records spontaneous activity of myocardial cells in real time. • Real-time ECG recording in the frequency range from 0 to 10,000 Hz. • Recording of micropotentials of more than 1 μV without filtering and averaging. • The activity of cardiomyocytes decreases significantly 1 day before the lethal outcome. Cardiovascular diseases are one of the leading causes of premature death worldwide. Sudden cardiac death is most often caused by previously undetected coronary heart disease. Therefore, it is necessary to create new high-resolution diagnostic equipment for non-invasive dynamic monitoring of spontaneous activity of myocardial cells in population screening. Nanosensors with greater sensitivity and noise immunity compared to existing electrodes were developed for ECG recording. A nanosensor-based hardware and software complex and special programs to ensure non-invasive recording and processing of micropotentials of the heart were created. Preliminary medical studies in volunteers showed the possibility of assessing a spontaneous activity of myocardial cells in dynamic observations. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) has emerged as a new biometric for human recognition due to its robustness against fraudulent attacks. This article presents a novel method of ECG biometric for human recognition using autocorrelation (AC) followed by one of the three transformation techniques, i.e. discrete cosine transform (DCT), discrete Fourier transform (DFT), and Walsh–Hadamard transform (WHT). The effectiveness of these transformations is evaluated on the dimensionality reduction techniques i.e. principal component analysis and linear discriminant analysis (LDA). Thus, the systems prepared by different combinations of transformations and dimensionality reduction techniques are evaluated on publically available databases of Physionet. The authentication and identification accuracies achieved by these systems are found the best on DFT and LDA combination. The authentication performance is reported to 99.98% (99.83%), whereas the average rank classification accuracy is reported to 100% (97%) on QT database (MIT‐BIH arrhythmia database) of Physionet. [ABSTRACT FROM AUTHOR]

Purpose: To characterize the effect of erdafitinib on electrocardiogram (ECG) parameters and the relationship between erdafitinib plasma concentrations and QTc interval changes in patients with advanced or refractory solid tumors.Methods: Triplicate ECGs and continuous 12-lead Holter data were collected in the dose escalation part (Part 1) of the first-in-human study, with doses ranging from 0.5 to 12 mg. Triplicate ECG monitoring continued in Parts 2-4 where 2 dose regimens selected from Part 1 were expanded in prespecified tumor types. Analyses of ECG data included central tendency analyses, identification of categorical outliers and morphological assessment. A concentration-QTc analysis was conducted using a linear mixed-effect model based on extracted time matching Holter data.Results: Central tendency, categorical outlier, and ECG morphologic analyses from 187 patients revealed no clinically significant effect of erdafitinib on heart rate, atrioventricular conduction or cardiac depolarization (PR and QRS), and no effect on cardiac repolarization (QTc). Concentration-QTc analysis from 62 patients indicated that the slopes of relationship between total and free erdafitinib plasma concentrations and QTcI (mean exponent of 0.395) were estimated as - 0.00269 ms/(ng/mL) and - 1.138 ms/(ng/mL), respectively. The predicted change in QTcI at the observed geometric mean of total and free concentration at the highest therapeutic erdafitinib dose (9 mg daily) was < 10 ms at the upper bound of the two-sided 90% confidence interval.Conclusions: ECG data and the concentration-QTc relationships demonstrate that erdafitinib does not prolong QTc interval and has no effects on cardiac repolarization or other ECG parameters. Clinical trial registration numbers NCT01703481, EudraCT: 2012-000697-34. [ABSTRACT FROM AUTHOR]

The Cardiovascular disease (CVD) is the most of death in the world. Electrocardiogram (ECG) is the graph that shows heart electrical activities. The physician record and detect the abnormal Electrocardiogram (ECG) signal by the Holter monitor that patient need to carry on the device for record ECG signal in 24 hours. Pan-Tomkins algorithm was appropriate for Real-time ECG signal recognition because high accuracy and rapidly analysis. This research propose the Real-time ECG Signal monitoring system for detect the abnormal ECG signal by using Pan-Tomkins algorithm with Wireless Sensor Network. The system separated into 2 part; sender module and receiver module. Experimental the system by using the ECG signal data from MIT-BIH database. Selected 20 samples of abnormal ECG signal then experimental at 10 and 20 meters sender module-receiver module distance, calculate R-R interval and R amplitude threshold The results show that the Real-time ECG signal monitoring system detect 17 abnormal ECG signal, the accuracy is 85%. This systems efficient for detect the abnormal of ECG signal in real-time. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) is an important diagnostic tool for identifying cardiac problems. Nowadays, new ways to record ECG signals outside of the hospital are being investigated. A promising technique is capacitively coupled ECG (ccECG), which allows ECG signals to be recorded through insulating materials. However, as the ECG is no longer recorded in a controlled environment, this inevitably implies the presence of more artefacts. Artefact detection algorithms are used to detect and remove these. Typically, the training of a new algorithm requires a lot of ground truth data, which is costly to obtain. As many labelled contact ECG datasets exist, we could avoid the use of labelling new ccECG signals by making use of previous knowledge. Transfer learning can be used for this purpose. Here, we applied transfer learning to optimise the performance of an artefact detection model, trained on contact ECG, towards ccECG. We used ECG recordings from three different datasets, recorded with three recording devices. We showed that the accuracy of a contact-ECG classifier improved between 5 and 8% by means of transfer learning when tested on a ccECG dataset. Furthermore, we showed that only 20 segments of the ccECG dataset are sufficient to significantly increase the accuracy.

Heartbeat detection for ambulatory cardiac monitoring is more challenging as the level of noise and artefacts induced by daily-life activities are considerably higher than monitoring in a hospital setting. It is valuable to understand the relationship between the characteristics of electrocardiogram (ECG) noises and the beat detection performance in the cardiac monitoring system. For this purpose, three well-known algorithms for the beat detection process were re-implemented. The beat detection algorithms were validated using two types of ambulatory datasets, which were the ECG signal from the MIT-BIH Arrhythmia Database and the simulated noise-contaminated ECG signal with different intensities of baseline wander (BW), muscle artefact (MA) and electrode motion (EM) artefact from the MIT-BIH Noise Stress Test Database. The findings showed that signals contaminated with noise and artefacts decreased the potential of beat detection in ambulatory signal with the poorest performance noted for ECG signal affected by the EM artefacts. In conclusion, none of the algorithms was able to detect all QRS complexes without any false detection at the highest level of noise. The EM noise influenced the beat detection performance the most in comparison to the MA and BW noises that resulted in the highest number of misdetections and false detections.