Historical Aspects of Electrocardiography

Delineation of 12-Lead ECG Representative Beats Using Convolutional Encoder-Decoders with Residual and Recurrent Connections

The aim of this study is to address the challenge of 12-lead ECG delineation by different encoder-decoder architectures of deep neural networks (DNNs). This study compares four concepts for encoder-decoders based on a fully convolutional architecture (CED-Net) and its modifications with a recurrent layer (CED-LSTM-Net), residual connections between symmetrical encoder and decoder feature maps (CED-U-Net), and sequential residual blocks (CED-Res-Net). All DNNs transform 12-lead representative beats to three diagnostic ECG intervals (P-wave, QRS-complex, QT-interval) used for the global delineation of the representative beat (P-onset, P-offset, QRS-onset, QRS-offset, T-offset). All DNNs were trained and optimized using the large PhysioNet ECG database (PTB-XL) under identical conditions, applying an advanced approach for machine-based supervised learning with a reference algorithm for ECG delineation (ETM, Schiller AG, Baar, Switzerland). The test results indicate that all DNN architectures are equally capable of reproducing the reference delineation algorithm's measurements in the diagnostic PTB database with an average P-wave detection accuracy (96.6%) and time and duration errors: mean values (-2.6 to 2.4 ms) and standard deviations (2.9 to 11.4 ms). The validation according to the standard-based evaluation practices of diagnostic electrocardiographs with the CSE database outlines a CED-Net model, which measures P-duration (2.6 ± 11.0 ms), PQ-interval (0.9 ± 5.8 ms), QRS-duration (-2.4 ± 5.4 ms), and QT-interval (-0.7 ± 10.3 ms), which meet all standard tolerances. Noise tests with high-frequency, low-frequency, and power-line frequency noise (50/60 Hz) confirm that CED-Net, CED-Res-Net, and CED-LSTM-Net are robust to all types of noise, mostly presenting a mean duration error < 2.5 ms when compared to measurements without noise. Reduced noise immunity is observed for the U-net architecture. Comparative analysis with other published studies scores this research within the lower range of time errors, highlighting its competitive performance.

Super-resolution techniques for biomedical applications and challenges

Super-resolution (SR) techniques have revolutionized the field of biomedical applications by detailing the structures at resolutions beyond the limits of imaging or measuring tools. These techniques have been applied in various biomedical applications, including microscopy, magnetic resonance imaging (MRI), computed tomography (CT), X-ray, electroencephalogram (EEG), ultrasound, etc. SR methods are categorized into two main types: traditional non-learning-based methods and modern learning-based approaches. In both applications, SR methodologies have been effectively utilized on biomedical images, enhancing the visualization of complex biological structures. Additionally, these methods have been employed on biomedical data, leading to improvements in computational precision and efficiency for biomedical simulations. The use of SR techniques has resulted in more detailed and accurate analyses in diagnostics and research, essential for early disease detection and treatment planning. However, challenges such as computational demands, data interpretation complexities, and the lack of unified high-quality data persist. The article emphasizes these issues, underscoring the need for ongoing development in SR technologies to further improve biomedical research and patient care outcomes.

Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases

Electrocardiography (ECG), improved by artificial intelligence (AI), has become a potential technique for the precise diagnosis and treatment of cardiovascular disorders. The conventional ECG is a frequently used, inexpensive, and easily accessible test that offers important information about the physiological and anatomical state of the heart. However, the ECG can be interpreted differently by humans depending on the interpreter's level of training and experience, which could make diagnosis more difficult. Using AI, especially deep learning convolutional neural networks (CNNs), to look at single, continuous, and intermittent ECG leads that has led to fully automated AI models that can interpret the ECG like a human, possibly more accurately and consistently. These AI algorithms are effective non-invasive biomarkers for cardiovascular illnesses because they can identify subtle patterns and signals in the ECG that may not be readily apparent to human interpreters. The use of AI in ECG analysis has several benefits, including the quick and precise detection of problems like arrhythmias, silent cardiac illnesses, and left ventricular failure. It has the potential to help doctors with interpretation, diagnosis, risk assessment, and illness management. Aside from that, AI-enhanced ECGs have been demonstrated to boost the identification of heart failure and other cardiovascular disorders, particularly in emergency department settings, allowing for quicker and more precise treatment options. The use of AI in cardiology, however, has several limitations and obstacles, despite its potential. The effective implementation of AI-powered ECG analysis is limited by issues such as systematic bias. Biases based on age, gender, and race result from unbalanced datasets. A model's performance is impacted when diverse demographics are inadequately represented. Potentially disregarded age-related ECG variations may result from skewed age data in training sets. ECG patterns are affected by physiological differences between the sexes; a dataset that is inclined toward one sex may compromise the accuracy of the others. Genetic variations influence ECG readings, so racial diversity in datasets is significant. Furthermore, issues such as inadequate generalization, regulatory barriers, and interpretability concerns contribute to deployment difficulties. The lack of robustness in models when applied to disparate populations frequently hinders their practical applicability. The exhaustive validation required by regulatory requirements causes a delay in deployment. Difficult models that are not interpretable erode the confidence of clinicians. Diverse dataset curation, bias mitigation strategies, continuous validation across populations, and collaborative efforts for regulatory approval are essential for the successful deployment of AI ECG in clinical settings and must be undertaken to address these issues. To guarantee a safe and successful deployment in clinical practice, the use of AI in cardiology must be done with a thorough understanding of the algorithms and their limits. In summary, AI-enhanced electrocardiography has enormous potential to improve the management of cardiovascular illness by delivering precise and timely diagnostic insights, aiding clinicians, and enhancing patient outcomes. Further study and development are required to fully realize AI's promise for improving cardiology practices and patient care as technology continues to advance.

History of arrhythmias

A historical overview is given on the techniques to record the electrical activity of the heart, some anatomical aspects relevant for the understanding of arrhythmias, general mechanisms of arrhythmias, mechanisms of some specific arrhythmias and nonpharmacological forms of therapy. The unravelling of arrhythmia mechanisms depends, of course, on the ability to record the electrical activity of the heart. It is therefore no surprise that following the construction of the string galvanometer by Einthoven in 1901, which allowed high-fidelity recording of the body surface electrocardiogram, the study of arrhythmias developed in an explosive way. Still, papers from McWilliam (1887), Garrey (1914) and Mines (1913, 1914) in which neither mechanical nor electrical activity was recorded provided crucial insights into re-entry as a mechanism for atrial and ventricular fibrillation, atrioventricular nodal re-entry and atrioventricular re-entrant tachycardia in hearts with an accessory atrioventricular connection. The components of the electrocardiogram, and of extracellular electrograms directly recorded from the heart, could only be well understood by comparing such registrations with recordings of transmembrane potentials. The first intracellular potentials were recorded with microelectrodes in 1949 by Coraboeuf and Weidmann. It is remarkable that the interpretation of extracellular electrograms was still controversial in the 1950s, and it was not until 1962 that Dower showed that the transmembrane action potential upstroke coincided with the steep negative deflection in the electrogram. For many decades, mapping of the spread of activation during an arrhythmia was performed with a "roving" electrode that was subsequently placed on different sites on the cardiac surface with a simultaneous recording of another signal as time reference. This method could only provide reliable information if the arrhythmia was strictly regular. When multiplexing systems became available in the late 1970s, and optical mapping in the 1980s, simultaneous registrations could be made from many sites. The analysis of atrial and ventricular fibrillation then became much more precise. The old question whether an arrhythmia is due to a focal or a re-entrant mechanism could be answered, and for atrial fibrillation, for instance, the answer is that both mechanisms may be operative. The road from understanding the mechanism of an arrhythmia to its successful therapy has been long: the studies of Mines in 1913 and 1914, microelectrode studies in animal preparations in the 1960s and 1970s, experimental and clinical demonstrations of initiation and termination of tachycardias by premature stimuli in the 1960s and 1970s, successful surgery in the 1980s, the development of external and implantable defibrillators in the 1960s and 1980s, and finally catheter ablation at the end of the previous century, with success rates that approach 99% for supraventricular tachycardias.

The search for Samojloff: a Russian physiologist in times of change

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