A novel method for 12-lead ECG reconstruction.

This paper presents a novel approach to synthesize a standard 12-lead electrocardiogram (ECG) from any three independent ECG leads using a patient-specific encoder-decoder convolutional neural network. The objective is to decrease the number of recording locations required to obtain the same information as a 12-lead ECG, thereby enhancing patients' comfort during the recording process. We evaluate the proposed algorithm on a dataset comprising fifteen patients, as well as a randomly selected cohort of patients from the PTB diagnostic database. To evaluate the precision of the reconstructed ECG signals, we present two metrics: the correlation coefficient and root mean square error. Our proposed method achieves superior performance compared to most existing synthesis techniques, with an average correlation coefficient of 0.976 and 0.97 for datasets, respectively. These results demonstrate the potential of our approach to improve the efficiency and comfort of ECG recording for patients, while maintaining high diagnostic accuracy.

Artificial Intelligence and Machine Learning in Cardiac Electrophysiology.

Cardiac electrophysiology requires the processing of several patient-specific data points in real time to provide an accurate diagnosis and determine an optimal therapy. Expanding beyond the traditional tools that have been used to extract information from patient-specific data, machine learning offers a new set of advanced tools capable of revealing previously unknown data patterns and features. This new tool set can substantially improve the speed and level of confidence with which electrophysiologists can determine patient-specific diagnoses and therapies. The ability to process substantial amounts of data in real time also paves the way to novel techniques for data collection and visualization. Extended realities such as virtual and augmented reality can now enable the real-time visualization of 3-dimensional images in space. This enables improved preprocedural planning and intraprocedural interventions. Machine learning supplemented with novel visualization technologies could substantially improve patient care and outcomes by helping physicians to make more informed patient-specific decisions. This article presents current applications of machine learning and their use in cardiac electrophysiology.

RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms.

There exists a gap in terms of the signals provided by pacemakers (i.e., intracardiac electrogram (EGM)) and the signals doctors use (i.e., 12-lead electrocardiogram (ECG)) to diagnose abnormal rhythms. Therefore, the former, even if remotely transmitted, are not sufficient for doctors to provide a precise diagnosis, let alone make a timely intervention. To close this gap and make a heuristic step towards real-time critical intervention in instant response to irregular and infrequent ventricular rhythms, we propose a new framework dubbed RT-RCG to automatically search for (1) efficient Deep Neural Network (DNN) structures and then (2) corresponding accelerators, to enable Real-Time and high-quality Reconstruction of ECG signals from EGM signals. Specifically, RT-RCG proposes a new DNN search space tailored for ECG reconstruction from EGM signals, and incorporates a differentiable acceleration search (DAS) engine to efficiently navigate over the large and discrete accelerator design space to generate optimized accelerators. Extensive experiments and ablation studies under various settings consistently validate the effectiveness of our RT-RCG. To the best of our knowledge, RT-RCG is the first to leverage neural architecture search (NAS) to simultaneously tackle both reconstruction efficacy and efficiency.

A novel convolutional neural network for reconstructing surface electrocardiograms from intracardiac electrograms and vice versa.

We propose a novel convolutional neural network framework for mapping a multivariate input to a multivariate output. In particular, we implement our algorithm within the scope of 12-lead surface electrocardiogram (ECG) reconstruction from intracardiac electrograms (EGM) and vice versa. The goal of performing this task is to allow for improved point-of-care monitoring of patients with an implanted device to treat cardiac pathologies. We will achieve this goal with 12-lead ECG reconstruction and by providing a new diagnostic tool for classifying five different ECG types. The algorithm is evaluated on a dataset retroactively collected from 14 patients. Correlation coefficients calculated between the reconstructed and the actual ECG show that the proposed convolutional neural network model represents an efficient, accurate, and superior way to synthesize a 12-lead ECG when compared to previous methods. We can also achieve the same reconstruction accuracy with only one EGM lead as input. We also tested the model in a non-patient specific way and saw a reasonable correlation coefficient. The model was also executed in the reverse direction to produce EGM signals from a 12-lead ECG and found that the correlation was comparable to the forward direction. Lastly, we analyzed the features learned in the model and determined that the model learns an overcomplete basis of our 12-lead ECG space. We then use this basis of features to create a new diagnostic tool for classifying different ECG arrhythmia's on the MIT-BIH arrhythmia database with an average accuracy of 0.98.

Novel Virtual Reality System for Auditory Tasks in Head-fixed Mice.

An emerging corpus of research seeks to use virtual realities (VRs) to understand the neural mechanisms underlying spatial navigation and decision making in rodents. These studies have primarily used visual stimuli to represent the virtual world. However, auditory cues play an important role in navigation for animals, especially when the visual system cannot detect objects or predators. We have developed a virtual reality environment defined exclusively by free-field acoustic landmarks for head-fixed mice. We trained animals to run in a virtual environment with 3 acoustic landmarks. We present evidence that they can learn to navigate in our context: we observed anticipatory licking and modest anticipatory slowing preceding the reward region. Furthermore, we found that animals were highly aware of changes in landmark cues: licking behavior changed dramatically when the familiar virtual environment was switched to a novel one, and then rapidly reverted to normal when the familiar virtual environment was re-introduced, all within the same session. Finally, while animals executed the task, we performed in-vivo calcium imaging in the CA1 region of the hippocampus using a modified Miniscope.org system. Our experiments point to a future in which auditory virtual reality can be used to expand our understanding of the neural bases of audition in locomoting animals and the variety of sensory cues which anchor spatial representations in a new virtual environment.

An essential N-terminal serine-rich motif in the AAV VP1 and VP2 subunits that may play a role in viral transcription.

Adeno-associated virus (AAV) is one of the most researched, clinically utilized gene therapy vectors. Though clinical success has been achieved, transgene delivery and expression may be hindered by cellular and tissue barriers. Understanding the role of receptor binding, entry, endosomal escape, cytoplasmic and nuclear trafficking, capsid uncoating, and viral transcription in therapeutic efficacy is paramount. Previous studies have shown that N-terminal regions of the AAV capsid proteins are responsible for endosomal escape and nuclear trafficking, however the mechanisms remain unknown. We identified a highly-conserved three-residue serine/threonine (S/T) motif in the capsid N-terminus, previously uncharacterized in its role in intracellular trafficking and transduction. Using alanine scanning mutagenesis, we found S155 and the flanking residues, D154 and G158, are essential for AAV2 transduction efficiency. Remarkably, specific capsid mutants show a 5 to 9-fold decrease in viral mRNA transcripts, highlighting a potential role of the S/T motif in transcription of the viral genome.