In recent years, deep learning has witnessed its blossom in the field of Electrocardiography (ECG) processing, outperforming traditional signal processing methods in various tasks, for example, classification, QRS detection, wave delineation. Although many neural architectures have been proposed in the literature, there is a lack of systematic studies and open-source libraries for ECG deep learning.
In this paper, we propose a deep learning framework, named \texttt{torch\_ecg}, which gathers a large number of neural networks, both from literature and novel, for various ECG processing tasks. It establishes a convenient and modular way for automatic building and flexible scaling of the networks, as well as a neat and uniform way of organizing the preprocessing procedures and augmentation techniques for preparing the input data for the models. Besides, \texttt{torch\_ecg} provides benchmark studies using the latest databases, illustrating the principles and pipelines for solving ECG processing tasks and reproducing results from the literature. \texttt{torch\_ecg} offers the ECG research community a powerful tool meeting the growing demand for the application of deep learning techniques.
The fetal electrocardiogram (fECG) was first recorded from the maternal abdominal surface in the early 1900s. During the past fifty years, the most advanced electronics technologies and signal processing algorithms have been used to convert noninvasive fetal electrocardiography into a reliable technology for fetal cardiac monitoring. In this chapter, the major signal processing techniques, which have been developed for the modeling, extraction and analysis of the fECG from noninvasive maternal abdominal recordings are reviewed and compared with one another in detail. The major topics of the ch…
Deconstructing Recurrence, Attention, and Gating: Investigating the transferability of Transformers and Gated Recurrent Neural Networks in forecasting of dynamical systems
School of Engineering and Applied Sciences, Harvard University（哈佛大学工程与应用科学学院）；Institute of Structural Engineering, ETH Zurich（苏黎世联邦理工学院结构工程研究所）
MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with Electrocardiography Using Mamba Capsule Network
State Key Laboratory of Transvascular Implantation Devices and TIDRI, Zhejiang University（浙江大学血管介入关键实验室及TIDRI）；School of Public Health, Zhejiang University and Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence（浙江大学公共卫生学院及浙江省医学影像人工智能重点实验室）；State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital and Liangzhu Laboratory, Zhejiang University（浙江大学第二附属医院血管介入关键实验室及梁渚实验室）；Department of Gastroenterology, First Hospital of Quanzhou Affiliated to Fujian Medical University（福建医科大学泉州第一医院消化内科）；School of Medicine, Stanford University（斯坦福大学医学院）
Ufa State Aviation Technical University（乌法国立航空技术大学）；Bashkir State Pedagogical University（巴什基尔国立师范大学）；Ural Federal University（乌拉尔联邦大学）；Institute of Immunology and Physiology UrB RAS（俄罗斯科学院乌拉尔分院免疫学与生理学研究所）
Machine learning shows great performance in various problems of electrocardiography (ECG) signal analysis. However, collecting a dataset for biomedical engineering is a very difficult task. Any dataset for ECG processing contains from 100 to 10,000 times fewer cases than datasets for image or text analysis. This issue is especially important because of physiological phenomena that can significantly change the morphology of heartbeats in ECG signals. In this preliminary study, we analyze the effects of lead choice from the standard ECG recordings, variation of ECG during 24-hours, and the effec…
Information Technologies Institute, CERTH（希腊研究与技术中心信息技术研究所）
Recent advances in content generation technologies (widely known as DeepFakes) along with the online proliferation of manipulated media content render the detection of such manipulations a task of increasing importance. Even though there are many DeepFake detection methods, only a few focus on the impact of dataset preprocessing and the aggregation of frame-level to video-level prediction on model performance. In this paper, we propose a pre-processing step to improve the training data quality and examine its effect on the performance of DeepFake detection. We also propose and evaluate the eff…
University of Essex（埃塞克斯大学）；University of Chemistry and Technology in Prague（布拉格化学与技术大学）；Charles University（查理大学）；Czech Technical University in Prague（布拉格捷克理工大学）；Nottingham Trent University（诺丁汉特伦特大学）
Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and …
National Institutes of Biomedical Innovation, Health and Nutrition（国立生物医学创新、健康与营养研究所）；Tohoku University（东北大学）；Tennessee Tech University（田纳西理工大学）；Benha University（本哈大学）；Lakehead University（湖首大学）；Thunder Bay Regional Health Research Institute（桑德贝地区健康研究所）
As we are about to embark upon the highly hyped "Society 5.0", powered by the Internet of Things (IoT), traditional ways to monitor human heart signals for tracking cardio-vascular conditions are challenging, particularly in remote healthcare settings. On the merits of low power consumption, portability, and non-intrusiveness, there are no suitable IoT solutions that can provide information comparable to the conventional Electrocardiography (ECG). In this paper, we propose an IoT device utilizing a spintronic ultra-sensitive sensor that measures the magnetic fields produced by cardio-vascular …
Investigating the Capabilities of Deep Learning for Processing and Interpreting One-Shot Multi-offset GPR Data: A Numerical Case Study for Lunar and Martian Environments
University of Aberdeen（阿伯丁大学）；Northumbria University（诺森比亚大学）；The University of Edinburgh（爱丁堡大学）；Interdisciplinary Centre for Data and AI（数据与人工智能跨学科中心）；Chinese Academy of Sciences（中国科学院）；China University of Geosciences (Wuhan)（中国地质大学（武汉））；Aristotle University of Thessaloniki（亚里士多德大学塞萨洛尼基分校）
TU Dresden（德累斯顿工业大学）；HZDR（亥姆霍兹德累斯顿罗森多夫研究中心）；UKE（汉堡埃彭多夫大学医院）
Functional connectivity plays an essential role in modern neuroscience. The modality sheds light on the brain's functional and structural aspects, including mechanisms behind multiple pathologies. One such pathology is schizophrenia which is often followed by auditory verbal hallucinations. The latter is commonly studied by observing functional connectivity during speech processing. In this work, we have made a step toward an in-depth examination of functional connectivity during a dichotic listening task via deep learning for three groups of people: schizophrenia patients with and without aud…
Most problems in natural language processing can be approximated as inverse problems such as analysis and generation at variety of levels from morphological (e.g., cat+Plural <-> cats) to semantic (e.g., (call + 1 2) <-> "Calculate one plus two."). Although the tasks in both directions are closely related, general approach in the field has been to design separate models specific for each task. However, having one shared model for both tasks, would help the researchers exploit the common knowledge among these problems with reduced time and memory requirements. We investigate a specific class of…
Investigating the generative dynamics of energy-based neural networks
Department of Mathematics, University of Padova（帕多瓦大学数学系）；Department of General Psychology and Padova Neuroscience Center, University of Padova（帕多瓦大学心理学系和帕多瓦神经科学中心）；IRCCS San Camillo Hospital（IRCCS圣卡米尔医院）
本文系统研究 RBM 的自上而下生成动力学，发现从编码多数字视觉特征的 chimera states 启动采样可提升原型多样性，但能量地形限制了单轨迹内的数字状态转换。
Convolutional neural networks (CNN) have recently achieved state-of-the-art results in various applications. In the case of image recognition, an ideal model has to learn independently of the training data, both local dependencies between the three components (R,G,B) of a pixel, and the global relations describing edges or shapes, making it efficient with small or heterogeneous datasets. Quaternion-valued convolutional neural networks (QCNN) solved this problematic by introducing multidimensional algebra to CNN. This paper proposes to explore the fundamental reason of the success of QCNN over …
Electrocardiography (ECG), an electrical measurement which captures cardiac activities, is the gold standard for diagnosing cardiovascular disease (CVD). However, ECG is infeasible for continuous cardiac monitoring due to its requirement for user participation. By contrast, photoplethysmography (PPG) provides easy-to-collect data, but its limited accuracy constrains its clinical usage. To combine the advantages of both signals, recent studies incorporate various deep learning techniques for the reconstruction of PPG signals to ECG; however, the lack of contextual information as well as the lim…
