We propose a method for generating an electrocardiogram (ECG) signal for one cardiac cycle using a variational autoencoder. Using this method we extracted a vector of new 25 features, which in many cases can be interpreted. The generated ECG has quite natural appearance. The low value of the Maximum Mean Discrepancy metric, 0.00383, indicates good quality of ECG generation too. The extracted new features will help to improve the quality of automatic diagnostics of cardiovascular diseases. Also, generating new synthetic ECGs will allow us to solve the issue of the lack of labeled ECG for use them in supervised learning.
Columbia University（哥伦比亚大学）；University of California at Berkeley（加州大学伯克利分校）；University of Chicago（芝加哥大学）
An electrocardiogram (EKG) is a common, non-invasive test that measures the electrical activity of a patient's heart. EKGs contain useful diagnostic information about patient health that may be absent from other electronic health record (EHR) data. As multi-dimensional waveforms, they could be modeled using generic machine learning tools, such as a linear factor model or a variational autoencoder. We take a different approach:~we specify a model that directly represents the underlying electrophysiology of the heart and the EKG measurement process. We apply our model to two datasets, including …
University of Technology Sydney（悉尼科技大学）；The Children’s Hospital at Westmead（韦斯特米德儿童医院）；Queen’s University at Kingston（金斯顿女王大学）
Feature extraction becomes increasingly important as data grows high dimensional. Autoencoder as a neural network based feature extraction method achieves great success in generating abstract features of high dimensional data. However, it fails to consider the relationships of data samples which may affect experimental results of using original and new features. In this paper, we propose a Relation Autoencoder model considering both data features and their relationships. We also extend it to work with other major autoencoder models including Sparse Autoencoder, Denoising Autoencoder and Variat…
VAE-MS: An Asymmetric Variational Autoencoder for Mutational Signature Extraction
Center for Clinical Data Science（临床数据科学中心）；Aalborg University（奥胡斯大学）；Aalborg University Hospital（奥胡斯大学医院）；Nuffield Department of Surgical Sciences（外科科学努尔菲尔德部门）；University of Oxford（牛津大学）；Nuffield Department of Women’s and Reproductive Health（妇女和生殖健康努尔菲尔德部门）
Quasiparticle Interference Kernel Extraction with Variational Autoencoders via Latent Alignment
Department of Computer Science \& \ University of Notre Dame South Bend, IN, USA；Department of Physics \& Astronomy University of Notre Dame South Bend, IN, USA
Hyperspectral Variational Autoencoders for Joint Data Compression and Component Extraction
Core Francisco Park 1,2,3,4（Core Francisco Park 团队）；Manuel Pérez-Carrasco 3（Manuel Pérez-Carrasco 团队）；Caroline Nowlan 3（Caroline Nowlan 团队）；Cecilia Garraffo 3,4（Cecilia Garraffo 团队）
Laboratoire des Images et des Signaux (LIS) – CNRS UMR 5083（图像与信号实验室（LIS）——法国国家科学研究中心 UMR 5083）；INPG（格勒诺布尔国立理工学院）；UJF（约瑟夫·傅里叶大学（格勒诺布尔第一大学））；Sharif University of Technology（谢里夫理工大学）；Université Catholique de Louvain (UCL)（鲁汶天主教大学（UCL））；Laboratoire Systémes Complexes (LSC) – CNRS FRE 2494（复杂系统实验室（LSC）——法国国家科学研究中心 FRE 2494）
Blind source separation (BSS) techniques have revealed to be promising approaches for, among other, biomedical signal processing applications. Specifically, for the noninvasive extraction of fetal cardiac signals from maternal abdominal recordings, where conventional filtering schemes have failed to extract the complete fetal ECG components. From previous studies, it is now believed that a carefully selected array of electrodes well-placed over the abdomen of a pregnant woman contains the required `information' for BSS, to extract the complete fetal components. Based on this idea, in previous …
University of New Mexico（新墨西哥大学）；The MIND Research Network（MIND研究网络）
Independent component analysis (ICA), as an approach to the blind source-separation (BSS) problem, has become the de-facto standard in many medical imaging settings. Despite successes and a large ongoing research effort, the limitation of ICA to square linear transformations have not been overcome, so that general INFOMAX is still far from being realized. As an alternative, we present feature analysis in medical imaging as a problem solved by Helmholtz machines, which include dimensionality reduction and reconstruction of the raw data under the same objective, and which recently have overcome …
ARC Centre of Excellence for Mathematical & Statistical Frontiers（ARC数学与统计前沿卓越中心）；Queensland University of Technology（昆士兰科技大学）；Beijing Institute of Technology（北京理工大学）；University of Technology Sydney（悉尼科技大学）
Construction of dictionaries using nonnegative matrix factorisation (NMF) has extensive applications in signal processing and machine learning. With the advances in deep learning, training compact and robust dictionaries using deep neural networks, i.e., dictionaries of deep features, has been proposed. In this study, we propose a probabilistic generative model which employs a variational autoencoder (VAE) to perform nonnegative dictionary learning. In contrast to the existing VAE models, we cast the model under a statistical framework with latent variables obeying a Gamma distribution and des…
Georgia Institute of Technology（佐治亚理工学院）；Google X Development LLC（Google X 开发有限责任公司）；Emory University（埃默里大学）；University of California San Francisco（加州大学旧金山分校）；University of North Carolina（北卡罗来纳大学）
Depression and post-traumatic stress disorder (PTSD) are psychiatric conditions commonly associated with experiencing a traumatic event. Estimating mental health status through non-invasive techniques such as activity-based algorithms can help to identify successful early interventions. In this work, we used locomotor activity captured from 1113 individuals who wore a research grade smartwatch post-trauma. A convolutional variational autoencoder (VAE) architecture was used for unsupervised feature extraction from four weeks of actigraphy data. By using VAE latent variables and the participant'…
Objective: Mixtures of temporally nonstationary signals are very common in biomedical applications. The nonstationarity of the source signals can be used as a discriminative property for signal separation. Herein, a semi-blind source separation algorithm is proposed for the extraction of temporally nonstationary components from linear multichannel mixtures of signals and noises. Methods: A hypothesis test is proposed for the detection and fusion of temporally nonstationary events, by using ad hoc indexes for monitoring the first and second order statistics of the innovation process. As proof o…
Reconstructing 12-Lead ECG from 3-Lead ECG using Variational Autoencoder to Improve Cardiac Disease Detection of Wearable ECG Devices
National Institute of Health Data Science, Peking University, Beijing, China（国家健康数据科学研究院，北京大学，北京，中国）；Institute for Artificial Intelligence, Peking University, Beijing, China（人工智能研究院，北京大学，北京，中国）；State Key Laboratory of General Artificial Intelligence, Beijing 100871, China（通用人工智能国家重点实验室，北京，中国）；School of Intelligence Science and Technology, Peking University, Beijing 100871, China（智能科学与技术学院，北京大学，北京，中国）；Department of Cardiology, Peking University People’s Hospital, Beijing 100044, China（北京大学人民医院心内科，北京，中国）；HeartVoice Medical Technology, Hefei 230088, China（HeartVoice医疗科技，合肥，中国）
Multimodal Variational Autoencoder for Low-cost Cardiac Hemodynamics Instability Detection
University of Sheffield（谢菲尔德大学）；IITRAM（印度理工学院拉杰科德校区）；Sheffield Teaching Hospitals（谢菲尔德教学医院）；INSIGNEO, Institute for in Silico Medicine, University of Sheffield（谢菲尔德大学INSIGNEO计算机医学研究所）
Indian Institute of Science（印度科学学院）；nference（nference公司）
Machine-Learning-as-a-Service providers expose machine learning (ML) models through application programming interfaces (APIs) to developers. Recent work has shown that attackers can exploit these APIs to extract good approximations of such ML models, by querying them with samples of their choosing. We propose VarDetect, a stateful monitor that tracks the distribution of queries made by users of such a service, to detect model extraction attacks. Harnessing the latent distributions learned by a modified variational autoencoder, VarDetect robustly separates three types of attacker samples from b…
Dr. Ing. h.c. F. Porsche AG（保时捷股份公司）；Ludwig Maximilian University（路德维希-马克西米利安大学）；University of Tübingen（蒂宾根大学）；Chemnitz University of Technology（开姆尼茨工业大学）；Silicon Austria Labs（奥地利硅谷实验室）
There is a growing need for sparse representational formats of human affective states that can be utilized in scenarios with limited computational memory resources. We explore whether representing neural data, in response to emotional stimuli, in a latent vector space can serve to both predict emotional states as well as generate synthetic EEG data that are participant- and/or emotion-specific. We propose a conditional variational autoencoder based framework, EEG2Vec, to learn generative-discriminative representations from EEG data. Experimental results on affective EEG recording datasets demo…
Qatar University（卡塔尔大学）；Universiti Kebangsaan Malaysia（马来西亚国立大学）；BUET（孟加拉国工程技术大学）
Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous Blood Pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most existing methods used in the hospitals for continuous monitoring of BP are invasive. On the contrary, cuff-based BP monitoring methods, which can predict Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP), cannot be used for continuous monitoring. Several studies attempted to predict BP fr…
Obesity is a common issue in modern societies today that can lead to various diseases and significantly reduced quality of life. Currently, research has been conducted to investigate resting state EEG (electroencephalogram) signals with an aim to identify possible neurological characteristics associated with obesity. In this study, we propose a deep learning-based framework to extract the resting state EEG features for obese and lean subject classification. Specifically, a novel variational autoencoder framework is employed to extract subject-invariant features from the raw EEG signals, which …
Deep learning applied to electrocardiogram (ECG) data can be used to achieve personal authentication in biometric security applications, but it has not been widely used to diagnose cardiovascular disorders. We developed a deep learning model for the detection of arrhythmia in which time-sliced ECG data representing the distance between successive R-peaks are used as the input for a convolutional neural network (CNN). The main objective is developing the compact deep learning based detect system which minimally uses the dataset but delivers the confident accuracy rate of the Arrhythmia detectio…
Compact Neural Network Algorithm for Electrocardiogram Classification
Centro de Física Aplicada y Tecnología Avanzada（应用物理与先进技术中心）；Instituto de Ciencias Nucleares（核科学研究所）；Instituto Tecnológico y de Estudios Superiores de Monterrey（蒙特雷技术与高等研究学院）
Northeastern University（东北大学）；Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究实验室（MERL））
Recent developments in biosignal processing have enabled users to exploit their physiological status for manipulating devices in a reliable and safe manner. One major challenge of physiological sensing lies in the variability of biosignals across different users and tasks. To address this issue, we propose an adversarial feature extractor for transfer learning to exploit disentangled universal representations. We consider the trade-off between task-relevant features and user-discriminative information by introducing additional adversary and nuisance networks in order to manipulate the latent r…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Beckman Institute for Advanced Science and Technology（贝克曼先进科学技术研究所）；Carle Illinois College of Medicine（卡尔伊利诺伊医学院）
Myocardial infarction is the leading cause of death worldwide. In this paper, we design domain-inspired neural network models to detect myocardial infarction. First, we study the contribution of various leads. This systematic analysis, first of its kind in the literature, indicates that out of 15 ECG leads, data from the v6, vz, and ii leads are critical to correctly identify myocardial infarction. Second, we use this finding and adapt the ConvNetQuake neural network model--originally designed to identify earthquakes--to attain state-of-the-art classification results for myocardial infarction,…
Self-Supervised Autoencoder Network for Robust Heart Rate Extraction from Noisy Photoplethysmogram: Applying Blind Source Separation to Biosignal Analysis
Mellowing Factory Co. Ltd.（梅洛灵工厂有限公司）；Chungnam National University Hospital（全南国立大学医院）；Department of Radiology, Seoul National University College of Medicine（首尔国立大学医学院放射科）
ECGtizer: a fully automated digitizing and signal recovery pipeline for electrocardiograms
IRD（法国发展研究院）；Sorbonne Université（索邦大学）；INSERM（法国国家健康与医学研究院）；Pitié-Salpêtrière University Hospital（皮蒂-萨尔皮特里耶大学医院）；Vanderbilt University Medical Center（范德堡大学医学中心）
The process of recording Electroencephalography (EEG) signals is onerous and requires massive storage to store signals at an applicable frequency rate. In this work, we propose the EventRelated Potential Encoder Network (ERPENet); a multi-task autoencoder-based model, that can be applied to any ERP-related tasks. The strength of ERPENet lies in its capability to handle various kinds of ERP datasets and its robustness across multiple recording setups, enabling joint training across datasets. ERPENet incorporates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), in an autoe…
Human Activity Recognition Based on Electrocardiogram Data Only
Department of Computer Science and Engineering, University of North Texas（计算机科学与工程系，北卡罗来纳州立大学）；Faculty of Computer Science, Technische Universität Dresden（计算机科学系，德累斯顿技术大学）
An electrocardiogram (ECG) feature extraction system has been developed and evaluated using Virtex-6 FPGA kit which belongs to Xilinx Ltd. In time domain, Pan-Tompkins algorithm is used for QRS detection and it is followed by a feature extractor block to extract ECG features. This whole system can be used to detect cardiac arrhythmia. The completed algorithm was implemented on Virtex-6(XC6VLX240-T) device and tested using hardware co-simulation in Modelsim and simulink environment. The software generated ECG signals are obtained from MIT-BIH arrhythmia Database [1]. The memory and time complex…
Goal: Although photoplethysmogram (PPG) and electrocardiogram (ECG) signals can be used to estimate blood pressure (BP) by extracting various features, the changes in morphological contours of both PPG and ECG signals due to various diseases of circulatory system and interaction of other physiological systems make the extraction of such features very difficult. Methods: In this work, we propose a waveform-based hierarchical Artificial Neural Network - Long Short Term Memory (ANN-LSTM) model for BP estimation. The model consists of two hierarchy levels, where the lower hierarchy level uses ANNs…
Masked Transformer for Electrocardiogram Classification
Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院国家心血管病中心阜外医院）
本文提出 MTECG 方法，将掩码自编码器应用于 ECG 时间序列的自监督表示学习，并在多个数据集上显著提升了分类的宏 F1 分数。
Johannes Gutenberg Universität-Mainz（美因茨约翰内斯·古腾堡大学）；Helmholtz-Institut Mainz（美因茨亥姆霍兹研究所）；GSI Helmholtzzentrum für Schwerionenforschung GmbH（GSI亥姆霍兹重离子研究中心）；University of California, Berkeley（加州大学伯克利分校）
In this work we demonstrate the use of neural networks for rapid extraction of signal parameters of discretely sampled signals. In particular, we use dense autoencoder networks to extract the parameters of interest from exponentially decaying signals and decaying oscillations. By using a three-stage training method and careful choice of the neural network size, we are able to retrieve the relevant signal parameters directly from the latent space of the autoencoder network at significantly improved rates compared to traditional algorithmic signal-analysis approaches. We show that the achievable…
Multi-Channel Masked Autoencoder and Comprehensive Evaluations for Reconstructing 12-Lead ECG from Arbitrary Single-Lead ECG
National Institute of Health Data Science, Peking University（北京大学国家健康数据科学研究院）；School of Biomedical Engineering, Sun Yat-sen University（中山大学医学院生物医学工程学院）；Shanghai Jiao Tong University（上海交通大学）；Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University（天津医科大学第二附属医院天津心脏病学研究所）；Institute of Medical Technology, Health Science Center of Peking University（北京大学健康科学中心医学技术研究院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）
Indian Institute of Technology, Roorkee（鲁尔基印度理工学院）；Leicester Royal Infirmary（莱斯特皇家医院）；Glenfield Hospital（格伦菲尔德医院）
The incidences of atrial fibrillation (AFib) are increasing at a daunting rate worldwide. For the early detection of the risk of AFib, we have developed an automatic detection system based on deep neural networks. For achieving better classification, it is mandatory to have good pre-processing of physiological signals. Keeping this in mind, we have proposed a two-fold study. First, an end-to-end model is proposed to denoise the electrocardiogram signals using denoising autoencoders (DAE). To achieve denoising, we have used three networks including, convolutional neural network (CNN), dense neu…
Masked Autoencoders that Feel the Heart: Unveiling Simplicity Bias for ECG Analyses
State Key Laboratory of Digital Medical Engineering, Southeast University（数字医疗工程国家重点实验室，东南大学）；School of Instrument Science and Engineering, Southeast University（仪器科学与工程学院，东南大学）；School of Computer Science and Engineering, Southeast University（计算机科学与工程学院，东南大学）
Decoding of motor imagery (MI) from Electroencephalogram (EEG) is an important component of the Brain-Computer Interface (BCI) system that helps motor-disabled people interact with the outside world via external devices. The main issue in developing the EEG based BCI is the informative confusion due to the non-stationary characteristics of EEG data. In this work, an innovative idea of transforming an EEG signal into the weight vector of an unsupervised neural network called the autoencoder is proposed for the first time to solve that problem. Separate autoencoders are trained for the individua…
Currently, the telehealth monitoring field has gained huge attention due to its noteworthy use in day-to-day life. This advancement has led to an increase in the data collection of electrophysiological signals. Due to this advancement, electrocardiogram (ECG) signal monitoring has become a leading task in the medical field. ECG plays an important role in the medical field by analysing cardiac physiology and abnormalities. However, these signals are affected due to numerous varieties of noises, such as electrode motion, baseline wander and white noise etc., which affects the diagnosis accuracy.…
