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 complexities of the implemented design were recorded and feature extraction has been done. We have achieved satisfactory results which is mainly due to parallel implementation. Therefore accurate arrhythmia detection using hardware implementation a viable approach.
Lobachevsky State University of Nizhni Novgorod（下诺夫哥罗德国立洛巴切夫斯基大学）
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 th…
University of Saskatchewan（萨斯喀彻温大学）；University of Isfahan（伊斯法罕大学）；University of North Dakota（北达科他大学）
Non-invasive fetal electrocardiogram (FECG) is used to monitor the electrical pulse of the fetal heart. Decomposing the FECG signal from maternal ECG (MECG) is a blind source separation problem, which is hard due to the low amplitude of FECG, the overlap of R waves, and the potential exposure to noise from different sources. Traditional decomposition techniques, such as adaptive filters, require tuning, alignment, or pre-configuration, such as modeling the noise or desired signal. to map MECG to FECG efficiently. The high correlation between maternal and fetal ECG parts decreases the performan…
College of Communication Engineering, Jilin University（吉林大学通信工程学院）；Zhuhai College of Jilin University（吉林大学珠海学院）
Feature extraction plays an important role in Electrocardiogram (ECG) Beats classification system. Compared to other popular methods, VQ method performs well in feature extraction from ECG with advantages of dimensionality reduction. In VQ method, a set of dictionaries corresponding to segments of ECG beats is trained, and VQ codes are used to represent each heartbeat. However, in practice, VQ codes optimized by k-means or k-means++ exist large quantization errors, which results in VQ codes for two heartbeats of the same type being very different. So the essential differences between different…
In this paper, the field programmable gate array (FPGA) implementation of a fetal heart rate (FHR) monitoring system is presented. The system comprises of a preprocessing unit to remove various types of noise, followed by a fetal electrocardiogram (FECG) extraction unit and an FHR detection unit. In order to improve the precision and accuracy of the arithmetic operations, a floating point unit is developed. A least mean squares algorithm based adaptive filter (LMS-AF) is used for the purpose of FECG extraction. Two different architectures, namely series and parallel, are proposed for the LMS-A…
In this paper, we present a novel Image Fusion Model (IFM) for ECG heart-beat classification to overcome the weaknesses of existing machine learning techniques that rely either on manual feature extraction or direct utilization of 1D raw ECG signal. At the input of IFM, we first convert the heart beats of ECG into three different images using Gramian Angular Field (GAF), Recurrence Plot (RP) and Markov Transition Field (MTF) and then fuse these images to create a single imaging modality. We use AlexNet for feature extraction and classification and thus employ end to end deep learning. We perfo…
Investigation on the electrocardiogram (ECG) signals is an essential way to diagnose heart disease since the ECG process is noninvasive and easy to use. This work presents a supraventricular arrhythmia prediction model consisting of a few stages, including filtering of noise, a unique collection of ECG characteristics, and automated learning classifying model to classify distinct types, depending on their severity. We de-trend and de-noise a signal to reduce noise to better determine functionality before extractions are performed. After that, we present one R-peak detection method and Q-S dete…
IBM Research - Africa（IBM非洲研究院）；University of Oxford（牛津大学）；Guangdong Cardiovascular Institute（广东省心血管病研究所）
Myocardial Infarction (MI) has the highest mortality of all cardiovascular diseases (CVDs). Detection of MI and information regarding its occurrence-time in particular, would enable timely interventions that may improve patient outcomes, thereby reducing the global rise in CVD deaths. Electrocardiogram (ECG) recordings are currently used to screen MI patients. However, manual inspection of ECGs is time-consuming and prone to subjective bias. Machine learning methods have been adopted for automated ECG diagnosis, but most approaches require extraction of ECG beats or consider leads independentl…
A Complex UNet Approach for Non-Invasive Fetal ECG Extraction Using Single-Channel Dry Textile Electrodes
SIGMA Laboratory, CAMPUS Institute, National University of Science and Technology Politehnica Bucharest（SIGMA实验室，CAMPUS研究所，波兰学院科学技术大学布加勒斯特分校）；Department of Information Engineering and Computer Science, University of Trento（信息工程与计算机科学系，特伦特大学）；Department of Electrical Engineering, Eindhoven University of Technology（电气工程系，埃因霍温理工大学）
The classification of electrocardiogram (ECG) signals, which takes much time and suffers from a high rate of misjudgment, is recognized as an extremely challenging task for cardiologists. The major difficulty of the ECG signals classification is caused by the long-term sequence dependencies. Most existing approaches for ECG signal classification use Recurrent Neural Network models, e.g., LSTM and GRU, which are unable to extract accurate features for such long sequences. Other approaches utilize 1-Dimensional Convolutional Neural Network (CNN), such as ResNet or its variant, and they can not m…
Many people are currently suffering from heart diseases that can lead to untimely death. The most common heart abnormality is arrhythmia, which is simply irregular beating of the heart. A prediction system for the early intervention and prevention of heart diseases, including cardiovascular diseases (CDVs) and arrhythmia, is important. This paper introduces the classification of electrocardiogram (ECG) heartbeats into normal or abnormal. The approach is based on the combination of swarm optimization algorithms with a modified PannTompkins algorithm (MPTA) and support vector machines (SVMs). Th…
University of New South Wales（新南威尔士大学）；Harvard University（哈佛大学）
Electrocardiography (ECG) signal is a highly applied measurement for individual heart condition, and much effort have been endeavored towards automatic heart arrhythmia diagnosis based on machine learning. However, traditional machine learning models require large investment of time and effort for raw data preprocessing and feature extraction, as well as challenged by poor classification performance. Here, we propose a novel deep learning model, named Attention-Based Convolutional Neural Networks (ABCNN) that taking advantage of CNN and multi-head attention, to directly work on the raw ECG sig…
The electrocardiogram (ECG) signal is one of the most vital signals that can be used to investigate the performance of heart. Based on the ECG graph, we can identify different disorders and diseases. Therefore, monitoring this signal is of great importance. Many electrodes (usually 12) are employed to acquire this signal in clinics and hospitals; therefore, a nurse must install them on the body of the patient to record the signal. In this project, we built a device that can acquire the real-time ECG signal and display it on the mobile screen with the least number of electrodes (three electrode…
Beyond Single-Channel: Multichannel Signal Imaging for PPG-to-ECG Reconstruction with Vision Transformers
Department of Computer Science, University of Toronto（多伦多大学计算机科学系）；Data Science Research Center, Duke Kunshan University（杜克昆山大学数据科学研究中心）；Data Science Lab, Fields Institute for Research in Mathematical Sciences（数学科学研究院数据科学实验室）；Department of Mathematics and Statistics, York University（约克大学数学与统计学系）
本文提出融合原始 PPG、一阶及二阶差分与曲线下面积的四通道信号图像表示，并以 ViT 建模心搏间和心搏内依赖，显著提升 PPG 到 ECG 的重建精度。
Università della Svizzera italiana（瑞士意大利语大学）；UiT the Arctic University of Norway（挪威北极大学）；TU Wien（维也纳技术大学）；University of Exeter（埃克塞特大学）
We tackle the problem of classifying Electrocardiography (ECG) signals with the aim of predicting the onset of Paroxysmal Atrial Fibrillation (PAF). Atrial fibrillation is the most common type of arrhythmia, but in many cases PAF episodes are asymptomatic. Therefore, in order to help diagnosing PAF, it is important to design procedures for detecting and, more importantly, predicting PAF episodes. We propose a method for predicting PAF events whose first step consists of a feature extraction procedure that represents each ECG as a multi-variate time series. Successively, we design a classificat…
The heart is one of the most vital organs in the human body. It supplies blood and nutrients in other parts of the body. Therefore, maintaining a healthy heart is essential. As a heart disorder, arrhythmia is a condition in which the heart's pumping mechanism becomes aberrant. The Electrocardiogram is used to analyze the arrhythmia problem from the ECG signals because of its fewer difficulties and cheapness. The heart peaks shown in the ECG graph are used to detect heart diseases, and the R peak is used to analyze arrhythmia disease. Arrhythmia is grouped into two groups - Tachycardia and Brad…
Personality Trait Recognition using ECG Spectrograms and Deep Learning
University of Engineering and Technology, Taxila（塔克西拉工程技术大学）；Sheikh Zayed Institute for Pediatric Surgical Innovation（谢赫·扎耶德儿科外科创新研究所）；Children’s National Hospital（儿童国家医院）；School of Medicine and Health Sciences, George Washington University（乔治·华盛顿大学医学与健康科学学院）
University of Isfahan（伊斯法罕大学）；Amirkabir University of Technology（阿米尔卡比尔理工大学）；New Jersey Institute of Technology（新泽西理工学院）；Rutgers University（罗格斯大学）
Electrocardiogram (ECG) is the most frequent and routine diagnostic tool used for monitoring heart electrical signals and evaluating its functionality. The human heart can suffer from a variety of diseases, including cardiac arrhythmias. Arrhythmia is an irregular heart rhythm that in severe cases can lead to heart stroke and can be diagnosed via ECG recordings. Since early detection of cardiac arrhythmias is of great importance, computerized and automated classification and identification of these abnormal heart signals have received much attention for the past decades. Methods: This paper in…
Indian Institute of Engineering Science and Technology, Shibpur（印度工程科学与技术学院，斯赫普尔）；IIT (ISM), Dhanbad（印度理工学院（印度矿业学院），丹巴德）
In primary diagnosis and analysis of heart defects, an ECG signal plays a significant role. This paper presents a model for the prediction of ventricular tachycardia arrhythmia using noise filtering, a unique set of ECG features, and a machine learning-based classifier model. Before signal feature extraction, we detrend and denoise the signal to eliminate the noise for detecting features properly. After that necessary features have been extracted and necessary parameters related to these features are measured. Using these parameters, we prepared one efficient multiclass classifier model using …
Institutes of Biomedical Engineering, Shanghai Jiaotong University（上海交通大学生物医学工程研究所）；Shukun (Beijing) Network Technology Co.,Ltd.（树坤（北京）网络科技有限公司）；Institutes of Medical Robotics, Shanghai Jiaotong University（上海交通大学医疗机器人研究所）
Objective: A novel structure based on channel-wise attention mechanism is presented in this paper. Embedding with the proposed structure, an efficient classification model that accepts multi-lead electrocardiogram (ECG) as input is constructed. Methods: One-dimensional convolutional neural networks (CNN) have proven to be effective in pervasive classification tasks, enabling the automatic extraction of features while classifying targets. We implement the Residual connection and design a structure which can learn the weights from the information contained in different channels in the input feat…
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）
MSW-Transformer: Multi-Scale Shifted Windows Transformer Networks for 12-Lead ECG Classification
Shantou University（汕头大学）；Sun Yet-sun University（中山大学）；The First Affiliated Hospital of Shantou University（汕头大学第一附属医院）；Shantou Institute of Ultrasonic Instruments Co., Ltd.（汕头超声仪器研究所有限公司）
Beihang University（北京航空航天大学）；Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University（北京航空航天大学大数据与脑计算北京高级创新中心）；Duke University（杜克大学）
Simultaneous Localization and Mapping (SLAM) is a critical task for autonomous navigation. However, due to the computational complexity of SLAM algorithms, it is very difficult to achieve real-time implementation on low-power platforms.We propose an energy efficient architecture for real-time ORB (Oriented-FAST and Rotated- BRIEF) based visual SLAM system by accelerating the most time consuming stages of feature extraction and matching on FPGA platform.Moreover, the original ORB descriptor pattern is reformed as a rotational symmetric manner which is much more hardware friendly. Optimizations …
Quantum random number generators (QRNG) based on continuous variable (CV) quantum fluctuations offer great potential for their advantages in measurement bandwidth, stability and integrability. More importantly, it provides an efficient and extensible path for significant promotion of QRNG generation rate. During this process, real-time randomness extraction using information theoretically secure randomness extractors is vital, because it plays critical role in the limit of throughput rate and implementation cost of QRNGs. In this work, we investigate parallel and real-time realization of sever…
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…
International Centre for Neuromorphic Systems, The MARCS Institute, Western Sydney University（西悉尼大学 MARCS 研究所国际神经形态系统中心）；Western Sydney University（西悉尼大学）
This paper presents a reconfigurable digital implementation of an event-based binaural cochlear system on a Field Programmable Gate Array (FPGA). It consists of a pair of the Cascade of Asymmetric Resonators with Fast Acting Compression (CAR FAC) cochlea models and leaky integrate and fire (LIF) neurons. Additionally, we propose an event-driven SpectroTemporal Receptive Field (STRF) Feature Extraction using Adaptive Selection Thresholds (FEAST). It is tested on the TIDIGTIS benchmark and compared with current event-based auditory signal processing approaches and neural networks.
This paper reported a bespoke adder-based deep learning network for time-domain fluorescence lifetime imaging (FLIM). By leveraging the l1-norm extraction method, we propose a 1-D Fluorescence Lifetime AdderNet (FLAN) without multiplication-based convolutions to reduce the computational complexity. Further, we compressed fluorescence decays in temporal dimension using a log-scale merging technique to discard redundant temporal information derived as log-scaling FLAN (FLAN+LS). FLAN+LS achieves 0.11 and 0.23 compression ratios compared with FLAN and a conventional 1-D convolutional neural netwo…
HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN
Universiti Malaysia Pahang（马来西亚彭亨大学）；Queensland University of Technology（昆士兰科技大学）；Islamic University（伊斯兰大学）；The University of Sydney（悉尼大学）；University of Cambridge（剑桥大学）；The Garvan Institute of Medical Research（加文医学研究所）；The University of Queensland（昆士兰大学）
Emory University School of Medicine（埃默里大学医学院）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔国立理工学院）
The extraction of nonstationary signals from blind and semi-blind multivariate observations is a recurrent problem. Numerous algorithms have been developed for this problem, which are based on the exact or approximate joint diagonalization of second or higher order cumulant matrices/tensors of multichannel data. While a great body of research has been dedicated to joint diagonalization algorithms, the selection of the diagonalized matrix/tensor set remains highly problem-specific. Herein, various methods for nonstationarity identification are reviewed and a new general framework based on hypot…
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…
Psychophysiology investigates the causal relationship of physiological changes resulting from psychological states. There are significant challenges with machine learning-based momentary assessments of physiology due to varying data collection methods, physiological differences, data availability and the requirement for expertly annotated data. Advances in wearable technology have significantly increased the scale, sensitivity and accuracy of devices for recording physiological signals, enabling large-scale unobtrusive physiological data gathering. This work contributes an empirical evaluation…
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）
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）
An electrocardiogram (ECG) is a time-series signal that is represented by one-dimensional (1-D) data. Higher dimensional representation contains more information that is accessible for feature extraction. Hidden variables such as frequency relation and morphology of segment is not directly accessible in the time domain. In this paper, 1-D time series data is converted into multi-dimensional representation in the form of multichannel 2-D images. Following that, deep learning was used to train a deep neural network based classifier to detect arrhythmias. The results of simulation on testing data…
Detection of Autonomic Dysreflexia in Individuals With Spinal Cord Injury Using Multimodal Wearable Sensors
SCAI-Lab, Department of Health Sciences and Technology (D-HEST), ETH Zurich（SCAI实验室，健康科学与技术系（D-HEST），苏黎世联邦理工学院）；School of Computation, Information and Technology, Technical University of Munich（计算、信息与技术学院，慕尼黑技术大学）；Swiss Paraplegic Research (SPF)（瑞士瘫痪研究机构（SPF））；Institute of Advanced Production Technologies, University of Valladolid（先进生产技术研究所，瓦尔达利德大学）；Swiss Paraplegic Centre (SPZ)（瑞士瘫痪中心（SPZ））；Balgrist University Hospital（巴尔格里斯大学医院）
