Wearable devices enable theoretically continuous, longitudinal monitoring of physiological measurements like step count, energy expenditure, and heart rate. Although the classification of abnormal cardiac rhythms such as atrial fibrillation from wearable devices has great potential, commercial algorithms remain proprietary and tend to focus on heart rate variability derived from green spectrum LED sensors placed on the wrist where noise remains an unsolved problem. Here, we develop a multi-task deep learning method to assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation (AF). We train our algorithm on over one million simulated unlabeled physiological signals and fine-tune on a curated dataset of over 500K labeled signals from over 100 individuals from 3 different wearable devices. We demonstrate that in comparison with a traditional random forest-based approach (precision:0.24, recall:0.58, f1:0.34, auPRC:0.44) and a single task CNN (precision:0.59, recall:0.69, f1:0.64, auPRC:0.68) our architecture using unsupervised transfer learning through convolutional denoising autoencoders dramatically improves the performance of AF detection in participants at rest (pr:0.94, rc:0.98, f1:0.96, auPRC:0.96). In addition, we validate algorithm performance on a prospectively derived replication cohort of ambulatory subjects using data derived from an independently engineered device. We show that two-stage training can help address the unbalanced data problem common to biomedical applications where large well-annotated datasets are scarce. In conclusion, though a combination of simulation and transfer learning and we develop and apply a multitask architecture to the problem of AF detection from wearable wrist sensors demonstrating high levels of accuracy and a solution for the vexing challenge of mechanical noise.
Low-power sensing technologies, such as wearables, have emerged in the healthcare domain since they enable continuous and non-invasive monitoring of physiological signals. In order to endow such devices with clinical value, classical signal processing has encountered numerous challenges. However, data-driven methods, such as machine learning, offer attractive accuracies at the expense of being resource and memory demanding. In this paper, we focus on the inference of neural networks running in microcontrollers and low-power processors which wearable sensors and devices are generally equipped w…
A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillance
Heartbeat classification using electrocardiogram (ECG) data is a vital assistive technology for wearable health solutions. We propose heartbeat feature classification based on a novel sparse representation using time-frequency joint distribution of ECG. Fundamental to this is a multi-layer perceptron, which incorporates these signatures to detect cardiac arrhythmia. This approach is validated with ECG data from MIT-BIH arrhythmia database. Results show that our approach has an average 95.7% accuracy, an improvement of 22% over state-of-the-art approaches. Additionally, ECG sparse distributed r…
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…
Wearable electrocardiograph (ECG) recording and processing systems have been developed to detect cardiac arrhythmia to help prevent heart attacks. Conventional wearable systems, however, suffer from high energy consumption at both circuit and system levels. To overcome the design challenges, this paper proposes an event-driven compressive ECG recording and neuromorphic processing system for cardiac arrhythmia detection. The proposed system achieves low power consumption and high arrhythmia detection accuracy via system level co-design with spike-based information representation. Event-driven l…
Cardiovascular disease (CVDs) is one of the universal deadly diseases, and the detection of it in the early stage is a challenging task to tackle. Recently, deep learning and convolutional neural networks have been employed widely for the classification of objects. Moreover, it is promising that lots of networks can be deployed on wearable devices. An increasing number of methods can be used to realize ECG signal classification for the sake of arrhythmia detection. However, the existing neural networks proposed for arrhythmia detection are not hardware-friendly enough due to a remarkable quant…
Texas A&M University（得克萨斯农工大学）；Dell Inc.（戴尔公司）；Heart Artery and Vein Center of Fresno（弗雷斯诺心脏动脉与静脉中心）
Recent introduction of wearable single-lead ECG devices of diverse configurations has caught the intrigue of the medical community. While these devices provide a highly affordable support tool for the caregivers for continuous monitoring and to detect acute conditions, such as arrhythmia, their utility for cardiac diagnostics remains limited. This is because clinical diagnosis of many cardiac pathologies is rooted in gleaning patterns from synchronous 12-lead ECG. If synchronous 12-lead signals of clinical quality can be synthesized from these single-lead devices, it can transform cardiac care…
University Medical Center Hamburg-Eppendorf (UKE)（汉堡-埃彭多夫大学医学中心（UKE））；Jacobs University gGmbH（雅各布大学gGmbH）
The reservoir computing paradigm is employed to classify heartbeat anomalies online based on electrocardiogram signals. Inspired by the principles of information processing in the brain, reservoir computing provides a framework to design, train, and analyze recurrent neural networks (RNNs) for processing time-dependent information. Due to its computational efficiency and the fact that training amounts to a simple linear regression, this supervised learning algorithm has been variously considered as a strategy to implement useful computations not only on digital computers but also on emerging u…
SSN College of Engineering（SSN工程学院）；Solarillion Foundation（Solarillion基金会）
Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide, with 2% of the population affected. It is associated with an increased risk of strokes, heart failure and other heart-related complications. Monitoring at-risk individuals and detecting asymptomatic AF could result in considerable public health benefits, as individuals with asymptomatic AF could take preventive measures with lifestyle changes. With increasing affordability to wearables, personalized health care is becoming more accessible. These personalized healthcare solutions require accurate classification of bio-…
ArrhythmiaVision: Resource-Conscious Deep Learning Models with Visual Explanations for ECG Arrhythmia Classification
Dipartimento di Informatica, Università di Verona, Verona, Italy（信息学院，威尼斯大学，威尼斯，意大利）；Department of Computer Science, School of Mathematics and Computer Science, Institute of Business Administration, Karachi, Pakistan（计算机科学系，数学与计算机科学学院，商学院，卡拉奇，巴基斯坦）；Faculty of Information Technology, Salim Habib University, Karachi, Pakistan（信息技术学院，萨利姆·哈比大学，卡拉奇，巴基斯坦）
Technische Universität Wien (TU Wien)（维也纳工业大学）；National University of Sciences and Technology (NUST)（巴基斯坦国立科技大学）；New York University Abu Dhabi (NYU-AD)（纽约大学阿布扎比分校）
In this work, we propose the BioNetExplorer framework to systematically generate and explore multiple DNN architectures for bio-signal processing in wearables. Our framework adapts key neural architecture parameters to search for an embedded DNN with a low hardware overhead, which can be deployed in wearable edge devices to analyse the bio-signal data and to extract the relevant information, such as arrhythmia and seizure. Our framework also enables hardware-aware DNN architecture search using genetic algorithms by imposing user requirements and hardware constraints (storage, FLOPs, etc.) duri…
Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise Arrhythmia Alarms
The recent developments in wearable devices and the Internet of Medical Things (IoMT) allow real-time monitoring and recording of electrocardiogram (ECG) signals. However, continuous monitoring of ECG signals is challenging in low-power wearable devices due to energy and memory constraints. Therefore, in this paper, we present a novel and energy-efficient methodology for continuously monitoring the heart for low-power wearable devices. The proposed methodology is composed of three different layers: 1) a Noise/Artifact detection layer to grade the quality of the ECG signals; 2) a Normal/Abnorma…
Personalized Deep Learning for Short-Term Forecasting of Impending Atrial Fibrillation from Continuous Wearable ECG Signals
Seoul National University（首尔大学）；CHA Bundang Medical Center and CHA University School of Medicine（CHA板桥医疗中心和CHA大学医学院）；SEERS Technology Co., Ltd（SEERS科技有限公司）；Department of Internal Medicine, Seoul National University College of Medicine and Seoul National University Hospital（首尔大学医学院内科学系和首尔大学医院）
Continuous long-term monitoring of electrocardiography (ECG) signals is crucial for the early detection of cardiac abnormalities such as arrhythmia. Non-clinical ECG recordings acquired by Holter and wearable ECG sensors often suffer from severe artifacts such as baseline wander, signal cuts, motion artifacts, variations on QRS amplitude, noise, and other interferences. Usually, a set of such artifacts occur on the same ECG signal with varying severity and duration, and this makes an accurate diagnosis by machines or medical doctors extremely difficult. Despite numerous studies that have attem…
The Chinese University of Hong Kong（香港中文大学）；Institute of Future Cities, The Chinese University of Hong Kong（香港中文大学未来城市研究所）；Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong（香港中文大学李嘉诚健康科学研究所）
Cardiovascular diseases (CVDs) are the number one cause of death worldwide. While there is growing evidence that the atrial fibrillation (AF) has strong associations with various CVDs, this heart arrhythmia is usually diagnosed using electrocardiography (ECG) which is a risk-free, non-intrusive, and cost-efficient tool. Continuously and remotely monitoring the subjects' ECG information unlocks the potentials of prompt pre-diagnosis and timely pre-treatment of AF before the development of any life-threatening conditions/diseases. Ultimately, the CVDs associated mortality could be reduced. In th…
uPVC-Net: A Universal Premature Ventricular Contraction Detection Deep Learning Algorithm
Faculty of Biomedical Engineering, Technion-IIT（生物医学工程学院，技术ion-IIT）；Ruth and Bruce Rappaport Faculty of Medicine, Technion-IIT（鲁思和布鲁斯·拉帕波特医学学院，技术ion-IIT）
Qatar University（卡塔尔大学）；Izmir University of Economics（伊兹密尔经济大学）；Tampere University（坦佩雷大学）
Noise and low quality of ECG signals acquired from Holter or wearable devices deteriorate the accuracy and robustness of R-peak detection algorithms. This paper presents a generic and robust system for R-peak detection in Holter ECG signals. While many proposed algorithms have successfully addressed the problem of ECG R-peak detection, there is still a notable gap in the performance of these detectors on such low-quality ECG records. Therefore, in this study, a novel implementation of the 1D Convolutional Neural Network (CNN) is used integrated with a verification model to reduce the number of…
Deciphering Heartbeat Signatures: A Vision Transformer Approach to Explainable Atrial Fibrillation Detection from ECG Signals
Walkky（沃基公司）；Chobanian & Avedisian School of Medicine, Boston University（波士顿大学乔巴尼亚与阿夫迪西安医学院）；Clemson University（克莱姆森大学）；Northwell Health Physician Partners（诺斯韦尔健康医师伙伴）；Lenox Hill Hospital（莱诺克斯山医院）
A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis
Computer Science Department, College of Science, University of Zakho（扎赫大学科学学院计算机科学系）；Department of Computer Science and Information Technology, The American University of Kurdistan（库尔德斯坦美国大学计算机科学与信息技术系）；PRIME Lab, Scientific Research Center, University of Zakho（扎赫大学科学研究中心PRIME实验室）
