With recently successful applications of deep learning in computer vision and general signal processing, deep learning has shown many unique advantages in medical signal processing. However, data labelling quality has become one of the most significant issues for AI applications, especially when it requires domain knowledge (e.g. medical image labelling). In addition, there might be noisy labels in practical datasets, which might impair the training process of neural networks. In this work, we propose a semi-supervised algorithm for training data samples with noisy labels by performing selected Positive Learning (PL) and Negative Learning (NL). To verify the effectiveness of the proposed scheme, we designed a portable ECG patch -- iRealCare -- and applied the algorithm on a real-life dataset. Our experimental results show that we can achieve an accuracy of 91.0 %, which is 6.2 % higher than a normal training process with ResNet. There are 65 patients in our dataset and we randomly picked 2 patients to perform validation.
We describe techniques and specifications of MATLAB software to process ambulatory electrocardiogram (ECG) data. Through template-based beat identification and simple pattern recognition models on the intervals between regular heart beats, we filter noisy sections of waveform and ectopic beats. Our end-to-end process can be used towards analysis of ECG and calculation of heart rate variability metrics after beat adjustments, removals and interpolation. Classification and noise detection is assessed on the human-annotated MIT-BIH Arrythmia and Noise Stress Test Databases.
Universitat Pompeu Fabra（庞培法布拉大学）；Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)（生物工程、生物材料与纳米医学生物医学研究网络中心（CIBER-BBN））；Aragón Institute of Engineering Research（阿拉贡工程研究所）；University of Zaragoza（萨拉戈萨大学）
Electrocardiogram (ECG) detection and delineation are key steps for numerous tasks in clinical practice, as ECG is the most performed non-invasive test for assessing cardiac condition. State-of-the-art algorithms employ digital signal processing (DSP), which require laborious rule adaptation to new morphologies. In contrast, deep learning (DL) algorithms, especially for classification, are gaining weight in academic and industrial settings. However, the lack of model explainability and small databases hinder their applicability. We demonstrate DL can be successfully applied to low interpretati…
IoT wearable devices are widely expected to reduce the cost and risk of personal healthcare. However, ambulatory data collected from such devices are often corrupted or contaminated with severe noises. Signal Quality Indicators (SQIs) can be used to assess the quality of data obtained from wearable devices, such that transmission/ storage of unusable data can be prevented. This article introduces a novel and generalized SQI which can be implemented on an edge device for detecting the quality of any quasi-periodic signal under observation, regardless of the type of noise present. The applicatio…
Deep Neural Network Architectures for Electrocardiogram Classification: A Comprehensive Evaluation
Independent Researcher（独立研究者）；Department of Materials Engineering, Xi'an Polytechnic University（材料工程系，西安理工大学）；Tandon School of Engineering, New York University（工程学院，纽约大学）；College of Computing, Georgia Institute of Technology（计算学院，佐治亚理工学院）
According to the World Health Organization, around 36% of the annual deaths are associated with cardiovascular diseases and 90% of heart attacks are preventable. Electrocardiogram signal analysis in ambulatory electrocardiography, during an exercise stress test, and in resting conditions allows cardiovascular disease diagnosis. However, during the acquisition, there is a variety of noises that may damage the signal quality thereby compromising their diagnostic potential. The baseline wander is one of the most undesirable noises. In this work, we propose a novel algorithm for BLW noise filterin…
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）
With the periodic rise and fall of COVID-19 and numerous countries being affected by its ramifications, there has been a tremendous amount of work that has been done by scientists, researchers, and doctors all over the world. Prompt intervention is keenly needed to tackle the unconscionable dissemination of the disease. The implementation of Artificial Intelligence (AI) has made a significant contribution to the digital health district by applying the fundamentals of deep learning algorithms. In this study, a novel approach is proposed to automatically diagnose the COVID-19 by the utilization …
A Novel end-to-end Digital Health System Using Deep Learning-based ECG Analysis
KIOS Research and Innovation Center of Excellence (KIOS CoE)（KIOS研究与创新中心（KIOS CoE））；University of Cyprus（塞浦路斯大学）；MEDTL Medical Technologies Ltd.（MEDTL医疗技术有限公司）；Department of Computer Science, University of Cyprus（塞浦路斯大学计算机科学系）
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）
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）
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…
HRVConformer: Neonatal Hypoxic-Ischemic Encephalopathy Classification from the Heart Rate signals
University College Cork（大学学院科克）；INFANT Research Centre（婴儿研究中心）；Department of Electrical & Electronic Engineering（电气与电子工程系）；School of Engineering and Architecture（工程与建筑学院）；Pediatrics and Child Health（儿科学与儿童健康）
