A novel Discrete Wavelet-Concatenated Mesh Tree and ternary chess pattern based ECG signal recognition method
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TLDR
In this paper, a novel Discrete Wavelet Concatenated Mesh Tree (DW-CMT) and ternary chess pattern (TCP) based ECG signal recognition method is presented.About:
This article is published in Biomedical Signal Processing and Control.The article was published on 2022-02-01 and is currently open access. It has received 10 citations till now. The article focuses on the topics: Pattern recognition (psychology) & Computer science.read more
Citations
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Diagnosis of arrhythmias with few abnormal ECG samples using metric-based meta learning
TL;DR: Wang et al. as mentioned in this paper proposed a Meta Siamese Network (MSN) based on metric learning to achieve high accuracy for automatic ECG arrhythmias diagnosis with limited ECG records.
Journal ArticleDOI
PPG-based blood pressure estimation can benefit from scalable multi-scale fusion neural networks and multi-task learning
TL;DR: Wang et al. as discussed by the authors proposed a continuous BP estimation method based on the end-to-end multi-scale fusion and multi-task learning neural network (MSF-MTLNet).
Journal ArticleDOI
Single-lead ECG recordings modeling for end-to-end recognition of atrial fibrillation with dual-path RNN
TL;DR: In this article , a dual-path recurrent neural network (DPRNN) was proposed to detect atrial fibrillation (AF) from single-lead ECG. But, the model requires specialized equipment and technical expertise, and accurate machine learning diagnosis of AF remains a dream.
Journal ArticleDOI
Semi-supervised active transfer learning for fetal ECG arrhythmia detection
TL;DR: In this paper , a modified active learning technique based on transfer learning, calibration probability, and autoencoder-based sampling was proposed to reduce the number of manually annotated samples.
Journal ArticleDOI
A fully-automated paper ECG digitisation algorithm using deep learning
Huiyi Wu,Kiran Haresh Kumar Patel,Xinyang Li,Bowen Zhang,Christoforos Galazis,Nikesh Bajaj,Arunashis Sau,Xili Shi,Lin Sun,Yanda Tao,Harith Adil Al-Qaysi,L. Tarusan,Najira Yasmin,Natasha Grewal,Gaurika Kapoor,Jonathan W. Waks,Daniel B. Kramer,Nicholas S Peters,Fu Siong Ng +18 more
TL;DR: In this article , the authors developed a fully-automated online ECG digitization tool to convert scanned paper ECGs into digital signals using horizontal and vertical anchor point detection, the algorithm automatically segments the ECG image into separate images for the 12 leads and a dynamical morphological algorithm is then applied to extract the signal of interest.
References
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Journal ArticleDOI
PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.
Ary L. Goldberger,Luís A. Nunes Amaral,Leon Glass,Jeffrey M. Hausdorff,Plamen Ch. Ivanov,Roger G. Mark,Joseph E. Mietus,George B. Moody,Chung-Kang Peng,H. Eugene Stanley +9 more
TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
Journal ArticleDOI
The impact of the MIT-BIH Arrhythmia Database
George B. Moody,Roger G. Mark +1 more
TL;DR: The history of the database, its contents, what is learned about database design and construction, and some of the later projects that have been stimulated by both the successes and the limitations of the MIT-BIH Arrhythmia Database are reviewed.
Proceedings Article
Neighbourhood Components Analysis
TL;DR: A novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm that directly maximizes a stochastic variant of the leave-one-out KNN score on the training set.
Journal ArticleDOI
Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet
TL;DR: In this paper, a Signaldatenbank for EKG-Diagnostik is presented, i.h.a. CARDIODAT, which enthält sowohl the digitalisierten eKGSignale sowie die entsprechenden zu diesen EKGs gehörenden Befunde und Patientenangaben.
Journal ArticleDOI
Arrhythmia detection using deep convolutional neural network with long duration ECG signals.
TL;DR: A new deep learning approach for cardiac arrhythmia (17 classes) detection based on long-duration electrocardiography (ECG) signal analysis based on a new 1D-Convolutional Neural Network model (1D-CNN).