4.7 Article

DConv-LSTM-Net: A Novel Architecture for Single- and 12-Lead ECG Anomaly Detection

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

MS-TCN plus plus : Multi-Stage Temporal Convolutional Network for Action Segmentation

Shijie Li et al.

Summary: In this paper, a multi-stage architecture is proposed for the temporal action segmentation task. By stacking multiple layers of dilated temporal convolutions in each stage, which cover a large receptive field, this method is able to capture long-range dependencies and achieves state-of-the-art results on three datasets.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2023)

Article Computer Science, Artificial Intelligence

Multi-stage stacked temporal convolution neural networks (MS-S-TCNs) for biosignal segmentation and anomaly localization

Theekshana Dissanayake et al.

Summary: In this study, a novel deep learning architecture called the multi-stage stacked TCN is proposed for biosignal segmentation and anomaly localization based on TCNs. The architecture uses multiple TCN modules with different dilation factors and employs convolution-based fusion for combining predictions. The model achieves state-of-the-art performance in five different tasks related to three 1D biosignal modalities and outperforms traditional multi-stage TCN models with similar configurations.

PATTERN RECOGNITION (2023)

Article Engineering, Biomedical

Inter-Patient Atrial Flutter Classification Using FFT-Based Features and a Low-Variance Stacking Classifier

Emre Besler et al.

Summary: This study used machine learning methods to analyze electrocardiogram (ECG) data from patients with atrial flutter (AFL) and found that machine learning can determine the electroanatomic circuit of AFL before invasive electrophysiological study, which has important clinical applications.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2022)

Article Computer Science, Information Systems

Robust and Interpretable Temporal Convolution Network for Event Detection in Lung Sound Recordings

Tharindu Fernando et al.

Summary: This paper introduces a novel framework for detecting lung sound events by using a multi-branch TCN architecture and feature fusion to identify discrete events in lung sound recordings. The proposed method shows promising results on multiple benchmarks, aiding in the identification of respiratory diseases. The feature concatenation strategy effectively suppresses non-informative features, leading to the construction of a lightweight network.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022)

Article Computer Science, Artificial Intelligence

Split 'n' merge net: A dynamic masking network for multi-task attention

Tharindu Fernando et al.

Summary: In this paper, a novel Multi-Task Learning (MTL) framework called Split 'n' Merge Net is proposed. Inspired by the multi-head attention formulation of Transformers, the framework offers a simple and interpretable pathway for processing information from multiple tasks. The proposed framework includes a splitting network design empowered with multi-head attention, a mutual information loss formulation to encourage distinct split embeddings, and a merging network to combine task-specific and shared information for individual downstream tasks. The evaluation results show that the Split 'n' Merge Network achieves state-of-the-art performance on diverse MTL tasks, indicating its robustness and applicability.

PATTERN RECOGNITION (2022)

Article Engineering, Electrical & Electronic

A Near-Sensor ECG Delineation and Arrhythmia Classification System

Xiaochen Tang et al.

Summary: This paper presents an ECG delineation and arrhythmia classification (EDAC) system suitable for wearable ECG biosensors. The proposed system uses a Delta-modulator-based analog-to-feature converter and a support vector machine classifier for arrhythmia classification. Experimental results show that the system achieves high classification accuracy while maintaining low power consumption.

IEEE SENSORS JOURNAL (2022)

Article Engineering, Electrical & Electronic

AFSense-ECG: Atrial Fibrillation Condition Sensing From Single Lead Electrocardiogram (ECG) Signals

Arijit Ukil et al.

Summary: This paper proposes AFSense-ECG, an intelligence-embedded single lead ECG sensor that accurately detects Atrial Fibrillation (AF) condition. AFSense-ECG acts as an early-warning sensor and demonstrates superior performance in terms of F1-measure compared to state-of-the-art methods. The proposed model is lean and affordable, making it an effective solution for intelligent sensing systems.

IEEE SENSORS JOURNAL (2022)

Article Computer Science, Artificial Intelligence

Self-Supervised ECG Representation Learning for Emotion Recognition

Pritam Sarkar et al.

Summary: We propose a self-supervised deep multi-task learning framework for emotion recognition based on electrocardiogram (ECG). By learning ECG representations through pretext tasks and transferring them to an emotion recognition network, our solution shows significantly improved performance compared to fully-supervised learning methods.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2022)

Article Engineering, Biomedical

Considerations on Performance Evaluation of Atrial Fibrillation Detectors

Monika Butkuviene et al.

Summary: This study implemented and studied three types of AF detectors, revealing crucial aspects of performance evaluation and highlighting the difference in performance depending on comparison methods and factors.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2021)

Article Computer Science, Interdisciplinary Applications

Automated ECG classification using a non-local convolutional block attention module

Jikuo Wang et al.

Summary: The study introduces a novel convolutional neural network structure with a non-local convolutional block attention module (NCBAM) to automatically classify ECG heartbeats. The method achieves high classification accuracy on arrhythmia database and ECG database.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2021)

Article Computer Science, Artificial Intelligence

Inter-patient ECG arrhythmia heartbeat classification based on unsupervised domain adaptation

Guijin Wang et al.

Summary: A novel Domain-Adaptative ECG Arrhythmia Classification (DAEAC) model is proposed based on convolutional network and unsupervised domain adaptation to enhance inter-patient performance, achieving competitive detection results for ventricular ectopic beats, supraventricular ectopic beats, and fusion beats.

NEUROCOMPUTING (2021)

Article Computer Science, Information Systems

Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL

Nils Strodthoff et al.

Summary: The article discusses the progress in automatic ECG analysis and the application of deep learning-based classification algorithms in this field. It proposes the PTB-XL dataset as a benchmark resource for ECG analysis algorithms and discusses potential research directions.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2021)

Article Engineering, Biomedical

MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG

Jing Zhang et al.

Summary: In this paper, a novel Multi-Lead-Branch Fusion Network (MLBF-Net) architecture is proposed for arrhythmia classification by integrating multi-loss optimization to jointly learn diversity and integrity of multi-lead ECG. Experimental results demonstrate that MLBF-Net achieves the highest arrhythmia classification performance on the China Physiological Signal Challenge 2018 dataset. The proposed method provides a promising solution for multi-lead ECG analysis from an information fusion perspective.

IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE (2021)

Article Computer Science, Artificial Intelligence

ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network

Jing Zhang et al.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2020)

Article Computer Science, Artificial Intelligence

Generating electrocardiogram signals by deep learning

Naren Wulan et al.

NEUROCOMPUTING (2020)

Article Computer Science, Information Systems

Deep Multi-Scale Fusion Neural Network for Multi-Class Arrhythmia Detection

Ruxin Wang et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2020)

Proceedings Paper Cardiac & Cardiovascular Systems

A Wide and Deep Transformer Neural Network for 12-Lead ECG Classification

Annamalai Natarajan et al.

2020 COMPUTING IN CARDIOLOGY (2020)

Proceedings Paper Cardiac & Cardiovascular Systems

Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020

Matthew A. Reyna et al.

2020 COMPUTING IN CARDIOLOGY (2020)

Article Computer Science, Theory & Methods

Learning Deep Off-the-Person Heart Biometrics Representations

Eduardo Jose da Silva Luz et al.

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (2018)

Article Mathematical & Computational Biology

An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection

Feifei Liu et al.

JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS (2018)

Article Computer Science, Information Systems

Multiscaled Fusion o Deep Convolutional Neural Networks for Screening Atrial Fibrillation From Single Lead Short ECG Recordings

Xiaomao Fan et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Time-Incremental Convolutional Neural Network for Arrhythmia Detection in Varied-length Electrocardiogram

Qihang Yao et al.

2018 16TH IEEE INT CONF ON DEPENDABLE, AUTONOM AND SECURE COMP, 16TH IEEE INT CONF ON PERVAS INTELLIGENCE AND COMP, 4TH IEEE INT CONF ON BIG DATA INTELLIGENCE AND COMP, 3RD IEEE CYBER SCI AND TECHNOL CONGRESS (DASC/PICOM/DATACOM/CYBERSCITECH) (2018)

Proceedings Paper Cardiac & Cardiovascular Systems

Identifying Normal, AF and other Abnormal ECG Rhythms using a Cascaded Binary Classifier

Shreyasi Datta et al.

2017 COMPUTING IN CARDIOLOGY (CINC) (2017)

Article Engineering, Biomedical

Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks

Serkan Kiranyaz et al.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2016)

Article Medicine, General & Internal

ABC of clinical electrocardiography - Introduction. II - Basic terminology

S Meek et al.

BRITISH MEDICAL JOURNAL (2002)