Papers Heartbeat Classification
“Heartbeat Classification” 태그가 달린 논문 28편 · 필터 해제
HeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal Analysis
The HeartBert model is introduced with three primary objectives: reducing the need for labeled data, minimizing computational resources, and simultaneously improving performance in machine learning systems that analyze E…
Heartbeat ClassificationSelf-Supervised LearningSleep Stage DetectionHeartbeat classification using various machine learning models: A comparative study
Cardiac arrhythmias, known as irregular heartbeats, pose a notable health threat that necessitates prompt diagnosis, as untreated arrhythmias can lead to severe heart complications. Among the various methods for arrhythm…
Arrhythmia DetectionECG ClassificationECG Denoisingfeature selection+2Multi-Feature Fusion and Compressed Bi-LSTM for Memory-Efficient Heartbeat Classification on Wearable Devices
In this article, we present a resource-efficient approach for electrocardiogram (ECG) based heartbeat classification using multi-feature fusion and bidirectional long short-term memory (Bi-LSTM). The dataset comprises fi…
Heartbeat ClassificationSparrowSNN: A Hardware/software Co-design for Energy Efficient ECG Classification
Heart disease is one of the leading causes of death worldwide. Given its high risk and often asymptomatic nature, real-time continuous monitoring is essential. Unlike traditional artificial neural networks (ANNs), spikin…
ECG ClassificationEdge-computingHeartbeat ClassificationECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning
In the medical field, current ECG signal analysis approaches rely on supervised deep neural networks trained for specific tasks that require substantial amounts of labeled data. However, our paper introduces ECGBERT, a s…
Arrhythmia DetectionHeartbeat ClassificationRepresentation LearningSleep apnea detection+1Cross-Database and Cross-Channel ECG Arrhythmia Heartbeat Classification Based on Unsupervised Domain Adaptation
The classification of electrocardiogram (ECG) plays a crucial role in the development of an automatic cardiovascular diagnostic system. However, considerable variances in ECG signals between individuals is a significant …
DiagnosticDomain AdaptationHeartbeat ClassificationUnsupervised Domain AdaptationHARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN
In this paper have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. This solves problems that arise when traditio…
DenoisingGenerative Adversarial NetworkHeartbeat ClassificationParameterization of state duration in Hidden semi-Markov Models: an application in electrocardiography
This work aims at providing a new model for time series classification based on learning from just one example. We assume that time series can be well characterized as a parametric random process, a sort of Hidden semi-M…
Heartbeat ClassificationTime SeriesTime Series AnalysisTime Series ClassificationECG Heartbeat classification using deep transfer learning with Convolutional Neural Network and STFT technique
Electrocardiogram (ECG) is a simple non-invasive measure to identify heart-related issues such as irregular heartbeats known as arrhythmias. While artificial intelligence and machine learning is being utilized in a wide …
BIG-bench Machine LearningHeartbeat ClassificationTransfer LearningGenerative Pre-Trained Transformer for Cardiac Abnormality Detection
ECG heartbeat classification plays a vital role in diagnosis of cardiac arrhythmia. The goal of the Physionet/CinC 2021 challenge was to accurately classify clinical diagnosis based on 12, 6, 4, 3 or 2-lead ECG recording…
Anomaly DetectionClassificationHeartbeat ClassificationMulti-Label Classification+3ECG Heartbeat Classification Using Multimodal Fusion
Electrocardiogram (ECG) is an authoritative source to diagnose and counter critical cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current machine learning techniques either depend on manuall…
ClassificationHeartbeat ClassificationATCN: Resource-Efficient Processing of Time Series on Edge
This paper presents a scalable deep learning model called Agile Temporal Convolutional Network (ATCN) for high-accurate fast classification and time series prediction in resource-constrained embedded systems. ATCN is a f…
General ClassificationHeartbeat ClassificationTime SeriesTime Series Analysis+1SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification
Generating training examples for supervised tasks is a long sought after goal in AI. We study the problem of heart signal electrocardiogram (ECG) synthesis for improved heartbeat classification. ECG synthesis is challeng…
ClassificationECG ClassificationGeneral ClassificationGenerative Adversarial Network+1Construe: a software solution for the explanation-based interpretation of time series
This paper presents a software implementation of a general framework for time series interpretation based on abductive reasoning. The software provides a data model and a set of algorithms to make inference to the best e…
Atrial Fibrillation DetectionHeartbeat ClassificationTime SeriesTime Series AnalysisA convolutional neural network approach to detect congestive heart failure
Congestive Heart Failure (CHF) is a severe pathophysiological condition associated with high prevalence, high mortality rates, and sustained healthcare costs, therefore demanding efficient methods for its detection. Desp…
Congestive Heart Failure detectionElectrocardiography (ECG)Heartbeat ClassificationHeart Rate VariabilityAnalysis and classification of heart diseases using heartbeat features and machine learning algorithms
This study proposed an ECG (Electrocardiogram) classification approach using machine learning based on several ECG features. An electrocardiogram (ECG) is a signal that measures the electric activity of the heart. The pr…
BIG-bench Machine LearningBinary ClassificationClassificationECG Classification+4Heartbeat Classification in Wearables Using Multi-layer Perceptron and Time-Frequency Joint Distribution of ECG
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-fre…
ClassificationGeneral ClassificationHeartbeat ClassificationInterpretability Analysis of Heartbeat Classification Based on Heartbeat Activity’s Global Sequence Features and BiLSTM-Attention Neural Network
Arrhythmia is a disease that threatens human life. Therefore, timely diagnosis of arrhythmia is of great significance in preventing heart disease and sudden cardiac death. The BiLSTM-Attention neural network model with h…
Arrhythmia DetectionElectrocardiography (ECG)General ClassificationHeartbeat ClassificationA Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection
We present a fully automatic and fast ECG arrhythmia classifier based on a simple brain-inspired machine learning approach known as Echo State Networks. Our classifier has a low-demanding feature processing that only req…
Arrhythmia DetectionBIG-bench Machine LearningElectrocardiography (ECG)feature selection+3Automated Heartbeat Classification Using 3-D Inputs Based on Convolutional Neural Network With Multi-Fields of View
A high-performance method of automated heartbeat classification based on Convolutional Neural Network (CNN) is proposed in this paper. To make full use of the electrocardiogram information acquired from different parts…
Heartbeat Classification