Heartbeat Classification
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Benchmarks
Most implemented
ECG Heartbeat Classification: A Deep Transferable Representation
Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach
HeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal Analysis
Heartbeat classification using various machine learning models: A comparative study
ECG Heartbeat Classification Using Multimodal Fusion
ATCN: Resource-Efficient Processing of Time Series on Edge
Papers
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 Adaptation