MIT-BIH Arrhythmia Database
PulseAugur coverage of MIT-BIH Arrhythmia Database — every cluster mentioning MIT-BIH Arrhythmia Database across labs, papers, and developer communities, ranked by signal.
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TinyML ECG personalization uses prototype adaptation on microcontrollers
Researchers have developed a novel method for personalizing on-device electrocardiogram (ECG) systems, addressing the challenge of adapting to individual patient physiology without requiring extensive computational reso…
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Deep learning model slashes power use for wearable arrhythmia detection
Researchers have developed a new deep learning architecture for wearable devices that significantly reduces power consumption for arrhythmia detection. By employing techniques like data precision reduction and approxima…
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TinyML models enable on-device arrhythmia detection
Researchers have developed ArrythML, a TinyML approach for on-device arrhythmia detection using autoencoder models. These INT8 quantized models are designed for resource-constrained embedded systems, processing over 95,…
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Manifold learning accurately detects cardiac arrhythmias without labels
Researchers have demonstrated the effectiveness of nonlinear dimensionality reduction (NLDR) algorithms, such as UMAP and t-SNE, for unsupervised detection of cardiac arrhythmias from electrocardiogram (ECG) signals. Un…