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1 public code

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1 public weights

DeepBeat

Stanford University (Ashley Lab) · 2020

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Code & model weights public

Multi-task model that jointly assesses signal quality and detects atrial fibrillation from wrist-worn wearable photoplethysmography (PPG), pretrained on roughly one million simulated unlabeled signals before fine-tuning on labeled wearable data. Uses a 1D CNN with separate output heads for signal quality and arrhythmia detection. Developed by Stanford's Ashley Lab.

PPG / wearable

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PPG / Wearable

Atrial fibrillation

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Arrhythmia

Binary classification

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Classification

CNN (1D)

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Convolutional (CNN)

Keras

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TensorFlow / Keras

GPL 3.0

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Copyleft


Model ID: 0046

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Subject Count: 100