We lifted 9 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| eth-siplab/beliefppg | canonical | 0 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| add_gaussian_noise | Not yet run | eth-siplab/beliefppg/beliefppg/util/augmentations.py code served (permissive licence) · get_code("c0811adb00c0c99b") |
| attention_block_1d | Not yet run | eth-siplab/beliefppg/beliefppg/model/belief_ppg.py code served (permissive licence) · get_code("389a9916e9dbd1e1") |
| attention_up_and_concate | Not yet run | eth-siplab/beliefppg/beliefppg/model/belief_ppg.py code served (permissive licence) · get_code("45d14789aa260103") |
| corrupt_one_representation | Not yet run | eth-siplab/beliefppg/beliefppg/util/augmentations.py code served (permissive licence) · get_code("cf508bedec0404ae") |
| get_timedomain_backbone | Not yet run | eth-siplab/beliefppg/beliefppg/model/timedomain_backbone.py code served (permissive licence) · get_code("4712dc30d2dfb621") |
| load_bami_1 | Not yet run | eth-siplab/beliefppg/beliefppg/datasets/file_reader.py code served (permissive licence) · get_code("24de07d5261e9504") |
| load_dalia | Not yet run | eth-siplab/beliefppg/beliefppg/datasets/file_reader.py code served (permissive licence) · get_code("3cdb7afa0c9abb7b") |
| load_wesad | Not yet run | eth-siplab/beliefppg/beliefppg/datasets/file_reader.py code served (permissive licence) · get_code("ad1093aee3f0d715") |
| prepare_session_labels | Not yet run | eth-siplab/beliefppg/beliefppg/datasets/pipeline_generator.py code served (permissive licence) · get_code("259bd59c4dc083e7") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present a novel learning-based method that achieves state-of-the-art performance on several heart rate estimation benchmarks extracted from photoplethysmography signals (PPG). We consider the evolution of the heart rate in the context of a discrete-time stochastic process that we represent as a hidden Markov model. We derive a distribution over possible heart rate values for a given PPG signal window through a trained neural network. Using belief propagation, we incorporate the statistical distribution of heart rate changes to refine these estimates in a temporal context. From this, we obtain a quantized probability distribution over the range of possible heart rate values that captures a meaningful and well-calibrated estimate of the inherent predictive uncertainty. We show the robustness of our method on eight public datasets with three different cross-validation experiments.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2306.07730")
get_code_for_paper("2306.07730")
have("2306.07730")
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