# Weak Supervision for Affordable Modeling of Electrocardiogram Data

arXiv: https://arxiv.org/abs/2201.02936 · HTML: https://mlsanity.com/paper/2201.02936/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 9 Jan 2022.

## Builds on (most cited first, 8 of 8)

- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling](https://mlsanity.com/paper/2012.06046/) (2020)
- [Cross-Modal Data Programming Enables Rapid Medical Machine Learning](https://mlsanity.com/paper/1903.11101/) (2019)
- [A Survey of Crowdsourcing in Medical Image Analysis](https://mlsanity.com/paper/1902.09159/) (2019)
- [LabelECG: A Web-based Tool for Distributed Electrocardiogram Annotation](https://mlsanity.com/paper/1908.06553/) (2019)

## Built on by (most cited first, 8 of 8)

- [MOMENT: A Family of Open Time-series Foundation Models](https://mlsanity.com/paper/2402.03885/) (2024)
- [Are We Done with MMLU?](https://mlsanity.com/paper/2406.04127/) (2024)
- [Unsupervised Model Selection for Time-series Anomaly Detection](https://mlsanity.com/paper/2210.01078/) (2022)
- [Exploring Representations and Interventions in Time Series Foundation Models](https://mlsanity.com/paper/2409.12915/) (2024)
- [AQuA: A Benchmarking Tool for Label Quality Assessment](https://mlsanity.com/paper/2306.09467/) (2023)
- [Classifying Unstructured Clinical Notes via Automatic Weak Supervision](https://mlsanity.com/paper/2206.12088/) (2022)
- [Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact](https://mlsanity.com/paper/2206.09074/) (2022)
- [A Benchmark Generative Probabilistic Model for Weak Supervised Learning](https://mlsanity.com/paper/2303.17841/) (2023)

## Abstract (arXiv)

Analysing electrocardiograms (ECGs) is an inexpensive and non-invasive, yet powerful way to diagnose heart disease. ECG studies using Machine Learning to automatically detect abnormal heartbeats so far depend on large, manually annotated datasets. While collecting vast amounts of unlabeled data can be straightforward, the point-by-point annotation of abnormal heartbeats is tedious and expensive. We explore the use of multiple weak supervision sources to learn diagnostic models of abnormal heartbeats via human designed heuristics, without using ground truth labels on individual data points. Our work is among the first to define weak supervision sources directly on time series data. Results show that with as few as six intuitive time series heuristics, we are able to infer high quality probabilistic label estimates for over 100,000 heartbeats with little human effort, and use the estimated labels to train competitive classifiers evaluated on held out test data.