# Learning ECG Signal Features Without Backpropagation Using Linear Laws

arXiv: https://arxiv.org/abs/2307.01930 · HTML: https://mlsanity.com/paper/2307.01930/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 84 references that are in the map.
- First posted on arXiv 4 Jul 2023.

## Builds on (most cited first, 18 of 18)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Measuring Catastrophic Forgetting in Neural Networks](https://mlsanity.com/paper/1708.02072/) (2017)
- [Towards a Definition of Disentangled Representations](https://mlsanity.com/paper/1812.02230/) (2018)
- [Which Neural Net Architectures Give Rise To Exploding and Vanishing Gradients?](https://mlsanity.com/paper/1801.03744/) (2018)
- [An exact mapping between the Variational Renormalization Group and Deep Learning](https://mlsanity.com/paper/1410.3831/) (2014)
- [RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior](https://mlsanity.com/paper/2010.00029/) (2020)
- [Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteria](https://mlsanity.com/paper/2211.01738/) (2022)
- [Reconstruction of observed mechanical motions with Artificial Intelligence tools](https://mlsanity.com/paper/2202.11447/) (2022)
- [Time series analysis with dynamic law exploration](https://mlsanity.com/paper/2104.10970/) (2021)
- [Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks](https://mlsanity.com/paper/1905.07136/) (2019)
- [VPNet: Variable Projection Networks](https://mlsanity.com/paper/2006.15590/) (2020)
- [Understanding understanding: a renormalization group inspired model of (artificial) intelligence](https://mlsanity.com/paper/2010.13482/) (2020)
- [Generalized Rational Variable Projection With Application in ECG Compression](https://mlsanity.com/paper/2003.00058/) (2020)
- [Abnormal Road Surface Detection Using Wheel Sensor Data](https://mlsanity.com/paper/2108.09162/) (2021)
- [LLT: An R package for Linear Law-based Feature Space Transformation](https://mlsanity.com/paper/2304.14211/) (2023)
- [Predicting the Price Movement of Cryptocurrencies Using Linear Law-based Transformation](https://mlsanity.com/paper/2305.04884/) (2023)

## Built on by (most cited first, 2 of 2)

- [Adaptive Law-Based Transformation (ALT): A Lightweight Feature Representation for Time Series Classification](https://mlsanity.com/paper/2501.09217/) (2025)
- [Optimized Hybrid Feature Engineering for Resource-Efficient Arrhythmia Detection in ECG Signals: An Optimization Framework](https://mlsanity.com/paper/2601.00192/) (2026)

## Abstract (arXiv)

This paper introduces LLT-ECG, a novel method for electrocardiogram (ECG) signal classification that leverages concepts from theoretical physics to automatically generate features from time series data. Unlike traditional deep learning approaches, LLT-ECG operates in a forward manner, eliminating the need for backpropagation and hyperparameter tuning. By identifying linear laws that capture shared patterns within specific classes, the proposed method constructs a compact and verifiable representation, enhancing the effectiveness of downstream classifiers. We demonstrate LLT-ECG's state-of-the-art performance on real-world ECG datasets from PhysioNet, underscoring its potential for medical applications where speed and verifiability are crucial.