# Similarity Learning based Few Shot Learning for ECG Time Series Classification

arXiv: https://arxiv.org/abs/2202.00612 · HTML: https://mlsanity.com/paper/2202.00612/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 38 references that are in the map.
- First posted on arXiv 31 Jan 2022.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Meta-Transfer Learning for Few-Shot Learning](https://mlsanity.com/paper/1812.02391/) (2018)
- [LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1709.05206/) (2017)
- [Induction Networks for Few-Shot Text Classification](https://mlsanity.com/paper/1902.10482/) (2019)
- [Diverse Few-Shot Text Classification with Multiple Metrics](https://mlsanity.com/paper/1805.07513/) (2018)
- [Transfer learning for time series classification](https://mlsanity.com/paper/1811.01533/) (2018)
- [Few-Shot Learning Through an Information Retrieval Lens](https://mlsanity.com/paper/1707.02610/) (2017)
- [Effects of padding on LSTMs and CNNs](https://mlsanity.com/paper/1903.07288/) (2019)
- [ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification](https://mlsanity.com/paper/1904.12546/) (2019)
- [The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version](https://mlsanity.com/paper/1602.01711/) (2016)
- [Data-efficient Online Classification with Siamese Networks and Active Learning](https://mlsanity.com/paper/2010.01659/) (2020)

## Built on by (most cited first, 3 of 3)

- [A Survey of Few-Shot Learning for Biomedical Time Series](https://mlsanity.com/paper/2405.02485/) (2024)
- [Large Language Models are Few-shot Multivariate Time Series Classifiers](https://mlsanity.com/paper/2502.00059/) (2025)
- [Domain-Adaptive Arrhythmia Classification Using a Hybrid Transformer on Wearable Heart Signals](https://mlsanity.com/paper/2605.08199/) (2026)

## Abstract (arXiv)

Using deep learning models to classify time series data generated from the Internet of Things (IoT) devices requires a large amount of labeled data. However, due to constrained resources available in IoT devices, it is often difficult to accommodate training using large data sets. This paper proposes and demonstrates a Similarity Learning-based Few Shot Learning for ECG arrhythmia classification using Siamese Convolutional Neural Networks. Few shot learning resolves the data scarcity issue by identifying novel classes from very few labeled examples. Few Shot Learning relies first on pretraining the model on a related relatively large database, and then the learning is used for further adaptation towards few examples available per class. Our experiments evaluate the performance accuracy with respect to K (number of instances per class) for ECG time series data classification. The accuracy with 5- shot learning is 92.25% which marginally improves with further increase in K. We also compare the performance of our method against other well-established similarity learning techniques such as Dynamic Time Warping (DTW), Euclidean Distance (ED), and a deep learning model - Long Short Term Memory Fully Convolutional Network (LSTM-FCN) with the same amount of data and conclude that our method outperforms them for a limited dataset size. For K=5, the accuracies obtained are 57%, 54%, 33%, and 92% approximately for ED, DTW, LSTM-FCN, and SCNN, respectively.