Similarity Learning based Few Shot Learning for ECG Time Series Classification

Abstract

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.

Enhancing ECG signal classification through pre-trained stacked-CNN embeddings: a transfer learning approach

“…This strategy, typically deployed within the deep learning paradigm, leverages existing knowledge from pre-trained models on extensive datasets, transferring this knowledge to the target task and significantly aiding the learning process [46]. Transfer learning has shown potential in improving model performance and reducing computational costs [47,48]. However, the incorporation of transfer learning within the ECG classification framework is an active research area with numerous untapped opportunities and challenges.…”