Similarity Learning based Few Shot Learning for ECG Time Series Classification
This work addresses data scarcity in IoT-based ECG classification, offering a practical solution for healthcare monitoring, but it is incremental as it builds on existing few-shot and similarity learning methods.
The paper tackles the problem of classifying ECG arrhythmia with limited labeled data from IoT devices by proposing a similarity learning-based few-shot learning method using Siamese Convolutional Neural Networks, achieving an accuracy of 92.25% with 5-shot learning and outperforming other techniques like DTW, ED, and LSTM-FCN.
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.