  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.
