Few-shot time series segmentation using prototype-defined infinite hidden Markov models

TL;DR

This paper introduces a novel few-shot time series segmentation framework combining prototype RBF neural networks with infinite hidden Markov models, enabling interpretable analysis of non-stationary data with limited training data.

Contribution

It presents a new RBF-iHMM model that integrates prototypical neural networks with HMMs for effective few-shot learning in time series segmentation.

Findings

Achieves state-of-the-art seizure detection performance on EEG data.

Requires significantly less training data than existing deep learning models.

Provides interpretable insights into non-stationary signal patterns.

Abstract

We propose a robust framework for interpretable, few-shot analysis of non-stationary sequential data based on flexible graphical models to express the structured distribution of sequential events, using prototype radial basis function (RBF) neural network emissions. A motivational link is demonstrated between prototypical neural network architectures for few-shot learning and the proposed RBF network infinite hidden Markov model (RBF-iHMM). We show that RBF networks can be efficiently specified via prototypes allowing us to express complex nonstationary patterns, while hidden Markov models are used to infer principled high-level Markov dynamics. The utility of the framework is demonstrated on biomedical signal processing applications such as automated seizure detection from EEG data where RBF networks achieve state-of-the-art performance using a fraction of the data needed to train long-short-term memory variational autoencoders.