Identifying Pairs in Simulated Bio-Medical Time-Series

TL;DR

This paper introduces a novel time-series classification method using a bio-medical simulator that leverages stock market data to identify similarities in simulated human health patterns, enabling real-time biomedical data analysis.

Contribution

It presents a new approach combining self-labeling and similarity ranking with decision trees for classifying unlabeled biomedical time-series data.

Findings

Effective classification of simulated biomedical signals in real-time

Successful application of machine learning techniques to unlabeled data

Demonstrated scalability with large datasets of 7,871 patterns

Abstract

The paper presents a time-series-based classification approach to identify similarities in pairs of simulated human-generated patterns. An example for a pattern is a time-series representing a heart rate during a specific time-range, wherein the time-series is a sequence of data points that represent the changes in the heart rate values. A bio-medical simulator system was developed to acquire a collection of 7,871 price patterns of financial instruments. The financial instruments traded in real-time on three American stock exchanges, NASDAQ, NYSE, and AMEX, simulate bio-medical measurements. The system simulates a human in which each price pattern represents one bio-medical sensor. Data provided during trading hours from the stock exchanges allowed real-time classification. Classification is based on new machine learning techniques: self-labeling, which allows the application of supervised learning methods on unlabeled time-series and similarity ranking, which applied on a decision tree learning algorithm to classify time-series regardless of type and quantity.