Clustering and machine learning framework for medical time series classification

Background and motivation:

The application of artificial intelligence in medical research, particularly unsupervised learning techniques, has shown promising potential. Medical time series data poses a unique challenge for analysis due to its complexity. Existing unsupervised learning methods often fail to effectively classify these variations, highlighting a gap in current approaches. We introduce a methodological clustering classification framework designed to accurately handle such data, aiming for improved classification tasks in biomedical signals.

Methods:

To address these challenges, we introduce a novel approach for the analysis and classification of medical time series data. Our method integrates agglomerative hierarchical clustering with Hilbert vector space representations of medical signals and biological sequences. We rigorously define the mathematical principles and conduct evaluations using simulations of cardiac signals, real-world neural signal datasets, open-source protein sequences, and the MNIST dataset for illustrative purposes.

Results:

The proposed method exhibited a 96% success rate in classifying protein sequences by function and effectively identifying families within a large protein set. In cardiac signal analysis, it retained 0.996 variance in a condensed 6-dimensional space, accurately classifying 87.4% of simulated atrial flutter groups and 99.91% of main groups when excluding conduction direction. For neural signals, it demonstrated near-perfect tracking accuracy of neural activity in mouse brain recordings, as confirmed by expert evaluations.

Conclusion:

Our proposed method offers a novel, translational approach for the treatment and classification of medical and biological time series, addressing some of the prevalent challenges in the field and paving the way for more reliable and effective biomedical signal analysis.