Discovering Similar Spike Patterns in High Dimensional Biomedical Signals

We discuss our progress towards solving a challenging biomedical problem: identifying similar patterns among multiple physiological nerve signals hidden in high throughput data, collected from micro electrical sensors implanted in several animal organs. The problem is difficult because patterns come as spikes within millisecond time-windows, data sets have high dimensionality and there is background electrical noise. A previous analytic system discovers patterns combining PCA dimensionality reduction and K-means clustering, which is slow and misses important patterns hidden by noise. Moreover, it requires reading the data set several times and it requires multiple languages and tools. With such limitations in mind, we present an improved, integrated system that effectively allows the discovery of more accurate patterns, with automated algorithm parameter tuning, by learning model parameters incrementally exploiting summarization. Our integrated solution combines signal filtering, variable construction (feature engineering) and multidimensional data summarization, for a tighter and more effective integration of PCA and K-means clustering. We present preliminary experiments on signals collected from key nerves in a rat. We show our method discovers more patterns in larger time-windows, with better noise filtering, taking less time. In the future, we plan to link signal patterns to specific physiological functions, paving the way for innovative medical treatment via nerve stimulation.