Unsupervised Machine Learning in Classification of Neurobiological Data
Konrad Andrzej Ciecierski , Tomasz Mandat
AbstractIn many cases of neurophysiological data analysis, the best results can be obtained using supervised machine learning approaches. Such very good results were obtained in detection of neurophysiological recordings recorded within Subthalamic Nucleus (STN) during deep brain stimulation (DBS) surgery for Parkinson disease. Supervised machine learning methods relay however on external knowledge provided by an expert. This becomes increasingly di�cult if the subject's domain is highly specialized as is the case in neurosurgery. The proper computation of features that are to be used for classi�cation without good domain knowledge can be diffcult and their proper construction heavily in uences quality of the �nal classi�cation. In such case one might wonder whether, how much and to what extent the unsupervised methods might become useful. Good result of unsupervised approach would indicate presence of a natural grouping within recordings and would also be a further con�rmation that features selected for classi�cation and clustering provide good basis for discrimination of recordings recorded within Subthalamic Nucleus (STN). For this test, the set of over 12 thousand of brain neurophysiological recordings with precalculated attributes were used. This paper shows comparison of results obtained from supervised - random forest based - method with those obtained from unsupervised approaches, namely K-Means and Hierarchical clustering approaches. It is also shown, how inclusion of certain types of attributes in uences the clustering based results.
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