K-means panning – Developing a new standard in automated MSNA signal recognition with a weakly supervised learning approach

Abstract Background Accessibility of labelled datasets is often a key limitation for the application of Machine Learning in clinical research. A novel semi-automated weak-labelling approach based on unsupervised clustering was developed to classify a large dataset of microneurography signals and subsequently used to train a Neural Network to reproduce the labelling process. Methods Clusters of microneurography signals were created with k-means and then labelled in terms of the validity of the signals contained in each cluster. Only purely positive or negative clusters were labelled, whereas clusters with mixed content were passed on to the next iteration of the algorithm to undergo another cycle of unsupervised clustering and labelling of the clusters. After several iterations of this process, only pure labelled clusters remained which were used to train a Deep Neural Network. Results Overall, 334,548 individual signal peaks form the integrated data were extracted and more than 99.99% of the data was labelled in six iterations of this novel application of weak labelling with the help of a domain expert. A Deep Neural Network trained based on this dataset achieved consistent accuracies above 95%. Discussion Data extraction and the novel iterative approach of labelling unsupervised clusters enabled creation of a large, labelled dataset combining unsupervised learning and expert ratings of signal-peaks on cluster basis in a time effective manner. Further research is needed to validate the methodology and employ it on other types of physiologic data for which it may enable efficient generation of large labelled datasets.