A Retrospective Study on Classifying Gait Signals Using Entropy Measures

The ability to differentiate sensor-induced physiological signals between healthy and diseased subjects is useful for developing an e-health system. Patients with neurodegenerative disorders are among those who can benefit from the use of e-health. Entropy methods have been utilized to quantify the complexity of such physiological signals for pattern classification. To date, these methods have been applied individually. In this retrospective study, several entropy methods are examined and used as feature extraction methods for machine learning to classify gait patterns in neurodegenerative diseases. Experimental results show that the combination of entropy methods and standard statistical measures performed much better than the individual measures for physiological pattern differentiation. Several machine learning models were also evaluated for learning on these features. This study also found that the one-dimensional convolutional network model trained with the combined features provided the most favorable results, where the best entropy mea-sures depend on certain values for time delays and embedding dimensions.