IEEE journal of biomedical and health informatics
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IEEE J Biomed Health Inform · Jan 2019
Electrode Density Affects the Robustness of Myoelectric Pattern Recognition System With and Without Electrode Shift.
With the availability of high-density (HD) electrodes technology, the electrodes used in myoelectric control can have much higher density than the current practice. In this study, we investigated the effects of electrode density on pattern recognition (PR) based myoelectric control. Four density levels were analyzed in two directions: parallel and perpendicular to muscle fibers. ⋯ The effect of electrode density varied among the different shift conditions: First, when there was no shift, increasing electrode density significantly improved the classification performance; second, when the shift was in the perpendicular direction, increasing electrode density resulted in deterioration in the classification performance; third, when the shift was in the parallel direction, the effect of the electrode density was more complicated-increasing the density in the parallel direction reduced the performance, while increasing density in the perpendicular direction would initially enhance the performance, but then reduce performance. To our best knowledge, this was the first study focusing on the role of electrode density in myoelectric control with the presence of electrode shift. Its outcome would benefit the design of electrode placement for future myoelectric prostheses with HD electrodes.
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IEEE J Biomed Health Inform · Jan 2019
Accounting for Label Uncertainty in Machine Learning for Detection of Acute Respiratory Distress Syndrome.
When training a machine learning algorithm for a supervised-learning task in some clinical applications, uncertainty in the correct labels of some patients may adversely affect the performance of the algorithm. For example, even clinical experts may have less confidence when assigning a medical diagnosis to some patients because of ambiguity in the patient's case or imperfect reliability of the diagnostic criteria. As a result, some cases used in algorithm training may be mislabeled, adversely affecting the algorithm's performance. ⋯ We represent uncertainty in the diagnosis of ARDS as a graded weight of confidence associated with each training label. We also performed a novel time-series sampling method to address the problem of intercorrelation among the longitudinal clinical data from each patient used in model training to limit overfitting. Preliminary results show that we can achieve meaningful improvement in the performance of algorithm to detect patients with ARDS on a hold-out sample, when we compare our method that accounts for the uncertainty of training labels with a conventional SVM algorithm.