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.
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IEEE J Biomed Health Inform · Nov 2018
Particle Filtering and Sensor Fusion for Robust Heart Rate Monitoring Using Wearable Sensors.
This paper describes a novel methodology leveraging particle filters for the application of robust heart rate monitoring in the presence of motion artifacts. Motion is a key source of noise that confounds traditional heart rate estimation algorithms for wearable sensors due to the introduction of spurious artifacts in the signals. In contrast to previous particle filtering approaches, we formulate the heart rate itself as the only state to be estimated, and do not rely on multiple specific signal features. ⋯ The signal processing methods described in this work were tested on real motion artifact affected electrocardiogram and photoplethysmogram data with concurrent accelerometer readings. Results show promising average error rates less than 2 beats/min for data collected during intense running activities. Furthermore, a comparison with contemporary signal processing techniques for the same objective shows how the proposed implementation is also computationally more efficient for comparable performance.
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IEEE J Biomed Health Inform · Jan 2018
BioPad: Leveraging off-the-Shelf Video Games for Stress Self-Regulation.
This paper presents an approach to use commercial videogames for biofeedback training. It consists of intercepting signals from the game controller and adapting them in real-time based on physiological measurements from the player. We present three sample implementations and a case study for teaching stress self-regulation via an immersive car racing game. ⋯ Experimental results show that our approach can promote deep breathing during gameplay, and also during a subsequent task, once biofeedback is removed. Our results also indicate that delivering biofeedback through subtle changes in gameplay can be as effective as delivering them directly through a visual display. These results open the possibility to develop low-cost and engaging biofeedback interventions using a variety of commercial videogames to promote adherence.
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IEEE J Biomed Health Inform · Jan 2018
Prediction of Patient-Controlled Analgesic Consumption: A Multimodel Regression Tree Approach.
Several factors contribute to individual variability in postoperative pain, therefore, individuals consume postoperative analgesics at different rates. Although many statistical studies have analyzed postoperative pain and analgesic consumption, most have identified only the correlation and have not subjected the statistical model to further tests in order to evaluate its predictive accuracy. ⋯ Cross-validation results indicated that the proposed approach significantly outperforms various existing regression methods. Moreover, a comparison between the predictions by anesthesiologists and medical specialists and those of the computational approach for an independent test data set of 60 patients further evidenced the superiority of the computational approach in predicting analgesic consumption because it produced markedly lower root mean squared errors.