Increasing Robustness in the Detection of Freezing of Gait in Parkinson’s Disease

Abstract

This paper focuses on detecting freezing of gait in Parkinson’s patients using body-worn accelerometers. In this study, we analyzed the robustness of four feature sets, two of which are new features adapted from speech processing: mel frequency cepstral coefficients and quality assessment metrics. For classification based on these features, we compared random forest, multilayer perceptron, hidden Markov models, and deep neural networks. These algorithms were evaluated using a leave-one-subject-out (LOSO) cross validation to match the situation where a system is being constructed for patients for whom there is no training data. This evaluation was performed using the Daphnet dataset, which includes recordings from ten patients using three accelerometers situated on the ankle, knee, and lower back. We obtained a reduction from 17.3% to 12.5% of the equal error rate compared to the previous best results using this dataset and LOSO testing. For high levels of sensitivity (such as 0.95), the specificity increased from 0.63 to 0.75. The biggest improvement across all of the feature sets and algorithms tested in this study was obtained by integrating information from longer periods of time in a deep neural network with convolutional layers.

Improved Deep Learning Technique to Detect Freezing of Gait in Parkinson’s Disease Based on Wearable Sensors

“…Based on the research of Bachlin et al, they further extracted five features of signal mean, standard deviation, variance, frequency entropy, and energy. However, San-Segundo et al [38] proved that the best results could be reproduced only Figure 9. Results of each patient for the proposed model using leave-one-subject-out (LOSO) evaluation, excluding patients #4 and #10.…”

“…Inspired by the excellent performance of DL in many classification and recognition tasks, some scholars [34,[36][37][38] tried to use deep learning methods to analyze human inertial signals. For example, Xia Yi et al designed a five-layer CNN to detect FOG, where the first three layers are used for feature extraction, the fourth layer is used for feature fusion, and the last layer is used for classification [37].…”

“…This section introduces the methods and metrics used to evaluate the performance of the proposed SEC-ALSTM. In order to compare with previous studies, we have done some experiments using two evaluation methods, 10-fold cross-validation (R10Fold) [37,46] and leave-one-subject-out (LOSO) cross-validation [34,38,47]. Among them, the specific implementation steps of R10Fold evaluation are as follows: first, we shuffle all samples and divide them into 10 folds as evenly as possible.…”

“…The next step is to add labels to the data segments. Some studies [38,49] pointed out that the sample label that appears most frequently in the window should be used as the label of the data segment, while other studies [34] think that it is more reasonable to use the label of the last frame sample as the label of the entire data segment. In this study, a method similar to the previous one is adopted ( Figure 6)-that is, the label with the most occurrences in the window is selected as the window label.…”

A Decision Support System for Irrigation Management: Analysis and Implementation of Different Learning Techniques

“…Two validation techniques (LOOCV and 90-10 shuffle cross-validation) were used to test the goodness of every model, as suggested in [44].…”