Exploiting Spectral and Cepstral Handwriting Features on Diagnosing Parkinson’s Disease

Nolazco-Flores, Juan Arturo; Faundez-Zanuy, Marcos; Cueva, V. M. De La; Mekyska, Jiří

doi:10.1109/access.2021.3119035

Cited by 10 publications

(12 citation statements)

References 37 publications

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“…The first difference is the addition of kinematic and statistical features in this study. We added these features because they proved to be effective in increasing the accuracy of the results obtained in our study on Parkinson's disease detection [17]. The second and most important difference is the inclusion of PCA, which is used to orthogonalise all features.…”

Section: Feature Extractionmentioning

confidence: 99%

“…The second step takes the features of the first step and selects the features with a correlation less than the threshold value. Algorithm 1 shows the pseudocode of our modified version of the function mFCBF [17]. This modified version differs from the original version in step 5, where the selected feature has high correlation with the output.…”

Section: Modified Fast Correlation-based Filtering (Mfcbf)mentioning

confidence: 99%

“…In our work on Parkinson's disease detection [16], we found it useful to add kinematic and statistical features; therefore, the first contribution of this work is to add these features to the user's features that we used in [17]. The second contribution of this work is the use of a PCA-mFCBF pipeline; in fact, it is probably the most important contribution.…”

Section: Introductionmentioning

confidence: 99%

“…In this paradigm, the features are orthogonalised using PCA. Then, the PCs are selected using mFCBF [17]. The third contribution of this work is the use of the automated machine learning (AutoML) approach.…”

Section: Introductionmentioning

confidence: 99%

“…This section also includes the augmentation method used in this work to synthetically increase the training dataset. Section 6 describes the feature selection (FS) methodology, which includes the PCA [18][19][20][21], mFCBF [17] and the new proposed PCA-mFCBF pipeline. Section 7 describes the hyperparameters of the front end.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Mood State Detection in Handwritten Tasks Using PCA–mFCBF and Automated Machine Learning

Nolazco-Flores

Faundez-Zanuy

Velázquez-Flores

et al. 2022

Sensors

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In this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood state. First, we calculated the features based on the temporal, kinematic, statistical, spectral and cepstral domains for the tablet pressure, the horizontal and vertical pen displacements and the azimuth of the pen’s position. Next, we selected features using a principal component analysis (PCA) pipeline, followed by modified fast correlation–based filtering (mFCBF). PCA was used to calculate the orthogonal transformation of the features, and mFCBF was used to select the best PCA features. The EMOTHAW database was used for depression, anxiety and stress scale (DASS) assessment. The process involved the augmentation of the training data by first augmenting the mood states such that all the data were the same size. Then, 80% of the training data was randomly selected, and a small random Gaussian noise was added to the extracted features. Automated machine learning was employed to train and test more than ten plain and ensembled classifiers. For all three moods, we obtained 100% accuracy results when detecting two possible grades of mood severities using this architecture. The results obtained were superior to the results obtained by using state-of-the-art methods, which enabled us to define the three mood states and provide precise information to the clinical psychologist. The accuracy results obtained when detecting these three possible mood states using this architecture were 82.5%, 72.8% and 74.56% for depression, anxiety and stress, respectively.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Modified Fast Correlation-based Filtering (Mfcbf)mentioning

confidence: 99%