Classification of Depressive Episodes Using Nighttime Data; a Multivariate and Univariate Analysis

Rodriguez-Ruiz, Julieta G.; Galván-Tejada, Carlos E.; Vázquez-Reyes, Sodel; Galván-Tejada, Jorge I.; Gamboa-Rosales, Hamurabi

doi:10.1134/s0361768820080198

Cited by 5 publications

(12 citation statements)

References 33 publications

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“…In this study, the data of nine dimensions, including psychoticism, paranoia, hostility, terror, anxiety, depression, obsessive-compulsive symptoms, interpersonal sensitivity, and somatization, were used as the input vectors of the LLE-SVM model, and the mental health status of college students was divided into healthy, mildly unhealthy, and unhealthy as the output vectors of the LLE-SVM model to establish a mental health status evaluation model of college students based on the LLE-SVM. e process of the evaluation algorithm based on LLE and SVM can be described in detail [16].…”

Section: Svm Multidimensional State Data Reduction Evaluationmentioning

confidence: 99%

[Retracted] Multidimensional State Data Reduction and Evaluation of College Students’ Mental Health Based on SVM

Peiqing

2022

Journal of Mathematics

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In response to the shortcomings of the traditional methods for evaluating the mental health status of college students in terms of computational complexity and low accuracy, a method for evaluating the mental health status of college students based on data reduction and support vector machines was proposed. A model experiment containing internal and external personality tendency classification, anxiety, and depression dichotomy was designed using logistic regression analysis, information entropy, and SVM algorithm to construct the feature dimensions of the network behavior data, combined with the labeled data of mental state to derive the sample data set for model experiments. In the experimental process, to reflect the difference in the effect of different models, various types of mathematical models were constructed for horizontal comparison; at the same time, to reflect the influence of the parameters of the same type of model, different combinations of parameters were constructed using a grid search algorithm to vertically compare the difference in the effect. The average accuracy of the dichotomous model for anxiety and depression in the sample of 1433 students was 0.80 or higher. The experiments show that the method of predicting students’ psychological status through their online behavioral data is feasible, and the mathematical classification model can be used to grasp students’ psychological status in real time and to warn students with abnormal psychological status, thus helping school counselors to intervene and prevent them promptly.

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Section: Svm Multidimensional State Data Reduction Evaluationmentioning

confidence: 99%

[Retracted] Multidimensional State Data Reduction and Evaluation of College Students’ Mental Health Based on SVM

Peiqing

2022

Journal of Mathematics

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“…Year of publication, n (%) [27,30,38,48,52,59,63,64,69,81] 10 ( 13) 2022 [19][20][21]23,25,28,41,45,49,54,61,62,68,73,74,77,78] 17 (25) 2021 [22,29,31,33,40,43,44,53,57,60,66,70,71,76,79] 15 (22) 2020 [26,32,34,42,46,47,51,56,…”

Section: References Values Featuresmentioning

confidence: 99%

Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review

Abd‐Alrazaq¹,

AlSaad²,

Aziz³

et al. 2023

J Med Internet Res

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Background Anxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of artificial intelligence (AI) into wearable devices (wearable AI) has been exploited to provide mental health services. Objective This review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues. Methods We searched 8 electronic databases (MEDLINE, PsycINFO, Embase, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar) and included studies that met the inclusion criteria. Then, we checked the studies that cited the included studies and screened studies that were cited by the included studies. The study selection and data extraction were carried out by 2 reviewers independently. The extracted data were aggregated and summarized using narrative synthesis. Results Of the 1203 studies identified, 69 (5.74%) were included in this review. Approximately, two-thirds of the studies used wearable AI for depression, whereas the remaining studies used it for anxiety. The most frequent application of wearable AI was in diagnosing anxiety and depression; however, none of the studies used it for treatment purposes. Most studies targeted individuals aged between 18 and 65 years. The most common wearable device used in the studies was Actiwatch AW4 (Cambridge Neurotechnology Ltd). Wrist-worn devices were the most common type of wearable device in the studies. The most commonly used category of data for model development was physical activity data, followed by sleep data and heart rate data. The most frequently used data set from open sources was Depresjon. The most commonly used algorithm was random forest, followed by support vector machine. Conclusions Wearable AI can offer great promise in providing mental health services related to anxiety and depression. Wearable AI can be used by individuals for the prescreening assessment of anxiety and depression. Further reviews are needed to statistically synthesize the studies’ results related to the performance and effectiveness of wearable AI. Given its potential, technology companies should invest more in wearable AI for the treatment of anxiety and depression.

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“…The third batch of 5 features is applied on the Fourier transformed data as this approach was proposed in [11]: maximum (fftMax), average (fftAverage), standard deviation (fftStdDev), variance (fftVar), coefficient of variation (fftVarCoeff) and kurtosis (fftKurtosis). The window size for the Fourier transformation is the length of the respective time series.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Rodríguez-Ruiz et al [11] also use the "depresjon" dataset and report an almost perfect accuracy of 99%. However, a closer look reveals inaccuracies.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Classification of Depression Using Motor Activity Data and Autoregressive Model

Schulte

Breiksch

Brockmann

et al. 2022

Studies in Health Technology and Informatics

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Machine learning based disease classification have already achieved amazing results in medicine: for example, models can find a tumor in computer tomography images at least as accurately as experts in the field. Since the development and widespread use of actigraphy watches, activity data has been used as a basis for diagnosing various diseases such as depression or Alzheimer’s disease. In this study, we use a dataset with activity measurements of mentally ill and healthy people, calculate various features and achieve a classification accuracy of over 78%. The paper describes and motivates the used features, discusses differences between healthy, bipolar 2 and unipolar participants and compares several well-known machine learning classifiers on different classification tasks and with different feature sets.

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Classification of Depressive Episodes Using Nighttime Data; a Multivariate and Univariate Analysis

Cited by 5 publications

References 33 publications

[Retracted] Multidimensional State Data Reduction and Evaluation of College Students’ Mental Health Based on SVM

[Retracted] Multidimensional State Data Reduction and Evaluation of College Students’ Mental Health Based on SVM

Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review

Machine Learning Based Classification of Depression Using Motor Activity Data and Autoregressive Model

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