Depress-DCNF: A deep convolutional neuro-fuzzy model for detection of depression episodes using IoMT

Kumar, Akshi; Sangwan, Saurabh Raj; Arora, Anshika; Menon, Varun G.

doi:10.1016/j.asoc.2022.108863

Cited by 18 publications

(16 citation statements)

References 35 publications

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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%

See 1 more Smart Citation

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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Section: References Values Featuresmentioning

confidence: 99%

“…Type of publication, n (%) [19,21,23,[25][26][27][28][29][30]34,[38][39][40][41][42][43][44][45][46][48][49][50][51][52][53][54][56][57][58][59][60][61][64][65][66][69][70][71][73][74][75][77][78][79]81,82,84,86,87] 49 (71) Journal article [20,22,24,…”

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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show abstract

“…As a result, the IoMT concept is suitable for building embedded technologies that can accurately diagnose diseases in the same manner that professionals perform. IoMT innovation, according to [ 26 ], has contributed to the establishment of vital healthcare systems. Physicians may now receive it in various settings, allowing them to better diagnose patients without affecting subjective features.…”

Section: Related Workmentioning

confidence: 99%

Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things

Mabrouk

Dahou

Elaziz

et al. 2022

Computational Intelligence and Neuroscience

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The Internet of Medical Things (IoMT) has dramatically benefited medical professionals that patients and physicians can access from all regions. Although the automatic detection and prediction of diseases such as melanoma and leukemia is still being investigated and studied in IoMT, existing approaches are not able to achieve a high degree of efficiency. Thus, with a new approach that provides better results, patients would access the adequate treatments earlier and the death rate would be reduced. Therefore, this paper introduces an IoMT proposal for medical images’ classification that may be used anywhere, i.e., it is an ubiquitous approach. It was designed in two stages: first, we employ a transfer learning (TL)-based method for feature extraction, which is carried out using MobileNetV3; second, we use the chaos game optimization (CGO) for feature selection, with the aim of excluding unnecessary features and improving the performance, which is key in IoMT. Our methodology was evaluated using ISIC-2016, PH2, and Blood-Cell datasets. The experimental results indicated that the proposed approach obtained an accuracy of 88.39% on ISIC-2016, 97.52% on PH2, and 88.79% on Blood-cell datsets. Moreover, our approach had successful performances for the metrics employed compared to other existing methods.

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“…Furthermore, ref. [ 25 ] studied the detection of depression using AI. In addition, studies [ 8 , 9 , 26 , 27 ] studied depression and anxiety.…”

Section: Literature Review: Anxiety and Depression In Snmentioning

confidence: 99%

“…[ 6 ] detected signs of depression in tweets in Spanish. In [ 25 ], the study investigated “Wearable Internet of Medical Things (IoMT) devices with sensors that collect motion data and provide objective measures of physical activity can help to better monitor and detect potential episodes related to the mental health conditions at earlier, more treatable stages”, while ref. [ 28 ] did the same using pictures from Instagram.…”

Section: Literature Review: Anxiety and Depression In Snmentioning

confidence: 99%

Factors Affecting the Use of Social Networks and Its Effect on Anxiety and Depression among Parents and Their Children: Predictors Using ML, SEM and Extended TAM

Abu-Taieh

AlHadid

Masa’deh

et al. 2022

IJERPH

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Previous research has found support for depression and anxiety associated with social networks. However, little research has explored parents’ depression and anxiety constructs as mediators that may account for children’s depression and anxiety. The purpose of this paper is to test the influence of different factors on children’s depression and anxiety, extending from parents’ anxiety and depression in Jordan. The authors recruited 857 parents to complete relevant web survey measures with constructs and items and a model based on different research models TAM and extended with trust, analyzed using SEM, CFA with SPSS and AMOS, and ML methods, using the triangulation method to validate the results and help predict future applications. The authors found support for the structural model whereby behavioral intention to use social media influences the parent’s anxiety and depression which correlate to their offspring’s anxiety and depression. Behavioral intention to use social media can be enticed by enjoyment, trust, ease of use, usefulness, and social influences. This study is unique in exploring rumination in the context of the relationship between parent–child anxiety and depression due to the use of social networks.

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Depress-DCNF: A deep convolutional neuro-fuzzy model for detection of depression episodes using IoMT

Cited by 18 publications

References 35 publications

Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review

Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review

Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things

Factors Affecting the Use of Social Networks and Its Effect on Anxiety and Depression among Parents and Their Children: Predictors Using ML, SEM and Extended TAM

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