Nighttime Depression Episodes Classification using a Formal Method: Knowledge Discovery in Databases

Deep‐SQA: A deep learning model using motor activity data for objective sleep quality assessment assisting digital wellness in healthcare 5.0

“…Several recent research gyrated around applying Machine Learning (ML) and DL methods to assess mental health indicators using actigraph measurements (Choi et al, 2021; Garcia‐Ceja, Riegler, Jakobsen, Tørresen, et al, 2018, 2020; Raihan et al, 2021; Rodríguez‐Ruiz et al, 2019, 2020; Tazawa et al, 2020). Several research (Frogner et al, 2019; Garcia‐Ceja, Riegler, Jakobsen, Torresen, et al, 2018; Jakobsen et al, 2020; Kulam, 2019; Meshram & Rambola, 2022) have been published that show how DL techniques may be used to inevitably extract features from the dataset recordings.…”

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

“…The prevalence & high risk associated with depression in individuals, early screening efforts and preventive interventions have been studied widely in literature. Recent studies have leveraged the data from wearable devices and soft computing techniques to assess various psychological disorders such as sleep quality [11,16], depression [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33], behavioral disorders [16], anxiety [17,19], and mental wellbeing [20,21]. Automated detection of depression and its severity assessment using activity monitoring and other multimodal cues is being researched extensively.…”

“…Subsequently, a number of researchers proposed models analyzing the Depresjon dataset. Rodríguez-Ruiz et al [30] have correlated depression with sleep patterns and analyzed depressive and non-depressive episodes during night time by implementing Random Forest model on Depresjon dataset. In another reported research [31], the authors compared the night and day motor activity to classify the depressive and non-depressive episodes on Depressjon dataset.…”