Exploring the relationship between response time sequence in scale answering process and severity of insomnia: a machine learning approach

• URL: http://arxiv.org/abs/2310.08817v1
• Date: Fri, 13 Oct 2023 02:06:52 GMT
• Title: Exploring the relationship between response time sequence in scale answering process and severity of insomnia: a machine learning approach
• Authors: Zhao Su, Rongxun Liu, Keyin Zhou, Xinru Wei, Ning Wang, Zexin Lin, Yuanchen Xie, Jie Wang, Fei Wang, Shenzhong Zhang, Xizhe Zhang
• Abstract summary: The relationship between insomnia symptoms and response time was investigated. A machine learning model was developed to predict the presence of insomnia using response time data.
• Score: 9.543953794971433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Objectives: The study aims to investigate the relationship between insomnia and response time. Additionally, it aims to develop a machine learning model to predict the presence of insomnia in participants using response time data. Methods: A mobile application was designed to administer scale tests and collect response time data from 2729 participants. The relationship between symptom severity and response time was explored, and a machine learning model was developed to predict the presence of insomnia. Results: The result revealed a statistically significant difference (p<.001) in the total response time between participants with or without insomnia symptoms. A correlation was observed between the severity of specific insomnia aspects and response times at the individual questions level. The machine learning model demonstrated a high predictive accuracy of 0.743 in predicting insomnia symptoms based on response time data. Conclusions: These findings highlight the potential utility of response time data to evaluate cognitive and psychological measures, demonstrating the effectiveness of using response time as a diagnostic tool in the assessment of insomnia.

Related papers

• Clustering and Data Augmentation to Improve Accuracy of Sleep Assessment and Sleep Individuality Analysis [1.9662978733004597]
  This study aims to construct a machine learning-based sleep assessment model providing evidence-based assessments, such as poor sleep due to frequent movement during sleep onset. Extracting sleep sound events, deriving latent representations using VAE, clustering with GMM, and training LSTM for subjective sleep assessment achieved a high accuracy of 94.8% in distinguishing sleep satisfaction.
  arXiv  Detail & Related papers  (2024-04-16T05:56:41Z)
• Domain Invariant Representation Learning and Sleep Dynamics Modeling for Automatic Sleep Staging [6.86283473936335]
  We propose a neural network based sleep staging model, DREAM, to learn domain generalized representations from physiological signals and models sleep dynamics. DREAM learns sleep related and subject invariant representations from diverse subjects' sleep signals and models sleep dynamics by capturing interactions between sequential signal segments and between sleep stages. We conducted a comprehensive empirical study to demonstrate the superiority of DREAM, including sleep stage prediction experiments, a case study, the usage of unlabeled data, and uncertainty.
  arXiv  Detail & Related papers  (2023-12-06T00:28:08Z)
• Signal Processing Grand Challenge 2023 -- e-Prevention: Sleep Behavior as an Indicator of Relapses in Psychotic Patients [27.6075917779323]
  This paper presents the approach and results of USC SAIL's submission to the Signal Processing Grand Challenge 2023 - e-Prevention (Task 2) on detecting relapses in psychotic patients. We investigate the use of sleep behavior features to estimate relapse days as outliers in an unsupervised machine learning setting. Our submission was ranked 3rd in the Task's official leaderboard, demonstrating the potential of such features as an objective and non-invasive predictor of psychotic relapses.
  arXiv  Detail & Related papers  (2023-04-17T21:02:46Z)
• Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data [67.60224656603823]
  Sleep Activity Recognition methods can provide indicators to assess, monitor, and characterize subjects' sleep-wake cycles and detect behavioral changes. We propose a general method that continuously operates on passively sensed data from smartphones to characterize sleep and identify significant sleep episodes. Thanks to their ubiquity, these devices constitute an excellent alternative data source to profile subjects' biorhythms in a continuous, objective, and non-invasive manner.
  arXiv  Detail & Related papers  (2023-01-17T15:18:45Z)
• Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data [67.60224656603823]
  Psychiatric patients' passive activity monitoring is crucial to detect behavioural shifts in real-time. Sleep Activity Recognition constitutes a behavioural marker to portray patients' activity cycles. Mobile passively sensed data captured from smartphones constitute an excellent alternative to profile patients' biorhythm.
  arXiv  Detail & Related papers  (2022-11-08T17:29:40Z)
• Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data [52.40058724040671]
  Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia. Detecting agitation episodes can assist in providing People Living with Dementia (PLWD) with early and timely interventions. This preliminary study presents a supervised learning model to analyse the risk of agitation in PLWD using in-home monitoring data.
  arXiv  Detail & Related papers  (2021-10-19T11:45:01Z)
• Identity and Posture Recognition in Smart Beds with Deep Multitask Learning [8.422257363944295]
  We propose a robust deep learning model capable of accurately detecting subjects and their sleeping postures. A combination of loss functions is used to discriminate subjects and their sleeping postures simultaneously. The proposed algorithm can ultimately be used in clinical and smart home environments.
  arXiv  Detail & Related papers  (2021-04-05T21:21:54Z)
• MSED: a multi-modal sleep event detection model for clinical sleep analysis [62.997667081978825]
  We designed a single deep neural network architecture to jointly detect sleep events in a polysomnogram. The performance of the model was quantified by F1, precision, and recall scores, and by correlating index values to clinical values.
  arXiv  Detail & Related papers  (2021-01-07T13:08:44Z)
• DeepRite: Deep Recurrent Inverse TreatmEnt Weighting for Adjusting Time-varying Confounding in Modern Longitudinal Observational Data [68.29870617697532]
  We propose Deep Recurrent Inverse TreatmEnt weighting (DeepRite) for time-varying confounding in longitudinal data. DeepRite is shown to recover the ground truth from synthetic data, and estimate unbiased treatment effects from real data.
  arXiv  Detail & Related papers  (2020-10-28T15:05:08Z)
• Predicting Parkinson's Disease with Multimodal Irregularly Collected Longitudinal Smartphone Data [75.23250968928578]
  Parkinsons Disease is a neurological disorder and prevalent in elderly people. Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the quality of a set of activity tests. We propose a novel time-series based approach to predicting Parkinson's Disease with raw activity test data collected by smartphones in the wild.
  arXiv  Detail & Related papers  (2020-09-25T01:50:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.