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Hammad G, Wulff K, Skene DJ, Münch M, Spitschan M. Open-Source Python Module for the Analysis of Personalized Light Exposure Data from Wearable Light Loggers and Dosimeters. LEUKOS 2024; 20:380-389. [PMID: 39021508 PMCID: PMC7616232 DOI: 10.1080/15502724.2023.2296863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 12/12/2023] [Indexed: 07/20/2024]
Abstract
Light exposure fundamentally influences human physiology and behavior, with light being the most important zeitgeber of the circadian system. Throughout the day, people are exposed to various scenes differing in light level, spectral composition and spatio-temporal properties. Personalized light exposure can be measured through wearable light loggers and dosimeters, including wrist-worn actimeters containing light sensors, yielding time series of an individual's light exposure. There is growing interest in relating light exposure patterns to health outcomes, requiring analytic techniques to summarize light exposure properties. Building on the previously published Python-based pyActigraphy module, here we introduce the module pyLight. This module allows users to extract light exposure data recordings from a wide range of devices. It also includes software tools to clean and filter the data, and to compute common metrics for quantifying and visualizing light exposure data. For this tutorial, we demonstrate the use of pyLight in one example dataset with the following processing steps: (1) loading, accessing and visual inspection of a publicly available dataset, (2) truncation, masking, filtering and binarization of the dataset, (3) calculation of summary metrics, including time above threshold (TAT) and mean light timing above threshold (MLiT). The pyLight module paves the way for open-source, large-scale automated analyses of light-exposure data.
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Affiliation(s)
- Grégory Hammad
- Sleep & Chronobiology Group, GIGA – CRC in Vivo Imaging, University of Liège, Liège, Belgium
- Chair of Neurogenetics, Institute of Human Genetics, University Hospital, Technical University of Munich, Munich, Germany
| | - Katharina Wulff
- Department of Molecular Biology, Umea University, Umea, Sweden
- Wallenberg Centre for Molecular Medicine (WCMM), Umea University, Umea, Sweden
| | - Debra J. Skene
- Chronobiology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Mirjam Münch
- Centre for Chronobiology, Psychiatric Hospital of the University of Basel, Basel, Switzerland
- Transfaculty Platform for Molecular and Cognitive Neuroscience, University of Basel, Basel, Switzerland
| | - Manuel Spitschan
- Translational Sensory & Circadian Neuroscience, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- TUM School of Medicine & Health, Technical University of Munich, Munich, Germany
- TUM Institute for Advanced Study, Technical University of Munich, Garching, Germany
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Bandyopadhyay A, Oks M, Sun H, Prasad B, Rusk S, Jefferson F, Malkani RG, Haghayegh S, Sachdeva R, Hwang D, Agustsson J, Mignot E, Summers M, Fabbri D, Deak M, Anastasi M, Sampson A, Van Hout S, Seixas A. Strengths, weaknesses, opportunities, and threats of using AI-enabled technology in sleep medicine: a commentary. J Clin Sleep Med 2024; 20:1183-1191. [PMID: 38533757 PMCID: PMC11217619 DOI: 10.5664/jcsm.11132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 03/20/2024] [Indexed: 03/28/2024]
Abstract
Over the past few years, artificial intelligence (AI) has emerged as a powerful tool used to efficiently automate several tasks across multiple domains. Sleep medicine is perfectly positioned to leverage this tool due to the wealth of physiological signals obtained through sleep studies or sleep tracking devices and abundance of accessible clinical data through electronic medical records. However, caution must be applied when utilizing AI, due to intrinsic challenges associated with novel technology. The Artificial Intelligence in Sleep Medicine Committee of the American Academy of Sleep Medicine reviews advancements in AI within the sleep medicine field. In this article, the Artificial Intelligence in Sleep Medicine committee members provide a commentary on the scope of AI technology in sleep medicine. The commentary identifies 3 pivotal areas in sleep medicine that can benefit from AI technologies: clinical care, lifestyle management, and population health management. This article provides a detailed analysis of the strengths, weaknesses, opportunities, and threats associated with using AI-enabled technologies in each pivotal area. Finally, the article broadly reviews barriers and challenges associated with using AI-enabled technologies and offers possible solutions. CITATION Bandyopadhyay A, Oks M, Sun H, et al. Strengths, weaknesses, opportunities, and threats of using AI-enabled technology in sleep medicine: a commentary. J Clin Sleep Med. 2024;20(7):1183-1191.
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Affiliation(s)
- Anuja Bandyopadhyay
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Margarita Oks
- Department of Medicine, Northwell Health System, New York, New York
| | - Haoqi Sun
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Bharati Prasad
- Department of Medicine, University of Illinois, Chicago, Illinois
| | - Sam Rusk
- EnsoData Research, EnsoData, Madison, Wisconsin
| | - Felicia Jefferson
- Department of Biochemistry and Molecular Biology, University of Nevada, Reno, Nevada
| | - Roneil Gopal Malkani
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Neurology Service, Jesse Brown Veterans Affairs Medical Center, Chicago, Illinois
| | - Shahab Haghayegh
- Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Ramesh Sachdeva
- Children’s Hospital of Michigan and Central Michigan University College of Medicine, Detroit, Michigan
| | - Dennis Hwang
- Kaiser Permanente Southern California, Los Angeles, California
| | | | - Emmanuel Mignot
- Stanford University, School of Medicine, Stanford, California
| | - Michael Summers
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Nebraska Medical Center, Omaha, Nebraska
| | | | | | | | | | | | - Azizi Seixas
- Department of Informatics and Health Data Science, University of Miami Miller School of Medicine, Miami, Florida
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Wang Y, Ren Y, Bi Y, Zhao F, Bai X, Wei L, Liu W, Ma H, Bai P. Multimodal transformer graph convolution attention isomorphism network (MTCGAIN): a novel deep network for detection of insomnia disorder. Quant Imaging Med Surg 2024; 14:3350-3365. [PMID: 38720838 PMCID: PMC11074748 DOI: 10.21037/qims-23-1594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 03/06/2024] [Indexed: 05/12/2024]
Abstract
Background In clinic, the subjectivity of diagnosing insomnia disorder (ID) often leads to misdiagnosis or missed diagnosis, as ID may have the same symptoms as those of other health problems. Methods A novel deep network, the multimodal transformer graph convolution attention isomorphism network (MTGCAIN) is proposed in this study. In this network, graph convolution attention (GCA) is first employed to extract the graph features of brain connectivity and achieve good spatial interpretability. Second, the MTGCAIN comprehensively utilizes multiple brain network atlases and a multimodal transformer (MT) to facilitate coded information exchange between the atlases. In this way, MTGCAIN can be used to more effectively identify biomarkers and arrive at accurate diagnoses. Results The experimental results demonstrated that more accurate and objective diagnosis of ID can be achieved using the MTGCAIN. According to fivefold cross-validation, the accuracy reached 81.29% and the area under the receiver operating characteristic curve (AUC) reached 0.8760. A total of nine brain regions were detected as abnormal, namely right supplementary motor area (SMA.R), right temporal pole: superior temporal gyrus (TPOsup.R), left temporal pole: superior temporal gyrus (TPOsup.L), right superior frontal gyrus, dorsolateral (SFGdor.R), right middle temporal gyrus (MTG.R), left middle temporal gyrus (MTG.L), right inferior temporal gyrus (ITG.R), right median cingulate and paracingulate gyri (DCG.R), left median cingulate and paracingulate gyri (DCG.L). Conclusions The brain regions in the default mode network (DMN) of patients with ID show significant impairment (occupies four-ninths). In addition, the functional connectivity (FC) between the right middle occipital gyrus and inferior temporal gyrus (ITG) has an obvious correlation with comorbid anxiety (P=0.008) and depression (P=0.005) among patients with ID.
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Affiliation(s)
- Yulong Wang
- College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Yande Ren
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yuzhen Bi
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Xingzhen Bai
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China
| | - Liangzhou Wei
- Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wanting Liu
- Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Hancheng Ma
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Peirui Bai
- College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China
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Li J, Dai Y, Mu Z, Wang Z, Meng J, Meng T, Wang J. Choice of refractive surgery types for myopia assisted by machine learning based on doctors' surgical selection data. BMC Med Inform Decis Mak 2024; 24:41. [PMID: 38331788 PMCID: PMC10854042 DOI: 10.1186/s12911-024-02451-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 02/02/2024] [Indexed: 02/10/2024] Open
Abstract
In recent years, corneal refractive surgery has been widely used in clinics as an effective means to restore vision and improve the quality of life. When choosing myopia-refractive surgery, it is necessary to comprehensively consider the differences in equipment and technology as well as the specificity of individual patients, which heavily depend on the experience of ophthalmologists. In our study, we took advantage of machine learning to learn about the experience of ophthalmologists in decision-making and assist them in the choice of corneal refractive surgery in a new case. Our study was based on the clinical data of 7,081 patients who underwent corneal refractive surgery between 2000 and 2017 at the Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences. Due to the long data period, there were data losses and errors in this dataset. First, we cleaned the data and deleted the samples of key data loss. Then, patients were divided into three groups according to the type of surgery, after which we used SMOTE technology to eliminate imbalance between groups. Six statistical machine learning models, including NBM, RF, AdaBoost, XGBoost, BP neural network, and DBN were selected, and a ten-fold cross-validation and grid search were used to determine the optimal hyperparameters for better performance. When tested on the dataset, the multi-class RF model showed the best performance, with agreement with ophthalmologist decisions as high as 0.8775 and Macro F1 as high as 0.8019. Furthermore, the results of the feature importance analysis based on the SHAP technique were consistent with an ophthalmologist's practical experience. Our research will assist ophthalmologists in choosing appropriate types of refractive surgery and will have beneficial clinical effects.
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Affiliation(s)
- Jiajing Li
- School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing, China.
- Wangganzhicha Information Technology Inc., Nanjing, Jiangsu Province, China.
| | - Yuanyuan Dai
- School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing, China
| | - Zhicheng Mu
- School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing, China
| | - Zhonghai Wang
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Juan Meng
- Community Health Service Center of Douhudi Town, Gongan County, Jingzhou, Hubei Province, China
| | - Tao Meng
- Wangganzhicha Information Technology Inc., Nanjing, Jiangsu Province, China
| | - Jimin Wang
- Department of Information Management, Peking University, Beijing, China
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Rani S, Shelyag S, Angelova M. Patterns of sedentary behaviour in adults with acute insomnia derived from actigraphy data. PLoS One 2023; 18:e0291095. [PMID: 37733680 PMCID: PMC10513233 DOI: 10.1371/journal.pone.0291095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 08/22/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Sleep disorders, such as insomnia, have been associated with extended periods of inactive, sedentary behaviour. Many factors contribute to insomnia, including stress, irregular sleep patterns, mental health issues, inadequate sleeping schedules, diseases, neurological disorders and prescription medications. OBJECTIVES Identification of the patterns of sedentary time and its duration in adults with acute insomnia and healthy controls to determine the statistically significant sedentary bouts; comparison of the sedentary behaviour patterns in acute insomnia adults with healthy controls. METHODS We investigate the daytime actigraphy data and identify temporal patterns of inactivity among adults with acute insomnia and healthy adults. Seven days of actigraphy data were utilised to calculate sedentary time and bouts of variable duration based on a threshold of activity counts (<100 counts per minute). Statistical analysis was applied to investigate sedentary bouts and total sedentary time during weekdays and weekend. A logistic regression model has been used to determine the significance of sedentary bouts. RESULTS We found that individuals with acute insomnia accumulate a significant amount of their sedentary time in medium (6-30 minutes and 31-60 minutes) and longer (more than 60 minutes) duration bouts in comparison to healthy adults. Furthermore, a low p value for total sedentary time (2.54 ⋅ 10-4) association with acute insomnia supports the finding that acute insomnia individuals are significantly more engaged in sedentary activities compared to healthy controls. Also, as shown by the weekend vs weekday analysis, the physical and sedentary activity patterns of acute insomnia adults demonstrate higher variability during the weekdays in comparison to the weekend. CONCLUSION The results of the present study demonstrate that adults with acute insomnia spend more time in low-intensity daily physical activities compared to healthy adults.
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Affiliation(s)
- Sunita Rani
- School of IT, Deakin University, Melbourne, Victoria, Australia
| | - Sergiy Shelyag
- School of IT, Deakin University, Melbourne, Victoria, Australia
- College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia
| | - Maia Angelova
- School of IT, Deakin University, Melbourne, Victoria, Australia
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Bitkina OV, Park J, Kim J. Modeling Sleep Quality Depending on Objective Actigraphic Indicators Based on Machine Learning Methods. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:9890. [PMID: 36011524 PMCID: PMC9408084 DOI: 10.3390/ijerph19169890] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/02/2022] [Accepted: 08/08/2022] [Indexed: 06/15/2023]
Abstract
According to data from the World Health Organization and medical research centers, the frequency and severity of various sleep disorders, including insomnia, are increasing steadily. This dynamic is associated with increased daily stress, anxiety, and depressive disorders. Poor sleep quality affects people's productivity and activity and their perception of quality of life in general. Therefore, predicting and classifying sleep quality is vital to improving the quality and duration of human life. This study offers a model for assessing sleep quality based on the indications of an actigraph, which was used by 22 participants in the experiment for 24 h. Objective indicators of the actigraph include the amount of time spent in bed, sleep duration, number of awakenings, and duration of awakenings. The resulting classification model was evaluated using several machine learning methods and showed a satisfactory accuracy of approximately 80-86%. The results of this study can be used to treat sleep disorders, develop and design new systems to assess and track sleep quality, and improve existing electronic devices and sensors.
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Affiliation(s)
- Olga Vl. Bitkina
- Department of Industrial and Management Engineering, Incheon National University (INU), Academy-ro 119, Incheon 22012, Korea
| | - Jaehyun Park
- Department of Industrial and Management Engineering, Incheon National University (INU), Academy-ro 119, Incheon 22012, Korea
| | - Jungyoon Kim
- Department of Computer Science, Kent State University, Kent, OH 44240, USA
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Rani S, Shelyag S, Karmakar C, Zhu Y, Fossion R, Ellis JG, Drummond SPA, Angelova M. Differentiating acute from chronic insomnia with machine learning from actigraphy time series data. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:1036832. [PMID: 36926085 PMCID: PMC10013073 DOI: 10.3389/fnetp.2022.1036832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 11/11/2022] [Indexed: 11/29/2022]
Abstract
Acute and chronic insomnia have different causes and may require different treatments. They are investigated with multi-night nocturnal actigraphy data from two sleep studies. Two different wrist-worn actigraphy devices were used to measure physical activities. This required data pre-processing and transformations to smooth the differences between devices. Statistical, power spectrum, fractal and entropy analyses were used to derive features from the actigraphy data. Sleep parameters were also extracted from the signals. The features were then submitted to four machine learning algorithms. The best performing model was able to distinguish acute from chronic insomnia with an accuracy of 81%. The algorithms were then used to evaluate the acute and chronic groups compared to healthy sleepers. The differences between acute insomnia and healthy sleep were more prominent than between chronic insomnia and healthy sleep. This may be associated with the adaptation of the physiology to prolonged periods of disturbed sleep for individuals with chronic insomnia. The new model is a powerful addition to our suite of machine learning models aiming to pre-screen insomnia at home with wearable devices.
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Affiliation(s)
- S Rani
- School of Information Technology, Deakin University, Geelong, VIC, Australia
| | - S Shelyag
- School of Information Technology, Deakin University, Geelong, VIC, Australia
| | - C Karmakar
- School of Information Technology, Deakin University, Geelong, VIC, Australia
| | - Ye Zhu
- School of Information Technology, Deakin University, Geelong, VIC, Australia
| | - R Fossion
- Centro de Ciencias de la Complejidad (C3) and Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, CDMX, Mexico.,Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, CDMX, Mexico
| | - J G Ellis
- Department of Psychology, Northumbria University, Newcastle Upon Tyne, United Kingdom
| | - S P A Drummond
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia
| | - M Angelova
- School of Information Technology, Deakin University, Geelong, VIC, Australia
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