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Your search keyword '"Penzel, Thomas"' showing total 2,179 results
Start Over Author "Penzel, Thomas" Publication Type Periodicals
2,179 results on '"Penzel, Thomas"'

1. Associations between obesity, a composite risk score for probable long COVID, and sleep problems in SARS-CoV-2 vaccinated individuals

Author

Xue, Pei, Merikanto, Ilona, Delale, Eva A., Bjelajac, Adrijana, Yordanova, Juliana, Chan, Rachel N. Y., Korman, Maria, Mota-Rolim, Sérgio A., Landtblom, Anne-Marie, Matsui, Kentaro, Reis, Catia, Penzel, Thomas, Inoue, Yuichi, Nadorff, Michael R., Holzinger, Brigitte, Morin, Charles M., Espie, Colin A., Plazzi, Giuseppe, De Gennaro, Luigi, Chung, Frances, Bjorvatn, Bjørn, Wing, Yun Kwok, Dauvilliers, Yves, Partinen, Markku, and Benedict, Christian

Published
2024
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2. STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine Applied to Examine the Utility of Photography-Based Phenotypes for OSA Prediction Across International Sleep Centers

Author

Urbanowicz, Ryan J., Bandhey, Harsh, Keenan, Brendan T., Maislin, Greg, Hwang, Sy, Mowery, Danielle L., Lynch, Shannon M., Mazzotti, Diego R., Han, Fang, Li, Qing Yun, Penzel, Thomas, Tufik, Sergio, Bittencourt, Lia, Gislason, Thorarinn, de Chazal, Philip, Singh, Bhajan, McArdle, Nigel, Chen, Ning-Hung, Pack, Allan, Schwab, Richard J., Cistulli, Peter A., and Magalang, Ulysses J.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

While machine learning (ML) includes a valuable array of tools for analyzing biomedical data, significant time and expertise is required to assemble effective, rigorous, and unbiased pipelines. Automated ML (AutoML) tools seek to facilitate ML application by automating a subset of analysis pipeline elements. In this study we develop and validate a Simple, Transparent, End-to-end Automated Machine Learning Pipeline (STREAMLINE) and apply it to investigate the added utility of photography-based phenotypes for predicting obstructive sleep apnea (OSA); a common and underdiagnosed condition associated with a variety of health, economic, and safety consequences. STREAMLINE is designed to tackle biomedical binary classification tasks while adhering to best practices and accommodating complexity, scalability, reproducibility, customization, and model interpretation. Benchmarking analyses validated the efficacy of STREAMLINE across data simulations with increasingly complex patterns of association. Then we applied STREAMLINE to evaluate the utility of demographics (DEM), self-reported comorbidities (DX), symptoms (SYM), and photography-based craniofacial (CF) and intraoral (IO) anatomy measures in predicting any OSA or moderate/severe OSA using 3,111 participants from Sleep Apnea Global Interdisciplinary Consortium (SAGIC). OSA analyses identified a significant increase in ROC-AUC when adding CF to DEM+DX+SYM to predict moderate/severe OSA. A consistent but non-significant increase in PRC-AUC was observed with the addition of each subsequent feature set to predict any OSA, with CF and IO yielding minimal improvements. Application of STREAMLINE to OSA data suggests that CF features provide additional value in predicting moderate/severe OSA, but neither CF nor IO features meaningfully improved the prediction of any OSA beyond established demographics, comorbidity and symptom characteristics., Comment: 23 pages, 7 figures, 1 table, 1 supplemental information document (77 pages), and 7 ancillary files

Published
2023

5. Estimation bias and agreement limits between two common self-report methods of habitual sleep duration in epidemiological surveys

Author

Korman, Maria, Zarina, Daria, Tkachev, Vadim, Merikanto, Ilona, Bjorvatn, Bjørn, Bjelajac, Adrijana Koscec, Penzel, Thomas, Landtblom, Anne-Marie, Benedict, Christian, Chan, Ngan Yin, Wing, Yun Kwok, Dauvilliers, Yves, Morin, Charles M., Matsui, Kentaro, Nadorff, Michael, Bolstad, Courtney J., Chung, Frances, Mota-Rolim, Sérgio, De Gennaro, Luigi, Plazzi, Giuseppe, Yordanova, Juliana, Holzinger, Brigitte, Partinen, Markku, and Reis, Cátia

Published
2024
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16. Associations between changes in habitual sleep duration and lower self-rated health among COVID-19 survivors: findings from a survey across 16 countries/regions

Author

Matsui, Kentaro, Chung, Frances, Bjelajac, Adrijana Koscec, Merikanto, Ilona, Korman, Maria, Mota-Rolim, Sérgio, Cunha, Ana Suely, Bjorvatn, Bjørn, Xue, Pei, Benedict, Christian, Morin, Charles M., Espie, Colin A., Landtblom, Anne-Marie, Penzel, Thomas, De Gennaro, Luigi, Holzinger, Brigitte, Hrubos-Strøm, Harald, Leger, Damien, Bolstad, Courtney J., Nadorff, Michael R., Plazzi, Giuseppe, Reis, Catia, Chan, Ngan Yin, Wing, Yun Kwok, Yordanova, Juliana, Dauvilliers, Yves, Partinen, Markku, and Inoue, Yuichi

Published
2023
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18. Persistent short nighttime sleep duration is associated with a greater post-COVID risk in fully mRNA-vaccinated individuals

Author

Xue, Pei, Merikanto, Ilona, Chung, Frances, Morin, Charles M., Espie, Colin, Bjorvatn, Bjørn, Cedernaes, Jonathan, Landtblom, Anne-Marie, Penzel, Thomas, De Gennaro, Luigi, Holzinger, Brigitte, Matsui, Kentaro, Hrubos-Strøm, Harald, Korman, Maria, Leger, Damien, Mota-Rolim, Sérgio, Bolstad, Courtney J., Nadorff, Michael, Plazzi, Giuseppe, Reis, Catia, Chan, Rachel Ngan Yin, Wing, Yun Kwok, Yordanova, Juliana, Bjelajac, Adrijana Koscec, Inoue, Yuichi, Partinen, Markku, Dauvilliers, Yves, and Benedict, Christian

Published
2023
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20. ENN-ICS - quality assurance and online evaluation of a multilingual learning management system for sleep medicine

Author

Knobl, Brigitte, Paiva, Teresa, Jungmann, Dieter, Böhme, Rico, Lomba, Antónia, Geldner, Lars, Jordán, Blanca, and Penzel, Thomas

Subjects
sleep medicine, sleep disleep disorders, learning management system, e-learning, e-health service, Computer applications to medicine. Medical informatics, R858-859.7, Infectious and parasitic diseases, RC109-216
Abstract

With the development and implementation of a multilingual interactive communication system the e-health project ENN-ICS, funded by the European Union, aims at the improvement of health care in Europe. The field of application is sleep physiology and sleep medicine. ENN-ICS Centre offers direct access to medical information for multiple users, i.e. health authorities, health care professionals, patients and citizen. Using XML technologies the new web based network integrates advanced e-learning and e-publishing methods for the training of healthcare professionals. Editorial and distributive processes are supported by customized central editorial, content management and learning management systems (CMS, LMS). ENN-ICS e-health services are evaluated by selected user groups in North, Middle and Southern Europe using reliable and scientifically accepted validation instruments. The compliance with essential quality requirements and criteria is tested and verified by using adapted online questionnaires based on the DISCERN questionnaire for evaluating patient information, on the HON principles for evaluating health-related websites and on the GMDS catalogue of quality criteria for electronic publications in medicine. The system architecture and its exemplary applications can be used as a model for future e-health services dealing with neurological and other medical topics.

Published
2006

21. A CNN-BiLSTM Deep Learning Model for Automatic Scoring of EEG Signals

Author

ElMoaqet Hisham, Eid Mohammad, Ryalat Mutaz, and Penzel Thomas

Subjects
eeg signal, cnn, bilstm, deep learning, Medicine
Abstract

Recently, several automatic sleep stage classification methods have been proposed based on deep learning using convolutional (CNN) and recurrent (RNN) neural networks. However, the state of the art CNN methods are still complex which usually require significant time and considerable computational resources in order to set up and sufficiently train a deep CNN from scratch. This study eliminates the need to establish and train a deep CNN from scratch by leveraging a pre-trained deep architecture that has been previously trained from sufficient labeled data in a different context. A convolutional neural network (CNN) and a Bidrectional long short term memory network (BiLSTM) are integrated for automatic feature extraction and sleep stage scoring using only a singlechannel EEG signal. To demonstrate the generalizability of our results, the proposed model was evaluated using PSG records of 81 patients that were collected in different environments, through different recording hardware, and annotated with different sleep experts. The use of a single EEG source and a one-to-one classification scheme in the proposed model can allow further development towards wearable systems and online in home monitoring applications.

Published
2023
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22. Mechanisms of phototherapy of Alzheimer’s disease during sleep and wakefulness: the role of the meningeal lymphatics

Author

Semyachkina-Glushkovskaya Oxana, Shirokov Alexander, Blokhina Inna, Fedosov Ivan, Terskov Andrey, Dubrovsky Alexander, Tsoy Maria, Elovenko Daria, Adushkina Viktoria, Evsukova Arina, Telnova Valeria, Tzven Anna, Krupnova Valeria, Manzhaeva Maria, Dmitrenko Alexander, Penzel Thomas, and Kurths Jürgen

Subjects
Alzheimer’s disease, Photobiomodulation, Brain lymphatics, Sleep, Mechanisms, Applied optics. Photonics, TA1501-1820
Abstract

Abstract With the increase in the aging population, the global number of people with Alzheimer’s disease (AD) progressively increased worldwide. The situation is aggravated by the fact that there is no the effective pharmacological therapy of AD. Photobiomodulation (PBM) is non-pharmacological approach that has shown very promising results in the therapy of AD in pilot clinical and animal studies. However, the mechanisms of therapeutic effects of PBM for AD are poorly understood. In this study on mice, we demonstrate that photodynamic effects of 5-aminolevulenic acid and laser 635 nm cause reduction of network of the meningeal lymphatic vessels (MLVs) leading to suppression of lymphatic removal of beta-amyloid (Aβ) from the right lateral ventricle and the hippocampus. Using the original protocol of PBM under electroencephalographic monitoring of wakefulness and sleep stages in non-anesthetized mice, we discover that the 7-day course of PBM during deep sleep vs. wakefulness provides better restoration of clearance of Aβ from the ventricular system of the brain and the hippocampus. Our results shed light on the mechanism of PBM and show the stimulating effects of PBM on the brain lymphatic drainage that promotes transport of Aβ via the lymphatic pathway. The effects of PBM on the brain lymphatics in sleeping brain open a new niche in the study of restorative functions of sleep as well as it is an important informative platform for the development of innovative smart sleep technologies for the therapy of AD. Graphical Abstract

Published
2023
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26. From sleep medicine to medicine during sleep: A clinical perspective

Author

Bar, Nitai, Sobel, Jonathan A., Penzel, Thomas, Shamay, Yosi, and Behar, Joachim A.

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Sleep has a profound influence on the physiology of body systems and biological processes. Molecular studies have shown circadian-regulated shifts in protein expression patterns across human tissues, further emphasizing the unique functional, behavioral and pharmacokinetic landscape of sleep. Thus, many pathological processes are also expected to exhibit sleep-specific manifestations. Nevertheless, sleep is seldom utilized for the study, detection and treatment of non-sleep-specific pathologies. Modern advances in biosensor technologies have enabled remote, non-invasive recording of a growing number of physiologic parameters and biomarkers. Sleep is an ideal time frame for the collection of long and clean physiological time series data which can then be analyzed using data-driven algorithms such as deep learning. In this perspective paper, we aim to highlight the potential of sleep as an auspicious time for diagnosis, management and therapy of nonsleep-specific pathologies. We introduce key clinical studies in selected medical fields, which leveraged novel technologies and the advantageous period of sleep to diagnose, monitor and treat pathologies. We then discuss possible opportunities to further harness this new paradigm and modern technologies to explore human health and disease during sleep and to advance the development of novel clinical applications: From sleep medicine to medicine during sleep.

Published
2021

27. Modification in ICU Design May Affect Delirium and Circadian Melatonin: A Proof of Concept Pilot Study

Author

Spies, Claudia, Piazena, Helmut, Deja, Maria, Wernecke, Klaus-Dieter, Willemeit, Thomas, Luetz, Alawi, Albrecht, Benjamin, Denke, Claudia, Dolata, Kai, Fietze, Ingo, Finke, Annette, he, Jing, Hegic, Dennis, Kronfeld, Susanne, Masswig, Ingrid, Penzel, Thomas, Quantz, Joachim, Sauter, Joachim, Sund, Andreas, Wassmuth, Norman, Weber-Carstens, Steffen, Weiss, Bjoern, Winninghoff, Ute, and Zimmermann, Susanne

Published
2024
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28. Modification in ICU Design May Affect Delirium and Circadian Melatonin: A Proof of Concept Pilot Study

Author

Spies, Claudia, Piazena, Helmut, Deja, Maria, Wernecke, Klaus-Dieter, Willemeit, Thomas, Luetz, Alawi, Albrecht, Benjamin, Denke, Claudia, Dolata, Kai, Fietze, Ingo, Finke, Annette, he, Jing, Hegic, Dennis, Kronfeld, Susanne, Masswig, Ingrid, Penzel, Thomas, Quantz, Joachim, Sauter, Joachim, Sund, Andreas, Wassmuth, Norman, Weber-Carstens, Steffen, Weiss, Bjoern, Winninghoff, Ute, and Zimmermann, Susanne

Published
2023
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32. Digital biomarkers and artificial intelligence for mass diagnosis of atrial fibrillation in a population sample at risk of sleep disordered breathing

Author

Chocron, Armand, Efraim, Roi, Mandel, Franck, Rueschman, Michael, Palmius, Niclas, Penzel, Thomas, Elbaz, Meyer, and Behar, Joachim A.

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with a five-fold increase in stroke risk. Many individuals with AF go undetected. These individuals are often asymptomatic. There are ongoing debates on whether mass screening for AF is to be recommended. However, there is incentive in performing screening for specific at risk groups such as individuals suspected of sleep-disordered breathing where an important association between AF and obstructive sleep apnea (OSA) has been demonstrated. We introduce a new methodology leveraging digital biomarkers and recent advances in artificial intelligence (AI) for the purpose of mass AF diagnosis. We demonstrate the value of such methodology in a large population sample at risk of sleep disordered breathing. Four databases, totaling n=3,088 patients and p=26,913 hours of ECG raw data were used. Three of the databases (n=125, p=2,513) were used for training a machine learning model in recognizing AF events from beat-to-beat interval time series. The visit 1 of the sleep heart health study database (SHHS1, n=2,963, p=24,400) consists of overnight polysomnographic (PSG) recordings, and was considered as the test set. In SHHS1, expert inspection identified a total of 70 patients with a prominent AF rhythm. Model prediction on the SHHS1 showed an overall Se=0.97,Sp=0.99,NPV=0.99,PPV=0.67 in classifying individuals with or without prominent AF. PPV was non-inferior (p=0.03) for individuals with an apnea-hypopnea index (AHI) > 15 versus AHI < 15. Over 22% of correctly identified prominent AF rhythm cases were not documented as AF in the SHHS1. Individuals with prominent AF can be automatically diagnosed from an overnight single channel ECG recording, with an accuracy unaffected by the presence of OSA. AF detection from overnight ECG recording revealed a large proportion of undiagnosed AF and may enhance the phenotyping of OSA.

Published
2020
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34. The association of insomnia with long COVID: An international collaborative study (ICOSS-II)

Author

Chen, Si-Jing, Morin, Charles M., Ivers, Hans, Wing, Yun Kwok, Partinen, Markku, Merikanto, Ilona, Holzinger, Brigitte, Espie, Colin A., De Gennaro, Luigi, Dauvilliers, Yves, Chung, Frances, Yordanova, Juliana, Vidović, Domagoj, Reis, Catia, Plazzi, Giuseppe, Penzel, Thomas, Nadorff, Michael R., Matsui, Kentaro, Mota-Rolim, Sergio, Leger, Damien, Landtblom, Anne-Marie, Korman, Maria, Inoue, Yuichi, Hrubos-Strøm, Harald, Chan, Ngan Yin, Bjelajac, Adrijana Koscec, Benedict, Christian, and Bjorvatn, Bjørn

Published
2023
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36. Obstructive Sleep Apnea with COVID-19

Author

Huang, Ying, Chen, DongMing, Fietze, Ingo, Penzel, Thomas, Crusio, Wim E., Series Editor, Dong, Haidong, Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, Steinlein, Ortrud, Series Editor, Xiao, Junjie, Series Editor, Penzel, Thomas, editor, and Hornero, Roberto, editor

Published
2022
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37. ECG and Heart Rate Variability in Sleep-Related Breathing Disorders

Author

Qin, Hua, Vaquerizo-Villar, Fernando, Steenbergen, Nicolas, Kraemer, Jan F., Penzel, Thomas, Crusio, Wim E., Series Editor, Dong, Haidong, Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, Steinlein, Ortrud, Series Editor, Xiao, Junjie, Series Editor, Penzel, Thomas, editor, and Hornero, Roberto, editor

Published
2022
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38. Cardiopulmonary Coupling

Author

Lu, Mi, Penzel, Thomas, Thomas, Robert J., Crusio, Wim E., Series Editor, Dong, Haidong, Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, Steinlein, Ortrud, Series Editor, Xiao, Junjie, Series Editor, Penzel, Thomas, editor, and Hornero, Roberto, editor

Published
2022
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42. Association between hypersomnolence and the COVID-19 pandemic: The International COVID-19 Sleep Study (ICOSS)

Author

Sarkanen, Tomi, Partinen, Markku, Bjorvatn, Bjørn, Merikanto, Ilona, Benedict, Christian, Nadorff, Michael R., Bolstad, Courtney J., Espie, Colin, Matsui, Kentaro, Chung, Frances, Morin, Charles M., Wing, Yun Kwok, Penzel, Thomas, Macêdo, Tainá, Mota-Rolim, Sérgio, Holzinger, Brigitte, Plazzi, Giuseppe, De Gennaro, Luigi, Landtblom, Anne-Marie, Inoue, Yuichi, Sieminski, Mariuz, Leger, Damien, and Dauvilliers, Yves

Published
2023
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44. Fieber

Author

Peter, Helga, primary and Penzel, Thomas, additional

Published
2023
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