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Publications

  1. Sutherland, K., Yee, B. J., Kairaitis, K., Wheatley, J., de Chazal, P., & Cistulli, P. A. (2021). A phenotypic approach for personalised management of obstructive sleep apnoea. Current Otorhinolaryngology Reports, 9(3), 223–237. https://doi.org/10.1007/s40136-021-00346-6
  2. Kang, J. M., Cho, S.-E., Lee, G. B., Cho, S.-J., Park, K. H., Kim, S. T., & Kang, S.-G. (2021). Relationship between the Spectral Power Density of Sleep Electroencephalography and Psychiatric Symptoms in Patients with Breathing-related Sleep Disorder. Clinical Psychopharmacology and Neuroscience : The Official Scientific Journal of the Korean College of Neuropsychopharmacology, 19(3), 521–529. https://doi.org/10.9758/cpn.2021.19.3.521
  3. Karhu, T., Myllymaa, S., Nikkonen, S., Mazzotti, D. R., Kulkas, A., Töyräs, J., & Leppänen, T. (2021). Diabetes and cardiovascular diseases are associated with the worsening of intermittent hypoxaemia. Journal of Sleep Research, e13441. https://doi.org/10.1111/jsr.13441
  4. Vaquerizo-Villar, F., Alvarez, D., Kheirandish-Gozal, L., Gutierrez-Tobal, G. C., Barroso-Garcia, V., Santamaria-Vazquez, E., Campo, F. D., Gozal, D., & Hornero, R. (2021). A convolutional neural network architecture to enhance oximetry ability to diagnose pediatric obstructive sleep apnea. IEEE Journal of Biomedical and Health Informatics, 25(8), 2906–2916. https://doi.org/10.1109/JBHI.2020.3048901
  5. Ye, G., Yin, H., Chen, T., Chen, H., Cui, L., & Zhang, X. (2021). Fenet: A frequency extraction network for obstructive sleep apnea detection. IEEE Journal of Biomedical and Health Informatics, 25(8), 2848–2856. https://doi.org/10.1109/JBHI.2021.3050113
  6. Korompili, G., Amfilochiou, A., Kokkalas, L., Mitilineos, S. A., Tatlas, N.-A., Kouvaras, M., Kastanakis, E., Maniou, C., & Potirakis, S. M. (2021). PSG-Audio, a scored polysomnography dataset with simultaneous audio recordings for sleep apnea studies. Scientific Data, 8(1), 197. https://doi.org/10.1038/s41597-021-00977-w
  7. Anzai, T., Grandinetti, A., Katz, A. R., Hurwitz, E. L., Wu, Y. Y., & Masaki, K. (2021). Cross-ethnic comparison of the association between central sleep apnea and atrial fibrillation/flutter: The Kuakini Honolulu-Asia Aging Study and the Osteoporotic Fractures in Men (Mr.OS) study. International Journal of Cardiology. Heart & Vasculature, 35, 100834. https://doi.org/10.1016/j.ijcha.2021.100834
  8. Huang, K., Zhou, Y., Huang, Z., Wang, R., Liu, Y., Chen, P., Xu, Y., Li, G., Chen, J., & Wang, L. (2021). Associations between nocturnal continuous blood pressure fluctuations and the characteristics of oxygen desaturation in patients with obstructive sleep apnea: a pilot study. Sleep Medicine, 84, 1–7. https://doi.org/10.1016/j.sleep.2021.05.005
  9. Magnusdottir, S., Thomas, R. J., & Hilmisson, H. (2021). Can improvements in sleep quality positively affect serum adiponectin-levels in patients with obstructive sleep apnea? Sleep Medicine, 84, 324–333. https://doi.org/10.1016/j.sleep.2021.05.032
  10. Morris, J. L., Mazzotti, D. R., Gottlieb, D. J., & Hall, M. H. (2021). Sex differences within symptom subtypes of mild obstructive sleep apnea. Sleep Medicine, 84, 253–258. https://doi.org/10.1016/j.sleep.2021.06.001
  11. Guillot, A., & Thorey, V. (2021). Robustsleepnet: transfer learning for automated sleep staging at scale. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 1441–1451. https://doi.org/10.1109/TNSRE.2021.3098968
  12. Ramachandran, A., & Karuppiah, A. (2021). A survey on recent advances in machine learning based sleep apnea detection systems. Healthcare (Basel), 9(7). https://doi.org/10.3390/healthcare9070914
  13. Yang, H.-W., Garaulet, M., Li, P., Bandin, C., Lin, C., Lo, M.-T., & Hu, K. (2021). Daily Rhythm of Fractal Cardiac Dynamics Links to Weight Loss Resistance: Interaction with CLOCK 3111T/C Genetic Variant. Nutrients, 13(7). https://doi.org/10.3390/nu13072463
  14. Liu, X., Pamula, Y., Immanuel, S., Kennedy, D., Martin, J., & Baumert, M. (2021). Utilisation of machine learning to predict surgical candidates for the treatment of childhood upper airway obstruction. Sleep & Breathing = Schlaf & Atmung. https://doi.org/10.1007/s11325-021-02425-w
  15. Nasifoglu, H., & Erogul, O. (2021). Obstructive sleep apnea prediction from electrocardiogram scalograms and spectrograms using convolutional neural networks. Physiological Measurement, 42(6). https://doi.org/10.1088/1361-6579/ac0a9c
  16. Li, R., Rueschman, M., Gottlieb, D. J., Redline, S., & Sofer, T. (2021). A composite sleep and pulmonary phenotype predicting hypertension. EBioMedicine, 68, 103433. https://doi.org/10.1016/j.ebiom.2021.103433
  17. Fulda, S. (2021). Periodic leg movements during sleep. Sleep Medicine Clinics, 16(2), 289–303. https://doi.org/10.1016/j.jsmc.2021.02.004
  18. Wu, Y., & Wang, L. (2021). Resampling-based confidence intervals for model-free robust inference on optimal treatment regimes. Biometrics, 77(2), 465–476. https://doi.org/10.1111/biom.13337
  19. Wallace, M. L., Coleman, T. S., Mentch, L. K., Buysse, D. J., Graves, J. L., Hagen, E. W., Hall, M. H., Stone, K. L., Redline, S., & Peppard, P. E. (2021). Physiological sleep measures predict time to 15-year mortality in community adults: Application of a novel machine learning framework. Journal of Sleep Research, e13386. https://doi.org/10.1111/jsr.13386
  20. Drews, H. J., & Drews, A. (2021). Couple Relationships Are Associated With Increased REM Sleep-A Proof-of-Concept Analysis of a Large Dataset Using Ambulatory Polysomnography. Frontiers in Psychiatry, 12, 641102. https://doi.org/10.3389/fpsyt.2021.641102
  21. Eldele, E., Chen, Z., Liu, C., Wu, M., Kwoh, C.-K., Li, X., & Guan, C. (2021). An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 809–818. https://doi.org/10.1109/TNSRE.2021.3076234
  22. Combs, D., Hsu, C.-H., Bailey, O., Patel, S. I., Mashaqi, S., Estep, L., Provencio-Dean, N., Lopez, S., & Parthasarathy, S. (2021). Differences in sleep timing and related effects between African Americans and non-Hispanic Whites. Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine, 17(5), 897–908. https://doi.org/10.5664/jcsm.9060
  23. Hathaway, E., Morgan, K., Carson, M., Shusterman, R., Fernandez-Corazza, M., Luu, P., & Tucker, D. M. (2021). Transcranial Electrical Stimulation targeting limbic cortex increases the duration of human deep sleep. Sleep Medicine, 81, 350–357. https://doi.org/10.1016/j.sleep.2021.03.001
  24. Karhu, T., Myllymaa, S., Nikkonen, S., Mazzotti, D. R., Töyräs, J., & Leppänen, T. (2021). Longer and deeper desaturations are associated with the worsening of mild sleep apnea: the sleep heart health study. Frontiers in Neuroscience, 15, 657126. https://doi.org/10.3389/fnins.2021.657126
  25. Wen, W. (2021). Sleep quality detection based on EEG signals using transfer support vector machine algorithm. Frontiers in Neuroscience, 15, 670745. https://doi.org/10.3389/fnins.2021.670745
  26. Baek, J., Banker, M., Jansen, E. C., She, X., Peterson, K. E., Pitchford, E. A., & Song, P. X. K. (2021). An Efficient Segmentation Algorithm to Estimate Sleep Duration from Actigraphy Data. Statistics in Biosciences. https://doi.org/10.1007/s12561-021-09309-3
  27. Perslev, M., Darkner, S., Kempfner, L., Nikolic, M., Jennum, P. J., & Igel, C. (2021). U-Sleep: resilient high-frequency sleep staging. Npj Digital Medicine, 4(1), 72. https://doi.org/10.1038/s41746-021-00440-5
  28. Guillet, A., Arneodo, A., & Argoul, F. (2021). Tracking Rhythms Coherence From Polysomnographic Records: A Time-Frequency Approach. Frontiers in Applied Mathematics and Statistics, 7. https://doi.org/10.3389/fams.2021.624456
  29. Hou, F., Zhang, L., Qin, B., Gaggioni, G., Liu, X., & Vandewalle, G. (2021). Changes in EEG permutation entropy in the evening and in the transition from wake to sleep. Sleep, 44(4). https://doi.org/10.1093/sleep/zsaa226
  30. German, C., Makarem, N., Fanning, J., Redline, S., Elfassy, T., McClain, A., Abdalla, M., Aggarwal, B., Allen, N., & Carnethon, M. (2021). Sleep, sedentary behavior, physical activity, and cardiovascular health: MESA. Medicine and Science in Sports and Exercise, 53(4), 724–731. https://doi.org/10.1249/MSS.0000000000002534
  31. Kang, J. M., Cho, S.-E., Na, K.-S., & Kang, S.-G. (2021). Spectral Power Analysis of Sleep Electroencephalography in Subjects with Different Severities of Obstructive Sleep Apnea and Healthy Controls. Nature and Science of Sleep, 13, 477–486. https://doi.org/10.2147/NSS.S295742
  32. Liu, C., Tan, B., Fu, M., Li, J., Wang, J., Hou, F., & Yang, A. (2021). Automatic sleep staging with a single-channel EEG based on ensemble empirical mode decomposition. Physica A: Statistical Mechanics and Its Applications, 567, 125685. https://doi.org/10.1016/j.physa.2020.125685
  33. Rivera, M. J., Teruel, M. A., Maté, A., & Trujillo, J. (2021). Diagnosis and prognosis of mental disorders by means of EEG and deep learning: a systematic mapping study. Artificial Intelligence Review. https://doi.org/10.1007/s10462-021-09986-y
  34. Li, X., Cui, L., Zhang, G.-Q., & Lhatoo, S. D. (2021). Can Big Data guide prognosis and clinical decisions in epilepsy? Epilepsia, 62 Suppl 2, S106–S115. https://doi.org/10.1111/epi.16786
  35. Wang, Q., Li, Y., Li, J., Wang, J., Shen, J., Wu, H., Guo, K., & Chen, R. (2021). Low arousal threshold: A potential bridge between OSA and periodic limb movements of sleep. Nature and Science of Sleep, 13, 229–238. https://doi.org/10.2147/NSS.S292617
  36. Ehsan, Z., Glynn, E. F., Hoffman, M. A., Ingram, D. G., & Al-Shawwa, B. (2021). Small sleepers, big data: leveraging big data to explore sleep-disordered breathing in infants and young children. Sleep, 44(2). https://doi.org/10.1093/sleep/zsaa176
  37. Ramaswamy, S. M., Weerink, M. A. S., Struys, M. M. R. F., & Nagaraj, S. B. (2021). Dexmedetomidine-induced deep sedation mimics non-rapid eye movement stage 3 sleep: large-scale validation using machine learning. Sleep, 44(2). https://doi.org/10.1093/sleep/zsaa167
  38. Baumert, M., & Stein, P. (2021). Comment on “The effect of persistent U-shaped patterns in RR night-time series on the heart rate variability complexity in healthy humans”. Physiological Measurement, 42(1), 018002. https://doi.org/10.1088/1361-6579/abd98d
  39. Soliński, M., Kuklik, P., Gierałtowski, J., Baranowski, R., Graff, B., & Żebrowski, J. (2021). Reply to comment on “The effect of persistent U-shaped patterns in RR night-time series on the heart rate variability complexity in healthy humans”. Physiological Measurement, 42(1), 018003. https://doi.org/10.1088/1361-6579/abd98e
  40. Uddin, M. B., Chow, C. M., Ling, S. H., & Su, S. W. (2021). A novel algorithm for automatic diagnosis of sleep apnea from airflow and oximetry signals. Physiological Measurement, 42(1), 015001. https://doi.org/10.1088/1361-6579/abd238
  41. Rachakonda, L., Bapatla, A. K., Mohanty, S. P., & Kougianos, E. (2021). SaYoPillow: Blockchain-Integrated Privacy-Assured IoMT Framework for Stress Management Considering Sleeping Habits. IEEE Transactions on Consumer Electronics, 67(1), 20–29. https://doi.org/10.1109/TCE.2020.3043683
  42. Hartmann, S., Bruni, O., Ferri, R., Redline, S., & Baumert, M. (2021). Cyclic alternating pattern in children with obstructive sleep apnea and its relationship with adenotonsillectomy, behavior, cognition, and quality of life. Sleep, 44(1). https://doi.org/10.1093/sleep/zsaa145
  43. Olesen, A. N., Jørgen Jennum, P., Mignot, E., & Sorensen, H. B. D. (2021). Automatic sleep stage classification with deep residual networks in a mixed-cohort setting. Sleep, 44(1). https://doi.org/10.1093/sleep/zsaa161
  44. Yan, R., Li, F., Zhou, D. D., Ristaniemi, T., & Cong, F. (2021). Automatic sleep scoring: A deep learning architecture for multi-modality time series. Journal of Neuroscience Methods, 348, 108971. https://doi.org/10.1016/j.jneumeth.2020.108971
  45. Levy, J., Álvarez, D., Rosenberg, A. A., Alexandrovich, A., Del Campo, F., & Behar, J. A. (2021). Digital oximetry biomarkers for assessing respiratory function: standards of measurement, physiological interpretation, and clinical use. Npj Digital Medicine, 4(1), 1. https://doi.org/10.1038/s41746-020-00373-5
  46. Djonlagic, I., Mariani, S., Fitzpatrick, A. L., Van Der Klei, V. M. G. T. H., Johnson, D. A., Wood, A. C., Seeman, T., Nguyen, H. T., Prerau, M. J., Luchsinger, J. A., Dzierzewski, J. M., Rapp, S. R., Tranah, G. J., Yaffe, K., Burdick, K. E., Stone, K. L., Redline, S., & Purcell, S. M. (2021). Macro and micro sleep architecture and cognitive performance in older adults. Nature Human Behaviour, 5(1), 123–145. https://doi.org/10.1038/s41562-020-00964-y
  47. Elgart, M., Redline, S., & Sofer, T. (2021). Machine and deep learning in molecular and genetic aspects of sleep research. Neurotherapeutics, 18(1), 228–243. https://doi.org/10.1007/s13311-021-01014-9
  48. Li, F., Yan, R., Mahini, R., Wei, L., Wang, Z., Mathiak, K., Liu, R., & Cong, F. (2021). End-to-end sleep staging using convolutional neural network in raw single-channel EEG. Biomedical Signal Processing and Control, 63, 102203. https://doi.org/10.1016/j.bspc.2020.102203
  49. Guillet, A., Arneodo, A., & Argoul, F. (2021). Data_Sheet_1_Tracking Rhythms Coherence From Polysomnographic Records: A Time-Frequency Approach.PDF.
  50. Leelaarporn, P., Wachiraphan, P., Kaewlee, T., Udsa, T., Chaisaen, R., Choksatchawathi, T., Laosirirat, R., Lakhan, P., Natnithikarat, P., Thanontip, K., Sangnark, S., Chen, W., Mukhopadhyay, S. C., & Wilaiprasitporn, T. (2021). Sensor-Driven Achieving of Smart Living: A Review. IEEE Sensors Journal, 1–1. https://doi.org/10.1109/JSEN.2021.3059304
  51. Nikolaidis, K., Plagemann, T., Kristiansen, S., Goebel, V., & Kankanhalli, M. (2021). Using Under-Trained Deep Ensembles to Learn Under Extreme Label Noise: A Case Study for Sleep Apnea Detection. IEEE Access : Practical Innovations, Open Solutions, 9, 45919–45934. https://doi.org/10.1109/ACCESS.2021.3067455
  52. Yan, R., Li, F., Zhou, D., Ristaniemi, T., Cong, F., & IEEE. (2021). A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series. 1090–1094.
  53. Yu, H., & Hu, M. (2021). Epilepsy SEEG data classification based on domain adversarial learning. IEEE Access : Practical Innovations, Open Solutions, 9, 82000–82009. https://doi.org/10.1109/ACCESS.2021.3086885
  54. Haghayegh, S., Khoshnevis, S., Smolensky, M. H., Diller, K. R., & Castriotta, R. J. (2020). Deep neural network sleep scoring using combined motion and heart rate variability data. Sensors (Basel, Switzerland), 21(1). https://doi.org/10.3390/s21010025
  55. Loh, H. W., Ooi, C. P., Vicnesh, J., Oh, S. L., Faust, O., Gertych, A., & Acharya, U. R. (2020). Automated detection of sleep stages using deep learning techniques: A systematic review of the last decade (2010–2020). Applied Sciences, 10(24), 8963. https://doi.org/10.3390/app10248963
  56. Tao, S., Zeng, N., Hands, I., Hurt-Mueller, J., Durbin, E. B., Cui, L., & Zhang, G.-Q. (2020). Web-based interactive mapping from data dictionaries to ontologies, with an application to cancer registry. BMC Medical Informatics and Decision Making, 20(Suppl 10), 271. https://doi.org/10.1186/s12911-020-01288-7
  57. Li, A., Chen, S., Quan, S. F., Powers, L. S., & Roveda, J. M. (2020). A deep learning-based algorithm for detection of cortical arousal during sleep. Sleep, 43(12). https://doi.org/10.1093/sleep/zsaa120
  58. Sridhar, N., Shoeb, A., Stephens, P., Kharbouch, A., Shimol, D. B., Burkart, J., Ghoreyshi, A., & Myers, L. (2020). Deep learning for automated sleep staging using instantaneous heart rate. Npj Digital Medicine, 3(1), 106. https://doi.org/10.1038/s41746-020-0291-x
  59. Abou Jaoude, M., Sun, H., Pellerin, K. R., Pavlova, M., Sarkis, R. A., Cash, S. S., Westover, M. B., & Lam, A. D. (2020). Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning. Sleep, 43(11). https://doi.org/10.1093/sleep/zsaa112
  60. Chocron, A., Efraim, R., Mandel, F., Rueschman, M., Palmius, N., Penzel, T., Elbaz, M., & Behar, J. A. (2020). Machine learning for nocturnal mass diagnosis of atrial fibrillation in a population at risk of sleep-disordered breathing. Physiological Measurement, 41(10), 104001. https://doi.org/10.1088/1361-6579/abb8bf
  61. Hesse, J., Malhan, D., Yalҫin, M., Aboumanify, O., Basti, A., & Relógio, A. (2020). An Optimal Time for Treatment-Predicting Circadian Time by Machine Learning and Mathematical Modelling. Cancers, 12(11). https://doi.org/10.3390/cancers12113103
  62. Calderón, J. M., Álvarez-Pitti, J., Cuenca, I., Ponce, F., & Redon, P. (2020). Development of a minimally invasive screening tool to identify obese pediatric population at risk of obstructive sleep apnea/hypopnea syndrome. Bioengineering, 7(4). https://doi.org/10.3390/bioengineering7040131
  63. Lechat, B., Hansen, K., Catcheside, P., & Zajamsek, B. (2020). Beyond K-complex binary scoring during sleep: probabilistic classification using deep learning. Sleep, 43(10). https://doi.org/10.1093/sleep/zsaa077
  64. Mayer, C. S., Williams, N., & Huser, V. (2020). Analysis of data dictionary formats of HIV clinical trials. Plos One, 15(10), e0240047. https://doi.org/10.1371/journal.pone.0240047
  65. Isaiah, A., Spanier, A. J., Grattan, L. M., Wang, Y., & Pereira, K. D. (2020). Predictors of behavioral changes after adenotonsillectomy in pediatric obstructive sleep apnea: A secondary analysis of a randomized clinical trial. JAMA Otolaryngology-- Head & Neck Surgery, 146(10), 900–908. https://doi.org/10.1001/jamaoto.2020.2432
  66. Berthomier, C., Muto, V., Schmidt, C., Vandewalle, G., Jaspar, M., Devillers, J., Gaggioni, G., Chellappa, S. L., Meyer, C., Phillips, C., Salmon, E., Berthomier, P., Prado, J., Benoit, O., Bouet, R., Brandewinder, M., Mattout, J., & Maquet, P. (2020). Exploring scoring methods for research studies: Accuracy and variability of visual and automated sleep scoring. Journal of Sleep Research, 29(5), e12994. https://doi.org/10.1111/jsr.12994
  67. Qu, W., Wang, Z., Hong, H., Chi, Z., Feng, D. D., Grunstein, R., & Gordon, C. (2020). A residual based attention model for EEG based sleep staging. IEEE Journal of Biomedical and Health Informatics, 24(10), 2833–2843. https://doi.org/10.1109/JBHI.2020.2978004
  68. Fallmann, S., Chen, L., & Chen, F. (2020). Enhanced multi-source data analysis for personalized sleep-wake pattern recognition and sleep parameter extraction. Personal and Ubiquitous Computing. https://doi.org/10.1007/s00779-020-01445-9
  69. Lhatoo, S. D., Bernasconi, N., Blumcke, I., Braun, K., Buchhalter, J., Denaxas, S., Galanopoulou, A., Josephson, C., Kobow, K., Lowenstein, D., Ryvlin, P., Schulze-Bonhage, A., Sahoo, S. S., Thom, M., Thurman, D., Worrell, G., Zhang, G.-Q., & Wiebe, S. (2020). Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy. Epilepsia, 61(9), 1869–1883. https://doi.org/10.1111/epi.16633
  70. Song, Y., Sun, F., Redline, S., & Wang, R. (2020). Random-effects meta-analysis of combined outcomes based on reconstructions of individual patient data. Research Synthesis Methods, 11(5), 594–616. https://doi.org/10.1002/jrsm.1406
  71. Azarbarzin, A., Sands, S. A., Taranto-Montemurro, L., Vena, D., Sofer, T., Kim, S.-W., Stone, K. L., White, D. P., Wellman, A., & Redline, S. (2020). The Sleep Apnea-Specific Hypoxic Burden Predicts Incident Heart Failure. Chest, 158(2), 739–750. https://doi.org/10.1016/j.chest.2020.03.053
  72. Hartmann, S., Bruni, O., Ferri, R., Redline, S., & Baumert, M. (2020). Characterization of cyclic alternating pattern during sleep in older men and women using large population studies. Sleep, 43(7). https://doi.org/10.1093/sleep/zsaa016
  73. Sun, H., Ganglberger, W., Panneerselvam, E., Leone, M. J., Quadri, S. A., Goparaju, B., Tesh, R. A., Akeju, O., Thomas, R. J., & Westover, M. B. (2020). Sleep staging from electrocardiography and respiration with deep learning. Sleep, 43(7). https://doi.org/10.1093/sleep/zsz306
  74. Li, X., Zhang, Y., Jiang, F., & Zhao, H. (2020). A novel machine learning unsupervised algorithm for sleep/wake identification using actigraphy. Chronobiology International, 37(7), 1002–1015. https://doi.org/10.1080/07420528.2020.1754848
  75. Olesen, A. N., Jennum, P., Mignot, E., & Sorensen, H. B. D. (2020). Deep transfer learning for improving single-EEG arousal detection. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020, 99–103. https://doi.org/10.1109/EMBC44109.2020.9176723
  76. Sadeghi, R., Banerjee, T., & Hughes, J. (2020). Predicting sleep quality in osteoporosis patients using electronic health records and heart rate variability. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020, 5571–5574. https://doi.org/10.1109/EMBC44109.2020.9175629
  77. Sadr, N., Bin, Y. S., Sutherland, K., Cook, K., Dissanayake, H., Cistulli, P., & Chazal, P. de. (2020). Is Cumulative Time of Oxygen Desaturation a Better Predictor of Cardiovascular Mortality than Apnoea Hypopnoea Index? Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020, 2788–2791. https://doi.org/10.1109/EMBC44109.2020.9176554
  78. Kwon, Y., Mariani, S., Gadi, S. R., Jacobs, D. R., Punjabi, N. M., Reid, M. L., Azarbarzin, A., Wellman, A. D., & Redline, S. (2020). Characterization of lung-to-finger circulation time in sleep study assessment: the Multi-Ethnic Study of Atherosclerosis. Physiological Measurement, 41(6), 065004. https://doi.org/10.1088/1361-6579/ab8e12
  79. Brink-Kjaer, A., Olesen, A. N., Peppard, P. E., Stone, K. L., Jennum, P., Mignot, E., & Sorensen, H. B. D. (2020). Automatic detection of cortical arousals in sleep and their contribution to daytime sleepiness. Clinical Neurophysiology, 131(6), 1187–1203. https://doi.org/10.1016/j.clinph.2020.02.027
  80. Anzai, T., Grandinetti, A., Katz, A. R., Hurwitz, E. L., Wu, Y. Y., & Masaki, K. (2020). Association between central sleep apnea and atrial fibrillation/flutter in Japanese-American men: The Kuakini Honolulu Heart Program (HHP) and Honolulu-Asia Aging Study (HAAS). Journal of Electrocardiology, 61, 10–17. https://doi.org/10.1016/j.jelectrocard.2020.05.005
  81. Olsen, M., Mignot, E., Jennum, P. J., & Sorensen, H. B. D. (2020). Robust, ECG-based detection of Sleep-disordered breathing in large population-based cohorts. Sleep, 43(5). https://doi.org/10.1093/sleep/zsz276
  82. Belur Nagaraj, S., Ramaswamy, S. M., Weerink, M. A. S., & Struys, M. M. R. F. (2020). Predicting Deep Hypnotic State From Sleep Brain Rhythms Using Deep Learning: A Data-Repurposing Approach. Anesthesia and Analgesia, 130(5), 1211–1221. https://doi.org/10.1213/ANE.0000000000004651
  83. Erdamar, A., & Aksahin, M. F. (2020). Quantitative sleep EEG synchronization analysis for automatic arousals detection. Biomedical Signal Processing and Control, 59, 101895. https://doi.org/10.1016/j.bspc.2020.101895
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