Sleep stage classification with ECG and respiratory effort P Fonseca, X Long, M Radha, R Haakma, RM Aarts, J Rolink Physiological measurement 36 (10), 2027, 2015 | 198 | 2015 |
Sleep stage classification from heart-rate variability using long short-term memory neural networks M Radha, P Fonseca, A Moreau, M Ross, A Cerny, P Anderer, X Long, ... Scientific reports 9 (1), 14149, 2019 | 156 | 2019 |
Validation of photoplethysmography-based sleep staging compared with polysomnography in healthy middle-aged adults P Fonseca, T Weysen, MS Goelema, EIS Møst, M Radha, ... Sleep 40 (7), zsx097, 2017 | 146 | 2017 |
Estimating blood pressure trends and the nocturnal dip from photoplethysmography M Radha, K De Groot, N Rajani, CCP Wong, N Kobold, V Vos, P Fonseca, ... Physiological measurement 40 (2), 025006, 2019 | 85* | 2019 |
A deep transfer learning approach for wearable sleep stage classification with photoplethysmography M Radha, P Fonseca, A Moreau, M Ross, A Cerny, P Anderer, X Long, ... NPJ digital medicine 4 (1), 135, 2021 | 82 | 2021 |
Comparison of feature and classifier algorithms for online automatic sleep staging based on a single EEG signal M Radha, G Garcia-Molina, M Poel, G Tononi 2014 36th annual international conference of the IEEE engineering in …, 2014 | 81 | 2014 |
Automatic sleep staging using heart rate variability, body movements, and recurrent neural networks in a sleep disordered population P Fonseca, MM van Gilst, M Radha, M Ross, A Moreau, A Cerny, ... Sleep 43 (9), zsaa048, 2020 | 62 | 2020 |
Deep learning approach for ECG-based automatic sleep state classification in preterm infants J Werth, M Radha, P Andriessen, RM Aarts, X Long Biomedical Signal Processing and Control 56, 101663, 2020 | 32 | 2020 |
Lifestyle recommendations for hypertension through rasch-based feasibility modeling M Radha, MC Willemsen, M Boerhof, WA IJsselsteijn Proceedings of the 2016 Conference on User Modeling Adaptation and …, 2016 | 29 | 2016 |
System and method for non-invasive determination of blood pressure dip based on trained prediction models KTJ De Groot, MG Radha US Patent 11,844,593, 2023 | 23 | 2023 |
Mediated interactions and musical expression—A survey D Reidsma, M Radha, A Nijholt Digital Da Vinci: Computers in Music, 79-98, 2014 | 18* | 2014 |
Arterial path selection to measure pulse wave velocity as a surrogate marker of blood pressure M Radha, G Zhang, J Gelissen, K de Groot, R Haakma, RM Aarts Biomedical Physics & Engineering Express 3 (1), 015022, 2017 | 17 | 2017 |
Direct application of an ECG-based sleep staging algorithm on reflective photoplethysmography data decreases performance MM Van Gilst, BM Wulterkens, P Fonseca, M Radha, M Ross, A Moreau, ... BMC Research Notes 13, 1-5, 2020 | 15 | 2020 |
LSTM knowledge transfer for HRV-based sleep staging M Radha, P Fonseca, M Ross, A Cerny, P Anderer, RM Aarts arXiv preprint arXiv:1809.06221, 2018 | 14 | 2018 |
Device and method for monitoring a physiological state of a subject R Haakma, PM Fonseca, MG Radha WO Patent WO2016206921A1, 2015 | 7 | 2015 |
Optical vital signs sensor JH Gelissen, R Hilbig, AR Hilgers, MG Radha, KTJ De Groot, R Haakma US Patent App. 16/318,567, 2019 | 5 | 2019 |
0436 Deep Learning for Scoring Sleep Based on Signals Available in Home Sleep Apnea Test Studies: Cardiorespiratory Sleep Staging P Anderer, M Ross, A Cerny, M Radha, P Fonseca Sleep 43, A167, 2020 | 3 | 2020 |
Pulse wave velocity determination, for example for blood pressure monitoring KTJ De Groot, MG Radha, JH Gelissen, R Haakma US Patent 10,898,085, 2021 | 2 | 2021 |
Method and system for delivering sensory simulation to a user MG Radha, S PFUNDTNER US Patent App. 16/832,927, 2020 | 2 | 2020 |
Assisting Home-Based Resistance Training for Normotensive and Prehypertensive Individuals Using Ambient Lighting and Sonification Feedback: Sensor-Based System Evaluation M Radha, N Den Boer, MC Willemsen, T Paardekooper, WA IJsselsteijn, ... JMIR cardio 4 (1), e16354, 2020 | 1 | 2020 |