| Acute pain intensity monitoring with the classification of multiple physiological parameters M Jiang, R Mieronkoski, E Syrjälä, A Anzanpour, V Terävä, AM Rahmani, ... Journal of clinical monitoring and computing 33, 493-507, 2019 | 76 | 2019 |
| Robust ECG R-peak detection using LSTM J Laitala, M Jiang, E Syrjälä, EK Naeini, A Airola, AM Rahmani, ND Dutt, ... Proceedings of the 35th annual ACM symposium on applied computing, 1104-1111, 2020 | 62 | 2020 |
| Developing a pain intensity prediction model using facial expression: A feasibility study with electromyography R Mieronkoski, E Syrjälä, M Jiang, A Rahmani, T Pahikkala, P Liljeberg, ... PloS one 15 (7), e0235545, 2020 | 19 | 2020 |
| Prospective study evaluating a pain assessment tool in a postoperative environment: protocol for algorithm testing and enhancement EK Naeini, M Jiang, E Syrjälä, MD Calderon, R Mieronkoski, K Zheng, ... JMIR Research Protocols 9 (7), e17783, 2020 | 13 | 2020 |
| Skin conductance response to gradual-increasing experimental pain E Syrjälä, M Jiang, T Pahikkala, S Salanterä, P Liljeberg 2019 41st Annual International Conference of the IEEE Engineering in …, 2019 | 11 | 2019 |
| Could the muscle corrugator supercilii serve as a signal of pain intensity? E Syrjälä, R Mieronkoski, M Jiang, N Hagelberg, S Salanterä, P Liljeberg Scandinavian Journal of Pain 18, 2018 | | 2018 |
| Classification of experimental acute pain intensity with multimodal biosignals E Syrjälä fi= Turun yliopisto| en= University of Turku|, 0 | | |
