{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T12:57:42Z","timestamp":1790686662523,"version":"4.1.0"},"reference-count":45,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Physiological-signal-based emotion recognition has received attention in human\u2013computer interaction. Electrocardiograms (ECGs) are readily acquired, and short windows contain repeated beat morphology and beat-wise variations that single-window encoding struggles to separate. Multiscale morphology modeling and training-sample diversity remain limited. We therefore propose Complementary Feature Fusion Dual-Path (CFF-DP), an ECG emotion recognition framework using beat-level complementary feature fusion, with three components: (1) a dual-path framework, where the morphology-stable path constructs representative beats with window-adaptive Gaussian weights, while the morphology-difference path combines beat-wise encoding, positional encoding, and additive attention; gated fusion integrates representations; (2) adaptive dilated convolution (ADConv), which extracts multiscale beat-morphology features using shared kernels and input-dependent scale weights; and (3) deviation-based beat-oriented augmentation (DBOA), which adjusts real-noise injection probability and target signal-to-noise ratio according to morphological deviation. CFF-DP achieved 43.39% mean Macro-F1, close to the best comparator, with the fewest multiply\u2013accumulate operations in five-seed WESAD three-class leave-one-subject-out (LOSO) evaluation, although recognition mainly distinguishes stress, with limited amusement discrimination. With short-gap calibration and testing within the same recording, fine-tuning using 40 s per class achieved 76.72% Macro-F1, exceeding six comparators. Binary DREAMER LOSO retained WESAD hyperparameters: valence Macro-F1 exceeded six comparators, whereas arousal fell below four; both remained below uniform random baselines, indicating limited recognition under current experimental conditions. The framework combines computational efficiency with within-record personalization advantages, although cross-subject recognition remains limited.<\/jats:p>","DOI":"10.3390\/s26196169","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T12:09:25Z","timestamp":1790683765000},"page":"6169","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ECG-Based Emotion Recognition Using Beat-Level Complementary Feature Fusion"],"prefix":"10.3390","volume":"26","author":[{"given":"Guandi","family":"Peng","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Ying","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,29]]},"reference":[{"key":"ref_1","first-page":"165","article-title":"A review of emotions, behavior and cognition","volume":"3","author":"Ali","year":"2023","journal-title":"J. Biomed. Sustain. Healthc. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"69111","DOI":"10.1007\/s11042-023-18096-6","article-title":"Current status and trends of technology, methods, and applications of Human-Computer Intelligent Interaction (HCII): A bibliometric research","volume":"83","author":"Ding","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Schlicher, M., Li, Y., Murthy, S.M.K., Sun, Q., and Schuller, B.W. (2025). Emotionally adaptive support: A narrative review of affective computing for mental health. Front. Digit. Health, 7.","DOI":"10.3389\/fdgth.2025.1657031"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wu, W., Zuo, E., Zhang, W., and Meng, X. (2024). Multi-physiological signal fusion for objective emotion recognition in educational human-computer interaction. Front. Public Health, 12.","DOI":"10.3389\/fpubh.2024.1492375"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Udahemuka, G., Djouani, K., and Kurien, A.M. (2024). Multimodal emotion recognition using visual, vocal and physiological signals: A review. Appl. Sci., 14.","DOI":"10.3390\/app14178071"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"124371","DOI":"10.1016\/j.eswa.2024.124371","article-title":"Emotion classification using electrocardiogram and machine learning: A study on the effect of windowing techniques","volume":"254","author":"Govarthan","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107039","DOI":"10.1016\/j.bspc.2024.107039","article-title":"Human emotion recognition using machine learning techniques based on the physiological signal","volume":"100","author":"Kumar","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103580","DOI":"10.1016\/j.bspc.2022.103580","article-title":"A new data augmentation convolutional neural network for human emotion recognition based on ECG signals","volume":"75","author":"Nita","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dar, M.N., Akram, M.U., Khawaja, S.G., and Pujari, A.N. (2020). CNN and LSTM-based emotion charting using physiological signals. Sensors, 20.","DOI":"10.3390\/s20164551"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, Z., and Wang, Y. (2025). Emotion recognition based on multimodal physiological electrical signals. Front. Neurosci., 19.","DOI":"10.3389\/fnins.2025.1512799"},{"key":"ref_11","first-page":"2526912","article-title":"Deep learning-based automated emotion recognition using multimodal physiological signals and time-frequency methods","volume":"73","author":"Kumar","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3948","DOI":"10.1109\/TMI.2026.3689332","article-title":"Multi-View Hilbert Curve-Based Hierarchical Information Aggregation for Incomplete Multimodal Alzheimer\u2019s Disease Diagnosis","volume":"45","author":"Liu","year":"2026","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4940","DOI":"10.1109\/TPAMI.2025.3546356","article-title":"Reliable Representation Learning for Incomplete Multi-View Missing Multi-Label Classification","volume":"47","author":"Liu","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105907","DOI":"10.1016\/j.bspc.2023.105907","article-title":"ECG-based emotion recognition using random convolutional kernel method","volume":"91","author":"Fang","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., and Yang, J. (2019, January 16\u201320). Selective Kernel Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00060"},{"key":"ref_16","unstructured":"Yang, B., Bender, G., Le, Q.V., and Ngiam, J. (2019, January 8\u201314). CondConv: Conditionally Parameterized Convolutions for Efficient Inference. Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver, BC, Canada."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., and Liu, Z. (2020, January 13\u201319). Dynamic Convolution: Attention over Convolution Kernels. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01104"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Son, H., Lee, J., Cho, S., and Lee, S. (2021, January 10\u201317). Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00264"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"109729","DOI":"10.1016\/j.bspc.2026.109729","article-title":"Spatially Enhanced Pyramid Split Attention for Improved ECG-Based Emotion Recognition","volume":"118","author":"Pan","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Guo, G., Gao, P., Zheng, X., and Ji, C. (2022, January 6\u20138). Multimodal emotion recognition using CNN-SVM with data augmentation. Proceedings of the 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Las Vegas, NV, USA.","DOI":"10.1109\/BIBM55620.2022.9994936"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"10313","DOI":"10.1007\/s13369-022-07585-9","article-title":"Augmenting ECG data with multiple filters for a better emotion recognition system","volume":"48","author":"Hasnul","year":"2023","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., Rivolta, M.W., Badilini, F., and Sassi, R. (2023). A systematic survey of data augmentation of ECG signals for AI applications. Sensors, 23.","DOI":"10.3390\/s23115237"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1016\/S1350-4533(99)00098-3","article-title":"Filtering of electromyogram artifacts from the electrocardiogram","volume":"21","author":"Christov","year":"1999","journal-title":"Med. Eng. Phys."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1186\/1475-925X-13-149","article-title":"Impedance spectroscopy of changes in skin-electrode impedance induced by motion","volume":"13","author":"Hyttinen","year":"2014","journal-title":"BioMed. Eng. Online"},{"key":"ref_25","unstructured":"Moody, G.B., Muldrow, W.K., and Mark, R.G. (1984, January 18\u201321). A noise stress test for arrhythmia detectors. Proceedings of the Computers in Cardiology, Salt Lake City, UT, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1038\/s41746-023-00869-w","article-title":"Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices","volume":"6","author":"Khunte","year":"2023","journal-title":"npj Digit. Med."},{"key":"ref_27","unstructured":"Arevalo, J., Solorio, T., Montes-y-G\u00f3mez, M., and Gonz\u00e1lez, F.A. (2017, January 24\u201326). Gated Multimodal Units for Information Fusion. Proceedings of the International Conference on Learning Representations Workshop, Toulon, France."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., and Van Laerhoven, K. (2018, January 16\u201320). Introducing WESAD, a multimodal dataset for wearable stress and affect detection. Proceedings of the 20th ACM International Conference on Multimodal Interaction (ICMI), Boulder, CO, USA.","DOI":"10.1145\/3242969.3242985"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/JBHI.2017.2688239","article-title":"DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals from Wireless Low-Cost Off-the-Shelf Devices","volume":"22","author":"Katsigiannis","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TBME.1985.325532","article-title":"A real-time QRS detection algorithm","volume":"BME-32","author":"Pan","year":"1985","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"108525","DOI":"10.1016\/j.compbiomed.2024.108525","article-title":"Explaining deep learning for ECG analysis: Building blocks for auditing and knowledge discovery","volume":"176","author":"Wagner","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.bspc.2009.01.002","article-title":"Methods of weighted averaging of ECG signals using Bayesian inference and criterion function minimization","volume":"4","author":"Momot","year":"2009","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Krasteva, V., Stoyanov, T., Schmid, R., and Jekova, I. (2024). Delineation of 12-lead ECG representative beats using convolutional encoder\u2013decoders with residual and recurrent connections. Sensors, 24.","DOI":"10.3390\/s24144645"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1007\/s10851-024-01196-9","article-title":"Discrete approximations of Gaussian smoothing and Gaussian derivatives","volume":"66","author":"Lindeberg","year":"2024","journal-title":"J. Math. Imaging Vis."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201322). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014, January 25\u201329). Learning Phrase Representations Using RNN Encoder\u2013Decoder for Statistical Machine Translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1109\/78.650093","article-title":"Bidirectional Recurrent Neural Networks","volume":"45","author":"Schuster","year":"1997","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_39","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention Is All You Need. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, USA."},{"key":"ref_40","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2015, January 7\u20139). Neural Machine Translation by Jointly Learning to Align and Translate. Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2721","DOI":"10.1109\/TBME.2020.2969719","article-title":"Real-Time Quality Assessment of Long-Term ECG Signals Recorded by Wearables in Free-Living Conditions","volume":"67","author":"Smital","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"162204","DOI":"10.1007\/s11432-025-4830-4","article-title":"CFAN: Convolutional Frequency-Attention Network for ECG-Based Emotion Recognition","volume":"69","author":"Ye","year":"2026","journal-title":"Sci. China Inf. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"106938","DOI":"10.1016\/j.compbiomed.2023.106938","article-title":"A new deep convolutional neural network incorporating attentional mechanisms for ECG emotion recognition","volume":"159","author":"Fan","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_45","unstructured":"Tan, M., and Le, Q.V. (2019, January 9\u201315). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6169\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T12:19:19Z","timestamp":1790684359000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6169"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,29]]},"references-count":45,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["s26196169"],"URL":"https:\/\/doi.org\/10.3390\/s26196169","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,29]]}}}