{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T16:16:53Z","timestamp":1781885813465,"version":"3.54.5"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T00:00:00Z","timestamp":1685404800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T00:00:00Z","timestamp":1685404800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s11760-023-02606-y","type":"journal-article","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T17:02:17Z","timestamp":1685466137000},"page":"3783-3791","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Emotion detection from ECG signals with different learning algorithms and automated feature engineering"],"prefix":"10.1007","volume":"17","author":[{"given":"Faruk Enes","family":"O\u011fuz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmet","family":"Alkan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thorsten","family":"Sch\u00f6ler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"key":"2606_CR1","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/T-AFFC.2011.25","volume":"3","author":"M Soleymani","year":"2012","unstructured":"Soleymani, M., Lichtenauer, J., Pun, T., Pantic, M.: A multimodal database for affect recognition and implicit tagging. IEEE Trans. Affect. Comput. 3, 42\u201355 (2012)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"2606_CR2","doi-asserted-by":"crossref","unstructured":"Smitha, K.G., Vinod, K.P.: Hardware efficient FPGA implementation of emotion recognizer for autistic children. In: IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), pp. 1\u20134 (2013)","DOI":"10.1109\/CONECCT.2013.6469294"},{"key":"2606_CR3","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1007\/BF02344719","volume":"42","author":"KH Kim","year":"2004","unstructured":"Kim, K.H., Bang, S.W., Kim, S.R.: Emotion recognition system using short-termmonitoring of physiological signals. Med. Biol. Eng. Comput. 42, 419\u2013427 (2004). https:\/\/doi.org\/10.1007\/BF02344719","journal-title":"Med. Biol. Eng. Comput."},{"key":"2606_CR4","doi-asserted-by":"publisher","first-page":"2067","DOI":"10.1109\/TPAMI.2008.26","volume":"30","author":"J Kim","year":"2008","unstructured":"Kim, J., Andr\u00e9, E.: Emotion recognition based on physiological changes in musiclistening. IEEE Trans. Pattern Anal. Mach. Intell. 30, 2067\u20132083 (2008). https:\/\/doi.org\/10.1109\/TPAMI.2008.26","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2606_CR5","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1109\/34.954607","volume":"23","author":"RW Picard","year":"2001","unstructured":"Picard, R.W., Vyzas, E., Healey, J.: Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1175\u20131191 (2001). https:\/\/doi.org\/10.1109\/34.954607","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"2606_CR6","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.imavis.2012.10.002","volume":"31","author":"S Koelstra","year":"2013","unstructured":"Koelstra, S., Patras, I.: Fusion of facial expressions and EEG for implicit affective tagging. Image Vis. Comput. 31(2), 164\u2013174 (2013)","journal-title":"Image Vis. Comput."},{"key":"2606_CR7","doi-asserted-by":"crossref","unstructured":"Ferdinando, H., Sepp\u00e4nen, T., Alasaarela, E.: Comparing features from ECG pattern and HRV analysis for emotion recognition system. In: 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 1\u20136. IEEE (2016)","DOI":"10.1109\/CIBCB.2016.7758108"},{"key":"2606_CR8","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1511\/2001.28.344","volume":"89","author":"R Plutchik","year":"2001","unstructured":"Plutchik, R.: The nature of emotions: human emotions have deep evolutionaryroots, a fact that may explain their complexity and provide tools for clinicalpractice. Am. Sci. 89, 344\u2013350 (2001)","journal-title":"Am. Sci."},{"key":"2606_CR9","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1037\/0003-066X.50.5.372","volume":"50","author":"PJ Lang","year":"1995","unstructured":"Lang, P.J.: The emotion probe: studies of motivation and attention. Am. Psychol. 50, 372 (1995). https:\/\/doi.org\/10.1037\/0003-066X.50.5.372","journal-title":"Am. Psychol."},{"issue":"1","key":"2606_CR10","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1109\/TAFFC.2017.2781732","volume":"11","author":"YL Hsu","year":"2017","unstructured":"Hsu, Y.L., Wang, J.S., Chiang, W.C., Hung, C.H.: Automatic ECG-based emotion recognition in music listening. IEEE Trans. Affect. Comput. 11(1), 85\u201399 (2017)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"2606_CR11","doi-asserted-by":"crossref","unstructured":"Wiem, M.B.H., Lachiri, Z.: Emotion classification in arousal valence model using MAHNOB-HCI database. Int. J. Adv. Comput. Sci. Appl. 8(3) (2017)","DOI":"10.14569\/IJACSA.2017.080344"},{"key":"2606_CR12","unstructured":"Siddharth, S., Jung, T.-P., Sejnowski, T.J.: Utilizing deep learning towards multi-modal bio-sensing and vision-based affective computing. IEEE Trans Affect Comput. (2019)"},{"key":"2606_CR13","doi-asserted-by":"publisher","first-page":"101902","DOI":"10.1016\/j.bspc.2020.101902","volume":"59","author":"M Baghizadeh","year":"2020","unstructured":"Baghizadeh, M., Maghooli, K., Farokhi, F., Dabanloo, N.J.: A new emotion detection algorithm using extracted features of the different time-series generated from ST intervals Poincar\u00e9 map. Biomed. Signal Process. Control 59, 101902 (2020)","journal-title":"Biomed. Signal Process. Control"},{"key":"2606_CR14","unstructured":"Lichtenauer, J., Soleymani, M.: MAHNOB-HCI-tagging database. (2011)"},{"key":"2606_CR15","doi-asserted-by":"crossref","unstructured":"Zhao, Z.D., Chen, Y.Q.: A new method for removal of baseline wander and power line interference in ECG signals. In: 2006 International Conference on Machine Learning and Cybernetics, pp. 4342\u20134347. IEEE (2006)","DOI":"10.1109\/ICMLC.2006.259082"},{"key":"2606_CR16","doi-asserted-by":"publisher","first-page":"101788","DOI":"10.1016\/j.artmed.2019.101788","volume":"103","author":"AK Sangaiah","year":"2020","unstructured":"Sangaiah, A.K., Arumugam, M., Bian, G.B.: An intelligent learning approach for improving ECG signal classification and arrhythmia analysis. Artif. Intell. Med. 103, 101788 (2020)","journal-title":"Artif. Intell. Med."},{"issue":"5","key":"2606_CR17","doi-asserted-by":"publisher","first-page":"941","DOI":"10.1007\/s11760-018-1237-5","volume":"12","author":"MM Bassiouni","year":"2018","unstructured":"Bassiouni, M.M., El-Dahshan, E.S.A., Khalefa, W., Salem, A.M.: Intelligent hybrid approaches for human ECG signals identification. SIViP 12(5), 941\u2013949 (2018)","journal-title":"SIViP"},{"key":"2606_CR18","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1109\/tbme.1985.325532","volume":"32","author":"J Pan","year":"1985","unstructured":"Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32, 230\u2013236 (1985). https:\/\/doi.org\/10.1109\/tbme.1985.325532","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"2606_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-0724-0","volume-title":"Ambulation analysis in wearable ECG","author":"S Chaudhuri","year":"2009","unstructured":"Chaudhuri, S., Pawar, T.D., Duttagupta, S.: Ambulation analysis in wearable ECG. Springer (2009)"},{"issue":"4","key":"2606_CR20","doi-asserted-by":"publisher","first-page":"3499","DOI":"10.1016\/j.eswa.2009.10.037","volume":"37","author":"YC Yeh","year":"2010","unstructured":"Yeh, Y.C., Wang, W.J., Chiou, C.W.: Feature selection algorithm for ECG signals using range-overlaps method. Expert Syst. Appl. 37(4), 3499\u20133512 (2010)","journal-title":"Expert Syst. Appl."},{"issue":"11","key":"2606_CR21","doi-asserted-by":"publisher","first-page":"2850","DOI":"10.1161\/01.CIR.94.11.2850","volume":"94","author":"H Tsuji","year":"1996","unstructured":"Tsuji, H., Larson, M.G., Venditti, F.J., Manders, E.S., Evans, J.C., Feldman, C.L., Levy, D.: Impact of reduced heart rate variability on risk for cardiac events: the Framingham Heart Study. Circulation 94(11), 2850\u20132855 (1996)","journal-title":"Circulation"},{"key":"2606_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-540-35488-8","volume-title":"Feature extraction","author":"I Guyon","year":"2006","unstructured":"Guyon, I., Elisseeff, A.: An introduction to feature extraction. In: Guyon, I., Nikravesh, M., Gunn, S., Zadeh, L.A. (eds.) Feature extraction, pp. 1\u201325. Springer (2006)"},{"key":"2606_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/SECON.2016.7506650","volume":"2016","author":"J Heaton","year":"2016","unstructured":"Heaton, J.: An empirical analysis of feature engineering for predictive modeling. SoutheastCon 2016, 1\u20136 (2016). https:\/\/doi.org\/10.1109\/SECON.2016.7506650","journal-title":"SoutheastCon"},{"key":"2606_CR24","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1016\/j.scs.2018.02.016","volume":"39","author":"C Zhang","year":"2018","unstructured":"Zhang, C., Cao, L., Romagnoli, A.: On the feature engineering of building energy data mining. Sustain. Cities Soc. 39, 508\u2013518 (2018)","journal-title":"Sustain. Cities Soc."},{"key":"2606_CR25","unstructured":"https:\/\/www.mathworks.com\/help\/stats\/gencfeatures.html"},{"key":"2606_CR26","doi-asserted-by":"crossref","unstructured":"Sunnetci, K.M., Alkan, A.: Biphasic majority voting-based comparative COVID-19 diagnosis using chest X-Ray images.\u00a0Expert Syst. Appl. p. 119430 (2023)","DOI":"10.1016\/j.eswa.2022.119430"},{"key":"2606_CR27","unstructured":"Boswell, D.: An introduction to support vector machines (2002)"},{"key":"2606_CR28","doi-asserted-by":"crossref","unstructured":"Alkan, A.: Analysis of knee osteoarthritis by using fuzzy c-means clustering and SVM classification. Sci. Res. Essays 6(20), 4213\u20134219 (2001)","DOI":"10.5897\/SRE11.068"},{"key":"2606_CR29","doi-asserted-by":"publisher","first-page":"103844","DOI":"10.1016\/j.bspc.2022.103844","volume":"77","author":"KM Sunnetci","year":"2022","unstructured":"Sunnetci, K.M., Ulukaya, S., Alkan, A.: Periodontal bone loss detection based on hybrid deep learning and machine learning models with a user-friendly application. Biomed. Signal Process. Control 77, 103844 (2022)","journal-title":"Biomed. Signal Process. Control"},{"key":"2606_CR30","doi-asserted-by":"crossref","unstructured":"Caputo, M., Denker, K., Franz, M. O., Laube, P., Umlauf, G.: Support vector machines for classification of geometric primitives in point clouds. In: International Conference on Curves and Surfaces, pp. 80\u201395. Springer, Cham (2014)","DOI":"10.1007\/978-3-319-22804-4_7"},{"key":"2606_CR31","volume-title":"Fundamentals of neural networks: architectures, algorithms, and applications","author":"LV Fausett","year":"1994","unstructured":"Fausett, L.V.: Fundamentals of neural networks: architectures, algorithms, and applications. Prentice Hall (1994)"},{"issue":"2","key":"2606_CR32","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1007\/s10489-016-0767-1","volume":"45","author":"H Faris","year":"2016","unstructured":"Faris, H., Aljarah, I., Mirjalili, S.: Training feedforward neural networks using multi-verse optimizer for binary classification problems. Appl. Intell. 45(2), 322\u2013332 (2016)","journal-title":"Appl. Intell."},{"key":"2606_CR33","doi-asserted-by":"crossref","unstructured":"Alkan, A., Sahin, Y. G., Karlik, B.: A novel mobile epilepsy warning system. In: Australasian Joint Conference on Artificial Intelligence, pp. 922\u2013928. Springer, Berlin, Heidelberg (2006)","DOI":"10.1007\/11941439_99"},{"key":"2606_CR34","doi-asserted-by":"crossref","unstructured":"Chen, Y., Xu, W., Zhu, W., Ma, G., Chen, X., Wang, L.: Beat-to-beat heart rate detection based on seismocardiogram using BiLSTM network. In: 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp. 1503\u20131507 (2021)","DOI":"10.1109\/TrustCom53373.2021.00216"},{"key":"2606_CR35","doi-asserted-by":"publisher","first-page":"51522","DOI":"10.1109\/ACCESS.2019.2909919","volume":"7","author":"G Xu","year":"2019","unstructured":"Xu, G., Meng, Y., Qiu, X., Yu, Z., Wu, X.: Sentiment analysis of comment texts based on BiLSTM. IEEE Access 7, 51522\u201351532 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2909919","journal-title":"IEEE Access"},{"key":"2606_CR36","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980. (2014)"},{"issue":"2","key":"2606_CR37","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1109\/TAFFC.2016.2625250","volume":"9","author":"R Subramanian","year":"2016","unstructured":"Subramanian, R., Wache, J., Abadi, M.K., Vieriu, R.L., Winkler, S., Sebe, N.: ASCERTAIN: emotion and personality recognition using commercial sensors. IEEE Trans. Affect. Comput. 9(2), 147\u2013160 (2016)","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"1","key":"2606_CR38","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/JBHI.2017.2688239","volume":"22","author":"S Katsigiannis","year":"2017","unstructured":"Katsigiannis, S., Ramzan, N.: DREAMER: a database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices. IEEE J. Biomed. Health Inform. 22(1), 98\u2013107 (2017)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"2606_CR39","first-page":"61","volume":"42","author":"M Gjoreski","year":"2018","unstructured":"Gjoreski, M., Lustrek, M., Gams, M., Mitrevski, B.: An inter-domain study for arousal recognition from physiological signals. Informatica (Slovenia) 42, 61\u201368 (2018)","journal-title":"Informatica (Slovenia)"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02606-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-023-02606-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02606-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T11:26:04Z","timestamp":1692271564000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-023-02606-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,30]]},"references-count":39,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["2606"],"URL":"https:\/\/doi.org\/10.1007\/s11760-023-02606-y","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,30]]},"assertion":[{"value":"24 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}