{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:17:37Z","timestamp":1780391857289,"version":"3.54.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s00521-022-07540-7","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T00:31:15Z","timestamp":1657153875000},"page":"19819-19830","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["A novel technique for stress detection from EEG signal using hybrid deep learning model"],"prefix":"10.1007","volume":"34","author":[{"given":"Lokesh","family":"Malviya","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1181-9615","authenticated-orcid":false,"given":"Sandip","family":"Mal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,7,6]]},"reference":[{"key":"7540_CR1","doi-asserted-by":"publisher","first-page":"108094","DOI":"10.1016\/j.patcog.2021.108094","volume":"119","author":"B Garc\u00eda-Mart\u00ednez","year":"2021","unstructured":"Garc\u00eda-Mart\u00ednez B, Fern\u00e1ndez-Caballero A, Alcaraz R, Mart\u00ednez-Rodrigo A (2021) Assessment of dispersion patterns for negative stress detection from electroencephalographic signals. Pattern Recogn 119:108094","journal-title":"Pattern Recogn"},{"key":"7540_CR2","doi-asserted-by":"crossref","unstructured":"Malviya L, Mal S, Lalwani P (2021) Eeg data analysis for stress detection. In: 2021 10th IEEE international conference on communication systems and network technologies (CSNT), pp 148\u2013152. IEEE","DOI":"10.1109\/CSNT51715.2021.9509713"},{"issue":"13","key":"7540_CR3","doi-asserted-by":"publisher","first-page":"1237","DOI":"10.1016\/j.jacc.2007.12.024","volume":"51","author":"JE Dimsdale","year":"2008","unstructured":"Dimsdale JE (2008) Psychological stress and cardiovascular disease. J Am Coll Cardiol 51(13):1237\u20131246","journal-title":"J Am Coll Cardiol"},{"key":"7540_CR4","doi-asserted-by":"publisher","first-page":"1258","DOI":"10.1016\/j.procs.2020.03.442","volume":"167","author":"A Priya","year":"2020","unstructured":"Priya A, Garg S, Tigga NP (2020) Predicting anxiety, depression and stress in modern life using machine learning algorithms. Proced Comput Sci 167:1258\u20131267","journal-title":"Proced Comput Sci"},{"issue":"3","key":"7540_CR5","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1037\/0022-006X.70.3.691","volume":"70","author":"P Lehrer","year":"2002","unstructured":"Lehrer P, Feldman J, Giardino N, Song H-S, Schmaling K (2002) Psychological aspects of asthma. J Consult Clin Psychol 70(3):691","journal-title":"J Consult Clin Psychol"},{"key":"7540_CR6","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1007\/s10462-021-09986-y","volume":"55","author":"MJ Rivera","year":"2022","unstructured":"Rivera MJ, Teruel MA, Mat\u00e9 A, Trujillo J (2022) Diagnosis and prognosis of mental disorders by means of EEG and deep learning: a systematic mapping study. Artif Intell Rev 55:1209\u20131251","journal-title":"Artif Intell Rev"},{"key":"7540_CR7","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1109\/TAFFC.2018.2890636","volume":"12","author":"B Garc\u00eda-Mart\u00ednez","year":"2019","unstructured":"Garc\u00eda-Mart\u00ednez B, Martinez-Rodrigo A, Alcaraz R, Fern\u00e1ndez-Caballero A (2019) A review on nonlinear methods using electroencephalographic recordings for emotion recognition. IEEE Trans Affect Comput 12:801","journal-title":"IEEE Trans Affect Comput"},{"issue":"1","key":"7540_CR8","first-page":"229","volume":"41","author":"R Sharma","year":"2020","unstructured":"Sharma R, Chopra K (2020) Eeg signal analysis and detection of stress using classification techniques. J Inf Optim Sci 41(1):229\u2013238","journal-title":"J Inf Optim Sci"},{"key":"7540_CR9","doi-asserted-by":"publisher","first-page":"101989","DOI":"10.1016\/j.bspc.2020.101989","volume":"60","author":"DD Chakladar","year":"2020","unstructured":"Chakladar DD, Dey S, Roy PP, Dogra DP (2020) Eeg-based mental workload estimation using deep blstm-lstm network and evolutionary algorithm. Biomed Signal Process Control 60:101989","journal-title":"Biomed Signal Process Control"},{"key":"7540_CR10","doi-asserted-by":"publisher","first-page":"37","DOI":"10.3389\/fnbot.2019.00037","volume":"13","author":"X Xing","year":"2019","unstructured":"Xing X, Li Z, Xu T, Shu L, Hu B, Xu X (2019) Sae+ lstm: a new framework for emotion recognition from multi-channel EEG. Front Neurorobot 13:37","journal-title":"Front Neurorobot"},{"key":"7540_CR11","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.compbiomed.2019.02.015","volume":"107","author":"A Asif","year":"2019","unstructured":"Asif A, Majid M, Anwar SM (2019) Human stress classification using eeg signals in response to music tracks. Comput Biol Med 107:182\u2013196","journal-title":"Comput Biol Med"},{"issue":"1","key":"7540_CR12","first-page":"25","volume":"8","author":"AR Gaurav","year":"2018","unstructured":"Gaurav AR, Kumar V (2018) Eeg-metric based mental stress detection. Netw Biol 8(1):25\u201334","journal-title":"Netw Biol"},{"key":"7540_CR13","first-page":"43","volume":"2018","author":"H Jebelli","year":"2018","unstructured":"Jebelli H, Khalili MM, Hwang S, Lee S (2018) A supervised learning-based construction workers\u2019 stress recognition using a wearable electroencephalography (eeg) device. Const Res Congress 2018:43\u201353","journal-title":"Const Res Congress"},{"issue":"23","key":"7540_CR14","doi-asserted-by":"publisher","first-page":"3037","DOI":"10.3390\/electronics10233037","volume":"10","author":"M\u00c1 Luj\u00e1n","year":"2021","unstructured":"Luj\u00e1n M\u00c1, Jimeno MV, Mateo Sotos J, Ricarte JJ, Borja AL (2021) A survey on eeg signal processing techniques and machine learning: applications to the neurofeedback of autobiographical memory deficits in schizophrenia. Electronics 10(23):3037","journal-title":"Electronics"},{"issue":"18","key":"7540_CR15","doi-asserted-by":"publisher","first-page":"6300","DOI":"10.3390\/s21186300","volume":"21","author":"A Hag","year":"2021","unstructured":"Hag A, Handayani D, Pillai T, Mantoro T, Kit MH, Al-Shargie F (2021) Eeg mental stress assessment using hybrid multi-domain feature sets of functional connectivity network and time-frequency features. Sensors 21(18):6300","journal-title":"Sensors"},{"issue":"8","key":"7540_CR16","doi-asserted-by":"publisher","first-page":"1849","DOI":"10.3390\/s19081849","volume":"19","author":"YS Can","year":"2019","unstructured":"Can YS, Chalabianloo N, Ekiz D, Ersoy C (2019) Continuous stress detection using wearable sensors in real life: algorithmic programming contest case study. Sensors 19(8):1849","journal-title":"Sensors"},{"issue":"spec01","key":"7540_CR17","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1142\/S0218339010003640","volume":"18","author":"SA Hosseini","year":"2010","unstructured":"Hosseini SA, Khalilzadeh MA, Changiz S (2010) Emotional stress recognition system for affective computing based on bio-signals. J Biol Syst 18(spec01):101\u2013114","journal-title":"J Biol Syst"},{"issue":"7","key":"7540_CR18","doi-asserted-by":"publisher","first-page":"1886","DOI":"10.3390\/s20071886","volume":"20","author":"SMU Saeed","year":"2020","unstructured":"Saeed SMU, Anwar SM, Khalid H, Majid M, Bagci U (2020) Eeg based classification of long-term stress using psychological labeling. Sensors 20(7):1886","journal-title":"Sensors"},{"key":"7540_CR19","unstructured":"Vanitha V,  Krishnan P (2016) Real time stress detection system based on EEG signals. Biomed Res-Tokyo, 271-275"},{"key":"7540_CR20","unstructured":"Nath D, Singh M, Sethia D, Kalra D, Indu S, et al. (2020) An efficient approach to eeg-based emotion recognition using lstm network. In: 2020 16th IEEE international colloquium on signal processing & its applications (CSPA), pp 88\u201392. IEEE"},{"key":"7540_CR21","doi-asserted-by":"crossref","unstructured":"Bird JJ, Ekart A, Buckingham CD, Faria DR (2019) Mental emotional sentiment classification with an eeg-based brain-machine interface. In: Proceedings of the international conference on digital image and signal processing (DISP\u201919)","DOI":"10.1109\/IS.2018.8710576"},{"issue":"3","key":"7540_CR22","first-page":"290","volume":"24","author":"S Zhang","year":"2021","unstructured":"Zhang S, Zhang Z, Chen Z, Lin S, Xie Z (2021) A novel method of mental fatigue detection based on cnn and lstm. Int J Comput Sci Eng 24(3):290\u2013300","journal-title":"Int J Comput Sci Eng"},{"key":"7540_CR23","doi-asserted-by":"publisher","first-page":"114693","DOI":"10.1016\/j.eswa.2021.114693","volume":"173","author":"L Mou","year":"2021","unstructured":"Mou L, Zhou C, Zhao P, Nakisa B, Rastgoo MN, Jain R, Gao W (2021) Driver stress detection via multimodal fusion using attention-based cnn-lstm. Expert Syst Appl 173:114693","journal-title":"Expert Syst Appl"},{"key":"7540_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07117-4","author":"S Masood","year":"2022","unstructured":"Masood S, Khan R, Abd El-Latif AA et al (2022) An FCN-LSTM model for neurological status detection from non-invasive multivariate sensor data. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-022-07117-4","journal-title":"Neural Comput Appl"},{"key":"7540_CR25","doi-asserted-by":"publisher","first-page":"101819","DOI":"10.1016\/j.bspc.2019.101819","volume":"57","author":"C Chen","year":"2020","unstructured":"Chen C, Hua Z, Zhang R, Liu G, Wen W (2020) Automated arrhythmia classification based on a combination network of cnn and lstm. Biomed Signal Process Control 57:101819","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"7540_CR26","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/data4010014","volume":"4","author":"I Zyma","year":"2019","unstructured":"Zyma I, Tukaev S, Seleznov I, Kiyono K, Popov A, Chernykh M, Shpenkov O (2019) Electroencephalograms during mental arithmetic task performance. Data 4(1):14","journal-title":"Data"},{"issue":"04","key":"7540_CR27","doi-asserted-by":"publisher","first-page":"1350018","DOI":"10.1142\/S0129065713500184","volume":"23","author":"A Temko","year":"2013","unstructured":"Temko A, Boylan G, Marnane W, Lightbody G (2013) Robust neonatal eeg seizure detection through adaptive background modeling. Int J Neural Syst 23(04):1350018","journal-title":"Int J Neural Syst"},{"issue":"4","key":"7540_CR28","first-page":"167","volume":"11","author":"CY Lee","year":"2018","unstructured":"Lee CY, Aliyu I, Lim CG (2018) Optimal eeg locations for eeg feature extraction with application to user\u2019s intension using a robust neuro-fuzzy system in bci. J Chosun Nat Sci 11(4):167\u2013183","journal-title":"J Chosun Nat Sci"},{"issue":"6","key":"7540_CR29","doi-asserted-by":"publisher","first-page":"1927","DOI":"10.1007\/s11269-021-02822-6","volume":"35","author":"R Kumar","year":"2021","unstructured":"Kumar R, Singh MP, Roy B, Shahid AH (2021) A comparative assessment of metaheuristic optimized extreme learning machine and deep neural network in multi-step-ahead long-term rainfall prediction for all-indian regions. Water Resour Manage 35(6):1927\u20131960","journal-title":"Water Resour Manage"},{"issue":"7","key":"7540_CR30","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1007\/s11760-012-0362-9","volume":"8","author":"Y Kumar","year":"2014","unstructured":"Kumar Y, Dewal M, Anand R (2014) Epileptic seizures detection in eeg using dwt-based apen and artificial neural network. SIViP 8(7):1323\u20131334","journal-title":"SIViP"},{"issue":"7","key":"7540_CR31","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1109\/34.192463","volume":"11","author":"SG Mallat","year":"1989","unstructured":"Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674\u2013693","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7540_CR32","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.seizure.2015.01.012","volume":"26","author":"O Faust","year":"2015","unstructured":"Faust O, Acharya UR, Adeli H, Adeli A (2015) Wavelet-based eeg processing for computer-aided seizure detection and epilepsy diagnosis. Seizure 26:56\u201364","journal-title":"Seizure"},{"key":"7540_CR33","doi-asserted-by":"publisher","first-page":"95","DOI":"10.3389\/fninf.2018.00095","volume":"12","author":"M Zhou","year":"2018","unstructured":"Zhou M, Tian C, Cao R, Wang B, Niu Y, Hu T, Guo H, Xiang J (2018) Epileptic seizure detection based on eeg signals and cnn. Front Neuroinform 12:95","journal-title":"Front Neuroinform"},{"key":"7540_CR34","unstructured":"Jespersen SL, Thygesen ME (2020) Fact extraction and verification in danish. PhD thesis, Master\u2019s thesis, IT University of Copenhagen"},{"issue":"11","key":"7540_CR35","doi-asserted-by":"publisher","first-page":"16979","DOI":"10.1007\/s11042-020-09406-3","volume":"80","author":"W Ullah","year":"2021","unstructured":"Ullah W, Ullah A, Haq IU, Muhammad K, Sajjad M, Baik SW (2021) Cnn features with bi-directional lstm for real-time anomaly detection in surveillance networks. Multimed Tools Appl 80(11):16979\u201316995","journal-title":"Multimed Tools Appl"},{"issue":"6","key":"7540_CR36","doi-asserted-by":"publisher","first-page":"1313","DOI":"10.2214\/ajr.147.6.1313","volume":"147","author":"WC Black","year":"1986","unstructured":"Black WC, Armstrong P (1986) Communicating the significance of radiologic test results: the likelihood ratio. Am J Roentgenol 147(6):1313\u20131318","journal-title":"Am J Roentgenol"},{"key":"7540_CR37","doi-asserted-by":"crossref","unstructured":"Ganguly B, Chatterjee A, Mehdi W, Sharma S, Garai S (2020) Eeg based mental arithmetic task classification using a stacked long short term memory network for brain-computer interfacing. In: 2020 IEEE VLSI device circuit and system (VLSI DCS), pp 89\u201394 . IEEE","DOI":"10.1109\/VLSIDCS47293.2020.9179949"},{"key":"7540_CR38","doi-asserted-by":"crossref","unstructured":"Priya TH, Mahalakshmi P, Naidu V, Srinivas M (2020) Stress detection from eeg using power ratio. In: 2020 International conference on emerging trends in information technology and engineering (ic-ETITE), pp 1\u20136. IEEE","DOI":"10.1109\/ic-ETITE47903.2020.401"},{"key":"7540_CR39","doi-asserted-by":"publisher","first-page":"13545","DOI":"10.1109\/ACCESS.2017.2723622","volume":"5","author":"AR Subhani","year":"2017","unstructured":"Subhani AR, Mumtaz W, Saad MNBM, Kamel N, Malik AS (2017) Machine learning framework for the detection of mental stress at multiple levels. IEEE Access 5:13545\u201313556","journal-title":"IEEE Access"},{"key":"7540_CR40","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1155\/2021\/9951905","volume":"2021","author":"M Kang","year":"2021","unstructured":"Kang M, Shin S, Jung J, Kim YT (2021) Classification of mental stress using CNN-LSTM algorithms with electrocardiogram signals. J Healthc Eng 2021:11. https:\/\/doi.org\/10.1155\/2021\/9951905","journal-title":"J Healthc Eng"},{"key":"7540_CR41","doi-asserted-by":"crossref","unstructured":"Purushotham S, Tripathy B (2011) Evaluation of classifier models using stratified tenfold cross validation techniques. In: International conference on computing and communication systems, pp 680\u2013690. Springer","DOI":"10.1007\/978-3-642-29216-3_74"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07540-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07540-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07540-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T20:48:48Z","timestamp":1666298928000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07540-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,6]]},"references-count":41,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["7540"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07540-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,6]]},"assertion":[{"value":"25 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Author Lokesh Malviya declares that he has no conflict of interest. Author Sandip Mal declares that he has no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}