{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T19:23:23Z","timestamp":1777058603155,"version":"3.51.4"},"reference-count":79,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T00:00:00Z","timestamp":1619568000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T00:00:00Z","timestamp":1619568000000},"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":["Appl Intell"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s10489-021-02426-y","type":"journal-article","created":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T12:03:42Z","timestamp":1619611422000},"page":"6449-6466","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Automated major depressive disorder detection using melamine pattern with EEG signals"],"prefix":"10.1007","volume":"51","author":[{"given":"Emrah","family":"Aydemir","sequence":"first","affiliation":[]},{"given":"Turker","family":"Tuncer","sequence":"additional","affiliation":[]},{"given":"Sengul","family":"Dogan","sequence":"additional","affiliation":[]},{"given":"Raj","family":"Gururajan","sequence":"additional","affiliation":[]},{"given":"U. Rajendra","family":"Acharya","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,28]]},"reference":[{"key":"2426_CR1","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1056\/NEJMra073096","volume":"358","author":"R Belmaker","year":"2008","unstructured":"Belmaker R, Agam G (2008) Major depressive disorder. N Engl J Med 358:55\u201368","journal-title":"N Engl J Med"},{"key":"2426_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/nrdp.2016.65","volume":"2","author":"C Otte","year":"2016","unstructured":"Otte C, Gold SM, Penninx BW, Pariante CM, Etkin A, Fava M et al (2016) Major depressive disorder. Nature reviews Disease primers 2:1\u201320","journal-title":"Nature reviews Disease primers"},{"key":"2426_CR3","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1007\/s11920-010-0150-6","volume":"12","author":"FW Lohoff","year":"2010","unstructured":"Lohoff FW (2010) Overview of the genetics of major depressive disorder. Current psychiatry reports 12:539\u2013546","journal-title":"Current psychiatry reports"},{"key":"2426_CR4","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1007\/s00542-018-4075-z","volume":"25","author":"S Mahato","year":"2019","unstructured":"Mahato S, Paul S (2019) Detection of major depressive disorder using linear and non-linear features from EEG signals. Microsyst Technol 25:1065\u20131076","journal-title":"Microsyst Technol"},{"key":"2426_CR5","volume-title":"The diagnostic and statistical manual of mental disorders","author":"JF Lehman","year":"2000","unstructured":"Lehman JF. The diagnostic and statistical manual of mental disorders. 2000"},{"key":"2426_CR6","doi-asserted-by":"crossref","unstructured":"Yasin S, Hussain SA, Aslan S, Raza I, Muzammel M, Othmani A. Neural Networks based approaches for Major Depressive Disorder and Bipolar Disorder Diagnosis using EEG signals: A review. arXiv preprint arXiv:200913402. 2020","DOI":"10.1016\/j.cmpb.2021.106007"},{"key":"2426_CR7","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1016\/j.jad.2014.11.061","volume":"174","author":"E Stockings","year":"2015","unstructured":"Stockings E, Degenhardt L, Lee YY, Mihalopoulos C, Liu A, Hobbs M, Patton G (2015) Symptom screening scales for detecting major depressive disorder in children and adolescents: a systematic review and meta-analysis of reliability, validity and diagnostic utility. J Affect Disord 174:447\u2013463","journal-title":"J Affect Disord"},{"key":"2426_CR8","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.compbiomed.2015.09.019","volume":"67","author":"SA Akar","year":"2015","unstructured":"Akar SA, Kara S, Agambayev S, Bilgi\u00e7 V (2015) Nonlinear analysis of EEGs of patients with major depression during different emotional states. Comput Biol Med 67:49\u201360","journal-title":"Comput Biol Med"},{"key":"2426_CR9","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1016\/j.jpsychires.2011.02.003","volume":"45","author":"EC Landsness","year":"2011","unstructured":"Landsness EC, Goldstein MR, Peterson MJ, Tononi G, Benca RM (2011) Antidepressant effects of selective slow wave sleep deprivation in major depression: a high-density EEG investigation. J Psychiatr Res 45:1019\u20131026","journal-title":"J Psychiatr Res"},{"key":"2426_CR10","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1186\/s12911-015-0227-6","volume":"15","author":"M Mohammadi","year":"2015","unstructured":"Mohammadi M, Al-Azab F, Raahemi B, Richards G, Jaworska N, Smith D et al (2015) Data mining EEG signals in depression for their diagnostic value. BMC medical informatics and decision making 15:108","journal-title":"BMC medical informatics and decision making"},{"key":"2426_CR11","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1159\/000381950","volume":"73","author":"UR Acharya","year":"2015","unstructured":"Acharya UR, Sudarshan VK, Adeli H, Santhosh J, Koh JE, Adeli A (2015) Computer-aided diagnosis of depression using EEG signals. Eur Neurol 73:329\u2013336","journal-title":"Eur Neurol"},{"key":"2426_CR12","doi-asserted-by":"publisher","DOI":"10.1201\/9781315371658","volume-title":"Machine learning: algorithms and applications: Crc press","author":"M Mohammed","year":"2016","unstructured":"Mohammed M, Khan MB, Bashier EBM. Machine learning: algorithms and applications: Crc press; 2016"},{"key":"2426_CR13","first-page":"1","volume":"9","author":"M Fatima","year":"2017","unstructured":"Fatima M, Pasha M (2017) Survey of machine learning algorithms for disease diagnostic. J Intell Learn Syst Appl 9:1\u201316","journal-title":"J Intell Learn Syst Appl"},{"key":"2426_CR14","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1016\/j.procs.2016.04.224","volume":"83","author":"H Asri","year":"2016","unstructured":"Asri H, Mousannif H, Al Moatassime H, Noel T (2016) Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Computer Science 83:1064\u20131069","journal-title":"Procedia Computer Science"},{"key":"2426_CR15","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1016\/j.cmpb.2011.03.018","volume":"104","author":"A Ozcift","year":"2011","unstructured":"Ozcift A, Gulten A (2011) Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Comput Methods Prog Biomed 104:443\u2013451","journal-title":"Comput Methods Prog Biomed"},{"key":"2426_CR16","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1186\/1471-2105-15-223","volume":"15","author":"R Palaniappan","year":"2014","unstructured":"Palaniappan R, Sundaraj K, Sundaraj S (2014) A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals. BMC bioinformatics 15:223","journal-title":"BMC bioinformatics"},{"key":"2426_CR17","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1159\/000504292","volume":"82","author":"U Raghavendra","year":"2019","unstructured":"Raghavendra U, Acharya UR, Adeli H (2019) Artificial intelligence techniques for automated diagnosis of neurological disorders. Eur Neurol 82:41\u201364","journal-title":"Eur Neurol"},{"key":"2426_CR18","doi-asserted-by":"crossref","unstructured":"Jiang C, Li Y, Tang Y, Guan C (2021) Enhancing EEG-based classification of depression patients using spatial information. IEEE Transactions on Neural Systems and Rehabilitation Engineering: a Publication of the IEEE Engineering in Medicine and Biology Society:1","DOI":"10.1109\/TNSRE.2021.3059429"},{"key":"2426_CR19","doi-asserted-by":"publisher","first-page":"102393","DOI":"10.1016\/j.bspc.2020.102393","volume":"66","author":"G Sharma","year":"2021","unstructured":"Sharma G, Parashar A, Joshi AM (2021) DepHNN: a novel hybrid neural network for electroencephalogram (EEG)-based screening of depression. Biomedical Signal Processing and Control 66:102393","journal-title":"Biomedical Signal Processing and Control"},{"key":"2426_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13755-021-00139-7","volume":"9","author":"H Akbari","year":"2021","unstructured":"Akbari H, Sadiq MT, Rehman AU (2021) Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain. Health Information Science and Systems 9:1\u201315","journal-title":"Health Information Science and Systems"},{"key":"2426_CR21","volume-title":"Krejcar O","author":"A Seal","year":"2021","unstructured":"Seal A, Bajpai R, Agnihotri J, Yazidi A, Herrera-Viedma E (2021) Krejcar O. A Deep Convolution Neural Networks Framework for Detecting Depression using EEG. IEEE Transactions on Instrumentation and Measurement, DeprNet"},{"key":"2426_CR22","doi-asserted-by":"publisher","first-page":"102337","DOI":"10.1016\/j.bspc.2020.102337","volume":"65","author":"C Kaur","year":"2021","unstructured":"Kaur C, Bisht A, Singh P, Joshi G (2021) EEG signal denoising using hybrid approach of Variational mode decomposition and wavelets for depression. Biomedical Signal Processing and Control. 65:102337","journal-title":"Biomedical Signal Processing and Control."},{"key":"2426_CR23","doi-asserted-by":"crossref","unstructured":"Mitra V, Tsiartas A, Shriberg E. Noise and reverberation effects on depression detection from speech. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): IEEE; 2016. p. 5795\u20139","DOI":"10.1109\/ICASSP.2016.7472788"},{"key":"2426_CR24","doi-asserted-by":"crossref","unstructured":"Afshan A, Guo J, Park SJ, Ravi V, Flint J, Alwan A. Effectiveness of Voice Quality Features in Detecting Depression. Interspeech2018. p. 1676\u20131680","DOI":"10.21437\/Interspeech.2018-1399"},{"key":"2426_CR25","doi-asserted-by":"crossref","unstructured":"Williamson JR, Quatieri TF, Helfer BS, Ciccarelli G, Mehta DD. Vocal and facial biomarkers of depression based on motor incoordination and timing. Proceedings of the 4th International Workshop on Audio\/Visual Emotion Challenge2014. p. 65\u201372","DOI":"10.1145\/2661806.2661809"},{"key":"2426_CR26","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1109\/TBME.2012.2228646","volume":"60","author":"KEB Ooi","year":"2012","unstructured":"Ooi KEB, Lech M, Allen NB (2012) Multichannel weighted speech classification system for prediction of major depression in adolescents. IEEE Trans Biomed Eng 60:497\u2013506","journal-title":"IEEE Trans Biomed Eng"},{"key":"2426_CR27","doi-asserted-by":"crossref","unstructured":"Sturim D, Torres-Carrasquillo PA, Quatieri TF, Malyska N, Mc Cree A. Automatic detection of depression in speech using gaussian mixture modeling with factor analysis. Twelfth Annual Conference of the International Speech Communication Association2011","DOI":"10.21437\/Interspeech.2011-746"},{"key":"2426_CR28","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.jad.2017.08.038","volume":"225","author":"T Taguchi","year":"2018","unstructured":"Taguchi T, Tachikawa H, Nemoto K, Suzuki M, Nagano T, Tachibana R, Nishimura M, Arai T (2018) Major depressive disorder discrimination using vocal acoustic features. J Affect Disord 225:214\u2013220","journal-title":"J Affect Disord"},{"key":"2426_CR29","doi-asserted-by":"crossref","unstructured":"Cohn JF, Kruez TS, Matthews I, Yang Y, Nguyen MH, Padilla MT, et al. Detecting depression from facial actions and vocal prosody. 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops: IEEE; 2009. p. 1\u20137","DOI":"10.1109\/ACII.2009.5349358"},{"key":"2426_CR30","doi-asserted-by":"crossref","unstructured":"Mitra V, Shriberg E. Effects of feature type, learning algorithm and speaking style for depression detection from speech. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): IEEE; 2015. p. 4774\u20138","DOI":"10.1109\/ICASSP.2015.7178877"},{"key":"2426_CR31","doi-asserted-by":"crossref","unstructured":"Williamson JR, Quatieri TF, Helfer BS, Horwitz R, Yu B, Mehta DD. Vocal biomarkers of depression based on motor incoordination. Proceedings of the 3rd ACM international workshop on Audio\/visual emotion challenge2013. p. 41\u20138","DOI":"10.1145\/2512530.2512531"},{"key":"2426_CR32","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1109\/TBME.2010.2091640","volume":"58","author":"L-SA Low","year":"2010","unstructured":"Low L-SA, Maddage NC, Lech M, Sheeber LB, Allen NB (2010) Detection of clinical depression in adolescents\u2019 speech during family interactions. IEEE Trans Biomed Eng 58:574\u2013586","journal-title":"IEEE Trans Biomed Eng"},{"key":"2426_CR33","unstructured":"Seneviratne N, Espy-Wilson C. Deep Learning Based Generalized Models for Depression Classification. arXiv preprint arXiv:201106739. 2020"},{"key":"2426_CR34","volume-title":"Duvvuri R","author":"L Zhang","year":"2020","unstructured":"Zhang L (2020) Duvvuri R. Nguyen T, Ghomi RH. Automated voice biomarkers for depression symptoms using an online cross-sectional data collection initiative. Depression and anxiety, Chandra KK"},{"key":"2426_CR35","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1109\/JBHI.2017.2676878","volume":"22","author":"H Dibeklio\u011flu","year":"2017","unstructured":"Dibeklio\u011flu H, Hammal Z, Cohn JF (2017) Dynamic multimodal measurement of depression severity using deep autoencoding. IEEE journal of biomedical and health informatics 22:525\u2013536","journal-title":"IEEE journal of biomedical and health informatics"},{"key":"2426_CR36","doi-asserted-by":"publisher","first-page":"105740","DOI":"10.1016\/j.cmpb.2020.105740","volume":"197","author":"O Yildirim","year":"2020","unstructured":"Yildirim O, Talo M, Ciaccio EJ, San Tan R, Acharya UR (2020) Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records. Comput Methods Prog Biomed 197:105740","journal-title":"Comput Methods Prog Biomed"},{"key":"2426_CR37","doi-asserted-by":"publisher","first-page":"103999","DOI":"10.1016\/j.compbiomed.2020.103999","volume":"126","author":"DCK Soh","year":"2020","unstructured":"Soh DCK, Ng E, Jahmunah V, Oh SL, San Tan R, Acharya UR (2020) Automated diagnostic tool for hypertension using convolutional neural network. Comput Biol Med 126:103999","journal-title":"Comput Biol Med"},{"key":"2426_CR38","doi-asserted-by":"publisher","first-page":"103939","DOI":"10.1016\/j.compbiomed.2020.103939","volume":"124","author":"R Panda","year":"2020","unstructured":"Panda R, Jain S, Tripathy R, Acharya UR (2020) Detection of shockable ventricular cardiac arrhythmias from ECG signals using FFREWT filter-bank and deep convolutional neural network. Comput Biol Med 124:103939","journal-title":"Comput Biol Med"},{"key":"2426_CR39","doi-asserted-by":"crossref","unstructured":"Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 103792","DOI":"10.1016\/j.compbiomed.2020.103792"},{"key":"2426_CR40","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition2016. p. 770\u20138","DOI":"10.1109\/CVPR.2016.90"},{"key":"2426_CR41","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition2017. p. 4700\u20138","DOI":"10.1109\/CVPR.2017.243"},{"key":"2426_CR42","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition2015. p. 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"2426_CR43","doi-asserted-by":"publisher","first-page":"2464","DOI":"10.1109\/78.157290","volume":"40","author":"MJ Shensa","year":"1992","unstructured":"Shensa MJ (1992) The discrete wavelet transform: wedding the a trous and Mallat algorithms. IEEE Trans Signal Process 40:2464\u20132482","journal-title":"IEEE Trans Signal Process"},{"key":"2426_CR44","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","volume":"24","author":"T Ojala","year":"2002","unstructured":"Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24:971\u2013987","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2426_CR45","first-page":"55","volume-title":"The support vector method of function estimation","author":"V Vapnik","year":"1998","unstructured":"Vapnik V (1998) The support vector method of function estimation. Springer, Nonlinear Modeling, pp 55\u201385"},{"key":"2426_CR46","volume-title":"The nature of statistical learning theory: springer science & business media","author":"V Vapnik","year":"2013","unstructured":"Vapnik V. The nature of statistical learning theory: springer science & business media; 2013"},{"key":"2426_CR47","doi-asserted-by":"publisher","unstructured":"Mumtaz W. MDD Patients and Healthy Controls EEG Data (New). figshare. Dataset. MDD Patients and Healthy Controls EEG Data generated by https:\/\/doi.org\/10.6084\/m9.figshare.4244171.v2. 2016","DOI":"10.6084\/m9.figshare.4244171.v2"},{"key":"2426_CR48","unstructured":"Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE. Neural message passing for quantum chemistry. International Conference on Machine Learning: PMLR; 2017. p. 1263\u20131272"},{"key":"2426_CR49","doi-asserted-by":"crossref","unstructured":"Ojala T, Pietik\u00e4inen M, M\u00e4enp\u00e4\u00e4 T. A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classification. International Conference on Advances in Pattern Recognition: Springer; 2001. p. 399\u2013408","DOI":"10.1007\/3-540-44732-6_41"},{"key":"2426_CR50","doi-asserted-by":"crossref","unstructured":"Ahonen T, Hadid A, Pietik\u00e4inen M. Face recognition with local binary patterns. European conference on computer vision: Springer; 2004. p. 469\u2013481","DOI":"10.1007\/978-3-540-24670-1_36"},{"key":"2426_CR51","doi-asserted-by":"publisher","first-page":"1368","DOI":"10.1109\/TIP.2016.2522378","volume":"25","author":"L Liu","year":"2016","unstructured":"Liu L, Lao S, Fieguth PW, Guo Y, Wang X, Pietik\u00e4inen M (2016) Median robust extended local binary pattern for texture classification. IEEE Trans Image Process 25:1368\u20131381","journal-title":"IEEE Trans Image Process"},{"key":"2426_CR52","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.eswa.2017.07.007","volume":"88","author":"Z Pan","year":"2017","unstructured":"Pan Z, Li Z, Fan H, Wu X (2017) Feature based local binary pattern for rotation invariant texture classification. Expert Syst Appl 88:238\u2013248","journal-title":"Expert Syst Appl"},{"key":"2426_CR53","doi-asserted-by":"publisher","first-page":"4862","DOI":"10.1016\/j.eswa.2008.05.052","volume":"36","author":"J Rafiee","year":"2009","unstructured":"Rafiee J, Tse P, Harifi A, Sadeghi M (2009) A novel technique for selecting mother wavelet function using an intelli gent fault diagnosis system. Expert Syst Appl 36:4862\u20134875","journal-title":"Expert Syst Appl"},{"key":"2426_CR54","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.epsr.2010.11.031","volume":"83","author":"S Avdakovic","year":"2012","unstructured":"Avdakovic S, Nuhanovic A, Kusljugic M, Music M (2012) Wavelet transform applications in power system dynamics. Electr Power Syst Res 83:237\u2013245","journal-title":"Electr Power Syst Res"},{"key":"2426_CR55","first-page":"513","volume":"17","author":"J Goldberger","year":"2004","unstructured":"Goldberger J, Hinton GE, Roweis S, Salakhutdinov RR (2004) Neighbourhood components analysis. Adv Neural Inf Proces Syst 17:513\u2013520","journal-title":"Adv Neural Inf Proces Syst"},{"key":"2426_CR56","doi-asserted-by":"crossref","unstructured":"Kuncan F, Kaya Y, Kuncan M (2019) Sens\u00f6r i\u015faretlerinden cinsiyet tan\u0131ma i\u00e7in yerel ikili \u00f6r\u00fcnt\u00fcler tabanl\u0131 yeni yakla\u015f\u0131mlar. Journal of the Faculty of Engineering & Architecture of Gazi University 34","DOI":"10.17341\/gazimmfd.426259"},{"key":"2426_CR57","first-page":"211","volume":"4","author":"V Kumar","year":"2014","unstructured":"Kumar V, Minz S (2014) Feature selection: a literature review. SmartCR. 4:211\u2013229","journal-title":"SmartCR."},{"key":"2426_CR58","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","volume":"40","author":"G Chandrashekar","year":"2014","unstructured":"Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Computers & Electrical Engineering 40:16\u201328","journal-title":"Computers & Electrical Engineering"},{"key":"2426_CR59","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.apacoust.2019.05.019","volume":"155","author":"T Tuncer","year":"2019","unstructured":"Tuncer T, Dogan S (2019) A novel octopus based Parkinson\u2019s disease and gender recognition method using vowels. Appl Acoust 155:75\u201383","journal-title":"Appl Acoust"},{"key":"2426_CR60","doi-asserted-by":"crossref","unstructured":"Ezuma M, Erden F, Anjinappa CK, Ozdemir O, Guvenc I. Micro-UAV detection and classification from RF fingerprints using machine learning techniques. 2019 IEEE Aerospace Conference: IEEE; 2019. p. 1\u201313","DOI":"10.1109\/AERO.2019.8741970"},{"key":"2426_CR61","doi-asserted-by":"publisher","first-page":"3079","DOI":"10.1016\/j.neucom.2010.06.024","volume":"73","author":"Y Gao","year":"2010","unstructured":"Gao Y, Gao F (2010) Edited AdaBoost by weighted kNN. Neurocomputing. 73:3079\u20133088","journal-title":"Neurocomputing."},{"key":"2426_CR62","doi-asserted-by":"publisher","first-page":"104923","DOI":"10.1016\/j.knosys.2019.104923","volume":"186","author":"T Tuncer","year":"2019","unstructured":"Tuncer T, Dogan S, P\u0142awiak P, Acharya UR (2019) Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals. Knowl-Based Syst 186:104923","journal-title":"Knowl-Based Syst"},{"key":"2426_CR63","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1111\/jcpp.12559","volume":"57","author":"D Bone","year":"2016","unstructured":"Bone D, Bishop SL, Black MP, Goodwin MS, Lord C, Narayanan SS (2016) Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion. J Child Psychol Psychiatry 57:927\u2013937","journal-title":"J Child Psychol Psychiatry"},{"key":"2426_CR64","doi-asserted-by":"crossref","unstructured":"Mantri S, Patil D, Agrawal P, Wadhai V. Non invasive EEG signal processing framework for real time depression analysis. 2015 SAI Intelligent Systems Conference (IntelliSys): IEEE; 2015. p. 518\u201321","DOI":"10.1109\/IntelliSys.2015.7361188"},{"key":"2426_CR65","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1159\/000438457","volume":"74","author":"UR Acharya","year":"2015","unstructured":"Acharya UR, Sudarshan VK, Adeli H, Santhosh J, Koh JE, Puthankatti SD et al (2015) A novel depression diagnosis index using nonlinear features in EEG signals. Eur Neurol 74:79\u201383","journal-title":"Eur Neurol"},{"key":"2426_CR66","doi-asserted-by":"publisher","first-page":"1607","DOI":"10.1007\/s00521-015-1959-z","volume":"27","author":"TT Erguzel","year":"2016","unstructured":"Erguzel TT, Sayar GH, Tarhan N (2016) Artificial intelligence approach to classify unipolar and bipolar depressive disorders. Neural Comput & Applic 27:1607\u20131616","journal-title":"Neural Comput & Applic"},{"key":"2426_CR67","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0171409","volume":"12","author":"W Mumtaz","year":"2017","unstructured":"Mumtaz W, Xia L, Mohd Yasin MA, Azhar Ali SS, Malik AS (2017) A wavelet-based technique to predict treatment outcome for major depressive disorder. PLoS One 12:e0171409","journal-title":"PLoS One"},{"key":"2426_CR68","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.3390\/s17061385","volume":"17","author":"S-C Liao","year":"2017","unstructured":"Liao S-C, Wu C-T, Huang H-C, Cheng W-T, Liu Y-H (2017) Major depression detection from EEG signals using kernel eigen-filter-bank common spatial patterns. Sensors. 17:1385","journal-title":"Sensors."},{"key":"2426_CR69","first-page":"1","volume":"8","author":"AY Kim","year":"2018","unstructured":"Kim AY, Jang EH, Kim S, Choi KW, Jeon HJ, Yu HY et al (2018) Automatic detection of major depressive disorder using electrodermal activity. Sci Rep 8:1\u20139","journal-title":"Sci Rep"},{"key":"2426_CR70","first-page":"1","volume":"2018","author":"H Cai","year":"2018","unstructured":"Cai H, Han J, Chen Y, Sha X, Wang Z, Hu B, Yang J, Feng L, Ding Z, Chen Y, Gutknecht J (2018) A pervasive approach to EEG-based depression detection. Complexity. 2018:1\u201313","journal-title":"Complexity."},{"key":"2426_CR71","doi-asserted-by":"publisher","first-page":"1244","DOI":"10.3390\/app8081244","volume":"8","author":"C-T Wu","year":"2018","unstructured":"Wu C-T, Dillon DG, Hsu H-C, Huang S, Barrick E, Liu Y-H (2018) Depression detection using relative EEG power induced by emotionally positive images and a conformal kernel support vector machine. Applied Sciences 8:1244","journal-title":"Applied Sciences"},{"key":"2426_CR72","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.cmpb.2018.04.012","volume":"161","author":"UR Acharya","year":"2018","unstructured":"Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H, Subha DP (2018) Automated EEG-based screening of depression using deep convolutional neural network. Comput Methods Prog Biomed 161:103\u2013113","journal-title":"Comput Methods Prog Biomed"},{"key":"2426_CR73","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1016\/j.cogsys.2018.07.010","volume":"52","author":"M Sharma","year":"2018","unstructured":"Sharma M, Achuth P, Deb D, Puthankattil SD, Acharya UR (2018) An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with EEG signals. Cogn Syst Res 52:508\u2013520","journal-title":"Cogn Syst Res"},{"key":"2426_CR74","doi-asserted-by":"publisher","first-page":"103983","DOI":"10.1016\/j.ijmedinf.2019.103983","volume":"132","author":"W Mumtaz","year":"2019","unstructured":"Mumtaz W, Qayyum A (2019) A deep learning framework for automatic diagnosis of unipolar depression. Int J Med Inform 132:103983","journal-title":"Int J Med Inform"},{"key":"2426_CR75","doi-asserted-by":"crossref","unstructured":"Sandheep P, Vineeth S, Poulose M, Subha D. Performance analysis of deep learning CNN in classification of depression EEG signals. TENCON 2019\u20132019 IEEE Region 10 Conference (TENCON): IEEE; 2019. p. 1339\u201344","DOI":"10.1109\/TENCON.2019.8929254"},{"key":"2426_CR76","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1007\/s11517-019-01959-2","volume":"57","author":"X Li","year":"2019","unstructured":"Li X, La R, Wang Y, Niu J, Zeng S, Sun S et al (2019) EEG-based mild depression recognition using convolutional neural network. Medical & biological engineering & computing 57:1341\u20131352","journal-title":"Medical & biological engineering & computing"},{"key":"2426_CR77","doi-asserted-by":"crossref","unstructured":"Mohammadi Y, Hajian M, Moradi MH. Discrimination of Depression Levels Using Machine Learning Methods on EEG Signals. 2019 27th Iranian Conference on Electrical Engineering (ICEE): IEEE; 2019. p. 1765\u20139","DOI":"10.1109\/IranianCEE.2019.8786540"},{"key":"2426_CR78","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s10916-019-1345-y","volume":"43","author":"B Ay","year":"2019","unstructured":"Ay B, Yildirim O, Talo M, Baloglu UB, Aydin G, Puthankattil SD, Acharya UR (2019) Automated depression detection using deep representation and sequence learning with EEG signals. J Med Syst 43:205","journal-title":"J Med Syst"},{"key":"2426_CR79","doi-asserted-by":"crossref","unstructured":"Duan L, Duan H, Qiao Y, Sha S, Qi S, Zhang X, Huang J, Huang X, Wang C (2020) Machine learning approaches for MDD detection and emotion decoding using EEG signals. Front Hum Neurosci 14","DOI":"10.3389\/fnhum.2020.00284"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02426-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02426-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02426-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,25]],"date-time":"2022-12-25T18:24:22Z","timestamp":1671992662000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02426-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,28]]},"references-count":79,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["2426"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02426-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,28]]},"assertion":[{"value":"8 April 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}