{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T22:34:31Z","timestamp":1784673271380,"version":"3.55.0"},"reference-count":43,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Bipolar Disorder (BD) is an illness with high prevalence and a huge social and economic impact. It is recurrent, with a long-term evolution in most cases. Early treatment and continuous monitoring have proven to be very effective in mitigating the causes and consequences of BD. However, no tools are currently available for a massive and semi-automatic BD patient monitoring and control. Taking advantage of recent technological developments in the field of wearables, this paper studies the feasibility of a BD episodes classification analysis while using entropy measures, an approach successfully applied in a myriad of other physiological frameworks. This is a very difficult task, since actigraphy records are highly non-stationary and corrupted with artifacts (no activity). The method devised uses a preprocessing stage to extract epochs of activity, and then applies a quantification measure, Slope Entropy, recently proposed, which outperforms the most common entropy measures used in biomedical time series. The results confirm the feasibility of the approach proposed, since the three states that are involved in BD, depression, mania, and remission, can be significantly distinguished.<\/jats:p>","DOI":"10.3390\/e22111243","type":"journal-article","created":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T20:05:25Z","timestamp":1604261125000},"page":"1243","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Classification of Actigraphy Records from Bipolar Disorder Patients Using Slope Entropy: A Feasibility Study"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0076-0515","authenticated-orcid":false,"given":"David","family":"Cuesta-Frau","sequence":"first","affiliation":[{"name":"Technological Institute of Informatics, Alcoi Campus, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jakub","family":"Schneider","sequence":"additional","affiliation":[{"name":"Department of Cybernetics, Czech Technical University in Prague, 166 36 Prague, Czech Republic"},{"name":"National Institute of Mental Health, 250 67 Klecany, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4672-4923","authenticated-orcid":false,"given":"Eduard","family":"Bak\u0161tein","sequence":"additional","affiliation":[{"name":"Department of Cybernetics, Czech Technical University in Prague, 166 36 Prague, Czech Republic"},{"name":"National Institute of Mental Health, 250 67 Klecany, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pavel","family":"Vostatek","sequence":"additional","affiliation":[{"name":"MINDPAX, Vinohrady, 128 00 Prague, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Filip","family":"Spaniel","sequence":"additional","affiliation":[{"name":"National Institute of Mental Health, 250 67 Klecany, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Nov\u00e1k","sequence":"additional","affiliation":[{"name":"Department of Cybernetics, Czech Technical University in Prague, 166 36 Prague, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1001\/archgenpsychiatry.2011.12","article-title":"Prevalence and Correlates of Bipolar Spectrum Disorder in the World Mental Health Survey Initiative","volume":"68","author":"Merikangas","year":"2011","journal-title":"Arch. Gen. Psychiatry"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1672","DOI":"10.1016\/S0140-6736(13)60857-0","article-title":"Treatment of bipolar disorder","volume":"381","author":"Geddes","year":"2013","journal-title":"Lancet"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.smrv.2016.05.003","article-title":"Actigraphic features of bipolar disorder: A systematic review and meta-analysis","volume":"33","author":"Crescenzo","year":"2017","journal-title":"Sleep Med. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders: DSM\u20135, American Psychiatric Association. [5th ed.].","DOI":"10.1176\/appi.books.9780890425596"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1111\/j.1399-5618.2010.00843.x","article-title":"Circadian rhythms and sleep in bipolar disorder","volume":"12","author":"Murray","year":"2010","journal-title":"Bipolar Disord."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s11920-017-0772-z","article-title":"Circadian Rhythm Dysregulation in Bipolar Spectrum Disorders","volume":"19","author":"Alloy","year":"2017","journal-title":"Curr. Psychiatry Rep."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1192\/bjp.133.5.429","article-title":"A Rating Scale for Mania: Reliability, Validity and Sensitivity","volume":"133","author":"Young","year":"1978","journal-title":"Br. J. Psychiatry"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1192\/bjp.134.4.382","article-title":"A New Depression Scale Designed to be Sensitive to Change","volume":"134","author":"Montgomery","year":"1979","journal-title":"Br. J. Psychiatry"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Krane-Gartiser, K., Henriksen, T.E.G., Morken, G., Vaaler, A., and Fasmer, O.B. (2014). Actigraphic Assessment of Motor Activity in Acutely Admitted Inpatients with Bipolar Disorder. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0089574"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"eaax1977","DOI":"10.1126\/scitranslmed.aax1977","article-title":"More random motor activity fluctuations predict incident frailty, disability, and mortality","volume":"11","author":"Li","year":"2019","journal-title":"Sci. Transl. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.3758\/s13428-013-0438-7","article-title":"Alternatives to polysomnography (PSG): A validation of wrist actigraphy and a partial-PSG system","volume":"46","author":"Kosmadopoulos","year":"2014","journal-title":"Behav. Res. Methods"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1678","DOI":"10.1017\/S0033291717000186","article-title":"Sleep and circadian rhythm disturbance in bipolar disorder","volume":"47","author":"Bradley","year":"2017","journal-title":"Psychol. Med."},{"key":"ref_13","first-page":"1","article-title":"Fractal biomarker of activity in patients with bipolar disorder","volume":"94","author":"Knapen","year":"2020","journal-title":"Psychol. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"R1078","DOI":"10.1152\/ajpregu.1996.271.4.R1078","article-title":"Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics","volume":"271","author":"Iyengar","year":"1996","journal-title":"Am. J. Physiol.-Regul. Integr. Comp. Physiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"174102","DOI":"10.1103\/PhysRevLett.88.174102","article-title":"Permutation Entropy: A Natural Complexity Measure for Time Series","volume":"88","author":"Bandt","year":"2002","journal-title":"Phys. Rev. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6842","DOI":"10.3934\/mbe.2019342","article-title":"Permutation entropy: Influence of amplitude information on time series classification performance","volume":"16","year":"2019","journal-title":"Math. Biosci. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"022911","DOI":"10.1103\/PhysRevE.87.022911","article-title":"Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information","volume":"87","author":"Fadlallah","year":"2013","journal-title":"Phys. Rev. E"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2711","DOI":"10.1109\/TBME.2017.2664105","article-title":"Bubble Entropy: An Entropy Almost Free of Parameters","volume":"64","author":"Manis","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cuesta-Frau, D. (2019). Slope Entropy: A New Time Series Complexity Estimator Based on Both Symbolic Patterns and Amplitude Information. Entropy, 21.","DOI":"10.3390\/e21121167"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Cuesta-Frau, D., Dakappa, P.H., Mahabala, C., and Gupta, A.R. (2020). Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis. Entropy, 22.","DOI":"10.3390\/e22091034"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/S0167-2789(02)00527-4","article-title":"Gradient pattern analysis of short nonstationary time series: An application to Lagrangian data from satellite tracked drifters","volume":"168\u2013169","author":"Assireu","year":"2002","journal-title":"Phys. Nonlinear Phenom."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.jcrc.2016.09.013","article-title":"Statistical models for fever forecasting based on advanced body temperature monitoring","volume":"37","author":"Vargas","year":"2017","journal-title":"J. Crit. Care"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ioannidis, Y., Scholl, M.H., Schmidt, J.W., Matthes, F., Hatzopoulos, M., Boehm, K., Kemper, A., Grust, T., and Boehm, C. (2006). Similarity Search on Time Series Based on Threshold Queries. Advances in Database Technology\u2014EDBT 2006, Springer.","DOI":"10.1007\/11687238"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1109\/TIT.1976.1055501","article-title":"On the Complexity of Finite Sequences","volume":"22","author":"Lempel","year":"1976","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sarlabous, L., Torres, A., Fiz, J.A., Gea, J., Galdiz, J.B., and Jane, R. (2009, January 3\u20136). Multistate Lempel-Ziv (MLZ) index interpretation as a measure of amplitude and complexity changes. Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, USA.","DOI":"10.1109\/IEMBS.2009.5333488"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1073\/pnas.88.6.2297","article-title":"Approximate entropy as a measure of system complexity","volume":"88","author":"Pincus","year":"1991","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"235","DOI":"10.3934\/mbe.2020013","article-title":"Classification of fever patterns using a single extracted entropy feature: A feasibility study based on Sample Entropy","volume":"17","author":"Dakappa","year":"2020","journal-title":"Math. Biosci. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"H319","DOI":"10.1152\/ajpheart.00561.2010","article-title":"Accurate estimation of entropy in very short physiological time series: The problem of atrial fibrillation detection in implanted ventricular devices","volume":"300","author":"Lake","year":"2011","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cuesta-Frau, D., Nov\u00e1k, D., Burda, V., Molina-Pic\u00f3, A., Vargas, B., Mraz, M., Kavalkova, P., Benes, M., and Haluzik, M. (2018). Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics. Entropy, 20.","DOI":"10.3390\/e20110871"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"69","DOI":"10.3389\/fninf.2014.00069","article-title":"Sample entropy reveals high discriminative power between young and elderly adults in short fMRI data sets","volume":"8","author":"Sokunbi","year":"2014","journal-title":"Front. Neuroinform."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zanin, M., G\u00f3mez-Andr\u00e9s, D., Pulido-Valdeolivas, I., Mart\u00edn-Gonzalo, J.A., L\u00f3pez-L\u00f3pez, J., Pascual-Pascual, S.I., and Rausell, E. (2018). Characterizing Normal and Pathological Gait through Permutation Entropy. Entropy, 20.","DOI":"10.3390\/e20010077"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"55","DOI":"10.3389\/fncom.2018.00055","article-title":"Epileptic Seizure Prediction Based on Permutation Entropy","volume":"12","author":"Yang","year":"2018","journal-title":"Front. Comput. Neurosci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Xia, Y., Yang, L., Zunino, L., Shi, H., Zhuang, Y., and Liu, C. (2018). Application of Permutation Entropy and Permutation Min-Entropy in Multiple Emotional States Analysis of RRI Time Series. Entropy, 20.","DOI":"10.3390\/e20030148"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"021906","DOI":"10.1103\/PhysRevE.85.021906","article-title":"Modified Permutation-entropy analysis of heartbeat dynamics","volume":"85","author":"Bian","year":"2012","journal-title":"Phys. Rev. E"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.cmpb.2016.02.008","article-title":"Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation","volume":"128","author":"Azami","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.3934\/mbe.2020086","article-title":"Permutation Entropy and Bubble Entropy: Possible interactions and synergies between order and sorting relations","volume":"17","author":"Vargas","year":"2020","journal-title":"Math. Biosci. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chicco, D., and Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom., 21.","DOI":"10.1186\/s12864-019-6413-7"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Patro, S.G.K., and Sahu, K.K. (2015). Normalization: A Preprocessing Stage. arXiv.","DOI":"10.17148\/IARJSET.2015.2305"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Khabou, M.A., and Parlato, M.V. (2013, January 4\u20137). Classification and feature analysis of actigraphy signals. Proceedings of the 2013 IEEE Southeastcon, Jacksonville, FL, USA.","DOI":"10.1109\/SECON.2013.6567450"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Cuesta-Frau, D., Mir\u00f3-Mart\u00ednez, P., Oltra-Crespo, S., Jord\u00e1n-N\u00fa\u00f1ez, J., Vargas, B., Gonz\u00e1lez, P., and Varela-Entrecanales, M. (2018). Model Selection for Body Temperature Signal Classification Using Both Amplitude and Ordinality-Based Entropy Measures. Entropy, 20.","DOI":"10.3390\/e20110853"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Cuesta-Frau, D., Murillo-Escobar, J.P., Orrego, D.A., and Delgado-Trejos, E. (2019). Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications. Entropy, 21.","DOI":"10.3390\/e21040385"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cuesta-Frau, D., Molina-Pic\u00f3, A., Vargas, B., and Gonz\u00e1lez, P. (2019). Permutation Entropy: Enhancing Discriminating Power by Using Relative Frequencies Vector of Ordinal Patterns Instead of Their Shannon Entropy. Entropy, 21.","DOI":"10.3390\/e21101013"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/11\/1243\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:27:51Z","timestamp":1760178471000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/11\/1243"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,1]]},"references-count":43,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["e22111243"],"URL":"https:\/\/doi.org\/10.3390\/e22111243","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,1]]}}}