Classification of bipolar disorder episodes based on analysis of voice and motor activity of patients

Pervasive and Mobile Computing

Volumn 31, Issue , 2016, Pages 50-66

  Maxhuni, Alban ^a   Muñoz Meléndez, Angélica ^b   Osmani, Venet ^a   Perez, Humberto ^b   Mayora, Oscar ^a   Morales, Eduardo F ^b  

^a CREATE NET   (Italy)

^b INSTITUTO NACIONAL DE ASTROFÍSICA   (Mexico)

Author keywords

Bipolar disorder; Depression; Mental health; Mobile computing; Motor activity; Personalized monitoring; Phone conversation; Speech emotions; Speech rate

Indexed keywords

MOBILE COMPUTING; SURVEYS; TELEPHONE SETS;

BIPOLAR DISORDER; DEPRESSION; MENTAL HEALTH; MOTOR ACTIVITY; PHONE CONVERSATIONS; SPEECH EMOTIONS; SPEECH RATES;

CLASSIFICATION (OF INFORMATION);

EID: 84957928382     PISSN: 15741192     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.pmcj.2016.01.008     Document Type: Article

References (45)

1. [1] WHO. World health organization. the who world health report 2001: New understanding- new hope, world health organization. geneva. switzerland. 2001.
2. [2] Bopp, J.M., Miklowitz, D.J., Goodwin, G.M., Stevens, W., Rendell, J.M., Geddes, J.R., The longitudinal course of bipolar disorder as revealed through weekly text messaging: a feasibility study. Bipolar Disord. 12:3 (2010), 327–334.
3. [3] Faurholt-Jepsen, M., Brage, S., Vinberg, M., Christensen, E.M., Knorr, U., Jensen, H.M., Kessing, L.V., Differences in psychomotor activity in patients suffering from unipolar and bipolar affective disorder in the remitted or mild/moderate depressive state. J. Affect. Disord. 141:2 (2012), 457–463.
4. [4] Sims, J., Smith, F., Duffy, A., Hilton, S., The vagaries of self-reports of physical activity: a problem revisited and addressed in a study of exercise promotion in the over 65 s in general practice. Fam. Pract. 16:2 (1999), 152–157.
5. [5] Alderfer, B.S., Allen, M.H., Treatment of agitation in bipolar disorder across the life cycle. J. Clin. Psychiatry 64 (2002), 3–9.
6. [6] Osmani, V., Smartphones in mental health: detecting depressive and manic episodes. IEEE Pervasive Comput. 14:3 (2015), 10–13.
7. [7] Gruenerbl, A., Osmani, V., Bahle, G., Carrasco, J.C., Oehler, S., Mayora, O., Haring, C., Lukowicz, P., Using smart phone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients. Proceedings of the 5th Augmented Human International Conference, 2014, ACM, 38.
8. [8] Grunerbl, A., Muaremi, A., Osmani, V., Bahle, G., Ohler, S., Troster, G., Mayora, O., Haring, C., Lukowicz, P., Smartphone-based recognition of states and state changes in bipolar disorder patients. Biomedical and Health Informatics, IEEE Journal of 19:1 (2015), 140–148.
9. [9] Osmani, V., Maxhuni, A., Grünerbl, A., Lukowicz, P., Haring, C., Mayora, O., Monitoring activity of patients with bipolar disorder using smart phones. Proceedings of International Conference on Advances in Mobile Computing and Multimedia, MoMM’13, 2013, ACM, New York, NY, USA, 85:85–85:92.
10. [10] Venta, L., Isomursu, M., Ahtinen, A., Ramiah, S., “my phone is a part of my soul”—how people bond with their mobile phones. Mobile Ubiquitous Computing, Systems, Services and Technologies, 2008. UBICOMM’08. The Second International Conference on, 2008, IEEE, 311–317.
11. [11] Marshall, S.J., Ramirez, E., Reducing sedentary behavior a new paradigm in physical activity promotion. Am. J. Lifestyle Med. 5:6 (2011), 518–530.
12. [12] Dantzig, S., Geleijnse, G., Halteren, A.T., Toward a persuasive mobile application to reduce sedentary behavior. Pers. Ubiquitous Comput. 17:6 (2013), 1237–1246.
13. [13] Mukhtar, H., Belaid, D., Using adaptive feedback for promoting awareness about physical activeness in adults. Ubiquitous Intelligence and Computing, 2013 IEEE 10th International Conference on and 10th International Conference on Autonomic and Trusted Computing (UIC/ATC), 2013, IEEE, 638–643.
14. [14] N.D. Lane, M. Mohammod, M. Lin, X. Yang, H. Lu, S. Ali, A. Doryab, E. Berke, T. Choudhury, A. Campbell, Bewell: A smartphone application to monitor, model and promote wellbeing, in: 5th International ICST Conference on Pervasive Computing Technologies for Healthcare, 2011, pp. 23–26.
15. [15] Ceja, E., Osmani, V., Mayora, O., Automatic stress detection in working environments from smartphones’ accelerometer data: A first step. IEEE J. Biomed. Health Inform., PP(99), 2015 1–1.
16. [16] Osmani, V., Ferdous, R., Mayora, O., Smartphone app usage as a predictor of perceived stress levels at workplace. 9th International Conference on Pervasive Computing Technologies for Healthcare, 2015, IEEE.
17. [17] Ferdous, R., Osmani, V., Marquez, J.B., Mayora, O., Investigating correlation between verbal interactions and perceived stress. Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, 2015, IEEE, Milano.
18. [18] Naranjo, C., Kornreich, C., Campanella, S., Noël, X., Vandriette, Y., Gillain, B., De~Longueville, X., Delatte, B., Verbanck, P., Constant, E., Major depression is associated with impaired processing of emotion in music as well as in facial and vocal stimuli. J. Affect. Disord. 128:3 (2011), 243–251.
19. [19] Schleusing, O., Renevey, P., Bertschi, M., Dasen, S., Koller, J.-M., Paradiso, R., Monitoring physiological and behavioral signals to detect mood changes of bipolar patients. Medical Information & Communication Technology (ISMICT), 2011 5th International Symposium on, 2011, IEEE, 130–134.
20. [20] Quer, G., Nwokafor, A., Ganguly, A., Rashid, N., Zhu, J., Navani, D., Rao, R.R., Bliss buzzer, a system to monitor health and stress with real-time feedback. Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, 2013, ACM, 83.
21. [21] Bauer, M., Wilson, T., Neuhaus, K., Sasse, J., Pfennig, A., Lewitzka, U., Grof, P., Glenn, T., Rasgon, N., Bschor, T., et al. Self-reporting software for bipolar disorder: validation of chronorecord by patients with mania. Psychiatry Res. 159:3 (2008), 359–366.
22. [22] J. Blum, E. Magill, M-psychiatry: Sensor networks for psychiatric health monitoring, in: Proceedings of the 9th Annual Postgraduate Symposium on the Convergence of Telecommunications, Networking and Broadcasting (PGNET 2008), 2008, pp. 33–37.
23. [23] Morriss, R., The early warning symptom intervention for patients with bipolar affective disorder. Adv. Psychiatr. Treat. 10:1 (2004), 18–26.
24. [24] Paradiso, R., Bianchi, A., Lau, K., Scilingo, E., Psyche: personalised monitoring systems for care in mental health. Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, 2010, IEEE, 3602–3605.
25. [25] O.A.P. Ltd, Optimism apps. www.findingoptimism.com. Accessed: 26-Mar-2014.
26. [26] Burke, L.E., Styn, M.A., Sereika, S.M., Conroy, M.B., Ye, L., Glanz, K., Sevick, M.A., Ewing, L.J., Using mhealth technology to enhance self-monitoring for weight loss: a randomized trial. Am. J. Prev. Med. 43:1 (2012), 20–26.
27. [27] Bartholomew, J.B., Morrison, D., Ciccolo, J.T., et al. Effects of acute exercise on mood and well-being in patients with major depressive disorder. Med. Sci. Sports Exerc., 37(12), 2005, 2032.
28. [28] Dunn, A.L., Trivedi, M.H., Kampert, J.B., Clark, C.G., Chambliss, H.O., Exercise treatment for depression: efficacy and dose response. Am. J. Prev. Med. 28:1 (2005), 1–8.
29. [29] Todder, D., Caliskan, S., Baune, B.T., Longitudinal changes of day-time and night-time gross motor activity in clinical responders and non-responders of major depression. World J. Biol. Psychiatry 10:4 (2009), 276–284.
30. [30] Judd, L.L., Schettler, P.J., Akiskal, H., Coryell, W., Fawcett, J., Fiedorowicz, J.G., Solomon, D.A., Keller, M.B., Prevalence and clinical significance of subsyndromal manic symptoms, including irritability and psychomotor agitation, during bipolar major depressive episodes. J. Affect. Disord. 138:3 (2012), 440–448.
31. [31] Kaplan, K.A., Talbot, L.S., Gruber, J., Harvey, A.G., Evaluating sleep in bipolar disorder: comparison between actigraphy, polysomnography, and sleep diary. Bipolar Disord. 14:8 (2012), 870–879.
32. [32] Burton, C., McKinstry, B., Szentagotai~Tătar, A., Serrano-Blanco, A., Pagliari, C., Wolters, M., Activity monitoring in patients with depression: A systematic review. J. Affect. Disord. 145:1 (2013), 21–28.
33. [33] Bos, E.H., Bouhuys, A.L., Geerts, E., van~Os, T.W., van~der Spoel, I.D., Brouwer, W.H., Ormel, J., Cognitive, physiological, and personality correlates of recurrence of depression. J. Affect. Disord. 87:2 (2005), 221–229.
34. [34] Moore, E., Clements, M., Peifer, J., Weisser, L., Analysis of prosodic variation in speech for clinical depression. Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE, vol.~3, 2003, IEEE, 2925–2928.
35. [35] Moore, E., Clements, M.A., Peifer, J.W., Weisser, L., Critical analysis of the impact of glottal features in the classification of clinical depression in speech. IEEE Trans. Biomed. Eng. 55:1 (2008), 96–107.
36. [36] Ozdas, A., Shiavi, R.G., Silverman, S.E., Silverman, M.K., Wilkes, D.M., Investigation of vocal jitter and glottal flow spectrum as possible cues for depression and near-term suicidal risk. IEEE Trans. Biomed. Eng. 51:9 (2004), 1530–1540.
37. [37] Vanello, N., Guidi, A., Gentili, C., Werner, S., Bertschy, G., Valenza, G., Lanata, A., Scilingo, E.P., Speech analysis for mood state characterization in bipolar patients. Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, 2012, IEEE, 2104–2107.
38. [38] S. Aimee, It's not just what you say, it's how you say it. http://www.slate.com/articles/technology/future_tense/2014/08/priori_smartphone_app_listens_to_bipolar_patients_to_detect_manic_episodes.html. Accessed: August 3 2014.
39. [39] Jeong, D.-U., Kim, S.-J., Chung, W.-Y., Classification of posture and movement using a 3-axis accelerometer. Convergence Information Technology, 2007. International Conference on, 2007, IEEE, 837–844.
40. [40] FUNF. Funf - open sensing framework. https://code.google.com/p/funf-opensensing-framework/source/browse/src/edu/mit/media/funf/probe/builtin/activityprobe.java?r=55f67cf53180dbba8ed4eddef422737f3abe030e Accessed: 2015-AUG-29.
41. [41] Mukhopadhyay, S., Ray, G., A new interpretation of nonlinear energy operator and its efficacy in spike detection. IEEE Trans. Biomed. Eng. 45:2 (1998), 180–187.
42. [42] Cover, T.M., Thomas, J.A., Elements of Information Theory. 2012, John Wiley & Sons.
43. [43] Eyben, F., Wollmer, M., Schuller, B., Openear—introducing the munich open-source emotion and affect recognition toolkit. Affective Computing and Intelligent Interaction and Workshops, 2009. ACII 2009. 3rd International Conference on, 2009, IEEE, 1–6.
44. [44] Boersma, P., Praat, a system for doing phonetics by computer. Glot international 5:9/10 (2002), 341–345.
45. [45] Pérez-Espinosa, H., Reyes-García, C.A., Villaseñor-Pineda, L., Acoustic feature selection and classification of emotions in speech using a 3d continuous emotion model. Biomed. Signal Process. Control 7:1 (2012), 79–87.