Chemotherapy-induced fatigue estimation using hidden Markov model

• Autorzy: Ameli Sina , Naghdy Fazel , Stirling David , Naghdy Golshah , Aghmesheh Morteza

Identyfikatory

DOI

10.1016/j.bbe.2018.11.002

• Języki publikacji: EN

Abstrakty

EN

Chemotherapy-induced fatigue undermines the physical performance and alter gait behaviour of patients. In clinics, there is not a well-established method to objectively assess the effects of chemotherapy-induced fatigue on gait characteristics. Clinical trials commonly use 6 Minute Walking Tests (6MWT) to assess patients' gait. However, these studies only measure the distance that patients can walk. The distance does not provide comprehensive information about variations in ambulatory motion characteristics and body postural behaviour which can more appropriately describe the fatigue effects on general physical performance. Gait characteristics provide a manifestation of relationships between muscular and cardiovascular fitness status and physical motions. Hence, an assessment of gait characteristics provides more appropriate information about the effects of chemotherapy-induced fatigue on gait behaviour. A novel approach is proposed to objectively assess the impacts of chemotherapy-induced fatigue on cancer gait by analysing the gait characteristics during 6MWT. The joint angles of the lower body segments are measured by inertial sensors and modelled through a Hidden Markov Model (HMM) with Gaussian emissions. A Gaussian clustering method classifies the joint angles of first gait cycle to determine the six gait phases of a normal gait as initial training values. A comparison of gait characteristics before and after chemotherapy-induced fatigue determines the gait abnormalities. The method is applied to four cancer patients and outcomes are benchmarked against the gait of a healthy subject before and after running program-induced fatigue. The results indicate a more accurate quantitative-based tool to measure the effects of chemotherapy-induce fatigue on gait and physical performance.

Słowa kluczowe

EN

chemotherapy induced fatigue; inertial sensor; gait analysis; hidden Markov model

PL

chemioterapia; czujnik inercyjny; analiza chodu; ukryty model Markowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2019
• Tom: Vol. 39, no. 1
• Strony: 176--187
• Opis fizyczny: Bibliogr. 37 poz., rys., tab., wykr.

Twórcy

autor

Ameli Sina

• University of Wollongong, School of Electrical, Computer & Telecommunication (SECTE), Australia

autor

Naghdy Fazel

• University of Wollongong, School of Electrical, Computer & Telecommunication (SECTE), Australia

autor

Stirling David

• University of Wollongong, School of Electrical, Computer & Telecommunication (SECTE), Australia

autor

Naghdy Golshah

• University of Wollongong, School of Electrical, Computer & Telecommunication (SECTE), Australia

autor

Aghmesheh Morteza

• Wollongong Hospital, Illawarra Cancer Care Centre, Australia

Bibliografia

• [1] Carroll JK, Kohli S, Mustian KM, Roscoe JA, Morrow GR. Pharmacologic treatment of cancer-related fatigue. Oncologist 2007;12(Suppl 1):43–51.
• [2] Hofman M, Ryan JL, Figueroa-Moseley CD, Jean-Pierre P, Morrow GR. Cancer-related fatigue: the scale of the problem. Oncologist 2007;12(Suppl 1):4–10.
• [3] van Waart H, Stuiver MM, van Harten WH, Sonke GS, Aaronson NK. Design of the Physical exercise during Adjuvant Chemotherapy Effectiveness Study (PACES): a randomized controlled trial to evaluate effectiveness and cost-effectiveness of physical exercise in improving physical fitness and reducing fatigue. BMC Cancer 2010;10:673.
• [4] de Vries Schultink AH, Suleiman AA, Schellens JH, Beijnen JH, Huitema AD. Pharmacodynamic modeling of adverse effects of anti-cancer drug treatment. Eur J Clin Pharmacol 2016;72(June):645–53.
• [5] Oken MM, Creech RH, Tormey DC, Horton J, Davis TE, McFadden ET, et al. Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol 1982;5(December):649–55.
• [6] Banipal R, Singh H, Singh B. Assessment of cancer-related fatigue among cancer patients receiving various therapies: a cross-sectional observational study. Indian J Palliat Care 2017;23(April):207–11.
• [7] Mesa RAK, Tefferi H, Cheville A. Functional assessment of performance status in patients with myelofibrosis (MF): utility and feasibility of the 6-minute walk test (6MWT). J Clin Oncol 2009;27.
• [8] Niederer D, Schmidt K, Vogt L, Egen J, Klingler J, Hübscher M, et al. Functional capacity and fear of falling in cancer patients undergoing chemotherapy. Gait Posture 2014;39:865–9.
• [9] McGinley JL, Baker R, Wolfe R, Morris ME. The reliability of three-dimensional kinematic gait measurements: a systematic review. Gait Posture 2009;29:360–9.
• [10] Ameli S, Naghdy F, Stirling D, Naghdy G, Aghmesheh M. Objective clinical gait analysis using inertial sensors and six minute walking test. Pattern Recognit 2017;3:246–57.
• [11] Clay CA, Perera S, Wagner JM, Miller ME, Nelson JB, Greenspan SL. Physical function in men with prostate cancer on androgen deprivation therapy. Phys Ther 2007;87:1325–33.
• [12] Cesari M, Cerullo F, Zamboni V, Di Palma R, Scambia G, Balducci L, et al. Functional status and mortality in older women with gynecological cancer. J Gerontol Ser A: Biol Sci Med Sci 2013;68:1129–33.
• [13] Wren TA, Gorton 3rd GE, Ounpuu S, Tucker CA. Efficacy of clinical gait analysis: a systematic review. Gait Posture 2011;34:149–53.
• [14] Sejdic E, Lowry KA, Bellanca J, Perera S, Redfern MS, Brach JS. Extraction of stride events from gait accelerometry during treadmill walking. IEEE J Transl Eng Health Med 2016;4.
• [15] Baker R. Gait analysis methods in rehabilitation. J Neuroeng Rehabil 2006;3:4.
• [16] Monfort SM, Pan X, Patrick R, Singaravelu J, Loprinzi CL, Lustberg MB, et al. Natural history of postural instability in breast cancer patients treated with taxane-based chemotherapy: a pilot study. Gait Posture 2016;48:237–42.
• [17] Syczewska M, Dembowska-Bagińska B, Perek-Polnik M, Kalinowska M, Perek D. Gait pathology assessed with Gillette Gait Index in patients after CNS tumour treatment. Gait Posture 2010;32:358–62.
• [18] Jia J, Barbera L, Sutradhar R. Using Markov multistate models to examine the progression of symptom severity among an ambulatory population of cancer patients: are certain symptoms better managed than others? J Pain Symptom Manage 2016;51(February):232–9.
• [19] Bae J, Tomizuka M. Gait phase analysis based on a hidden Markov model. Mechatronics 2011;21:961–70.
• [20] Palladino JL, Davis Iii RB. Biomechanics. In: Enderle JD, Bronzino JD, editors. Introduction to biomedical engineering. 3rd ed. Boston: Academic Press; 2012. p. 133– 218 [Chapter 4].
• [21] Sperlich PF, Holmberg HC, Reed JL, Zinner C, Mester J, Sperlich B. Individual versus standardized running protocols in the determination of VO(2max). J Sports Sci Med 2015;14:386–93.
• [22] Wright KE, Lyons TS, Navalta JW. Effects of exercise-induced fatigue on postural balance: a comparison of treadmill versus cycle fatiguing protocols. Eur J Appl Physiol 2013;113(May):1303–9.
• [23] Ameli S, Naghdy F, Stirling D, Naghdya G, Aghmesheh M. Objective clinical fitness assessment using inertial sensors. International Symposium on Robotics and Intelligent Sensors, Malaysia; 2012.
• [24] Pfau T, Ferrari M, Parsons K, Wilson A. A hidden Markov model-based stride segmentation technique applied to equine inertial sensor trunk movement data. J Biomech 2008;41:216–20.
• [25] Rabiner JB. An introduction to hidden Markov models. IEEE ASSP Mag 1986;3:4–16.
• [26] Murphy K. Hidden Markov model (HMM) toolbox for Matlab; 1998.
• [27] de Jong N, Courtens AM, Abu-Saad HH, Schouten HC. Fatigue in patients with breast cancer receiving adjuvant chemotherapy: a review of the literature. Cancer Nurs 2002;25:283–97.
• [28] Ameli S, Naghdy F, Stirling D, Naghdy G, Aghmesheh M. Quantitative and non-invasive measurement of exercise-induced fatigue. Proc Inst Mech Eng Pt P: J Sports Eng Technol 2018. http://dx.doi.org/10.1177/1754337118775548.
• [29] Bower JE. Cancer-related fatigue – mechanisms, risk factors, and treatments. Nat Rev Clin Oncol 2014;11(October):597–609.
• [30] Piper BF, Borneman T, Sun VC-Y, Koczywas M, Uman G, Ferrell B, et al. Assessment of cancer-related fatigue: role of the oncology nurse in translating NCCN assessment guidelines into practice. Clin J Oncol Nurs 2008;12:37–47.
• [31] Cai B, Allexandre D, Rajagopalan V, Jiang Z, Siemionow V, Ranganathan VK, et al. Evidence of significant central fatigue in patients with cancer-related fatigue during repetitive elbow flexions till perceived exhaustion. PLoS One 2014;9:e115370.
• [32] Zarogoulidis P, Kerenidi T, Huang H, Kontakiotis T, Tremma O, Porpodis K, et al. Six minute walking test and carbon monoxide diffusing capacity for non-small cell lung cancer: easy performed tests in every day practice. J Thorac Dis 2012;4(December):569–76.
• [33] Huehnchen P, Boehmerle W, Endres M. Assessment of paclitaxel induced sensory polyneuropathy with catwalk automated gait analysis in mice. PLoS One 2013;8:e76772.
• [34] Panahandeh G, Mohammadiha N, Leijon A, Handel P. Continuous hidden Markov model for pedestrian activity classification and gait analysis. IEEE Trans Instrum Meas 2013;62:1073–83.
• [35] Chee Seng C, Honghai L. Fuzzy qualitative human motion analysis. IEEE Trans Fuzzy Syst 2009;17:851–62.
• [36] Karg M, Venture G, Hoey J, Kulic D. Human movement analysis as a measure for fatigue: a hidden Markov-based approach. IEEE Trans Neural Syst Rehabil Eng 2014;22: 470–481.
• [37] Nakagawa T, Chiba N, Saito M, Sakaguchi Y, Ishikawa S. Clinical relevance of decreased oxygen saturation during 6-min walk test in preoperative physiologic assessment for lung cancer surgery. Gen Thorac Cardiovasc Surg 2014;62(October):620–6.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).