Альманах клинической медицины. 2015; : 15-21
ПРИМЕНЕНИЕ КОМПЛЕКСА «ИНТЕРФЕЙС “МОЗГ – КОМПЬЮТЕР” И ЭКЗОСКЕЛЕТ» И ТЕХНИКИ ВООБРАЖЕНИЯ ДВИЖЕНИЯ ДЛЯ РЕАБИЛИТАЦИИ ПОСЛЕ ИНСУЛЬТА
https://doi.org/10.18786/2072-0505-2015-39-15-21Аннотация
Актуальность. Эффективность физических упражнений и воображения движений для восстановления двигательных нарушений после инсульта считается доказанной. Однако применение воображения движений осложняется невозможностью объективного и субъективного контроля за выполнением упражнений, а также отсутствием их двигательного подкрепления. Интерфейс «мозг – компьютер» на основе электроэнцефалограммы – метод, позволяющий осуществлять обратную связь при выполнении воображения движений.
Материал и методы. Обследованы 10 пациентов (6 мужчин и 4 женщины) в возрасте от 30 до 66 лет (средний возраст 47 ± 7,7 года), перенесших ишемический (n = 9) и геморрагический (n = 1) инсульт в срок от 2 месяцев до 4 лет. Онлайн-распознавание воображения движений осуществлялось классификатором с помощью интерфейса «мозг – компьютер». Экзоскелет осуществлял пассивное движение в паретичной кисти под управлением интерфейса «мозг – компьютер». Пациенты получали по 10 занятий длительностью 45–90 минут в течение 2 недель. В качестве контроля использовали данные 5 пациентов, перенесших инсульт, которым в дополнение к стандартной терапии проводилась имитация реабилитационной процедуры без воображения движения и обратной связи. Для оценки эффективности проводимых мероприятий использовали модифицированную шкалу Ашворта, шкалу Fugl-Meyer, тест исследования функций руки ARAT, Британскую шкалу оценки мышечной силы MRC-SS; уровень дееспособности и повседневной активности определяли при помощи модифицированной шкалы Рэнкина и индекса Бартел; когнитивные функции исследовали с использованием таблиц Шульте.
Результаты. Онлайн-распознавание воображения движений по реакции десинхронизации μ-ритма зарегистрировано у пациентов в 50–75%. Субъективно все пациенты отметили улучшение двигательных функций и дееспособности. Положительный результат по данным одного и более показателей был отмечен у всех пациентов, однако статистически значимого различияпоказателей до и после проведения реабилитационных мероприятий не получено за исключением когнитивной сферы (степень врабатываемости, p < 0,02).
Заключение. У пациентов, перенесших инсульт, процедура с использованием воображения движений, интерфейса «мозг – компьютер» и экзоскелета не оказывала отрицательного влияния на процесс реабилитации. Во всех наблюдениях был достигнут положительный результат как в отношении восстановления движений, так и дееспособности и повседневной активности. Результат применения реабилитационной процедуры перспективен, однако следует продолжить исследование.
Список литературы
1. Prasad G, Herman P, Coyle D, McDonough S, Crosbie J. Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study. J Neuroeng Rehabil. 2010;7:60.
2. Yoon JA, Koo BI, Shin MJ, Shin YB, Ko HY, Shin YI. Effect of constraint-induced movement therapy and mirror therapy for patients with subacute stroke. Ann Rehabil Med. 2014;38(4): 458–66.
3. Котов СВ. Новые технологии в диагностике и лечении больных в остром периоде инсульта. Русский медицинский журнал. 2014;22(10):712–6. Kotov SV. Novye tekhnologii v diagnostike i lechenii bol'nykh v ostrom periode insul'ta [New technologies in diagnostics and treatment of acute stroke patients]. Russkiy meditsinskiy zhurnal. 2014;22(10):712–6 (in Russian).
4. Albert SJ, Kesselring J. Neurorehabilitation. In: Brainin M, Heiss WD, editors. Textbook of Stroke Medicine. Cambridge: Cambridge University Press; 2010. p. 283–306.
5. Nichols-Larsen DS, Clark PC, Zeringue A, Greenspan A, Blanton S. Factors influencing stroke survivors' quality of life during subacute recovery. Stroke. 2005;36(7):1480–4.
6. Plautz EJ, Milliken GW, Nudo RJ. Effects of repetitive motor training on movement representations in adult squirrel monkeys: role of use versus learning. Neurobiol Learn Mem. 2000;74(1):27–55.
7. Kwakkel G. Impact of intensity of practice after stroke: issues for consideration. Disabil Rehabil. 2006;28(13–14):823–30. 8. Imam B, Jarus T. Virtual reality rehabilitation from social cognitive and motor learning theoretical perspectives in stroke population. Rehabil Res Pract. 2014;2014:594540.
8. Lohse KR, Hilderman CG, Cheung KL, Tatla S, Van der Loos HF. Virtual reality therapy for adults post-stroke: a systematic review and meta-analysis exploring virtual environments and commercial games in therapy. PLoS One. 2014;9(3):e93318.
9. Lazaridou A, Astrakas L, Mintzopoulos D, Khanicheh A, Singhal AB, Moskowitz MA, Rosen B, Tzika AA. Diffusion tensor and volumetric magnetic resonance imaging using an MR-compatible hand-induced robotic device suggests training-induced neuroplasticity in patients with chronic stroke. Int J Mol Med. 2013;32(5):995–1000.
10. Котов СВ, Турбина ЛГ, Бобров ПД, Фролов АА, Павлова ОГ, Курганская МЕ, Бирюкова ЕВ. Реабилитация больных, перенесших инсульт, с помощью биоинженерного комплекса «интерфейс мозг-компьютер + экзоскелет». Журнал неврологии и психиатрии им. С.С. Корсакова. 2014;14(12–2):66–72. Kotov SV, Turbina LG, Bobrov PD, Frolov AA, Pavlova OG, Kurganskaya ME, Biryukova EV. Reabilitatsiya bol'nykh, perenesshikh insul't, s pomoshch'yu bioinzhenernogo kompleksa «interfeys mozg-komp'yuter + ekzoskelet» [Rehabilitation of post stroke patients using a bioengineering system “brain-computer interface + exoskeleton”]. Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova. 2014; 14(12–2):66–72 (in Russian).
11. Bobrov P, Frolov A, Cantor C, Fedulova I, Bakhnyan M, Zhavoronkov A. Brain-computer interface based on generation of visual images. PLoS One. 2011;6(6):e20674.
12. Takahashi M, Takeda K, Otaka Y, Osu R, Hanakawa T, Gouko M, Ito K. Event related desynchronization- modulated functional electrical stimulation system for stroke rehabilitation: a feasibility study. J Neuroeng Rehabil. 2012;9:56.
13. Faller J, Scherer R, Friedrich EV, Costa U, Opisso E, Medina J, Muller-Putz GR. Non-motor tasks improve adaptive brain-computer interface performance in users with severe motor impairment. Front Neurosci. 2014;8:320.
14. Bohannon RW, Smith MB. Interrater reliability of a modified Ashworth scale of muscle spasticity. Phys Ther. 1987;67(2):206–7.
15. Fugl-Meyer AR, Jaasko L, Leyman I, Olsson S, Steglind S. The post-stroke hemiplegic patient. 1. A method for evaluation of physical performance. Scand J Rehabil Med. 1975;7(1):13–31.
16. Lyle RC. A performance test for assessment of upper limb function in physical rehabilitation treatment and research. Int J Rehabil Res.
17. ;4(4):483–92.
18. Белова АН, ред. Шкалы, тесты и опросники в медицинской реабилитации. М.: Антидор; 2002. 440 с. Belova AN, editor. Shkaly, testy i oprosniki v meditsinskoy reabilitatsii [Scales, tests and questionnaires in medical rehabilitation]. Moscow Antidor; 2002. 440 p. (in Russian).
Almanac of Clinical Medicine. 2015; : 15-21
THE USE OF A COMPLEX “BRAIN-COMPUTER INTERFACE AND EXO-SKELETON” AND MOVEMENT IMAGINATION TECHNIQUE FOR POST-STROKE REHABILITATION
https://doi.org/10.18786/2072-0505-2015-39-15-21Abstract
Background: Efficacy of physical exercise and movement imagination for restoration of motor dysfunction after a stroke is seen as proven. However, the use of movement imagination is complicated by impossibility of objective and subjective control over the exercise, as well as by the absence of their motor support. The brain-computer interface based on electroencephalography is a technique that enables a feedback during movement imagination.
Materials and methods: We assessed 10 patients (6 men and 4 women) aged from 30 to 66 years (mean age, 47 ± 7.7 years) with an ischemic (n = 9) and hemorrhagic (n = 1) stroke during the last 2 months to 4 years. Online recognition of movement imagination was done by a classifier with a brain computer interface. An exo-skeleton supported passive movements in a paretic hand managed by the brain-computer interface. During 2 weeks the patients had 10 sessions of 45–90 minute duration each. For control, we used data from 5 stroke patients who, in addition to their standard treatment, underwent an imitation of rehabilitation procedures without movement imagination and feedback. To assess efficacy of treatment, we used a modified Ashworth scale, Fugl-Meyer scale, test for evaluation of hand functions ARAT, British scale for assessment of muscle force MRC-SS. Level of everyday activity and working ability was measured with a modified Rankin scale and Bartel index. Cognitive functions were assessed with Schulte tables.
Results: Online recognition of movement imagination according to desynchronization of μ rhythm was registered in 50–75% of patients. All patients reported a subjective improvement of motor functions and working ability. Positive results for at least one parameter were observed in all patients; however, there were no significant difference between the parameters before and after rehabilitation procedures, excluding cognitive functions (degree of warming-up, p < 0.02).
Conclusion: In post stroke patients, the use of movement imagination, brain-computer interface and exo-skeleton does not seem to affect the rehabilitation process negatively. In all cases, some positive results were achieved in motor recovery, as well as in working ability and daily activity. The results of the rehabilitation procedure are promising; however, the study should be continued.
References
1. Prasad G, Herman P, Coyle D, McDonough S, Crosbie J. Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study. J Neuroeng Rehabil. 2010;7:60.
2. Yoon JA, Koo BI, Shin MJ, Shin YB, Ko HY, Shin YI. Effect of constraint-induced movement therapy and mirror therapy for patients with subacute stroke. Ann Rehabil Med. 2014;38(4): 458–66.
3. Kotov SV. Novye tekhnologii v diagnostike i lechenii bol'nykh v ostrom periode insul'ta. Russkii meditsinskii zhurnal. 2014;22(10):712–6. Kotov SV. Novye tekhnologii v diagnostike i lechenii bol'nykh v ostrom periode insul'ta [New technologies in diagnostics and treatment of acute stroke patients]. Russkiy meditsinskiy zhurnal. 2014;22(10):712–6 (in Russian).
4. Albert SJ, Kesselring J. Neurorehabilitation. In: Brainin M, Heiss WD, editors. Textbook of Stroke Medicine. Cambridge: Cambridge University Press; 2010. p. 283–306.
5. Nichols-Larsen DS, Clark PC, Zeringue A, Greenspan A, Blanton S. Factors influencing stroke survivors' quality of life during subacute recovery. Stroke. 2005;36(7):1480–4.
6. Plautz EJ, Milliken GW, Nudo RJ. Effects of repetitive motor training on movement representations in adult squirrel monkeys: role of use versus learning. Neurobiol Learn Mem. 2000;74(1):27–55.
7. Kwakkel G. Impact of intensity of practice after stroke: issues for consideration. Disabil Rehabil. 2006;28(13–14):823–30. 8. Imam B, Jarus T. Virtual reality rehabilitation from social cognitive and motor learning theoretical perspectives in stroke population. Rehabil Res Pract. 2014;2014:594540.
8. Lohse KR, Hilderman CG, Cheung KL, Tatla S, Van der Loos HF. Virtual reality therapy for adults post-stroke: a systematic review and meta-analysis exploring virtual environments and commercial games in therapy. PLoS One. 2014;9(3):e93318.
9. Lazaridou A, Astrakas L, Mintzopoulos D, Khanicheh A, Singhal AB, Moskowitz MA, Rosen B, Tzika AA. Diffusion tensor and volumetric magnetic resonance imaging using an MR-compatible hand-induced robotic device suggests training-induced neuroplasticity in patients with chronic stroke. Int J Mol Med. 2013;32(5):995–1000.
10. Kotov SV, Turbina LG, Bobrov PD, Frolov AA, Pavlova OG, Kurganskaya ME, Biryukova EV. Reabilitatsiya bol'nykh, perenesshikh insul't, s pomoshch'yu bioinzhenernogo kompleksa «interfeis mozg-komp'yuter + ekzoskelet». Zhurnal nevrologii i psikhiatrii im. S.S. Korsakova. 2014;14(12–2):66–72. Kotov SV, Turbina LG, Bobrov PD, Frolov AA, Pavlova OG, Kurganskaya ME, Biryukova EV. Reabilitatsiya bol'nykh, perenesshikh insul't, s pomoshch'yu bioinzhenernogo kompleksa «interfeys mozg-komp'yuter + ekzoskelet» [Rehabilitation of post stroke patients using a bioengineering system “brain-computer interface + exoskeleton”]. Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova. 2014; 14(12–2):66–72 (in Russian).
11. Bobrov P, Frolov A, Cantor C, Fedulova I, Bakhnyan M, Zhavoronkov A. Brain-computer interface based on generation of visual images. PLoS One. 2011;6(6):e20674.
12. Takahashi M, Takeda K, Otaka Y, Osu R, Hanakawa T, Gouko M, Ito K. Event related desynchronization- modulated functional electrical stimulation system for stroke rehabilitation: a feasibility study. J Neuroeng Rehabil. 2012;9:56.
13. Faller J, Scherer R, Friedrich EV, Costa U, Opisso E, Medina J, Muller-Putz GR. Non-motor tasks improve adaptive brain-computer interface performance in users with severe motor impairment. Front Neurosci. 2014;8:320.
14. Bohannon RW, Smith MB. Interrater reliability of a modified Ashworth scale of muscle spasticity. Phys Ther. 1987;67(2):206–7.
15. Fugl-Meyer AR, Jaasko L, Leyman I, Olsson S, Steglind S. The post-stroke hemiplegic patient. 1. A method for evaluation of physical performance. Scand J Rehabil Med. 1975;7(1):13–31.
16. Lyle RC. A performance test for assessment of upper limb function in physical rehabilitation treatment and research. Int J Rehabil Res.
17. ;4(4):483–92.
18. Belova AN, red. Shkaly, testy i oprosniki v meditsinskoi reabilitatsii. M.: Antidor; 2002. 440 s. Belova AN, editor. Shkaly, testy i oprosniki v meditsinskoy reabilitatsii [Scales, tests and questionnaires in medical rehabilitation]. Moscow Antidor; 2002. 440 p. (in Russian).
