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Klasik Türk Müziği Makamlarının Minör/Majör Depresyon Hastalarının Üzerindeki Duygu Değişimlerine ve Tedavi Süreçlerine Etkilerinin Beyin EEG Sinyalleri Kullanılarak Analiz Edilmesi Potansiyelinin Meta-Sentez Yöntemi ile İncelenmesi

Yıl 2020, Cilt: 9 Sayı: 27, 947 - 978, 15.12.2020

Öz

Müzikle tedaviyi sistemli olarak ilk defa kullanan Türklerde, bu tedavi yöntemi yaklaşık 6000 yıl kadar eskiye dayanmaktadır. Modern bilimin gelişimine paralel olarak müzikle tedavi üzerine bir çok literatür çalışması gerçekleştirilmiştir. Klasik Türk Müziği makam esasına dayanan bir Türk müzik türüdür. Klasik Türk Müziği ile müzik terapisi çok eski dönemlerden itibaren uygulanmasına rağmen bu kapsamdaki uluslararası modern literatür çalışmalarının gerçekleştirilmesine ancak 2000’li yılların ikinci yarısından itibaren başlandığı görülmektedir. Müzik dinlemenin beyin EEG (Elektroensefalografi) sinyallerine etkisinin incelenmesine ilişkin ilk çalışmalar 1980’li yılların başlarında yapılmıştır. Literatürde dinlenilen müzik eserinin beğenilip beğenilmediği ya da hangi duyguları
uyandırdığının beyin EEG sinyalleri ile tahmin edilmesi-sınıflandırılması yönünde birçok çalışma gerçekleştirilmiştir. Ayrıca, son yıllarda yapılan literatür çalışmaları dikkate alındığında müzik terapisinin oluşturduğu EEG sinyallerinin mühendislik analizi ile hastalığın gidişatı üzerindeki tıbbi değerlendirmelerin birlikte yapıldığı multi-disipliner literatür çalışmalarında artış görülmektedir. Müzik ile beyin EEG sinyalleri arasındaki ilişkileri inceleyen çalışmalarda kullanılan müzik eserlerinin, çalışmayı yapan ekiplerin etnik ve kültürel kökenlerinin de etkisiyle genellikle Klasik Batı Müziği, Klasik Hint Müziği, Rock Müzik, Klasik İran Müziği gibi müzik türlerinden seçildiği görülmektedir. Sınırlı sayıda olmakla birlikte Klasik Türk Müziği ile beyin EEG sinyalleri arasındaki ilişkiyi inceleyen çalışmalarda gerçekleştirilmiştir. Depresyon; uyaranlara karşı duyarlığın azalması, girişim gücünün ve kendine
güvenin yiterek umutsuzluğun, karamsarlığın güçlenmesi biçiminde beliren ruhsal bozukluk hali olarak tanımlanabilir. Depresyon genel olarak majör depresyon ve minör depresyon olmak üzere iki başlıkta incelenmektedir. Sağlık Bakanlığının antidepresan kullanımına ilişkin verileri yıllık ortalama % 10 civarında bir artışa işaret etmektedir. Ayrıca, bu veriler depresyonun ülkemiz adına bir hastalık olmaktan ziyade bir halk sağlığı problemine dönüşmek üzere olduğunu açıkça ortaya koymaktadır.
Bu meta-sentez çalışmasında literatür üzerinde kapsamlı bir inceleme yapılarak Klasik Türk Müziği makamlarının minör/majör depresyon hastaları üzerindeki duygu değişimlerine ve tedavi süreçlerine etkilerinin beyin EEG sinyalleri
kullanılarak analiz edilmesine yönelik potansiyelinin incelenmesi ve açığa çıkarılması hedeflenmiştir. Bu kapsamda gerçekleştirilen çalışmanın araştırmacıların konu hakkında multi-disipliner çalışmalar yapmalarını teşvik edeceği ve kolaylaştıracağı değerlendirilmektedir.

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  • SENGUPTA, Sourya, Sayan. BISWAS, Shankha. SANYAL, Archi. BANERJEE, Ranjan. SENGUPTA. ve Dipak. GHOSH, (2016), “Quantification and categorization of emotion using cross cultural music: An EEG based fractal study”, 2nd International Conference on Next Generation Computing Technologies (NGCT), 759-764.
  • SHAHABI, Hossein. ve Sahar. MOGHIMI, (2016), “Toward automatic detection of brain responses to emotional music through analysis of EEG effective connectivity”, Computers in Human Behavior, 58:231-239.
  • SHAHNAZ, Celia, Shoaib. MASUD. ve S. M. Shafiul. HASAN, (2016), “Emotion recognition based on wavelet analysis of Empirical Mode Decomposed EEG signals responsive to music videos”, Region 10 Conference (TENCON), 424-427.
  • SOURINA, Olga, Yisi. LIU. ve Minh. K. NGUYEN, (2012), “Real-time EEG-based emotion recognition for music therapy”, Journal on Multimodal User Interfaces, V, 1-2:27-35.
  • SREEDEVI M, A. AJESH, R. AJITHNATH. ve L. BINU, (2009), “A study of effect of music pitch variation in EEG using factor analysis and neural networks”, 2nd International Conference on Biomedical Engineering and Informatics,1-3.
  • STURM, Irene, Sven. DÄHNE, Benjamin. BLANKERTZ. ve Gabriel. CURIO, (2015), “Multi-variate EEG analysis as a novel tool to examine brain responses to naturalistic music stimuli”, PloS One, X, 10:e0141281.
  • ŞENGÜL, Enver, (2008), “Kültür Tarihi İçinde Müzikle Tedavi ve Edirne Sultan II. Bayezid Darüşşifası”, Trakya Üniversitesi Sosyal Bilimler Enstitüsü, Yüksek Lisans Tezi, Edirne.
  • UÇAN, Ali, (1985), “İnsan ve Müzik”, Gazi Üniversitesi Gazi Eğitim Fakültesi Dergisi, I, 1:74-92.
  • UĞRAŞ, Gülay, Güven. YILDIRIM, Serpil. YÜKSEL, Yusuf. ÖZTÜRKÇÜ, Mustafa. KUZDERE. ve Seher. D. ÖZTEKİN, (2018), “The effect of different types of music on patients’ preoperative anxiety: A randomized controlled trial”, Complementary Therapies in Clinical Practice, 31:158-163.
  • USLU, Gonca, (2017), “Influence of Music Therapy on the State of Anxiety During Radiotherapy”, Turkish Journal of Oncology, XXXII, 4:141-147. TANDLE, Avinash, Nandini. JOG, Ambrish. DHARMADHIKARI, Suyog.
  • JAISWAL. ve Vishal. SWANT, (2016), “Study of valence of musical emotions and its laterality evoked by instrumental Indian classical music: An EEG study”, International Conference on Communication and Signal Processing (ICCSP), 0327- 0331.
  • THAMMASAN, Nattapong, Ken-ichi. FUKUI. ve Masayuki. NUMAO, (2016), “An investigation of annotation smoothing for eeg-based continuous music-emotion recognition”, IEEE International Conference on Systems, Man, and Cybernetics (SMC), 003323-003328.
  • THAMMASAN, Nattapong, Ken-ichi. FUKUI. ve Masayuki. NUMAO, (2016), “Application of deep belief networks in eeg-based dynamic music-emotion recognition”, International Joint Conference on Neural Networks (IJCNN), 881-888.
  • THAMMASAN, Nattapong, Ken-ichi. FUKUI. ve Masayuki. NUMAO, (2017), “Multimodal Fusion of EEG and Musical Features in Music-Emotion Recognition”, Association for the Advancement of Artificial Intelligence, 4991-4992.
  • TORNEK, Alexandra, Tiffany. FIELD, Maria. HERNANDEZ-REIF, Miguel. DIEGO. ve Nancy. JONES, (2003), “Music effects on EEG in intrusive and withdrawn mothers with depressive symptoms”, Psychiatry, LXVI, 3:234-243.
  • TREDER, Matthias Sebastian, Hendrik. PURWINS, Daniel. MIKLODY, Irene. STURM. ve Benjamin. BLANKERTZ, (2014), “Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification”, Journal of Neural Engineering, XI, 2:026009.
  • TSENG, Kevin, Bor-Shyh. LIN, Chang-Mu. HAN. ve Psi-Shi. WANG, (2013), “Emotion recognition of EEG underlying favourite music by support vector machine”, International Conference on Orange Technologies (ICOT), 155-158.
  • VAROTTO Giulia, Patrik. FAZIO, D. Rossi. SEBASTIANO, Giuliano. AVANZINI, Silvana. FRANCESCHETTI. ve Ferruccio. PANZICA, (2012), “Music and emotion: An EEG connectivity study in patients with disorders of consciousness”, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 5206-5209.
  • VERMA, Tapish. ve Indu. SAINI, (2016), “Age-related variation in EEG to music stimulation: A nonlinear analysis”, 2nd International Conference on Next Generation Computing Technologies, 137-143.
  • VIJAYALAKSHMI, K, Susmita. SRIDHAR. ve Payal. KHANWANI, (2010), “Estimation of effects of alpha music on EEG components by time and frequency domain analysis”, International Conference on Computer and Communication Engineering, 1-5.
  • VIJAYARAGAVAN, Gautham Raj, Revathy. RAGHAV, Kompella. PHANI. ve Vivek. VAIDYANATHAN, (2015), “EEG monitored mind de-stressing smart phone application using Yoga and Music Therapy”, International Conference on Green Computing and Internet of Things (ICGCIoT), 412-415.
  • WALKER, James, (1980), “Alpha EEG correlates of performance on a music recognition task”, Physiological Psychology, VIII, 3:417-420. WEED, Mike, (2005), ““Meta Interpretation”: A Method for the Interpretive Synthesis of Qualitative Research”, Forum Qualitative Sozialforschung/Forum:Qualitative Social Research, VI, 1.
  • YİĞİTBAŞ, Sadık, (1972), Musiki ile Tedavi (1. Basım), İstanbul: İstanbul Yayınevi. ZHAO, Wei, Xinxi. WANG. ve Ye. WANG, (2010), “Automated sleep quality measurement using EEG signal: first step towards a domain specific music recommendation system”, 18th ACM International Conference on Multimedia, 1079- 1082
Toplam 124 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Eğitim Üzerine Çalışmalar
Bölüm Eğitim ve Toplum Sayı 27
Yazarlar

Naciye Hardalaç 0000-0001-9380-2640

Hüseyin Yaşar 0000-0003-2972-7441

Pınar Akdemir Özışık Bu kişi benim 0000-0002-9630-0707

Yayımlanma Tarihi 15 Aralık 2020
Gönderilme Tarihi 21 Nisan 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 9 Sayı: 27

Kaynak Göster

APA Hardalaç, N., Yaşar, H., & Özışık, P. A. (2020). Klasik Türk Müziği Makamlarının Minör/Majör Depresyon Hastalarının Üzerindeki Duygu Değişimlerine ve Tedavi Süreçlerine Etkilerinin Beyin EEG Sinyalleri Kullanılarak Analiz Edilmesi Potansiyelinin Meta-Sentez Yöntemi ile İncelenmesi. 21. Yüzyılda Eğitim Ve Toplum, 9(27), 947-978.

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