A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests
المؤلفون المشاركون
المصدر
International Journal of Telemedicine and Applications
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-04-12
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Recently, speech pattern analysis applications in building predictive telediagnosis and telemonitoring models for diagnosing Parkinson’s disease (PD) have attracted many researchers.
For this purpose, several datasets of voice samples exist; the UCI dataset named “Parkinson Speech Dataset with Multiple Types of Sound Recordings” has a variety of vocal tests, which include sustained vowels, words, numbers, and short sentences compiled from a set of speaking exercises for healthy and people with Parkinson’s disease (PWP).
Some researchers claim that summarizing the multiple recordings of each subject with the central tendency and dispersion metrics is an efficient strategy in building a predictive model for PD.
However, they have overlooked the point that a PD patient may show more difficulty in pronouncing certain terms than the other terms.
Thus, summarizing the vocal tests may lead into loss of valuable information.
In order to address this issue, the classification setting must take what has been said into account.
As a solution, we introduced a new framework that applies an independent classifier for each vocal test.
The final classification result would be a majority vote from all of the classifiers.
When our methodology comes with filter-based feature selection, it enhances classification accuracy up to 15%.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Behroozi, Mahnaz& Sami, Ashkan. 2016. A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests. International Journal of Telemedicine and Applications،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1107092
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Behroozi, Mahnaz& Sami, Ashkan. A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests. International Journal of Telemedicine and Applications No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1107092
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Behroozi, Mahnaz& Sami, Ashkan. A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests. International Journal of Telemedicine and Applications. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1107092
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1107092
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر