کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8799756 1604172 2018 32 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Identification of regional activation by factorization of high-density surface EMG signals: A comparison of Principal Component Analysis and Non-negative Matrix factorization
ترجمه فارسی عنوان
شناسایی فعال سازی منطقه ای با فاکتور سازی سیگنال های سطح الکترومغناطیسی سطح بالا: مقایسۀ تجزیه و تحلیل مولفه های اصلی و تقسیم ماتریس غیر منفی
موضوعات مرتبط
علوم پزشکی و سلامت پزشکی و دندانپزشکی ارتوپدی، پزشکی ورزشی و توانبخشی
چکیده انگلیسی
In this study, we investigated whether principal component analysis (PCA) and non-negative matrix factorization (NMF) perform similarly for the identification of regional activation within the human vastus medialis. EMG signals from 64 locations over the VM were collected from twelve participants while performing a low-force isometric knee extension. The envelope of the EMG signal of each channel was calculated by low-pass filtering (8 Hz) the monopolar EMG signal after rectification. The data matrix was factorized using PCA and NMF, and up to 5 factors were considered for each algorithm. Association between explained variance, spatial weights and temporal scores between the two algorithms were compared using Pearson correlation. For both PCA and NMF, a single factor explained approximately 70% of the variance of the signal, while two and three factors explained just over 85% or 90%. The variance explained by PCA and NMF was highly comparable (R > 0.99). Spatial weights and temporal scores extracted with non-negative reconstruction of PCA and NMF were highly associated (all p < 0.001, mean R > 0.97). Regional VM activation can be identified using high-density surface EMG and factorization algorithms. Regional activation explains up to 30% of the variance of the signal, as identified through both PCA and NMF.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Electromyography and Kinesiology - Volume 41, August 2018, Pages 116-123
نویسندگان
, , ,