İnsan Kafasındaki Dokuların Öziletkenliklerinin in vivo E/MEG Verileri ile Kestirilmesi ve Üç Değişik Kestirim Algoritma Sonuçlarının Karşılaştırılması
Date
2004-08-03
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Signal Processing and Communications Applications Conference
Abstract
Doku öziletkenliklerinin bilinmesi, insan vücudunun güvenilir
hacim iletken modellerinin oluşturulmasında ve ileri/ters
biyoelektrik alan problemlerinin çözümünde gereklidir. Bu
çalışmada, insan kafasindaki dokulann öziletkenliklerinin
EEG ve MEG verileri kullanılarak in vivo kestirimi ipin üç
farklı kestirim algoritmasi kullanılarak elde edilen sonuçlar
karşılaştırılmıştır. Uygulanan bu algoritmalar; En Küçük
Hatalar Karesi (E.K.H.K) kestirim algoritmasi, Bayesian
MAP kestirim algoritmasi ve istatistiksel Kısıtlı Minimum
Ortalama Hatalar Karesi (1.K.M.O.H.K) algoritmasıdır.
Algoritmalar, geometrik yapı, ön bilgisi ile doku
öziletkenlikleri ile doğrusallaştırma ve enstrümantasyon
gürültüsünün istatistiksel ön bilgilerini girdi olarak kullanır.
E/MEG verileri, medyan sinirin uyarıkdığı kaynak
konumlandırma deneyinden sırasıyla 32 kanallı EEG ve 31
kanallı magnetometre ile somatosensory korteks üzerinden
ölçülmüştür. Kafanın anatomik geometri bilgisi 256 adet TI
ağırlıklı MRI görüntüden elde edilmiş ve kafa derisi, kafatası
ve beyin olarak homojen üç bölgeye bölütlendirilmiştir.
Sözkonusu algoritmalar kullanılarak kafa derisi, kafatası ve
beyin öziletkenlikleri ve hata oranları üç farklı algoritma ile
kestirilmiştir. Hata oranları E.K.H.K için %90, Bayesian
Map kestirim algoriması için % 20.5 ve İ.K.M.O.H.K
algoritması için %12.5 olarak hesaplanmıştır. Sonuçta İ.K.M.O.H.K algoritmasının diğer algoritmalara kıyasla daha düşük
hata oranları verdiği gösterilmiştir.
Description
Estimation Of Human Head Tissue Conductivities by Using In
vivo EMEG Data and Comparison of Three Different
Estimation Algorithm Results
ABSTRACT: Knowledge of tissue conductivities is needed to construct reliable volume conductor models ofthe human body and the head in solving forward and inverse bioelectric field problems. In this study three different estimation algorithms are applied to in vivo human head tissue resistivity estimation by using EEG and MEG data. The applied algorithms are conventional Least-Squared EROI Algorithm (LSEE), Bayesian MAP Algorithm and statistically constrained Minimum Mean Squared Error Estimator (MiMSEE). The algorithms intake a priori information on body geometry (realistic boundary element model), statistical properties of regional conductivities (assumed to be uniformly dishihuted between upper and lower hounds), linearization error and instrumentation noise. The EEG-MEG data set has been obtained from a source localization experiment in which the median nerve has been stimulated. The anatomical boundary information has been extracted from 256 TI-weighted MRI images. The MEG data have been obtained by using a 31- channel magnetometer over the somatosensory cortex. By using the data, scalp, skull and brain conductivities have been estimated and estimation variances are calculated by using the algorithms. It is shown that MiMSEE algorithm gives lower error rates than the other two algorithms. The calculated error rates are % 90 for the LSEE, %20.5 for the Bayesian Map estimator and %12.5 for the MiMSEE.
ABSTRACT: Knowledge of tissue conductivities is needed to construct reliable volume conductor models ofthe human body and the head in solving forward and inverse bioelectric field problems. In this study three different estimation algorithms are applied to in vivo human head tissue resistivity estimation by using EEG and MEG data. The applied algorithms are conventional Least-Squared EROI Algorithm (LSEE), Bayesian MAP Algorithm and statistically constrained Minimum Mean Squared Error Estimator (MiMSEE). The algorithms intake a priori information on body geometry (realistic boundary element model), statistical properties of regional conductivities (assumed to be uniformly dishihuted between upper and lower hounds), linearization error and instrumentation noise. The EEG-MEG data set has been obtained from a source localization experiment in which the median nerve has been stimulated. The anatomical boundary information has been extracted from 256 TI-weighted MRI images. The MEG data have been obtained by using a 31- channel magnetometer over the somatosensory cortex. By using the data, scalp, skull and brain conductivities have been estimated and estimation variances are calculated by using the algorithms. It is shown that MiMSEE algorithm gives lower error rates than the other two algorithms. The calculated error rates are % 90 for the LSEE, %20.5 for the Bayesian Map estimator and %12.5 for the MiMSEE.
Keywords
electrical & electronics engineering