Discontinuous doppler signals simulating respiratory misregistration: Effect on autoregressive frequency spectra

Kemalolu, Semra; Erdoan, Nuri; Kara, Sadık

doi:10.1016/j.compbiomed.2005.03.002

Cited by 2 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yule-Walker, Burg, covariance, least squares, and maximum likelihood estimation are examples to the various estimation methods for the parameters of AR method [1,2]. Burg AR method stands as a powerful calculation method in terms of computational efficiency, and resulting in consistent estimates, is frequently used in PSD estimates of internal carotid artery Doppler signals [8,19,20].…”

Section: Introductionmentioning

confidence: 99%

Detection of Carotid Artery Disease by Using Learning Vector Quantization Neural Network

Uğuz

2010

J Med Syst

View full text Add to dashboard Cite

Doppler ultrasound has been usually preferred for investigation of the artery conditions in the last two decades, because it is a non-invasive, easy to apply and reliable technique. In this study, a biomedical system based on Learning Vector Quantization Neural Network (LVQ NN) has been developed in order to classify the internal carotid artery Doppler signals obtained from the 191 subjects, 136 of them had suffered from internal carotid artery stenosis and rest of them had been healthy subject. The system is composed of feature extraction and classification parts, basically. In the feature extraction stage, power spectral density (PSD) estimates of internal carotid artery Doppler signals were obtained by using Burg autoregressive (AR) spectrum analysis technique in order to obtain medical information. In the classification stage, LVQ NN was used classify features from Burg AR method. In experiments, LVQ NN based method reached 97.91% classification accuracy with 5 fold Cross Validation (CV) technique. In addition, the classification performance of the LVQ NN was compared with some methods such as Multi Layer Perceptron (MLP) NN, Naive Bayes (NB), K-Nearest Neighbor (KNN), decision tree and Support Vector Machine (SVM) with sensitivity and specificity statistical parameters. The classification results showed that the LVQ NN method is effective for classification of internal carotid artery Doppler signals.

show abstract

Section: Introductionmentioning

confidence: 99%

Detection of Carotid Artery Disease by Using Learning Vector Quantization Neural Network

Uğuz

2010

J Med Syst

View full text Add to dashboard Cite

show abstract

Quantitative Grading Using Grey Relational Analysis on Ultrasonographic Images of a Fatty Liver

2011

View full text Add to dashboard Cite

A quantitative graduation system based on Grey Relational Analysis is proposed to recognize fatty livers in B-scan ultrasonic images. We evaluated ultrasonography liver images from 95 subjects having fatty livers (Grade I, II, III) and 45 normal subjects, as diagnosed by an expert radiologist. In practice, ultrasonographical findings of fatty liver are based on the brightness level of the liver in comparison to the renal parenchyma. The development of a non-invasive and accurate method would be of great clinical value as an alternative to diagnosing fatty liver based on the radiologist's visual perception. In this study, we also evaluated AST and ALT liver enzymes for fatty liver having different grades. A high correlation between enzymes and Grey Relational Grades were found. The Receiver Operating Characteristic (ROC) curves were obtained and yielded satisfactory classification results using sensitivity, specificity and area under the curve for computing graduation and distinguishing fatty livers from healthy livers. With the proposed method based on Grey Relational Analysis, not only misdiagnosis caused by subjective differences in clinical evaluation will be reduced, but also the early diagnosis fatty liver and quantitative assessment of its degree will be achieved.

show abstract

Discontinuous doppler signals simulating respiratory misregistration: Effect on autoregressive frequency spectra

Cited by 2 publications

References 15 publications

Detection of Carotid Artery Disease by Using Learning Vector Quantization Neural Network

Detection of Carotid Artery Disease by Using Learning Vector Quantization Neural Network

Quantitative Grading Using Grey Relational Analysis on Ultrasonographic Images of a Fatty Liver

Contact Info

Product

Resources

About