Statistics over features for internal carotid arterial disorders detection

íbeylï, Elif Derya

doi:10.1016/j.compbiomed.2007.12.002

Cited by 8 publications

(7 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If longer time frames are used, the frequency spectra will be smeared and the consecutive frames will not provide a detailed indication of how the velocities within the artery are changing with respect to time. The Doppler power spectrum has a shape similar to the histogram of the blood velocities within the sample volume (in the arteries) and thus spectral analysis of the signal produces information concerning the velocity distribution in the artery [2,3,[9][10][11][12]. The estimation of the power spectral density (PSD) of the signal is performed by applying the spectral analysis methods.…”

Section: Data Acquisition From Ophthalmic Arteriesmentioning

confidence: 99%

“…The signals recorded from OA is conventionally interpreted by analyzing its spectral content. Diagnosis and disease monitoring are assessed by analysis of spectral shape and parameters [2,3,[9][10][11][12]. OA arterial signals are processed by the spectral analysis methods to achieve the PSD estimates and then OA disorders can be determined.…”

Section: Spectral Analysis Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of doppler ultrasound signals: Ophthalmic arterial disorders detection case

Übeylı

2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

The paper includes illustrative and detailed information about implementation of automated diagnostic systems and feature extraction/selection from signals recorded from ophthalmic arteries. The major objective of the paper is to be a guide for the readers, who want to develop an automated diagnostic systems for detection of arterial disorders. Toward achieving this objective, this paper present the techniques which should be considered in developing automated diagnostic systems. The paper will assist to the people in gaining a better understanding of the techniques in the detection of arterial disorders.

show abstract

Section: Data Acquisition From Ophthalmic Arteriesmentioning

confidence: 99%

Section: Spectral Analysis Techniquesmentioning

confidence: 99%

Analysis of doppler ultrasound signals: Ophthalmic arterial disorders detection case

Übeylı

2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

show abstract

“…The power spectral density (PSD) estimates can be used to track the velocity distribution, since the velocity components will be proportional to the frequency shifts (Güler, Kiymik, Akin, & Aklan, 2001;Übeyli andGüler, 2003b, 2004). Spectrum analysis techniques were used to obtain the Doppler PSD estimates of carotid arterial Doppler signals.…”

Section: Introductionmentioning

confidence: 99%

Classification of internal carotid artery Doppler signals using fuzzy discrete hidden Markov model

Uğuz¹,

Kodaz²

2011

Expert Systems with Applications

View full text Add to dashboard Cite

“…AR method is a common practice based on the fact that solving linear equations makes it easy to estimate the AR parameters. For that reason, AR method is widely used as a spectrum analysis technique [6,[8][9][10][11][12][13]. 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].…”

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

Statistics over features for internal carotid arterial disorders detection

Cited by 8 publications

References 72 publications

Analysis of doppler ultrasound signals: Ophthalmic arterial disorders detection case

Analysis of doppler ultrasound signals: Ophthalmic arterial disorders detection case

Classification of internal carotid artery Doppler signals using fuzzy discrete hidden Markov model

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

Contact Info

Product

Resources

About