Comparison of Short-Time Fourier Transform and Eigenvector MUSIC Methods Using Discrete Wavelet Transform for Diagnosis of Atherosclerosis

Abstract

In this paper, a more effective use of Doppler techniques is presented for the purpose of diagnosing atherosclerosis in its early stages using the carotid artery Doppler signals. The power spectral density (PSD) graphics are obtained by applying the short-time Fourier transform (STFT)-Welch and the Eigenvector MUSIC methods to the discrete wavelet transform (DWT) of Doppler signals. The PSDs for the fourth approximation component (A4) of both methods estimated that the patients with atherosclerosis in its early phase had lower maximum frequency components. On the other hand, the healthy subjects had higher maximum frequency components. The area under the curve (AUC), which belongs to the receiver operating characteristic (ROC) curve for the frequency level of the maximum PSDs of the A4 approximation obtained from the STFT modeling, is computed as 0.97. The AUC for the MUSIC modeling is computed as 0.996. The AUC belonging to the ROC curve for the higher maximum frequency component is computed as 0.87. The AUC belonging to the ROC curve for the test parameter of the frequency level of the maximum PSDs derived from the MUSIC modeling is determined to be 0.882. The results of this study clearly demonstrate that it is possible to distinguish between the healthy people and the patients with atherosclerosis by using the frequency level of the maximum PSDs for the A4 approximation. Furthermore, it is concluded that the power of Eigenvector-MUSIC method in terms of the resolution of the high frequencies is better than that of the STFT methods.

Fingerprinting of Doppler audio signals from the common carotid artery

“…The feature extraction method presented in this study is relatively simple. Other, more complex and elaborate methods of feature extraction have been described 10,23 . However, a strength of the relatively simple feature extraction described here is that it is robust against variations in insonated blood volume, heart rate, and insonation angle.…”

“…They are all time based parameters or ratios of such parameters and are described in detail in the methods. Objective analysis of the actual Doppler sounds in the frequency domain has been reported, but is not common practice in medical imaging [9][10][11] . In fact, the Doppler audio signal is rarely recorded.…”

Non-Invasive Diagnosis of Stress Urinary Incontinence Sub Types Using Wavelet Analysis, Shannon Entropy and Principal Component Analysis

“…Nonstationary signal are time-varying signals and require adaptable window size, wavelet transforms provide an ideal windows size; for low-frequency components, it provides a larger time window, and for high-frequency components, it provides a smaller time window [20]. Wavelet transform can compress the characteristics of biomedical signals consisting of many data points into fewer parameters and is therefore is frequently used in biomedical applications [21][22][23][24]. In this study, the preprocessed urethral artery Doppler ultrasound signal is decomposed with "db3" Daubechies Wavelets to the fifth level.…”