Comparison of RootMUSIC and Discrete Wavelet Transform Analysis of Doppler Ultrasound Blood Flow Waveforms to Detect Microvascular Abnormalities in Type I Diabetes

Abstract

The earliest signs of cardiovascular disease occur in microcirculations. Changes to mechanical and structural properties of these small resistive vessels alter the impedance to flow, subsequent reflected waves, and consequently, flow waveform morphology. In this paper, we compare two frequency analysis techniques: 1) rootMUSIC and 2) the discrete wavelet transform (DWT) to extract features of flow velocity waveform morphology captured using Doppler ultrasound from the ophthalmic artery (OA) in 30 controls and 38 age and sex matched Type I diabetics. Conventional techniques for characterizing Doppler velocity waveforms, such as mean velocity, resistive index, and pulsatility index, revealed no significant differences between the groups. However, rootMUSIC and the DWT provided highly correlated results with the spectral content in bands 2-7 (30-0.8 Hz) significantly elevated in the diabetic group (p < 0.05). The spectral distinction between the groups may be attributable to manifestations of underlying pathophysiological processes in vascular impedance and consequent wave reflections, with bands 5 and 7 related to age. Spectral descriptors of OA blood velocity waveforms are better indicators of preclinical microvascular abnormalities in Type I diabetes than conventional measures. Although highly correlated DWT proved slightly more discriminatory than rootMUSIC and has the advantage of extending to subheart rate frequencies, which may be of interest.

Multimodal functional and anatomic imaging identifies preclinical microvascular abnormalities in type 1 diabetes mellitus

“…The resistive index was calculated along standard lines using the maximum and minimum velocities only (1). We used the discrete wavelet transform, a time-frequency signal analysis method, to quantify changes in the Doppler flow waveform structure.…”

“…1. A detailed mathematical description of the discrete wavelet transform is beyond the scope of this paper (1).…”

Refinement for Ocular Ultrasound Images Quality by Utilizing Combination of Enhancement Techniques

“…Many researchers relied on different ways, wavelet transform is mostly used because of its efficient properties especially the De-Correlation property of components of image with the high and low frequency content (Übeyl & Inan, 2004) (Agnew et al, 2011) (Florian & Thierry, 2007) (Akhilesh, 2012). Some researchers used discrete wavelet transform (DWT) (Fodor & Kamath, 2003) (Ferreira & Borges, 2003) in ultrasound images.…”

Comparison of Wavelet Transform and Time-Domain Analysis of Second Trimester Uterine Artery Doppler Waveforms in Screening for Pre-Eclampsia

“…Traditional time-domain analysis using the resistance index (RI) and pulsatility index (PI) utilizes systolic, diastolic and mean velocities derived from Doppler waveforms to assess the downstream vascular resistance. As these indices are derived from isolated points along the entire waveform, they largely ignore its dynamic nature [14,15,16]. Frequency-domain analysis incorporates the entire waveform data and extracts information from blood flow velocity waveforms not achieved through the use of established time-domain parameters such as the RI and PI [14,16].…”

“…As these indices are derived from isolated points along the entire waveform, they largely ignore its dynamic nature [14,15,16]. Frequency-domain analysis incorporates the entire waveform data and extracts information from blood flow velocity waveforms not achieved through the use of established time-domain parameters such as the RI and PI [14,16]. Frequency-domain analysis has also been shown to be superior to conventional time-domain analysis in detecting microvascular abnormalities in patients with diabetes and autoimmune diseases [14,15,16].…”