A brief history of Doppler ultrasound in the diagnosis of peripheral vascular disease

Cross-sectional study of lipoprotein(a) and the severity of coronary artery disease, cerebrovascular disease, and peripheral vascular disease in a group of South Asian patients

Background

Atherosclerotic cardiovascular diseases (ASCVD), including coronary artery disease (CAD), cerebrovascular disease (CVD), and peripheral vascular disease (PVD), remain the leading cause of death globally. South Asians exhibit a higher incidence of cardiovascular diseases than other ethnicities, attributed to a range of genetic, environmental, and lifestyle factors. Lipoprotein(a) [Lp(a)] with a unique apolipoprotein(a) component, has emerged as a marker of atherosclerosis and ASCVD risk, with evidence to promote arterial plaque formation and thrombogenesis.

Objective

The aim of this study was to explore the associations between Lp(a) levels and the severity of CAD, CVD, and PVD in a group of South Asian patients.

Methods

Following ethical approval, 60 consecutive patients who underwent coronary angiography for any indication were reviewed. There were 51 eligible participants who were evaluated for Lp(a) level, Synergy Between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) score, and severity of CVD and PVD. The SYNTAX-I score was calculated using two-observer consensus on coronary angiograms. Assessment of CVD was by ultrasound/Doppler, and PVD by estimating ankle-brachial index using Doppler. The multisite arterial disease score 2 (MADS2) and SYNTAX score tertiles were used to group the patients. Statistical analysis was performed using the SPSS software.

Results and discussion

In this group, we identified a statistically significant difference with higher Lp(a) levels being associated with more severe coronary disease (SYNTAX tertile 2,3). Despite a numerical trend, statistical significance was not confirmed for Lp(a) levels in relation to MADS2-CVD or MADS2-PVD scores. A larger study may be required to assess these aspects.

Robotic Ultrasound Scanning End-Effector with Adjustable Constant Contact Force

In modern medical treatment, ultrasound scanning provides a radiation-free medical imaging method for the diagnosis of soft tissues via skin contact. However, the exerted contact force heavily relies on the skill and experience of the operator, which poses great inspection instability. This article reports on a robotic ultrasound scanning system with a constant-force end-effector. Its uniqueness is the introduction of a hybrid active-passive force control approach to maintaining a constant contact force between the ultrasound probe and the continually changing surface. In particular, the passive constant-force mechanism provides strong buffering to the force variation. The active force control system improves flexibility and provides long-stroke positioning. Experimental tests on both silicone models and human volunteers demonstrate the capability of the proposed robotic ultrasound scanning system for obtaining qualified ultrasound images with high repeatability. Moreover, the ease of operation of the robotic US scanning system is verified. This work provides a promising method to assist doctors in conducting better and cushier ultrasound scanning imaging.

Bendable Phased-Array Ultrasound Transducer for Imaging on Curved Surfaces

Flexible phased-array ultrasound transducers (PAUTs), promising for nondestructive testing in biomedical and industrial applications, are classified as stretchable or bendable. Stretchable PAUTs offer a superior solution for complex curved surfaces but face substantial variations in piezoelectric element pitches, particularly on surfaces with small radii, complicating position correction algorithms. Meanwhile, real-time measurement of the pitches remains a technical difficulty. In contrast, bendable PAUTs with limited variable pitches are currently more practical for engineering applications. This paper introduces an innovative bendable PAUT featuring a constant-pitch-preservation (CPP) design. It includes a flexible 12 × 12 piezo-composite element array bonded with silicone, allowing conformity to surfaces with varying curvatures while maintaining constant element pitches. This design enables accurate progressive time delays for precise ultrasound beam steering and focusing. Individual backing and matching blocks for each piezoelectric element enhance detection performance. Experimental results from pulse-echo inspections and sector scans validate its effectiveness in high-quality imaging.

Multiwavelength laser diode based portable photoacoustic and ultrasound imaging system for point of care applications

Vascular diseases are a leading cause of death and disability worldwide. Despite having precursor conditions like peripheral arterial disease (PAD), they are often only diagnosed after the onset of stroke or heart attack. Low-cost, portable, noninvasive, point-of-care (POC), label-free assessment of deep vascular function benefits PAD diagnosis, especially in resource poor settings of the world. Doppler ultrasound-based blood flow measurements can diagnose PAD, albeit with limited sensitivity and specificity. To overcome this, here, we propose the first-of-its-kind dual-modality photoacoustic-and-ultrasound (PAUS) imaging system that integrates a multiwavelength pulsed laser diode (PLD) with a compact ultrasound data acquisition unit. The mesoscopic imaging depth of the portable PLD-PAUS system was validated using tissue phantoms, and its multispectral photoacoustic imaging capabilities were validated using an atherosclerosis-mimicking phantom. Furthermore, we demonstrated high-contrast volumetric in vivo photoacoustic imaging of rodent abdominal vasculature and quantified vessel reactivity due to hypercapnia stimulation. The multiparametric functional and molecular imaging capabilities of the PLD-PAUS system holds promise for POC applications.

Diagnostic value of ophthalmic artery Doppler indices for prediction of preeclampsia at 28-32 weeks of gestation

OBJECTIVE

The aim of this study was to examine the diagnostic value of ophthalmic artery Doppler indices in predicting preeclampsia along with other markers in the third trimester of pregnancy.

METHODS

Normotensive pregnancies were included during 28-32 weeks of gestation to undergo uterine and ophthalmic artery Doppler ultrasound. Maternal and fetal characteristics were documented at the visit between the 28 and 32 weeks of gestation, and pregnancy-associated plasma protein A (PAPP-A) values in the first trimester were collected to be integrated into a multiparametric prediction model.

RESULTS

Of 795 included participants, 48 cases progressed to preeclampsia. All assessed ophthalmic Doppler parameters including first and second peak systolic velocities (PSVs), second to first peak ratio (PR), and pulsatility index (PI), were statistically different in patients who developed preeclampsia later on. The average PR (sensitivity: 100% [95% CI, 0.81-1.00]; specificity: 90% [95% CI, 0.86-0.93]) and PI between the eyes, PAPP-A multiple of median and uterine artery PI were determined to be the most important predictors of PE, which were subsequently integrated into a multiple regression model (sensitivity: 94% [95% CI, 0.70-1.00]; specificity: 93% [95% CI, 0.89-0.96]).

CONCLUSION

This study provided a screening method for individuals at higher risk of progressing to preeclampsia in the third trimester of pregnancy.

Piezoelectric Micromachined Ultrasonic Transducer for Arterial Wall Dynamics Monitoring

In this article, a [Formula: see text] piezoelectric micromachined ultrasonic transducer (PMUT) array is designed and driven with one cycle of a 5-MHz sinusoid at 10 [Formula: see text] for radial artery motion tracking. The transmit and receive performance figure of merit (FOM) of an individual PMUT over operating frequency is modeled and validated using laser Doppler vibrometer (LDV) measurements. Given a fixed cross section, the FOM inversely scales with frequency. The array aperture size is selected to obtain enough pressure and received signal to measure the radial artery wall reflection at a 5-mm depth in tissue. The 2-mm acoustic beamwidth provides enough lateral resolution for radial artery wall motion tracking. Single-line ultrasonic pulse-echo measurements with high time resolution, also called M-mode ultrasound imaging, are demonstrated to reproduce a known target motion profile with a precision of around 0.5 [Formula: see text]. In vivo radial artery dynamics are measured by placing the sensor on the wrist of a volunteer. The measured diameter change waveform of the radial artery is consistent with reports in the literature and captures key arterial pulse waveform features, including systolic upstroke, systolic decline, dicrotic notch, and diastolic runoff. The system has sufficient accuracy and precision to measure both the 50 [Formula: see text] overall diameter change and the 5- [Formula: see text] diameter change due to the dicrotic notch. A heart rate of 70 beats/min is also derived. This demonstrates the great potential of custom PMUT arrays for continuous cardiovascular system monitoring.

Translation of Simultaneous Vessel Wall Motion and Vectorial Blood Flow Imaging in Healthy and Diseased Carotids to the Clinic: A Pilot Study

This study aims to investigate the clinical feasibility of simultaneous extraction of vessel wall motion and vectorial blood flow at high frame rates for both extraction of clinical markers and visual inspection. If available in the clinic, such a technique would allow a better estimation of plaque vulnerability and improved evaluation of the overall arterial health of patients. In this study, both healthy volunteers and patients were recruited and scanned using a planewave acquisition scheme that provided a data set of 43 carotid recordings in total. The vessel wall motion was extracted based on the complex autocorrelation of the signals received, while the vector flow was extracted using the transverse oscillation technique. Wall motion and vector flow were extracted at high frame rates, which allowed for a visual appreciation of tissue movement and blood flow simultaneously. Several clinical markers were extracted, and visual inspections of the wall motion and flow were conducted. From all the potential markers, young healthy volunteers had smaller artery diameter (7.72 mm) compared with diseased patients (9.56 mm) ( p -value ≤ 0.001), 66% of diseased patients had backflow compared with less than 10% for the other patients ( p -value ≤ 0.05), a carotid with a pulse wave velocity extracted from the wall velocity greater than 7 m/s was always a diseased vessel, and the peak wall shear rate decreased as the risk increases. Based on both the pathological markers and the visual inspection of tissue motion and vector flow, we conclude that the clinical feasibility of this approach is demonstrated. Larger and more disease-specific studies using such an approach will lead to better understanding and evaluation of vessels, which can translate to future use in the clinic.

Chemical Resonance, Beats, and Frequency Locking in Forced Chemical Oscillatory Systems

Resonance, beats, and synchronization are general and fundamental phenomena in physics. Their existence and their in-depth understanding in physical systems have led to several applications and technological developments shaping our world today. Here we show the existence of chemical resonance, chemical beats, and frequency locking phenomena in periodically forced pH oscillatory systems (sulfite-hydrogen peroxide and sulfite-formaldehyde-gluconolactone pH oscillatory systems). Periodic forcing was realized by a superimposed sinusoidal modulation on the inflow rates of the reagents in the continuous-flow stirred tank reactor. The dependence of the time period of beats follows the relation known from classical physics for forced physical oscillators. Our developed numerical model describes qualitatively the resonance and beat phenomena experimentally revealed. Application of periodic forcing in autonomously oscillating systems can provide new types of oscillators with a controllable frequency and new insight into controlling irregular chemical oscillation regimes.

The variation in frequency locations in Doppler ultrasound spectra for maximum blood flow velocities in narrowed vessels

This study assessed the variation in the frequency locations in the Doppler ultrasound spectra for the maximum blood flow velocities of in vessels with different degrees of bilaterally axisymmetric stenosis. This was done by comparing the relationship between the velocity distributions and corresponding Doppler power spectra. First, a geometric vessel model with axisymmetric stenosis was established. This made it possible to obtain the blood flow velocity distributions for different degrees of stenosis from the solutions of the Navier-Stokes equations. Then, the Doppler spectra were calculated for the entire segment of the vessel that was covered by the sound field. Finally, the maximum frequency locations for the spectra were determined based on the intersections of the maximum values chosen from the calculated blood flow velocity distributions and their corresponding spectra. The computational analysis showed that the maximum frequencies, which corresponded to the maximum blood flow velocities for different degrees of stenosis, were located at different positions along the spectral falling edges. The location for a normal (stenosis free) vessel was in the middle of the falling edge. For vessels with increasing degrees of stenosis, this location shifted approximately linearly downward along the falling edge. For 40% stenosis, the location reached a position at the falling edge of 0.32. Results obtained using the Field II simulation tool demonstrated the validity of the theoretical analysis and calculations, and may help to improve the maximum velocity estimation accuracy for Doppler blood flow spectra in stenosed vessels.

Secondary Stroke Prevention: Improving Diagnosis and Management with Newer Technologies

Treatment of hypertension, diabetes, high cholesterol, smoking cessation, and healthy lifestyle have all contributed to the decline in the incidence of vascular disease over the last several decades. Patients who suffer an acute stroke are at a high risk for recurrence. Introduction of newer technologies and their wider use allows for better identification of patients in whom the risk of recurrence following an acute stroke may be very high. Traditionally, the major focus for diagnosis and management has focused on patient history, examination, imaging for carotid stenosis/occlusion, and detection of AF and paroxysmal AF (PAF) with 24-48 h cardiac monitoring. This review focuses on the usefulness of three newer investigative tools that are becoming widely available and lead to better prevention. Continuous ambulatory blood pressure measurements for 24 h or longer and 3D Doppler measures of the carotid arteries provide key useful information on the state of vascular health and enhance our ability to monitor the response to preventive therapies. Furthermore, the detection of PAF can be significantly improved with prolonged cardiac monitoring for 3 weeks or longer, enabling the initiation of appropriate prevention therapy. This review will focus on the potential impact and importance of these emerging technologies on the prevention of recurrent stroke in high-risk patients.

The Role of Ultrasound in Lower Extremity Wound Management

Imaging and hemodynamic information are essential in determining the etiology of lower extremity wounds. Ultra- sound offers a noninvasive method of studying vessel walls and measuring blood flow, thereby providing reliable diagnostic data. This article reviews the role of ultrasound in the management of the lower extremity wound.

Development of a system for the automatic detection of air embolism using a precordial Doppler

Venous air embolism (VAE) is the air bubble accumulation in the right side of the heart. Changes in Doppler heart sound (DHS) are characteristic of VAE, and the anesthesiologist has to pay attention to this event continuously, which may not always be possible. This work aims to study different features of the heart sound through a precordial Doppler, that may provide useful information on VAE episodes. A clinical protocol was designed, and DHS was collected at baseline and following infusions of saline with 4 distinct volumes (1ml, 5ml, 8ml and 10ml), and two infusion rates (slow and fast), given by central and peripheric catheters. Signal was pre-processed, the envelope of each signal was extracted, and five features were implemented and evaluated: frequency corresponding to 95% of Welch periodogram power (f95), frequency corresponding to 50% of Welch periodogram power (f50), frequency corresponding to maximum power spectral density (fm), entropy (E), and frequency corresponding to maximum energy of a wavelet transform (freqwav). Relation between extracted features and saline infusions were studied and compared to baseline values. A Graphical User Interface (GUI) with a database of Doppler heart sounds and annotations was also developed. Although features present a high variability between patients, E presents a better performance showing an increase in response to saline injections (in 75% injections), followed by f95 (62%), fm (56.3%), freqwav (37.5%) and f50 (0%).

Quantifying Blood Flow in the DIEP Flap: An Ultrasonographic Study

Supplemental Digital Content is available in the text.

Background:

The maximum weight of tissue that a single perforator can perfuse remains an important question in reconstructive microsurgery. An empirically based equation, known as the flap viability index (FVI), has been established to determine what weight of tissue will survive on one or more perforators. The equation is FVI = Sum d(n)^4/W, where d is the internal diameter of each perforator and W is the final weight of the flap. It has been shown that if FVI exceeds 10, total flap survival is likely, but if under 10, partial flap necrosis is probable. The aim of this study was to measure absolute flow rates in deep inferior epigastric perforator (DIEP) flap pedicles and assess correlation with the determinants of the FVI, perforator diameter and flap weight.

Methods:

Color Doppler ultrasound was used to quantify arterial flow in 10 consecutive DIEP flap pedicles 24 hours after anastomosis.

Results:

In single-perforator DIEP flaps, flow rate was highly correlated with perforator diameter (r = 0.82, P = 0.01). Mean arterial flow rate was significantly reduced in DIEP flaps with 2 or more perforators (6 vs 38 cm³/min; P < 0.05).

Conclusions:

This study confirms that perforator size is a critical factor in optimizing blood flow in perforator-based free tissue transfer. Further research is required to understand the flow dynamics of perforator flaps based on multiple perforators. However, surgeons should be cognizant that a single large perforator may have substantially higher flow rates than multiple small perforators. Routine FVI calculation is recommended to ensure complete flap survival.

Oesophageal Doppler cardiac output monitoring: a longstanding tool with evolving indications and applications

Much work has been done over the years to assess cardiac output and better grasp haemodynamic profiles of patients in critical care and during major surgery. Pulmonary artery catheterization has long been considered as the standard of care, especially in critical care environments, however this dogma has been challenged over the last 10-15 years. This has led to a greater focus on alternate, lesser invasive technologies. This review focuses on the scientific and clinical outcomes basis of oesophageal Doppler monitoring. The science underpinning Doppler shift assessment of velocity stretches back over 100 years, whereas the clinical applicability, and specifically clinical outcomes improvement can be attributed to the last 20 years. Oesophageal Doppler monitoring (ODM), and its associated protocol-guided fluid administration, has been shown to reduce complications, length of stay, and overall healthcare cost when incorporated into perioperative fluid management algorithms. However, more recent advances in enhanced recovery after surgery programs have led to similar improvements, leading the clinician to consider the role of Oesophageal Doppler Monitor to be more focused in high-risk surgery and/or the high-risk patient.

In search of venous thromboembolism: the first 2913 years

OBJECTIVE

Venous thromboembolism was first described in India around 600-900 BC. It was not until the 17th through 19th centuries that Western researchers began to understand the anatomy, physiology, and pathology of deep venous thrombosis and pulmonary embolism. Roentgen's discovery of x-rays in 1895 led to the first objective imaging.

CONCLUSION

Currently, scintigraphy, helical CT, MRI, and sonography provide accurate in vivo images. These high-quality images have forced clinicians to reevaluate many preimaging assumptions about and treatments for venous thromboembolism.

A comparison of the Doppler ultrasound interpretation by student and registered podiatrists

BACKGROUND

Hand held Doppler ultrasound machines are routinely used by podiatrists to assess the arterial perfusion of the lower limb. They are practical, painless and effective as a screening tool, and the available general evidence would suggest that interpretation by practitioners is reliable. This study compared the abilities of student and Health and Care Professions Council (HCPC) registered podiatrists to identify correctly Doppler ultrasound outputs.

METHOD

A prospective single blind comparative study design was utilised. Fifteen Doppler recordings of the blood flow in the posterior tibial artery, five each of monophasic, biphasic and triphasic blood flow, were used to compare the interpretation abilities of 30 undergraduate podiatry students and 30 HCPC registered podiatrists. Chi-squared analysis of the results was undertaken.

RESULTS

Chi-squared analysis found that there was no statistically significant difference between the overall abilities of student podiatrists and HCPC registered podiatrists to identify correctly Doppler ultrasound recordings (p = 0.285). No significant difference was found in their ability to identify Doppler ultrasound recordings of monophasic, biphasic or triphasic blood flow (p > 0.050).

CONCLUSION

The results of this relatively small study suggest that both student and HCPC registered podiatrists are in general able to identify the nature of blood flow based on the output of handheld Doppler ultrasound units. However, the results raise an issue regarding professional development of practitioners who might have been expected to have enhanced their skills of Doppler ultrasound sound identification since professional registration.

Real-time monitoring of water quality using fish and crayfish as bio-indicators: a review

Water quality monitoring using fish and crayfish as bio-indicators requires an understanding of the state of pollution of waters, choice of bio-indicators, physiological and behavioral endpoints of fish and crayfish, and principles of the methodology and their potential applications. Here, we discuss telemetry, acoustic monitoring, vision-based monitoring, measures of ventilatory activity, electrocardiography, and fiber-optic plethysmography. Assessment of water quality must be based, not only on physicochemical characteristics of the current environment as determined by chemical analyses, but also on observations of the physiology and behavior of its inhabitants. Real-time biomonitoring is suggested as the most reliable method, since it incorporates living organisms into the system to serve as biosensors. The potential application of the methods discussed includes use at water treatment plants and water supply stations for prevention of hazardous toxicological events, and, for aquaculture, in ponds, lakes, and aquariums for monitoring growth, population size, and behavior traits.

Swept-source based, single-shot, multi-detectable velocity range Doppler optical coherence tomography

Phase-Resolved Doppler Optical Coherence Tomography (PR-DOCT) allows visualization and characterization of the location, direction, velocity, and profile of flow activity embedded in a static sample structure. The detectable Velocity Dynamic Range (VDR) of each particular PR-DOCT system is governed by a detectable Doppler phase shift, a flow angle, and an acquisition time interval used to determine the Doppler phase shift. In general, the lower boundary of the detectable Doppler phase shift is limited by the phase stability of the system, while the upper boundary is limited by the π phase ambiguity. For a given range of detectable Doppler phase shift, shortening the acquisition duration will increase not only the maximum detectable velocity but unfortunately also the minimum detectable velocity, which may lead to the invisibility of a slow flow. In this paper, we present an alternative acquisition scheme for PR-DOCT that extends the lower limit of the velocity dynamic range, while maintaining the maximum detectable velocity, hence increasing the overall VDR of PR-DOCT system. The essence of the approach is to implement a technique of multi-scale measurement to simultaneously acquire multiple VDRs in a single measurement. We demonstrate an example of implementation of the technique in a dual VDR DOCT, where two Doppler maps having different detectable VDRs were simultaneously detected, processed, and displayed in real time. One was a fixed VDR DOCT capable of measuring axial velocity of up to 10.9 mm/s without phase unwrapping. The other was a variable VDR DOCT capable of adjusting its detectable VDR to reveal slow flow information down to 11.3 μm/s. The technique is shown to effectively extend the overall detectable VDR of the PR-DOCT system. Examples of real time Doppler imaging of an African frog tadpole are demonstrated using the dual-VDR DOCT system.

Doppler imaging with dual-detection full-range frequency domain optical coherence tomography

Most of full-range techniques for Frequency Domain Optical Coherence Tomography (FD-OCT) reported to date utilize the phase relation between consecutive axial lines to reconstruct a complex interference signal and hence may exhibit degradation in either mirror image suppression performance or detectable velocity dynamic range or both when monitoring a moving sample such as flow activity. We have previously reported a technique of mirror image removal by simultaneous detection of the quadrature components of a complex spectral interference called a Dual-Detection Frequency Domain OCT (DD-FD-OCT) [Opt. Lett. 35, 1058-1060 (2010)]. The technique enables full range imaging without any loss of acquisition speed and is intrinsically less sensitive to phase errors generated by involuntary movements of the subject. In this paper, we demonstrate the application of the DD-FD-OCT to a phase-resolved Doppler imaging without degradation in either mirror image suppression performance or detectable velocity dynamic range that were observed in other full-range Doppler methods. In order to accommodate for Doppler imaging, we have developed a fiber-based DD-FD-OCT that more efficiently utilizes the source power compared with the previous free-space DD-FD-OCT. In addition, the velocity sensitivity of the phase-resolved DD-FD-OCT was investigated, and the relation between the measured Doppler phase shift and set flow velocity of a flow phantom was verified. Finally, we demonstrate the Doppler imaging using the DD-FD-OCT in a biological sample.

In vivo Doppler ultrasound quantification of turbulence intensity using a high-pass frequency filter method

The objective of this investigation was to implement a high-pass frequency filter method to analyze Doppler ultrasound velocity waveforms and quantify turbulence intensity (TI) in vivo. Doppler velocity data were analyzed using two techniques, based on either ensemble averaging or high-pass frequency domain filtering of the periodic waveforms. The accuracy and precision of TI measurements were determined with controlled in vitro experiments, using a pulsatile-flow model of a stenosed carotid bifurcation. The high-pass filter technique was also applied in vivo to determine whether this technique could successfully distinguish between pertinent hemodynamic sites within the carotid artery bifurcation. Twenty-five seconds of Doppler audio data were acquired at three sites (common carotid artery [CCA], internal carotid artery [ICA] stenosis and distal ICA) within 10 human carotid arteries, and repeated three times. Doppler velocity data were analyzed using a ninth-order high-pass Butterworth filter with a 12-Hz inflection point. TI measured within the CCA and distal ICA was found to be significantly different (p < 0.0001) for moderate to nearly occluded carotid artery classifications. Also, TI measured within the distal ICA increased with stenosis severity, with the ability to distinguish between each stenosis class (p < 0.05). This investigation demonstrated the ability to precisely quantify TI using a conventional Doppler ultrasound machine in human subjects, without interfering with normal clinical protocols.

Comparison of short-time Fourier transform and Eigenvector MUSIC methods using discrete wavelet transform for diagnosis of atherosclerosis

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.

In vitro Doppler ultrasound investigation of turbulence intensity in pulsatile flow with simulated cardiac variability

An in vitro investigation of turbulence intensity (TI) associated with a severe carotid stenosis in the presence of physiological cardiac variability is described. The objective of this investigation was to determine if fluctuations due to turbulence could be quantified with conventional Doppler ultrasound (DUS) in the presence of normal physiological cycle-to-cycle cardiac variability. An anthropomorphic model of a 70% stenosed carotid bifurcation was used in combination with a programmable flow pump to generate pulsatile flow with a mean flow rate of 6 mL/s. Utilizing the pump, we studied normal, nonrepetitive cycle-to-cycle cardiac variability (+/-3.9%) in flow, as well as waveform shapes with standard deviations equal to 0, 2 and 3 times the normal variation. Eighty cardiac cycles of Doppler data were acquired at two regions within the model, representing either laminar or turbulent flow; each measurement was repeated six times. Turbulence intensity values were found to be 11 times higher (p < 0.001), on average, in the turbulent region than in the laminar region, with a mean difference of 24 cm/s. Twenty cardiac cycles were required for confidence in TI values. In conclusion, these results indicate that it is possible to quantify TI in vitro, even in the presence of normal and exaggerated cycle-to-cycle cardiac variability.

Use of an ultrasound blood-mimicking fluid for Doppler investigations of turbulence in vitro

Turbulence is an important factor in the assessment of stenotic disease and a possible causative mechanism for thromboembolism. Previous Doppler studies of turbulence have typically used whole-blood preparations or suspensions of erythrocytes. Recently, a water-glycerol based blood-mimicking fluid (BMF) has been developed for use in Doppler ultrasound studies. This fluid has desirable ultrasound properties but it has not previously been described during in vitro investigations of turbulence intensity. We report on investigations of grid-generated and constrained-jet turbulence in an in vitro test system. The BMF was found to generate significant levels of turbulence during steady flow at physiological flow rates, producing turbulent patterns in the distal region that were consistent with previous studies. Turbulence intensity increased significantly with flow rate (p < 0.005) for both the constrained jet and the constrained grid. Based on our observations, we conclude that a water-glycerol based BMF provides a suitable working fluid during in vitro investigations of turbulence using Doppler ultrasound.

Usage of eigenvector methods to improve reliable classifier for Doppler ultrasound signals

A new approach based on the implementation of the automated diagnostic systems for Doppler ultrasound signals classification with the features extracted by eigenvector methods is presented. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Because of the importance of making the right decision, the present work is carried out for searching better classification procedures for the Doppler ultrasound signals. Decision making was performed in two stages: feature extraction by the eigenvector methods and classification using the classifiers trained on the extracted features. The aim of the study is classification of the Doppler ultrasound signals by the combination of eigenvector methods and the classifiers. The present research demonstrated that the power levels of the power spectral density (PSD) estimates obtained by the eigenvector methods are the features which well represent the Doppler ultrasound signals and the probabilistic neural networks (PNNs), recurrent neural networks (RNNs) trained on these features achieved high classification accuracies.

A new supervised classification algorithm in artificial immune systems with its application to carotid artery Doppler signals to diagnose atherosclerosis

Because of its self-regulating nature, immune system has been an inspiration source for usually unsupervised learning methods in classification applications of Artificial Immune Systems (AIS). But classification with supervision can bring some advantages to AIS like other classification systems. Indeed, there have been some studies, which have obtained reasonable results and include supervision in this branch of AIS. In this study, we have proposed a new supervised AIS named as Supervised Affinity Maturation Algorithm (SAMA) and have presented its performance results through applying it to diagnose atherosclerosis using carotid artery Doppler signals as a real-world medical classification problem. We have employed the maximum envelope of the carotid artery Doppler sonograms derived from Autoregressive (AR) method as an input of proposed classification system and reached a maximum average classification accuracy of 98.93% with 10-fold cross-validation method used in training-test portioning. To evaluate this result, comparison was done with Artificial Neural Networks and Decision Trees. Our system was found to be comparable with those systems, which are used effectively in literature with respect to classification accuracy and classification time. Effects of system's parameters were also analyzed in performance evaluation applications. With this study and other possible contributions to AIS, classification algorithms with effective performances can be developed and potential of AIS in classification can be further revealed.

Determining fractal dimension of umbilical artery Doppler signals using Hurst exponent

Doppler signals from the umbilical artery of 20 women with normal pregnancy between 18 and 20 weeks of gestation were recorded. The AR spectral analysis method has been used to obtain the Doppler sonograms of umbilical artery belonging to normal pregnant subjects and fractal dimension curves were calculated using Hurst exponent. RI; PI and S/D indexes have been calculated from the maximum frequency envelope of Doppler sonograms and from the fractal dimension curve. Area under the curve from ROC curve for RI, PI and S/D indexes derived from maximum frequency waveform were calculated as 0.931, 0.959, 0.938, respectively and area under the curve for RI, PI and S/D indexes derived from fractal dimension curve were calculated as 0.933, 0.961, and 0.941, respectively. These results show that, the Doppler indexes derived from fractal dimension curve are as sensitive as Doppler indexes derived from maximum velocity curve. Power Spectral Density graphics were derived from Doppler signals and Hurst exponent values calculated to evaluate the blood flow changing during pregnancy. ROC curve for PSD(HURST) index was calculated as 0.97. According to this result, PSD(HURST) index is more sensitive to detect the blood flow changing than traditional Doppler indexes.

Probabilistic neural networks employing Lyapunov exponents for analysis of Doppler ultrasound signals

The implementation of probabilistic neural networks (PNNs) with the Lyapunov exponents for Doppler ultrasound signals classification is presented. This study is directly based on the consideration that Doppler ultrasound signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Decision making was performed in two stages: computation of Lyapunov exponents as representative features of the Doppler ultrasound signals and classification using the PNNs trained on the extracted features. The present research demonstrated that the Lyapunov exponents are the features which well represent the Doppler ultrasound signals and the PNNs trained on these features achieved high classification accuracies.

Combining eigenvector methods and support vector machines for detecting variability of Doppler ultrasound signals

In this paper, the multiclass support vector machines (SVMs) with the error correcting output codes (ECOC) were presented for detecting variabilities of the multiclass Doppler ultrasound signals. The ophthalmic arterial (OA) Doppler signals were recorded from healthy subjects, subjects suffering from OA stenosis, subjects suffering from ocular Behcet disease. The internal carotid arterial (ICA) Doppler signals were recorded from healthy subjects, subjects suffering from ICA stenosis, subjects suffering from ICA occlusion. Methods of combining multiple classifiers with diverse features are viewed as a general problem in various application areas of pattern recognition. Because of the importance of making the right decision, better classification procedures for Doppler ultrasound signals are searched. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the SVMs trained on the extracted features. The research demonstrated that the multiclass SVMs trained on extracted features achieved high accuracy rates.

Medical diagnosis of atherosclerosis from Carotid Artery Doppler Signals using principal component analysis (PCA), k-NN based weighting pre-processing and Artificial Immune Recognition System (AIRS)

In this study, we proposed a new medical diagnosis system based on principal component analysis (PCA), k-NN based weighting pre-processing, and Artificial Immune Recognition System (AIRS) for diagnosis of atherosclerosis from Carotid Artery Doppler Signals. The suggested system consists of four stages. First, in the feature extraction stage, we have obtained the features related with atherosclerosis disease using Fast Fourier Transformation (FFT) modeling and by calculating of maximum frequency envelope of sonograms. Second, in the dimensionality reduction stage, the 61 features of atherosclerosis disease have been reduced to 4 features using PCA. Third, in the pre-processing stage, we have weighted these 4 features using different values of k in a new weighting scheme based on k-NN based weighting pre-processing. Finally, in the classification stage, AIRS classifier has been used to classify subjects as healthy or having atherosclerosis. Hundred percent of classification accuracy has been obtained by the proposed system using 10-fold cross validation. This success shows that the proposed system is a robust and effective system in diagnosis of atherosclerosis disease.

Pattern Detection of Atherosclerosis from Carotid Artery Doppler Signals using Fuzzy Weighted Pre-Processing and Least Square Support Vector Machine (LSSVM)

Carotid Artery Doppler Signals were recorded from 114 subjects, 60 of whom had Atherosclerosis disease while the rest were healthy controls. Diagnosis of Atherosclerosis from Carotid Artery Doppler Signals was conducted using Fuzzy weighted pre-processing and Least Square Support Vector Machine (LSSVM). First, in order to determine the LSSVM inputs, spectral analysis of Carotid Artery Doppler Signals was performed via Autoregressive (AR) modeling. Then, fuzzy weighted pre-processing based is proposed expert system, applied to inputs obtained from spectral analysis of Carotid Artery Doppler Signals. LSSVM was used to detect Atherosclerosis from Carotid Artery Doppler Signals. All data set were obtained from Carotid Artery Doppler Signals of healthy subjects and subjects suffering from Atherosclerosis disease. The employed expert system has achieved 100% classification accuracy using a 10-fold Cross Validation (CV) method.

Spectral analysing of portal vein Doppler signals in the cirrhosis patients

In this study, we have researched the efficacy of short-time Fourier transformation (STFT) of Doppler signals from the portal veins of healthy volunteers and cirrhosis patients. Sonogram and power spectral distribution for portal vein Doppler spectral waveform changes in the cirrhosis disease were utilized and these graphics compared with healthy volunteers. Five parameters were used to compare power spectrum graphics. Clear differences were detected in the calculated parameters between healthy and cirrhosis patients. It was seen that power spectral graphics and sonograms of portal vein Doppler signals may be used to determine cirrhosis disease.

Medical application of artificial immune recognition system (AIRS): diagnosis of atherosclerosis from carotid artery Doppler signals

This study was conducted to distinguish between atherosclerosis and healthy subjects. Hence, we have employed the maximum envelope of the carotid artery Doppler sonograms derived from Fast Fourier Transformation-Welch method and Artificial Immune Recognition System (AIRS). The fuzzy appearance of the carotid artery Doppler signals makes physicians suspicious about the existence of diseases and sometimes causes false diagnosis. Our technique gets around this problem using AIRS to decide and assist the physician to make the final judgment in confidence. AIRS has reached 99.29% classification accuracy using 10-fold cross validation. Results show that the proposed method classified Doppler signals successfully.

Wavelet-Based Neural Network Analysis of Internal Carotid Arterial Doppler Signals

In this study, internal carotid arterial Doppler signals recorded from 130 subjects, where 45 of them suffered from internal carotid artery stenosis, 44 of them suffered from internal carotid artery occlusion and the rest of them were healthy subjects, were classified using wavelet-based neural network. Wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of the internal carotid arterial Doppler signals. Multi-layer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to detect stenosis and occlusion in internal carotid arteries. In order to determine the MLPNN inputs, spectral analysis of the internal carotid arterial Doppler signals was performed using wavelet transform (WT). The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All these data sets were obtained from internal carotid arteries of healthy subjects, subjects suffering from internal carotid artery stenosis and occlusion. The correct classification rate was 96% for healthy subjects, 96.15% for subjects having internal carotid artery stenosis and 96.30% for subjects having internal carotid artery occlusion. The classification results showed that the MLPNN trained with the Levenberg-Marquardt algorithm was effective to detect internal carotid artery stenosis and occlusion.

A mixture of experts network structure for modelling Doppler ultrasound blood flow signals

Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide modelling Doppler ultrasound blood flow signals. Expectation-Maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The ME network structures were implemented for diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models.

Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals

In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of internal carotid artery stenosis and occlusion. The internal carotid arterial Doppler signals were recorded from 130 subjects that 45 of them suffered from internal carotid artery stenosis, 44 of them suffered from internal carotid artery occlusion and the rest of them were healthy subjects. The three ANFIS classifiers were used to detect internal carotid artery conditions (normal, stenosis and occlusion) when two features, resistivity and pulsatility indices, defining changes of internal carotid arterial Doppler waveforms were used as inputs. To improve diagnostic accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of internal carotid artery stenosis and occlusion were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of classification accuracies and the results confirmed that the proposed ANFIS classifiers have some potential in detecting the internal carotid artery stenosis and occlusion. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.

Improving medical diagnostic accuracy of ultrasound Doppler signals by combining neural network models

There are a number of different quantitative models that can be used in a medical diagnostic decision support system including parametric methods (linear discriminant analysis or logistic regression), nonparametric models (k nearest neighbor or kernel density) and several neural network models. The complexity of the diagnostic task is thought to be one of the prime determinants of model selection. Unfortunately, there is no theory available to guide model selection. This paper illustrates the use of combined neural network models to guide model selection for diagnosis of ophthalmic and internal carotid arterial disorders. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first-level networks were implemented for the diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the second-level networks were trained using the outputs of the first-level networks as input data. The combined neural network models achieved accuracy rates which were higher than that of the stand-alone neural network models.

Determining variability of ophthalmic arterial Doppler signals using Lyapunov exponents

The new method presented in this study was directly based on the consideration that ophthalmic arterial Doppler signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architecture was formulated and used as a basis for determining variabilities such as stenosis, ocular Behcet disease, and uveitis disease in the physical state of ophthalmic arterial Doppler signals. The computed Lyapunov exponents of the ophthalmic arterial Doppler signals were used as inputs of the MLPNN. Receiver operating characteristic (ROC) curve was used to assess the performance of the detection process. The ophthalmic arterial Doppler signals were classified with the accuracy varying from 93.75% to 97.06%. The results confirmed that the proposed MLPNN trained with Levenberg-Marquardt algorithm has potential in detecting stenosis, Behcet disease and uveitis disease in ophthalmic arteries.

Detecting variability of internal carotid arterial Doppler signals by Lyapunov exponents

The new method presented in this study was directly based on the consideration that internal carotid arterial Doppler signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architecture was formulated and used as a basis for detecting variabilities such as stenosis and occlusion in the physical state of internal carotid arterial Doppler signals. The computed Lyapunov exponents of the internal carotid arterial Doppler signals were used as inputs of the MLPNN. Receiver operating characteristic (ROC) curve was used to assess the performance of the detection process. The internal carotid arterial Doppler signals were classified with the accuracy varying from 94.87% to 97.44%. The results confirmed that the proposed MLPNN trained with Levenberg-Marquardt algorithm has potential in detecting stenosis and occlusion in internal carotid arteries.

Spectral analysis of internal carotid arterial Doppler signals using FFT, AR, MA, and ARMA methods

In this study, Doppler signals recorded from internal carotid artery of 45 subjects were processed by PC-computer using classical (fast Fourier transform) and model-based (autoregressive, moving average, autoregressive moving average (ARMA) methods) methods. Power spectral density estimates of internal carotid arterial Doppler signals were obtained using these spectral analysis methods. The variations in the shape of the Doppler power spectra as a function of time were presented in the form of sonograms in order to determine the degree of internal carotid artery stenosis. These Doppler power spectra and sonograms were then used to compare the applied methods in terms of their frequency resolution and the impact on determining stenosis in internal carotid arteries. Based on the results, performance characteristics of the autoregressive and ARMA methods were found extremely valuable for spectral analysis of internal carotid arterial Doppler signals obtained from healthy subjects and unhealthy subjects having artery stenosis.