A novel artifact reconstruction method applied to blood pressure signals

Peer ReviewedPostprint (published version

Characterization and interpretation of cardiovascular and cardiorespiratory dynamics in cardiomyopathy patients

Aplicat embargament des de la data de defensa fins el dia 20/5/2022The main objective of this thesis was to study the variability of the cardiac, respiratory and vascular systems through electrocardiographic (ECG), respiratory flow (FLW) and blood pressure (BP) signals, in patients with idiopathic (IDC), dilated (DCM), or ischemic (ICM) disease. The aim of this work was to introduce new indices that could contribute to characterizing these diseases. With these new indices, we propose methods to classify cardiomyopathy patients (CMP) according to their cardiovascular risk or etiology. In addition, a new tool was proposed to reconstruct artifacts in biomedical signals. From the ECG, BP and FLW signals, different data series were extracted: beat to beat intervals (BBI - ECG), systolic and diastolic blood pressure (SBP and DBP - BP), and breathing duration (TT - FLW). -Firstly, we propose a novel artifact reconstruction method applied to biomedical signals. The reconstruction process makes use of information from neighboring events while maintaining the dynamics of the original signal. The method is based on detecting the cycles and artifacts, identifying the number of cycles to reconstruct, and predicting the cycles used to replace the artifact segments. The reconstruction results showed that most of the artifacts were correctly detected, and physiological cycles were incorrectly detected as artifacts in fewer than 1% of the cases. The second part is related to the cardiac death risk stratification of patients based on their left ventricular ejection (LVEF), using the Poincaré plot analysis, and classified as low (LVEF > 35%) or high (LVEF = 35%) risk. The BBI, SBP, and IT series of 46 CMP patients were applied. The linear discriminant analysis and support vector machines (SVM) classification methods were used. When comparing low risk vs high risk, an accuracy of 98 12% was obtained. Our results suggest that a dysfunction in the vagal activity could prevent the body from correctly maintaining circulatory homeostasis Next, we studied cardio-vascular couplings based on heart rate (HRV) and blood pressure (BPV) variability analyses in order to introduce new indices for noninvasive risk stratification in IDC patients. The ECG and BP signals of 91 IDC patients, and 49 healthy subjects were used. The patients were stratified by their sudden cardiac death risk as: high risk (IDCHR), when after two years the subject either died or suffered complications, or low risk (IDCLR) otherwise. Several indices were extracted from the BBI and SBP, and analyzed using the segmented Poincaré plot analysis, the high-resolution joint symbolic dynamics, and the normalized short time partial directed coherence methods. SVM models were built to classify these patients based on their sudden cardiac death risk. The SVM IDCLR vs IDCHR model achieved 98 9% accuracy with an area under the curve (AUC) of 0.96. Our results suggest that IDCHR patients have decreased HRV and increased BPV compared to both the IDCLR patients and the control subjects, suggesting a decrease in their vagal activity and the compensation of sympathetic activity. Lastly, we analyzed the cardiorespiratory interaction associated with the systems related to ICM and DCM disease. We propose an analysis based on vascular activity as the input and output of the baroreflex response. The aim was to analyze the suitability of cardiorespiratory and vascular interactions for the classification of ICM and DCM patients. We studied 41 CMP patients and 39 healthy subjects. Three new sub-spaces were defined: 'up' for increasing values, 'down' for decreasing values, and 'no change' otherwise, and a three-dimensional representation was created for each sub-space that was characterized statistically and morphologically. The resulting indices were used to classify the patients by their etiology through SVM models achieving 92.7% accuracy for ICM vs DCM patients comparison. The results reflected a more pronounced deterioration of the autonomous regulation in DCM patients.El objetivo de esta tesis fue estudiar la variabilidad de los sistemas cardíaco, respiratorio y vascular a través de señales electrocardiográficas (ECG), de flujo respiratorio (FLW) y de presión arterial (BP), en pacientes con cardiopatía idiopática (IDC). dilatada (DCM) o isquémica (ICM). El objetivo de este trabajo fue introducir nuevos indices que contribuyan a caracterizar estas enfermedades. Proponemos métodos para clasificar pacientes con cardiomiopatía (CMP) de acuerdo con su riesgo cardiovascular o etiología. Además, se propuso una nueva herramienta para reconstruir artefactos en señales biomédicas. De las señales de ECG, BP y FLW, se extrajeron diferentes series temporales: intervalos latido-a-latido (BBI - ECG), presión arterial sistólica y diastólica (SBP y DBP - BP) y la duración de la respiración (TT - FLW). En primer lugar, proponemos un método de reconstrucción de artefactos aplicado a señales biomédicas. El proceso de reconstrucción usa la información de eventos vecinos manteniendo la dinámica de la señal. El método se basa en detectar ciclos y artefactos, en identificar el número de ciclos a reconstruir y en predecir los ciclos utilizados para reemplazar los artefactos. La mayoría de los artefactos probados fueron detectados y reconstruidos correctamente y los ciclos fisiológicos fueron detectados incorrectamente como artefactos en menos del 1% de los casos, La segunda parte está relacionada con la estratificación de riesgo de muerte cardiovascular en función de la fracción de eyección ventricular izquierda (FEVI), mediante el análisis de Poincaré, en bajo (FEVI > 35%) y alto riesgo (FEVI 5 35%). Se utilizaron las series BBI, SBP y TT de 46 pacientes con CMP. Se utilizaron para la clasificación el análisis discriminante lineal y las máquinas de soporte vectorial (SVM). Al comparar los pacientes de bajo y alto riesgo, se obtuvo una exactitud del 98%. Los resultados sugieren la disfunción de la actividad vagal en pacientes de alto riesgo. A continuación, estudiamos los acoplamientos cardiovasculares basados en el análisis de la variabilidad de la frecuencia cardiaca (HRV) y la presión arterial (BPV) para introducir nuevos índices de estratificación de riesgo en pacientes con IDC. Se utilizaron las señales de ECG y BP de 91 pacientes con IDC y 49 sujetos sanos. Los pacientes fueron estratificados por su riesgo cardíaco como: alto riesgo (IDCHR), cuando después de dos años el sujeto murió, o bajo riesgo (IDCLR) en otro caso. Se extrajeron indices utilizando el análisis de Poincaré segmentado, la dinámica simbólica articulada de alta resolución y la coherencia parcial dirigida a corto plazo normalizada. Se construyeron modelos SVM para clasificar a estos pacientes en función de su riesgo cardiovascular. El modelo IDCLR vs IDCHR logró una exactitud del 98% con un área bajo la curva de 0.96. Los resultados sugieren que los pacientes IDCHR tienen sus HRV y BPV disminuidos en comparación con los pacientes IDCLR, lo que sugiere una disminución en su actividad vagal y la compensación de la actividad simpática. Finalmente, analizamos la interacción cardiorrespiratoria asociada con los sistemas relacionados con ICM y DCM. Proponemos un análisis basado en la actividad vascular como entrada y salida de la respuesta baroreflectora. El objetivo fue analizar la capacidad de las interacciones cardiorrespiratorias y vasculares para la clasificación de pacientes con ICM y DCM. Estudiamos 41 pacientes con CMP y 39 sujetos sanos. Se definieron tres sub-espacios: 'up' para valores crecientes, 'down' para los decrecientes, y 'no-change' en otro caso, y se creó una representación tridimensional que se caracterizó estadística y morfológicamente. Los indices resultantes se usaron para clasificar a los pacientes por su etiología con modelos SVM que lograron una exactitud de 92% cuando los pacientes ICM y DCM fueron comparados. Los resultados reflejaron un deterioro más pronunciado de la regulación autónoma en pacientes con DCM.Postprint (published version

Photoacoustic-guided focused ultrasound (PAFUSion) for identifying reflection artifacts in photoacoustic imaging

AbstractInfluence of acoustic inhomogeneities and resulting reflection artifacts is an important problem in reflection-mode photoacoustic imaging. Absorption of light by skin and superficial optical absorbers will generate photoacoustic transients, which traverse into the tissue and get reflected from structures having different acoustic impedance. These reflected photoacoustic signals, when reconstructed, may appear in the region of interest, which causes difficulties in image interpretation. We propose a novel method to identify and potentially eliminate reflection artifacts in photoacoustic images using photoacoustic-guided focused ultrasound [PAFUSion]. Our method uses focused ultrasound pulses to mimic the wave field produced by photoacoustic sources and thus provides a way to identify reflection artifacts in clinical combined photoacoustic and pulse-echo ultrasound. Simulation and phantom results are presented to demonstrate the validity and impact of this method. Results show that PAFUSion can identify reflections in photoacoustic images and thus envisages potential for improving photoacoustic imaging of acoustically inhomogeneous tissue

Respiratory organ motion in interventional MRI : tracking, guiding and modeling

Respiratory organ motion is one of the major challenges in interventional MRI, particularly in interventions with therapeutic ultrasound in the abdominal region. High-intensity focused ultrasound found an application in interventional MRI for noninvasive treatments of different abnormalities. In order to guide surgical and treatment interventions, organ motion imaging and modeling is commonly required before a treatment start. Accurate tracking of organ motion during various interventional MRI procedures is prerequisite for a successful outcome and safe therapy. In this thesis, an attempt has been made to develop approaches using focused ultrasound which could be used in future clinically for the treatment of abdominal organs, such as the liver and the kidney. Two distinct methods have been presented with its ex vivo and in vivo treatment results. In the first method, an MR-based pencil-beam navigator has been used to track organ motion and provide the motion information for acoustic focal point steering, while in the second approach a hybrid imaging using both ultrasound and magnetic resonance imaging was combined for advanced guiding capabilities. Organ motion modeling and four-dimensional imaging of organ motion is increasingly required before the surgical interventions. However, due to the current safety limitations and hardware restrictions, the MR acquisition of a time-resolved sequence of volumetric images is not possible with high temporal and spatial resolution. A novel multislice acquisition scheme that is based on a two-dimensional navigator, instead of a commonly used pencil-beam navigator, was devised to acquire the data slices and the corresponding navigator simultaneously using a CAIPIRINHA parallel imaging method. The acquisition duration for four-dimensional dataset sampling is reduced compared to the existing approaches, while the image contrast and quality are improved as well. Tracking respiratory organ motion is required in interventional procedures and during MR imaging of moving organs. An MR-based navigator is commonly used, however, it is usually associated with image artifacts, such as signal voids. Spectrally selective navigators can come in handy in cases where the imaging organ is surrounding with an adipose tissue, because it can provide an indirect measure of organ motion. A novel spectrally selective navigator based on a crossed-pair navigator has been developed. Experiments show the advantages of the application of this novel navigator for the volumetric imaging of the liver in vivo, where this navigator was used to gate the gradient-recalled echo sequence

Measuring Cerebral Activation From fNIRS Signals: An Approach Based on Compressive Sensing and Taylor-Fourier Model

Functional near-infrared spectroscopy (fNIRS) is a noninvasive and portable neuroimaging technique that uses NIR light to monitor cerebral activity by the so-called haemodynamic responses (HRs). The measurement is challenging because of the presence of severe physiological noise, such as respiratory and vasomotor waves. In this paper, a novel technique for fNIRS signal denoising and HR estimation is described. The method relies on a joint application of compressed sensing theory principles and Taylor-Fourier modeling of nonstationary spectral components. It operates in the frequency domain and models physiological noise as a linear combination of sinusoidal tones, characterized in terms of frequency, amplitude, and initial phase. Algorithm performance is assessed over both synthetic and experimental data sets, and compared with that of two reference techniques from fNIRS literature

A Novel Equivalent Time Sampling-Based Method for Pulse Transit Time Estimation with Applications into the Cardiovascular Disease Diagnosis

The increasing incidence of cardiovascular diseases (CVDs) is reflected in additional costs for healthcare systems all over the world. To date, pulse transit time (PTT) is considered a key index of cardiovascular health status and for diagnosis of CVDs. In this context, the present study focuses on a novel image analysis-based method for PTT estimation through the application of equivalent time sampling. The method, which post-processes color Doppler videos, was tested on two different setups: a Doppler flow phantom set in pulsatile mode and an in-house arterial simulator. In the former, the Doppler shift was due to the echogenic properties of the blood mimicking fluid only, since the phantom vessels are non-compliant. In the latter, the Doppler signal relied on the wall movement of compliant vessels in which a fluid with low echogenic properties was pumped. Therefore, the two setups allowed the measurement of the flow average velocity (FAV) and the pulse wave velocity (PWV), respectively. Data were collected through an ultrasound diagnostic system equipped with a phased array probe. Experimental outcomes confirm that the proposed method can represent an alternative tool for the local measurement of both FAV in non-compliant vessels and PWV in compliant vessels filled with low echogenic fluids

Linear-Array Photoacoustic Imaging Using Minimum Variance-Based Delay Multiply and Sum Adaptive Beamforming Algorithm

In Photoacoustic imaging (PA), Delay-and-Sum (DAS) beamformer is a common beamforming algorithm having a simple implementation. However, it results in a poor resolution and high sidelobes. To address these challenges, a new algorithm namely Delay-Multiply-and-Sum (DMAS) was introduced having lower sidelobes compared to DAS. To improve the resolution of DMAS, a novel beamformer is introduced using Minimum Variance (MV) adaptive beamforming combined with DMAS, so-called Minimum Variance-Based DMAS (MVB-DMAS). It is shown that expanding the DMAS equation results in multiple terms representing a DAS algebra. It is proposed to use the MV adaptive beamformer instead of the existing DAS. MVB-DMAS is evaluated numerically and experimentally. In particular, at the depth of 45 mm MVB-DMAS results in about 31 dB, 18 dB and 8 dB sidelobes reduction compared to DAS, MV and DMAS, respectively. The quantitative results of the simulations show that MVB-DMAS leads to improvement in full-width-half-maximum about 96 %, 94 % and 45 % and signal-to-noise ratio about 89 %, 15 % and 35 % compared to DAS, DMAS, MV, respectively. In particular, at the depth of 33 mm of the experimental images, MVB-DMAS results in about 20 dB sidelobes reduction in comparison with other beamformers.Comment: This is the final version of this paper, which is accepted in the "Journal of Biomedical Optics". Compared to previous versions, this version contains more experiments and evaluatio