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Title: Analysis and modeling of swallowing sounds
Author(s): Mohammad Aboofazeli (author), Zahra Moussavi (thesis advisor), University of Manitoba Electrical and Computer Engineering (Degree granting institution)
Date: 2006
Abstract: Swallowing function is a complicated process involving several highly coordinated events. In recent years, acoustical analysis of the swallowing mechanism has received considerable attention due to its diagnostic potentials. This thesis presents novel approaches for analysis and modeling of swallowing sounds in individuals with and without swallowing disorder (dysphagia). Different techniques based on nonlinear dynamics, recuffence quantification analysis, hidden Markov model (HMM), and multiresolution wavelet analysis were used to detect characteristic features of swallowing sounds for automatic swallowing sound detection, swallowing sound segmentation, as well as classification of normal and dysphagic swallowing sounds. Data from 27 healthy subjects and 11 dysphagic patients were used, in which swallowing and breath sounds were recorded with an accelerometer placed over the trachea (suprasternal notch), while the subjects were fed three different textures with a bolus size of 5 ml. Submental electromyogram signals were also recorded in 12 normal subjects simultaneously with their swallowing sounds. In terms of swallowing sound detection in the swallow and breath sound recordings, the performance of the HMM based method using recurrence plot features was superior to that of the other methods. As for the segmentation of the swallowing sounds, multiscale products of wavelet coefficients along with an HMM yielded the least error in detecting the boundaries between the segments. Lastly, in terms of classification of normal and dysphagic swallowing sounds, nonlinear metric tools, i.e., correlation dimension and time delay, resulted in a high accuracy of 837o. The results of HMM based methods for classification of swallowing sounds between the two groups of healthy and dysphagic subjects using RMS achieved 85.5Vo accuracy. The results of the study on the timing of submental muscle contraction in relation to swallowing sounds showed that the movement of the larynx precedes the fust audible segment of swallowing sounds. It also showed that the lag between the onset of submental muscle contraction and the beginning of initial discrete sounds is shorter for thin liquid texture. Overall, the outcomes of this study have paved the way for a better understanding of the swallowing mechanism and improving clinical assessment techniques of swallowing disorders using its acoustic signatures.
Department: Computing Science

Title: Analysis of swallowing sounds using hidden Markov models
Author(s): Mohammad Aboofazeli (author), Zahra Moussavi (author)
Date: 2008
Abstract: In recent years, acoustical analysis of the swallowing mechanism has received considerable attention due to its diagnostic potentials. This paper presents a hidden Markov model (HMM) based method for the swallowing sound segmentation and classification. Swallowing sound signals of 15 healthy and 11 dysphagic subjects were studied. The signals were divided into sequences of 25 ms segments each of which were represented by seven features. The sequences of features were modeled by HMMs. Trained HMMs were used for segmentation of the swallowing sounds into three distinct phases, i.e., initial quiet period, initial discrete sounds (IDS) and bolus transit sounds (BTS). Among the seven features, accuracy of segmentation by the HMM based on multi-scale product of wavelet coefficients was higher than that of the other HMMs and the linear prediction coefficient (LPC)-based HMM showed the weakest performance. In addition, HMMs were used for classification of the swallowing sounds of healthy subjects and dysphagic patients. Classification accuracy of different HMM configurations was investigated. When we increased the number of states of the HMMs from 4 to 8, the classification error gradually decreased. In most cases, classification error for N = 9 was higher than that of N = 8. Among the seven features used, root mean square (RMS) and waveform fractal dimension (WFD) showed the best performance in the HMM-based classification of swallowing sounds. When the sequences of the features of IDS segment were modeled separately, the accuracy reached up to 85.5%. As a second stage classification, a screening algorithm was used which correctly classified all the subjects but one healthy subject when RMS was used as characteristic feature of the swallowing sounds and the number of states was set to N = 8.
Department: Computing Science

Title: Comparison of recurrence plot features of swallowing and breath sounds
Author(s): Mohammad Aboofazeli (author), Zahra K. Moussavi (author)
Date: 2008
Abstract: Nonlinear dynamics theory has been a tool of choice in the analysis of physiological signals and systems. This paper presents a novel approach in the analysis of tracheal swallowing and breath sounds based on nonlinear dynamics theory. As the tracheal sound signal is nonstationary, the signal was studied based on the recurrence quantification analysis (RQA) method, which is a useful technique in the analysis of nonstationary and noisy signals. Tracheal sound recordings of 15 healthy and 9 dysphagic subjects were studied. The multidimensional state space trajectory of each signal was reconstructed using Taken’s method of delays. The reconstructed trajectories were analyzed by the RQA technique. The preliminary results suggested that some recurrence parameters were appreciably different between swallowing and breath sounds. In order to confirm discriminating ability of the parameters, an automated method for extraction of swallowing sounds in the records of the tracheal swallowing and breath sounds was investigated. The swallowing sound extraction results were validated by manual inspection of the simultaneously recorded airflow signal and spectrogram of the sounds and also by auditory means. Experimental results proved that the investigated method more accurately detected the boundaries of swallowing sounds than methods proposed previously. Swallowing sound detection may be employed in a system for automated swallowing assessment and diagnosis of swallowing disorders (dysphagia) by acoustical means.
Department: Computing Science

Title: Swallowing sound detection using hidden Markov modeling of recurrence plot features
Author(s): Mohammad Aboofazeli (author), Zahra Moussavi (author)
Date: 2009
Abstract: Automated detection of swallowing sounds in swallowing and breath sound recordings is of importance for monitoring purposes in which the recording durations are long. This paper presents a novel method for swallowing sound detection using hidden Markov modeling of recurrence plot features. Tracheal sound recordings of 15 healthy and nine dysphagic subjects were studied. The multidimensional state space trajectory of each signal was reconstructed using the Taken method of delays. The sequences of three recurrence plot features of the reconstructed trajectories (which have shown discriminating capability between swallowing and breath sounds) were modeled by three hidden Markov models. The Viterbi algorithm was used for swallowing sound detection. The results were validated manually by inspection of the simultaneously recorded airflow signal and spectrogram of the sounds, and also by auditory means. The experimental results suggested that the performance of the proposed method using hidden Markov modeling of recurrence plot features was superior to the previous swallowing sound detection methods.
Department: Computing Science

Title: Automated classification of swallowing and breadth sounds
Author(s): Mohammad Aboofazeli (author), Zahra Moussavi (author)
Date: 2004
Abstract: The goal of this study was to develop an automated and objective method to separate swallowing sounds from breath sounds. Swallowing sound detection can be utilized as part of a system for swallowing mechanism assessment and diagnosis of swallowing dysfunction (dysphagia) by acoustical means. In this study, an algorithm based on multilayer feed forward neural networks is proposed for decomposition of tracheal sound into swallowing and respiratory segments. Among many features examined, root-mean-square of the signal, the average power of the signal over 150-450 Hz and waveform fractal dimension were selected features applied to the neural network as inputs. Findings from previous studies about temporal and durational patterns of swallowing and respiration were used in a smart algorithm for further identification of the swallow and breath segments. The proposed method was applied to 18 tracheal sound recordings of 7 healthy subjects (ages 13-30 years, 4 males). The results were validated manually by visual inspection using airflow measurement and spectrogram of the sounds and auditory means. The algorithm was able to detect 91.7% of swallows correctly. The average of missed swallows and average of false detection were 8.3% and 9.5%, respectively. With additional preprocessing and post processing, the proposed method may be used for automated extraction of swallowing sounds from breath sounds in healthy and dysphagic individuals.
Department: Computing Science

Title: Automated extraction of swallowing sounds using a wavelet-based filter
Author(s): Mohammad Aboofazeli (author), Zahra Moussavi (author)
Date: 2006
Abstract: This paper presents an automated and objective method for extraction of swallowing sounds in a record of the tracheal breath and swallowing sounds. The proposed method takes advantage of the fact that swallowing sounds have more non-stationarity comparing with breath sounds and have large components in many wavelet scales whereas wavelet transform coefficients of breath sounds in higher wavelet scales are small. Therefore, a wavelet transform based filter was utilized in which a multiresolution decomposition-reconstruction process filters the signal. Swallowing sounds are detected in the filtered signal. The proposed method was applied to the tracheal sound recordings of 15 healthy and 11 dysphagic subjects. The results were validated manually by visual inspection using airflow measurement and spectrogram of the sounds and auditory means. Experimental results prove that the proposed method is more accurate, efficient, and objective than the methods proposed previously. Swallowing sound detection may be employed in a system for automated swallowing assessment and diagnosis of swallowing disorders (dysphagia) by acoustical means.
Department: Computing Science

Title: Analysis of normal swallowing sounds using nonlinear dynamic metric tools
Author(s): Mohammad Aboofazeli (author), Zahra Moussavi (author)
Date: 2004
Abstract: Several metric tools for quantative analysis of scalar time series have been developed using the theory of nonlinear dynamics. The goal of this work was to study the characteristics of swallowing sound using these metric tools. Takens method of delays was used to reconstruct multidimensional state space representation of the swallowing sounds of 6 healthy subjects (ages 13-30 years, 3 males) being fed thin and thick liquid textures. The optimum time delay for different subjects varied from 3 to 9 samples. False nearest neighbors method was used to obtain proper embedding dimension. The correlation dimension was calculated based on Grassberger-Procaccia algorithm. The results suggest that swallowing sound is well characterized by a small number of dimensions. The largest Lyapunov exponent was also estimated to evaluate the presence of chaos. As the largest Lyapunov exponent for some cases was negative, it may be concluded that swallowing sound is not necessarily a chaotic process.
Department: Computing Science

Title: Analysis of temporal pattern of swallowing mechanism
Author(s): Mohammad Aboofazeli (author), Zahra Moussavi (author)
Date: 2006
Abstract: This paper presents an objective method for analysis of temporal pattern of swallowing mechanism based on analysis of swallowing sounds and submental surface electromyogram (EMG). In this study, swallowing sound signal and submental EMG of 12 healthy subjects were recorded. Swallowing sound signals were divided into 25 millisecond segments each of which was represented by waveform fractal dimension (WED). Temporal pattern of swallowing sound signal was identified based on hidden Markov modeling (HMM) of the WED sequences. Submental muscle contraction was marked by thresholding the RMS values of the EMG signals. Duration of the swallowing sound phases, duration of the submental muscle contraction, and time difference between the onset of submental muscle contraction and the opening of cricopharynx were calculated. Experimental results suggest that the proposed method is efficient in the study of temporal pattern of swallowing mechanism and can provide an objective and accurate approach for swallowing mechanism analysis.
Department: Computing Science

Title: Automated detection of prostate cancer using wavelet transform features of ultrasound RF time series
Author(s): Mohammad Aboofazeli (author), Purang Abolmaesumi (author), Mehdi Moradi (author), Eric Sauerbrei (author), Robert Siemens (author), Alexander Boag (author), Parvin Mousavi (author)
Date: 2009
Abstract: The aim of this research was to investigate the performance of wavelet transform based features of ultrasound radiofrequency (RF) time series for automated detection of prostate cancer tumors in transrectal ultrasound images. Sequential frames of RF echo signals from 35 extracted prostate specimens were recorded in parallel planes, while the ultrasound probe and the tissue were fixed in position in each imaging plane. The sequence of RF echo signal samples corresponding to a particular spot in tissue imaging plane constitutes one RF time series. Each region of interest (ROI) of ultrasound image was represented by three groups of features of its time series, namely, wavelet, spectral and fractal features. Wavelet transform approximation and detail sequences of each ROI were averaged and used as wavelet features. The average value of the normalized spectrum in four quarters of the frequency range along with the intercept and slope of a regression line fitted to the values of the spectrum versus normalized frequency plot formed six spectral features. Fractal dimension (FD) of the RF time series were computed based on the Higuchi's approach. A support vector machine (SVM) classifier was used to classify the ROIs. The results indicate that combining wavelet coefficient based features with previously proposed spectral and fractal features of RF time series data would increase the area under ROC curve from 93.1% to 95.0%, respectively. Furthermore, the accuracy, sensitivity, and specificity increases to 91.7%, 86.6%, and 94.7%, from 85.7%, 85.2%, and 86.1%, respectively, using only spectral and fractal features. [ABSTRACT FROM AUTHOR]
Department: Computing Science

Title

Validation platform for ultrasound-based monitoring of thermal ablation

Author(s)

Alexandra M. Pompeu-Robinson (author), James Gray (author), Joshua Marble (author), Hamed Peikari (author), Jena Hall (author), Paweena U-Thainual (author), Mohammad Aboofazeli (author), Andras Lasso (author), Gabor Fichtinger (author)

Date

2010

Abstract

PURPOSE: A ground-truth validation platform was developed to provide spatial correlation between ultrasound (US), temperature measurements and histopathology images to validate US based thermal ablation monitoring methods.

METHOD: The test-bed apparatus consists of a container box with integrated fiducial lines. Tissue samples are suspended within the box using agar gel as the fixation medium. Following US imaging, the gel block is sliced and pathology images are acquired. Interactive software segments the fiducials as well as structures of interest in the pathology and US images. The software reconstructs the regions in 3D space and performs analysis and comparison of the features identified from both imaging modalities.

RESULTS: The apparatus and software were constructed to meet technical requirements. Tissue samples were contoured, reconstructed and registered in the common coordinate system of fiducials. There was agreement between the sample shapes, but systematic shift of several millimeters was found between histopathology and US. This indicates that during pathology slicing shear forces tend to dislocate the fiducial lines. Softer fiducial lines and harder gel material can eliminate this problem.

CONCLUSION: Viability of concept was presented. Despite our straightforward approach, further experimental work is required to optimize all materials and customize software. [ABSTRACT FROM AUTHOR]

Department

Computing Science

Title: Analysis and classification of swallowing sounds using reconstructed phase space features
Author(s): Mohammad Aboofazeli (author), Zahra Moussavi (author)
Date: 2005
Abstract: The paper presents a quantitative analysis of swallowing sounds in normal and dysphagic subjects based on nonlinear dynamics metric tools. In addition, an automated method is proposed to identify patients at risk of dysphagia. Multidimensional phase space representation of the swallowing sound was reconstructed using Takens method of delays. Rosenstein and false nearest neighbor (FNN) methods were employed to evaluate the optimum time delay and proper embedding dimension, respectively. A Grassberger-Procaccia algorithm was utilized to calculate the correlation dimension as a measure of the complexity of the reconstructed attractor. The analysis demonstrated the low-dimensional dynamic characteristics of normal and dysphagic swallowing sounds. The optimum time delay and correlation dimension of the opening and transmission phases of swallowing sounds were used as features for a 3-nearest neighbor classifier to identify individuals at risk of dysphagia. The method was applied to tracheal sound recordings of 15 healthy subjects and 11 patients with some degree of dysphagia. The algorithm was able to classify 83% of swallows correctly. Finally, a screening algorithm was used which correctly classified 24 out of 26 subjects. This study suggests that nonlinear analysis is a promising tool for quantitative analysis of swallowing sounds and swallowing disorders.
Department: Computing Science

Title: Tissue characterization using multiscale products of wavelet transform of ultrasound radio frequency echoes
Author(s): Mohammad Aboofazeli (author), Purang Abolmaesumi (author), Gabor Fichtinger (author), Parvin Mousavi (author)
Date: 2009
Abstract: This paper presents a novel method for tissue characterization using wavelet transform of ultrasound radio frequency (RF) echo signals. We propose the use of multiscale products of wavelet transform sequences of RF echoes to estimate the scatterer distribution in the tissue. The proposed method is based on the fact that when emitted ultrasound beams interact with scatterers in the tissue, backscattered beams contain singularities corresponding to the location of the scatterers. The singularities will exist in multiple scales of wavelet sequences of the echo signals. Therefore, peaks of wavelet transform multiscale products correspond to the location of scatterers. Estimation of scatterer spacing can be used for tissue characterization. The efficacy of the proposed method was validated in RF echo signals of in-vitro human prostate to characterize normal and cancerous tissue. The results confirm that wavelet transform multiscale products of RF echo signals contain tissue typing information that can be used as an effective tool to differentiate normal and cancerous prostate tissue.
Department: Computing Science

Title: A wavelet transform based digital image watermarking scheme
Author(s): Mohammad Aboofazeli (author), G. Thomas (author), Zahra Moussavi (author)
Date: 2004
Abstract: Digital image watermarking techniques have been proposed to prevent unauthorized distribution of multimedia data. A digital watermark encodes the owner's license information and embeds it into the image. Several discrete wavelet transform (DWT) based techniques are used for watermarking. In this paper, a watermarking scheme is proposed in which the image is decomposed into wavelet coefficients and a visual recognizable logo is embedded in the wavelet coefficients. Wavelet coefficients corresponding to the points located in a neighborhood that have maximum entropy are proposed for embedding the watermark. This method embeds the maximum amount of watermark while the watermark is imperceptible. Watermarking techniques must be robust to some attacks such as smoothing, sharpening and compression. These maximum entropy areas can survive a variety of attacks and can be used as reference points for watermark embedding. The experimental results confirmed that the technique is robust to a variety of attacks.
Department: Computing Science

Title: A new scheme for curved needle segmentation in three-dimensional ultrasound images
Author(s): Mohammad Aboofazeli (author), Purang Abolmaesumi (author), Parvin Mousavi (author), Gabor Fichtinger (author)
Date: 2009
Abstract: Ultrasound image guided needle insertion is the method of choice for a wide variety of medical diagnostic and therapeutic procedures. When flexible needles are inserted in soft tissue, these needles generally follow a curved path. Segmenting the trajectory of the needles in ultrasound images will facilitate guiding them within the tissue. In this paper, a novel algorithm for curved needle segmentation in three-dimensional (3D) ultrasound images is presented. The algorithm is based on the projection of a filtered 3D image onto a two-dimensional (2D) image. Detection of the needle in the resulting 2D image determines a surface on which the needle is located. The needle is then segmented on the surface. The proposed technique is able to detect needles without any previous assumption about the needle shape, or any a priori knowledge about the needle insertion axis line.
Department: Computing Science

Title: Respiratory flow estimation from tracheal sound by adaptive filters
Author(s): M. Golabbakhsh (author), Zahra Moussavi (author), Mohammad Aboofazeli (author)
Date: 2005
Abstract: In this study, average power of tracheal sound (P ave ) was used to estimate flow by parametric method as well as adaptive filters as a nonparametric method. Based on some preliminary studies, an exponential model was used for describing the relationship between flow and P ave for parametric method. It was assumed that flow signal of at least one breath from each target flow is available for calibration. The error for flow estimation with parametric method, was found to be 9plusmn3% and 10plusmn4% for inspiration and expiration, respectively. Considering nonparametric method, the estimation error was the least for the third order adaptive filter using the average power of the tracheal sound (dB), which was 10plusmn3 % and 11plusmn4 % for inspiration and expiration, respectively.
Department: Computing Science