Classification of Prolapsed Mitral Valve versus Healthy Heart from Phonocardiograms by Multifractal Analysis

Phonocardiography has shown a great potential for developing low-cost computer-aided diagnosis systems for cardiovascular monitoring. So far, most of the work reported regarding cardiosignal analysis using multifractals is oriented towards heartbeat dynamics. This paper represents a step towards automatic detection of one of the most common pathological syndromes, so-called mitral valve prolapse (MVP), using phonocardiograms and multifractal analysis. Subtle features characteristic for MVP in phonocardiograms may be difficult to detect. The approach for revealing such features should be locally based rather than globally based. Nevertheless, if their appearances are specific and frequent, they can affect a multifractal spectrum. This has been the case in our experiment with the click syndrome. Totally, 117 pediatric phonocardiographic recordings (PCGs), 8 seconds long each, obtained from 117 patients were used for PMV automatic detection. We propose a two-step algorithm to distinguish PCGs that belong to children with healthy hearts and children with prolapsed mitral valves (PMVs). Obtained results show high accuracy of the method. We achieved 96.91% accuracy on the dataset (97 recordings). Additionally, 90% accuracy is achieved for the evaluation dataset (20 recordings). Content of the datasets is confirmed by the echocardiographic screening

Detection of road structure composition and geometry changes by processing measured parameters, for the purpose of road network categorization

U ovoj disertaciji razvijen je algoritam za predikciju rizika proklizavanja vozila, čime je omogućeno mapiranje rizičnih zona duž putne infrastrukture. Predloženim algoritmom se realizuju automatska detekcija i prepoznavanje finih promena sastava i geometrije putne površi. Ovo se zasniva na obradi teksture slike dobijene skeniranjem puta, iz specijalnog vozila koje se kreće kolovoznom trakom duž deonice putne mreže. Merni podaci prikupljeni su upotrebom multisenzorske platforme montirane na vozilo. Ovakav pristup analizi putne infrastrukture ima za cilj adekvatnu i blagovremenu reakciju na promene stanja površi puta, koje nisu vidljive golim okom od strane direktnih učesnika u saobraćaju. Ovo je od posebnog značaja i za potrebe službi koje se bave održavanjem puteva i sanacijom oštećenja. Na osnovu eksperimentalnih rezultata i obradom izmerenih parametara, razvijen je i predstavljen algoritam čiji je glavni cilj predviđanje rizika i lokalizacija regiona potencijalnih saobraćajnih nezgoda koje mogu nastati kao posledica proklizavanja vozila sa putne površi. U pogledu strukturnog kvaliteta, putna površ se najčešće opisuje svojom teksturom. Njena geomerijska svojstva direktno utiču na druge činioce bezbednosti u saobraćaju, kao što su interakcija pneumatika sa površinskim slojem puteva, odvođenje tj. drenaža vode i otpornost na proklizavanje. U osnovi razvoja pomenutog algoritma urađene su analize jednodimenzionalnih i dvodimenzionalnih signala dobijenih uređajima za beskontaktno skeniranje. Akvizicija jednodimenzionalnih signala vršena je na osnovu interakcije koherentne svetlosti sa površinskim materijalima puta, upotrebom laserskog profilometra. Dvodimenzionalni signal je dobijen upotrebom video-kamere kojom je snimana putna površ. Oba dijagnostička pristupa realizovana su uređajima sa istog specijalnog vozila. U ovoj disertaciji je najpre potvrđena multifraktalna priroda profila putne površi, čime je dokazana mogućnost primene multifraktalnog pristupa u analizi teksture puta, koja se pokazala kao veoma pouzdan alat za detekciju i lokalizaciju granulometrijskih promena na putnoj površi. Rezultati multifraktalne analize su iskorišćeni kao potvrda stohastičke prirode jednodimenzionalnog signala, i pretpostavka da dvodimenzionalni signal pripada sličnoj familiji slučajnih/pseudoslučajnih vremenskih serija. Novi algoritam predikcije rizika, predložen u disertaciji, bazira se na obradi i analizi dvodimenzionalnog signala. Obrada i analiza slike vršena je testiranjem četiri metode za ekstrakciju obeležja teksture: Gaborovom transformacijom, transformacijom talasićima, matricom kopojavljivanja nivoa sivog i obeležjima histograma ivica. Od svih navedenih metoda, primena Gaborove transformacije je pokazala najbolje rezultate. Ekstrakcija vektora obeležja teksture praćena statističkim algoritmima za merenje sličnosti vektora obeležja i selekcija referentnog vektora, dovela je do klasifikacije teksture slike. Sâm algoritam je nadograđen inkorporiranjem istovremenih merenja temperature površine, kako bi se kreirala i validirala finalna klasifikacija finih tekstura površine. Put je klasifikovan u klase rizika visokog, srednjeg i niskog nivoa, u skladu sa opasnostima od proklizavanja, što je omogućilo formiranje mape rizičnih zona. Algoritam predviđanja rizika je potvrđenna osnovu podataka o saobraćajnim nezgodama, koje su se desile u periodu od tri sukcesivne godine na istoj deonici puta, pribavljenih iz baze Agencije za bezbednost drumskog saobraćaja Srbije. Razvijeni algoritam omogućava predikciju lokacija rizičnih zona sa mapiranjem, koje upozoravaju na potencijalne saobraćajne nezgode usled proklizavanja vozila. Može se koristiti kao podrška za navigaciju, autonomnu vožnju, a moguće je unaprediti celu proceduru sa ciljem adekvatne reakcije u realnom vremenu, putem globalne mreže (IoV - Internet of Vehicles), koja postaje sastavni deo tzv. pametnih gradova (smart cities). Ovakav pristup analizi putne površi će svakako, u svojoj daljoj primeni, rezultirati u smeru precizne i objektivne klasifikacije i kategorizacije kompletne putne infrastrukture, a sve u pravcu povećanja bezbednosti učesnika u saobraćaju, sa naročitim akcentom na rešenje problema predviđanja rizika na putu za donošenje odluka pri autonomnoj vožnji.This dissertation describes the development of an algorithm for predicting the risk of vehicle skidding by mapping high-risk zones along road surfaces. The algorithm enables the automatic detection and recognition of fine changes in the composition and geometry of road surfaces. It is based on image texture processing of the metrics obtained from scanning the road surface using a vehicle-mounted multi-sensory platform. The objective of this algorithm is to provide the means to a real time response to invisible to the bare eye changes in road surface conditions for the benefit of road maintenance and damage repair services, as well as general motorists and autonomously driven vehicles. The algorithm will be capable of being used to identify and assess the accident risk posed by inadequate and compromised road surfaces that potentiate the possibility of vehicles skidding and sliding. In terms of structural quality, road surface is most often described according to its texture. Its geometric properties have a direct impact on other road safety factors, such as interaction with vehicle tires, water drainage and skid resistance. The development of the algorithm was based on analysis of onedimensional and two-dimensional signals obtained by contactless scanning devices. The acquisition of one-dimensional signals was performed with a laser profiler, and the two-dimensional signal was obtained with a combination of a video camera and a surface temperature sensor. All diagnostic devices were mounted on the same special vehicle. For this research, the multifractal nature of the road surface profile was firstly confirmed, thus proving the feasibility of applying a multifractal approach to analyze road texture. This has proven to be a very reliable tool for detecting and locating real changes in the geometry of road surfaces. The results of multifractal analysis were used to confirm the stochastic nature of the one-dimensional signal, and the assumption that the two-dimensional signal belongs to a similar family of random / pseudorandom time series. The new risk prediction algorithm proposed in this dissertation is based on processing and analyzing a two-dimensional signal. Image processing and analysis were tested by comparing four texture extraction methods: Gabor transform, wavelet transform, gray level co-occurrence matrix and edge histogram descriptor. Of all the above methods, the Gabor transform produced the best results. Texture feature vector extraction, followed by statistical algorithms to measure feature vector similarity and reference vector selection, led to the classification of the image texture. The algorithm itself has been upgraded by incorporating simultaneous surface temperature measurements to create and validate the final classification of fine surface textures. The road was classified into high, medium and low level risk areas according to skid hazard, which enabled the formation of a map of risk zones. The algorithm for risk prediction was validated on the basis of traffic accidents which occurred over three successive years on the same section of road, information for which was obtained from the database of the Road Traffic Safety Agency of Serbia.The algorithm that has been developed enables risk assessment mapping of dangerous locations. In this way, potential traffic accident sites due to vehicle skidding can be flagged. It could be used as a support for navigation or for autonomous driving. The entire procedure could be improved and updated by integrating real time responses through the global network (Internet of Vehicles - IoV), to become an integral part of so-called smart cities. The approach to road surface analysis described in this research paper could potentially be applied to the precise and objective classification and categorization of the entire road surface infrastructure. Road safety could be increased, with particular emphasis on solving the risk prediction problems for decision making for autonomous driving

Multifractal techniques for analysis and classification of emphysema images

This thesis proposes, develops and evaluates different multifractal methods for detection, segmentation and classification of medical images. This is achieved by studying the structures of the image and extracting the statistical self-similarity measures characterized by the Holder exponent, and using them to develop texture features for segmentation and classification. The theoretical framework for fulfilling these goals is based on the efficient computation of fractal dimension, which has been explored and extended in this work. This thesis investigates different ways of computing the fractal dimension of digital images and validates the accuracy of each method with fractal images with predefined fractal dimension. The box counting and the Higuchi methods are used for the estimation of fractal dimensions. A prototype system of the Higuchi fractal dimension of the computed tomography (CT) image is used to identify and detect some of the regions of the image with the presence of emphysema. The box counting method is also used for the development of the multifractal spectrum and applied to detect and identify the emphysema patterns. We propose a multifractal based approach for the classification of emphysema patterns by calculating the local singularity coefficients of an image using four multifractal intensity measures. One of the primary statistical measures of self-similarity used in the processing of tissue images is the Holder exponent (α-value) that represents the power law, which the intensity distribution satisfies in the local pixel neighbourhoods. The fractal dimension corresponding to each α-value gives a multifractal spectrum f(α) that was used as a feature descriptor for classification. A feature selection technique is introduced and implemented to extract some of the important features that could increase the discriminating capability of the descriptors and generate the maximum classification accuracy of the emphysema patterns. We propose to further improve the classification accuracy of emphysema CT patterns by combining the features extracted from the alpha-histograms and the multifractal descriptors to generate a new descriptor. The performances of the classifiers are measured by using the error matrix and the area under the receiver operating characteristic curve (AUC). The results at this stage demonstrated the proposed cascaded approach significantly improves the classification accuracy. Another multifractal based approach using a direct determination approach is investigated to demonstrate how multifractal characteristic parameters could be used for the identification of emphysema patterns in HRCT images. This further analysis reveals the multi-scale structures and characteristic properties of the emphysema images through the generalized dimensions. The results obtained confirm that this approach can also be effectively used for detecting and identifying emphysema patterns in CT images. Two new descriptors are proposed for accurate classification of emphysema patterns by hybrid concatenation of the local features extracted from the local binary patterns (LBP) and the global features obtained from the multifractal images. The proposed combined feature descriptors of the LBP and f(α) produced a very good performance with an overall classification accuracy of 98%. These performances outperform other state-of-the-art methods for emphysema pattern classification and demonstrate the discriminating power and robustness of the combined features for accurate classification of emphysema CT images. Overall, experimental results have shown that the multifractal could be effectively used for the classifications and detections of emphysema patterns in HRCT images

Classification of prolapsed mitral valve versus healthy heart from phonocardiograms by multifractal analysis. Computational and mathematical methods in medicine

Phonocardiography has shown a great potential for developing low-cost computer-aided diagnosis systems for cardiovascular monitoring. So far, most of the work reported regarding cardiosignal analysis using multifractals is oriented towards heartbeat dynamics. This paper represents a step towards automatic detection of one of the most common pathological syndromes, socalled mitral valve prolapse (MVP), using phonocardiograms and multifractal analysis. Subtle features characteristic for MVP in phonocardiograms may be difficult to detect. The approach for revealing such features should be locally based rather than globally based. Nevertheless, if their appearances are specific and frequent, they can affect a multifractal spectrum. This has been the case in our experiment with the click syndrome. Totally, 117 pediatric phonocardiographic recordings (PCGs), 8 seconds long each, obtained from 117 patients were used for PMV automatic detection. We propose a two-step algorithm to distinguish PCGs that belong to children with healthy hearts and children with prolapsed mitral valves (PMVs). Obtained results show high accuracy of the method. We achieved 96.91% accuracy on the dataset (97 recordings). Additionally, 90% accuracy is achieved for the evaluation dataset (20 recordings). Content of the datasets is confirmed by the echocardiographic screening

Automatic analysis and classification of cardiac acoustic signals for long term monitoring

Objective: Cardiovascular diseases are the leading cause of death worldwide resulting in over 17.9 million deaths each year. Most of these diseases are preventable and treatable, but their progression and outcomes are significantly more positive with early-stage diagnosis and proper disease management. Among the approaches available to assist with the task of early-stage diagnosis and management of cardiac conditions, automatic analysis of auscultatory recordings is one of the most promising ones, since it could be particularly suitable for ambulatory/wearable monitoring. Thus, proper investigation of abnormalities present in cardiac acoustic signals can provide vital clinical information to assist long term monitoring. Cardiac acoustic signals, however, are very susceptible to noise and artifacts, and their characteristics vary largely with the recording conditions which makes the analysis challenging. Additionally, there are challenges in the steps used for automatic analysis and classification of cardiac acoustic signals. Broadly, these steps are the segmentation, feature extraction and subsequent classification of recorded signals using selected features. This thesis presents approaches using novel features with the aim to assist the automatic early-stage detection of cardiovascular diseases with improved performance, using cardiac acoustic signals collected in real-world conditions. Methods: Cardiac auscultatory recordings were studied to identify potential features to help in the classification of recordings from subjects with and without cardiac diseases. The diseases considered in this study for the identification of the symptoms and characteristics are the valvular heart diseases due to stenosis and regurgitation, atrial fibrillation, and splitting of fundamental heart sounds leading to additional lub/dub sounds in the systole or diastole interval of a cardiac cycle. The localisation of cardiac sounds of interest was performed using an adaptive wavelet-based filtering in combination with the Shannon energy envelope and prior information of fundamental heart sounds. This is a prerequisite step for the feature extraction and subsequent classification of recordings, leading to a more precise diagnosis. Localised segments of S1 and S2 sounds, and artifacts, were used to extract a set of perceptual and statistical features using wavelet transform, homomorphic filtering, Hilbert transform and mel-scale filtering, which were then fed to train an ensemble classifier to interpret S1 and S2 sounds. Once sound peaks of interest were identified, features extracted from these peaks, together with the features used for the identification of S1 and S2 sounds, were used to develop an algorithm to classify recorded signals. Overall, 99 features were extracted and statistically analysed using neighborhood component analysis (NCA) to identify the features which showed the greatest ability in classifying recordings. Selected features were then fed to train an ensemble classifier to classify abnormal recordings, and hyperparameters were optimized to evaluate the performance of the trained classifier. Thus, a machine learning-based approach for the automatic identification and classification of S1 and S2, and normal and abnormal recordings, in real-world noisy recordings using a novel feature set is presented. The validity of the proposed algorithm was tested using acoustic signals recorded in real-world, non-controlled environments at four auscultation sites (aortic valve, tricuspid valve, mitral valve, and pulmonary valve), from the subjects with and without cardiac diseases; together with recordings from the three large public databases. The performance metrics of the methodology in relation to classification accuracy (CA), sensitivity (SE), precision (P+), and F1 score, were evaluated. Results: This thesis proposes four different algorithms to automatically classify fundamental heart sounds – S1 and S2; normal fundamental sounds and abnormal additional lub/dub sounds recordings; normal and abnormal recordings; and recordings with heart valve disorders, namely the mitral stenosis (MS), mitral regurgitation (MR), mitral valve prolapse (MVP), aortic stenosis (AS) and murmurs, using cardiac acoustic signals. The results obtained from these algorithms were as follows: • The algorithm to classify S1 and S2 sounds achieved an average SE of 91.59% and 89.78%, and F1 score of 90.65% and 89.42%, in classifying S1 and S2, respectively. 87 features were extracted and statistically studied to identify the top 14 features which showed the best capabilities in classifying S1 and S2, and artifacts. The analysis showed that the most relevant features were those extracted using Maximum Overlap Discrete Wavelet Transform (MODWT) and Hilbert transform. • The algorithm to classify normal fundamental heart sounds and abnormal additional lub/dub sounds in the systole or diastole intervals of a cardiac cycle, achieved an average SE of 89.15%, P+ of 89.71%, F1 of 89.41%, and CA of 95.11% using the test dataset from the PASCAL database. The top 10 features that achieved the highest weights in classifying these recordings were also identified. • Normal and abnormal classification of recordings using the proposed algorithm achieved a mean CA of 94.172%, and SE of 92.38%, in classifying recordings from the different databases. Among the top 10 acoustic features identified, the deterministic energy of the sound peaks of interest and the instantaneous frequency extracted using the Hilbert Huang-transform, achieved the highest weights. • The machine learning-based approach proposed to classify recordings of heart valve disorders (AS, MS, MR, and MVP) achieved an average CA of 98.26% and SE of 95.83%. 99 acoustic features were extracted and their abilities to differentiate these abnormalities were examined using weights obtained from the neighborhood component analysis (NCA). The top 10 features which showed the greatest abilities in classifying these abnormalities using recordings from the different databases were also identified. The achieved results demonstrate the ability of the algorithms to automatically identify and classify cardiac sounds. This work provides the basis for measurements of many useful clinical attributes of cardiac acoustic signals and can potentially help in monitoring the overall cardiac health for longer duration. The work presented in this thesis is the first-of-its-kind to validate the results using both, normal and pathological cardiac acoustic signals, recorded for a long continuous duration of 5 minutes at four different auscultation sites in non-controlled real-world conditions.Open Acces