This paper presents an overview of approaches to analysis of heart sound signals. The paper reviews the milestones in the development of phonocardiogram (PCG) signal analysis. It describes the various stages involved in the analysis of heart sounds and discrete wavelet transform as a preferred method for bio-signal processing. In addition, the gaps that still exist between contemporary methods of signal analysis of heart sounds and their applications for clinical diagnosis is reviewed. A lot of progress has been made but crucial gaps still exist. The findings of this review paper are as follows: there is a lack of consensus in research outputs; inter-patient adaptability of signal processing algorithm is still problematic; the process of clinical validation of analysis techniques was not sufficiently rigorous in most of the reviewed literature; and as such data integrity and measurement are still in doubt, which most of the time led to inaccurate interpretation of results. In addition, the existing diagnostic systems are too complex and expensive. The paper concluded that the ability to correctly acquire, analyse and interpret heart sound signals for improved clinical diagnostic processes has become a priority. 相似文献
This paper presents an overview of approaches to analysis of heart sound signals. The paper reviews the milestones in the development of phonocardiogram (PCG) signal analysis. It describes the various stages involved in the analysis of heart sounds and discrete wavelet transform as a preferred method for bio-signal processing. In addition, the gaps that still exist between contemporary methods of signal analysis of heart sounds and their applications for clinical diagnosis is reviewed. A lot of progress has been made but crucial gaps still exist. The findings of this review paper are as follows: there is a lack of consensus in research outputs; inter-patient adaptability of signal processing algorithm is still problematic; the process of clinical validation of analysis techniques was not sufficiently rigorous in most of the reviewed literature; and as such data integrity and measurement are still in doubt, which most of the time led to inaccurate interpretation of results. In addition, the existing diagnostic systems are too complex and expensive. The paper concluded that the ability to correctly acquire, analyse and interpret heart sound signals for improved clinical diagnostic processes has become a priority. 相似文献
In the past decades, machine learning (ML) tools have been successfully used in several medical diagnostic problems. While they often significantly outperform expert physicians (in terms of diagnostic accuracy, sensitivity, and specificity), they are mostly not being used in practice. One reason for this is that it is difficult to obtain an unbiased estimation of diagnose's reliability. We discuss how reliability of diagnoses is assessed in medical decision-making and propose a general framework for reliability estimation in machine learning, based on transductive inference. We compare our approach with a usual (machine learning) probabilistic approach as well as with classical stepwise diagnostic process where reliability of diagnose is presented as its post-test probability. The proposed transductive approach is evaluated on several medical datasets from the University of California (UCI) repository as well as on a practical problem of clinical diagnosis of the coronary artery disease (CAD). In all cases, significant improvements over existing techniques are achieved. 相似文献
Many disease of the heart cause changes in heart sounds and additional murmurs before other signs and symptoms appear. Hence, heart sound analysis by auscultation is the primary test conducted by physicians to assess the condition of the heart. Yet, heart sound analysis by auscultation as well as analysis of the phonocardiogram (PCG) signal have not gained widespread acceptance. This is due mainly to many controversies regarding the genesis of the sounds and the lack of quantitative techniques for reliable analysis of the signal features. The heart sound signal has much more information than can be assessed by the human ear or by visual inspection of the signal tracings on paper as currently practiced. Here, we review the nature of the heart sound signal and the various signal-processing techniques that have been applied to PCG analysis. Some new research directions are also outlined. 相似文献
There are lots of work being done to develop computer-assisted diagnosis and detection (CAD) technologies and systems to improve
the diagnostic quality for pulmonary nodules. Another way to improve accuracy of diagnosis on new images is to recall or find
images with similar features from archived historical images which already have confirmed diagnostic results, and the content-based
image retrieval (CBIR) technology has been proposed for this purpose. In this paper, we present a method to find and select
texture features of solitary pulmonary nodules (SPNs) detected by computed tomography (CT) and evaluate the performance of
support vector machine (SVM)-based classifiers in differentiating benign from malignant SPNs. Seventy-seven biopsy-confirmed
CT cases of SPNs were included in this study. A total of 67 features were extracted by a feature extraction procedure, and
around 25 features were finally selected after 300 genetic generations. We constructed the SVM-based classifier with the selected
features and evaluated the performance of the classifier by comparing the classification results of the SVM-based classifier
with six senior radiologists′ observations. The evaluation results not only showed that most of the selected features are
characteristics frequently considered by radiologists and used in CAD analyses previously reported in classifying SPNs, but
also indicated that some newly found features have important contribution in differentiating benign from malignant SPNs in
SVM-based feature space. The results of this research can be used to build the highly efficient feature index of a CBIR system
for CT images with pulmonary nodules. 相似文献
OBJECTIVE: Recently, gene expression profiling using microarray techniques has been shown as a promising tool to improve the diagnosis and treatment of cancer. Gene expression data contain high level of noise and the overwhelming number of genes relative to the number of available samples. It brings out a great challenge for machine learning and statistic techniques. Support vector machine (SVM) has been successfully used to classify gene expression data of cancer tissue. In the medical field, it is crucial to deliver the user a transparent decision process. How to explain the computed solutions and present the extracted knowledge becomes a main obstacle for SVM. MATERIAL AND METHODS: A multiple kernel support vector machine (MK-SVM) scheme, consisting of feature selection, rule extraction and prediction modeling is proposed to improve the explanation capacity of SVM. In this scheme, we show that the feature selection problem can be translated into an ordinary multiple parameters learning problem. And a shrinkage approach: 1-norm based linear programming is proposed to obtain the sparse parameters and the corresponding selected features. We propose a novel rule extraction approach using the information provided by the separating hyperplane and support vectors to improve the generalization capacity and comprehensibility of rules and reduce the computational complexity. RESULTS AND CONCLUSION: Two public gene expression datasets: leukemia dataset and colon tumor dataset are used to demonstrate the performance of this approach. Using the small number of selected genes, MK-SVM achieves encouraging classification accuracy: more than 90% for both two datasets. Moreover, very simple rules with linguist labels are extracted. The rule sets have high diagnostic power because of their good classification performance. 相似文献
Single-photon emission tomography (SPECT) imaging has been widely used to guide clinicians in the early Alzheimer's disease (AD) diagnosis challenge. However, AD detection still relies on subjective steps carried out by clinicians, which entail in some way subjectivity to the final diagnosis. In this work, kernel principal component analysis (PCA) and linear discriminant analysis (LDA) are applied on functional images as dimension reduction and feature extraction techniques, which are subsequently used to train a supervised support vector machine (SVM) classifier. The complete methodology provides a kernel-based computer-aided diagnosis (CAD) system capable to distinguish AD from normal subjects with 92.31% accuracy rate for a SPECT database consisting of 91 patients. The proposed methodology outperforms voxels-as-features (VAF) that was considered as baseline approach, which yields 80.22% for the same SPECT database. 相似文献
This paper is concerned with a synthesis study of the fast Fourier transform (FFT), the short-time Fourier transform (STFT), the Wigner distribution (WD) and the wavelet transform (WT) in analysing the phonocardiogram signal (PCG). It is shown that these transforms provide enough features of the PCG signals that will help clinics to obtain qualitative and quantitative measurements of the time-frequency (TF) PCG signal characteristics and consequently aid diagnosis. Similarly, it is shown that the frequency content of such a signal can be determined by the FFT without difficulties. The studied techniques (FT, STFT, WD, CWT, DWT and PWT) of analysis can thus be regarded as complementary in the TF analysis of the PCG signal; each will relate to a part distinct from the analysis in question. 相似文献
This paper presents the applications of the spectrogram, Wigner distribution and wavelet transform analysis methods to the phonocardiogram (PCG) signals. A comparison between these three methods has shown the resolution differences between them. It is found that the spectrogram short-time Fourier transform (STFT), cannot detect the four components of the first sound of the PCG signal. Also, the two components of the second sound are inaccurately detected. The Wigner distribution can provide time-frequency characteristics of the PCG signal, but with insufficient diagnostic information: the four components of the first sound, SI, are not accurately detected and the two components of the second sound, S2, seem to be one component. It is found that the wavelet transform is capable of detecting the two components, the aortic valve component A2 and pulmonary value component P2, of the second sound S2 of a normal PCG signal. These components are not detectable using the spectrogram or the Wigner distribution. However, the standard Fourier transform can display these two components in frequency but not the time delay between them. Furthermore, the wavelet transform provides more features and characteristics of the PCG signals that mill help physicians to obtain qualitative and quantitative measurements of the time-frequency characteristics. 相似文献
Currently, the best way to reduce the mortality of cancer is to detect and treat it in the earliest stages. Technological advances in genomics and proteomics have opened a new realm of methods for early detection that show potential to overcome the drawbacks of current strategies. In particular, pattern analysis of mass spectra of blood samples has attracted attention as an approach to early detection of cancer. Mass spectrometry provides rapid and precise measurements of the sizes and relative abundances of the proteins present in a complex biological/chemical mixture. This article presents a review of the development of clinical decision support systems using mass spectrometry from a machine learning perspective. The literature is reviewed in an explicit machine learning framework, the components of which are preprocessing, feature extraction, feature selection, classifier training, and evaluation. 相似文献
There are many research studies and emerging tools using artificial intelligence (AI) and machine learning to augment flow and mass cytometry workflows. Emerging AI tools can quickly identify common cell populations with continuous improvement of accuracy, uncover patterns in high-dimensional cytometric data that are undetectable by human analysis, facilitate the discovery of cell subpopulations, perform semi-automated immune cell profiling, and demonstrate potential to automate aspects of clinical multiparameter flow cytometric (MFC) diagnostic workflow. Utilizing AI in the analysis of cytometry samples can reduce subjective variability and assist in breakthroughs in understanding diseases.Here we review the diverse types of AI that are being applied to clinical cytometry data and how AI is driving advances in data analysis to improve diagnostic sensitivity and accuracy. We review supervised and unsupervised clustering algorithms for cell population identification, various dimensionality reduction techniques, and their utilities in visualization and machine learning pipelines, and supervised learning approaches for classifying entire cytometry samples.Understanding the AI landscape will enable pathologists to better utilize open source and commercially available tools, plan exploratory research projects to characterize diseases, and work with machine learning and data scientists to implement clinical data analysis pipelines. 相似文献
Phonocardiograms (PCG) are recordings of the acoustic waves produced by the mechanical action of the heart. They generally consist of two kinds of acoustic vibrations: heart sounds and heart murmurs. Heart murmurs are often the first signs of pathological changes of the heart valves, and are usually found during auscultation in primary health care. Heart auscultation has been recognized for a long time as an important tool for the diagnosis of heart disease, although its accuracy is still insufficient to diagnose some heart diseases. It does not enable the analyst to obtain both qualitative and quantitative characteristics of the PCG signals. The efficiency of diagnosis can be improved considerably by using modern digital signal processing techniques. Therefore, these last can provide useful and valuable information on these signals. The aim of this study is to analyse PCG signals using wavelet transform. This analysis is based on an algorithm for the detection of heart sounds (the first and second sounds, S1 and S2) and heart murmurs using the PCG signal as the only source. The segmentation algorithm, which separates the components of the heart signal, is based on denoising by wavelet transform (DWT). This algorithm makes it possible to isolate individual sounds (S1 or S2) and murmurs. Thus, the analysis of various PCGs signals using wavelet transform can provide a wide range of statistical parameters related to the phonocardiogram signal. 相似文献
Phonocardiograms (PCG) are recordings of the acoustic waves produced by the mechanical action of the heart. They generally consist of two kinds of acoustic vibrations: heart sounds and heart murmurs. Heart murmurs are often the first signs of pathological changes of the heart valves, and are usually found during auscultation in primary health care. Heart auscultation has been recognized for a long time as an important tool for the diagnosis of heart disease, although its accuracy is still insufficient to diagnose some heart diseases. It does not enable the analyst to obtain both qualitative and quantitative characteristics of the PCG signals. The efficiency of diagnosis can be improved considerably by using modern digital signal processing techniques. Therefore, these last can provide useful and valuable information on these signals. The aim of this study is to analyse PCG signals using wavelet transform. This analysis is based on an algorithm for the detection of heart sounds (the first and second sounds, S1 and S2) and heart murmurs using the PCG signal as the only source. The segmentation algorithm, which separates the components of the heart signal, is based on denoising by wavelet transform (DWT). This algorithm makes it possible to isolate individual sounds (S1 or S2) and murmurs. Thus, the analysis of various PCGs signals using wavelet transform can provide a wide range of statistical parameters related to the phonocardiogram signal. 相似文献
The paper provides an overview of the development of intelligent data analysis in medicine from a machine learning perspective: a historical view, a state-of-the-art view, and a view on some future trends in this subfield of applied artificial intelligence. The paper is not intended to provide a comprehensive overview but rather describes some subareas and directions which from my personal point of view seem to be important for applying machine learning in medical diagnosis. In the historical overview, I emphasize the naive Bayesian classifier, neural networks and decision trees. I present a comparison of some state-of-the-art systems, representatives from each branch of machine learning, when applied to several medical diagnostic tasks. The future trends are illustrated by two case studies. The first describes a recently developed method for dealing with reliability of decisions of classifiers, which seems to be promising for intelligent data analysis in medicine. The second describes an approach to using machine learning in order to verify some unexplained phenomena from complementary medicine, which is not (yet) approved by the orthodox medical community but could in the future play an important role in overall medical diagnosis and treatment. 相似文献
Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality reduction, and classification. This paper proposes a system for classifying a six-channel EMG signal from 14 finger movements. A feature vector of 66 elements was determined from the six-channel EMG signal for each finger movement. Subsequently, various feature extraction techniques and classifiers were tested and evaluated. We compared the performance of six feature extraction techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), uncorrelated linear discriminant analysis (ULDA), orthogonal fuzzy neighborhood discriminant analysis (OFNDA), spectral regression linear discriminant analysis (SRLDA), and spectral regression extreme learning machine (SRELM). In addition, we also evaluated the performance of seven classifiers consisting of support vector machine (SVM), linear classifier (LC), naive Bayes (NB), k-nearest neighbors (KNN), radial basis function extreme learning machine (RBF-ELM), adaptive wavelet extreme learning machine (AW-ELM), and neural network (NN). The results showed that the combination of SRELM as the feature extraction technique and NN as the classifier yielded the best classification accuracy of 99%, which was significantly higher than those from the other combinations tested.