Feature saliency using signal-to-noise ratios in automated diagnostic systems developed for ECG beats

Artificial neural networks (ANNs) have been used in a great number of medical diagnostic decision support system applications and within feedforward ANNs framework there are a number of established measures such as saliency measures for identifying important input features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of multilayer perceptron neural networks (MLPNNs) used in classification of electrocardiogram (ECG) beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database. The SNR saliency measure determines the saliency of a feature by comparing it to that of an injected noise feature and the SNR screening method utilizes the SNR saliency measure to select a parsimonious set of salient features. ECG signals were decomposed into time–frequency representations using discrete wavelet transform. Input feature vectors were extracted using statistics over the set of the wavelet coefficients. The MLPNNs used in the ECG beats-classification were trained for the SNR screening method. The application results of the SNR screening method to the ECG signals demonstrated that classification accuracies of the MLPNNs with salient input features are higher than that of the MLPNNs with salient and non-salient input features.     

Signal‐to‐noise ratios for measuring saliency of features extracted by eigenvector methods from ophthalmic arterial Doppler signals

Abstract: Features are used to represent patterns with minimal loss of important information. The feature vector, which is composed of the set of all features used to describe a pattern, is a reduced‐dimensional representation of that pattern. Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal‐to‐noise ratio saliency measure was employed to determine the saliency of input features of recurrent neural networks (RNNs) used in classification of ophthalmic arterial Doppler signals. Eigenvector methods were used to extract features representing the ophthalmic arterial Doppler signals. The RNNs used in the ophthalmic arterial Doppler signal classification were trained for the signal‐to‐noise ratio screening method. The application results of the signal‐to‐noise ratio screening method to the ophthalmic arterial Doppler signals demonstrated that classification accuracies of RNNs with salient input features are higher than those of RNNs with salient and non‐salient input features.     

Measuring saliency of features representing EEG signals using signal-to-noise ratios

Various methodologies of automated diagnosis have been adopted, however the entire process can generally be subdivided into a number of disjoint processing modules: pre-processing, feature extraction/selection, and classification. Features are used to represent patterns with minimal loss of important information. The feature vector, which is comprised of the set of all features used to describe a pattern, is a reduced-dimensional representation of that pattern. Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of probabilistic neural networks (PNNs) used in classification of electroencephalogram (EEG) signals. In order to extract features representing the EEG signals, eigenvector methods were used. The PNNs used in the EEG signals classification were trained for the SNR screening method. The application results of the SNR screening method to the EEG signals demonstrated that classification accuracies of the PNNs with salient input features are higher than that of the PNNs with salient and non-salient input features.     

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.     

Expert systems for time-varying biomedical signals using eigenvector methods

In this paper, we present the expert systems for time-varying biomedical signals classification and determine their accuracies. The combined neural network (CNN), mixture of experts (ME), and modified mixture of experts (MME) were tested and benchmarked for their performance on the classification of the studied time-varying biomedical signals (ophthalmic arterial Doppler signals, internal carotid arterial Doppler signals and electroencephalogram signals). Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The inputs of these expert systems composed of diverse or composite features were chosen according to the network structures. The present study was conducted with the purpose of answering the question of whether the expert system with diverse features (MME) or composite feature (CNN, ME) improve the capability of classification of the time-varying biomedical signals. The purpose was to determine an optimum classification scheme for the problem and also to infer clues about the extracted features. Our research demonstrated that the power levels of power spectral density (PSD) estimations obtained by the eigenvector methods are the valuable features which are representing the time-varying biomedical signals and the CNN, ME, and MME trained on these features achieved high classification accuracies.     

Combined neural network model to compute wavelet coefficients

Abstract: In recent years a novel model based on artificial neural networks technology has been introduced in the signal processing community for modelling the signals under study. The wavelet coefficients characterize the behaviour of the signal and computation of the wavelet coefficients is particularly important for recognition and diagnostic purposes. Therefore, we dealt with wavelet decomposition of time-varying biomedical signals. In the present study, we propose a new approach that takes advantage of combined neural network (CNN) models to compute the wavelet coefficients. The computation was provided and expressed by applying the CNNs to ophthalmic arterial and internal carotid arterial Doppler signals. The results were consistent with theoretical analysis and showed good promise for discrete wavelet transform of the time-varying biomedical signals. Since the proposed CNNs have high performance and require no complicated mathematical functions of the discrete wavelet transform, they were found to be effective for the computation of wavelet coefficients.     

Application of an adaptive neuro-fuzzy inference system for classification of Behcet disease using the fast Fourier transform method

Abstract: In this study, ophthalmic arterial Doppler signals were obtained from 200 subjects, 100 of whom suffered from ocular Behcet disease while the rest were healthy subjects. An adaptive neuro-fuzzy inference system (ANFIS) was used to detect the presence of ocular Behcet disease. Spectral analysis of the ophthalmic arterial Doppler signals was performed by the fast Fourier transform method for determining the ANFIS inputs. The ANFIS was trained with a training set and tested with a testing set. All these data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ocular Behcet disease. Performance indicators and statistical measures were used for evaluating the ANFIS. The correct classification rate was 94% for healthy subjects and 90% for unhealthy subjects suffering from ocular Behcet disease. The classification results showed that the ANFIS was effective at detecting ophthalmic arterial Doppler signals from subjects with Behcet disease.     

Neural network analysis of ophthalmic arterial doppler signals with Uveitis disease

Doppler ultrasound is known as a reliable technique, which demonstrates the flow characteristics and resistance of ophthalmic arteries. In this study, ophthalmic arterial Doppler signals were obtained from 95 subjects—that 45 of them had suffered from Uveitis disease and the rest of them had been healthy subjects. Multilayer perceptron neural network (MLPNN) employing quick propagation training algorithm was used to detect the presence of Uveitis disease. Spectral analysis of ophthalmic arterial Doppler signals was performed by autoregressive moving average (ARMA) method for determining the MLPNN inputs. The MLPNN was trained with training set, cross validated with cross validation set and tested with testing set. All these data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from Uveitis disease. Performance indicators and statistical measures were used for evaluating the MLPNN. The correct classification rate was 95.83% for healthy subjects and 91.30% for subjects suffering from Uveitis disease. Based on the accuracy of the MLPNN detections, it can be mentioned that the classification of ophthalmic arterial Doppler signals with Uveitis disease is feasible by the MLPNN employing quick propagation training algorithm.     

Classification of modulation signals using statistical signal characterization and artificial neural networks

Modulation recognition systems have to be able to correctly classify the incoming signal modulation scheme in the presence of noise. A new method for classification of analogue and digital modulated signals at low signal-to-noise ratio (SNR) is introduced in this paper. This method uses the statistical signal characterization (SSC) to extract parameters to classify the different modulation signals. The SSC technique produces a set of four numerical parameters for a specific modulated signal. Subsequent comparison of these parameters to those of other waveforms provides the basis for our classification system. The results of SSC technique are applied to an artificial neural network (ANN) to have a robust classification system in the presence of noise down to SNR of 3 dB. No a priori information is required by this technique about the set of input waveforms. The input to the classification system can be analogue or digital signals or a combination of both. The proposed technique shows a 100% efficiency of classification of analogue signals or digital signals at SNR of 7 dB. This classification efficiency reduces to 83% and 86% for analogue or digital signals at SNR of 3 dB. The SSC technique shows better classification results in comparison with other techniques with an important advantage over other methods, which is the simplicity of the neural network needed with this technique due to the small number of features used in the classification.     

基于小波域滤波的电子通信信道恶意干扰信号分离方法

针对电子通信信号与恶意干扰信号严重重叠的现象,设计了基于小波域滤波的电子通信信道恶意干扰信号分离方法。在分析信道特征并划分噪声种类后明确噪声信号形式,然后利用小波域滤波算法合理设置分解层数,对信号做分解重构与降噪,再利用极限学习机建立神经网络学习模型。根据干扰信号模型与信道衰减程度提取干扰信号特征,并将特征样本输入到神经网络中,直到输出分离结果。仿真结果表明：该方法能够有效去除信道噪声、降低通信误码率、均衡信道负载。     

Application of special-purpose artificial neural networks for speckle reduction in SAR images

Synthetic aperture radar (SAR) is used extensively for remote-sensing applications due to its ability to operate under all weather conditions and provide high-resolution images. However, high-resolution images constructed from SAR data often suffer from speckle, which makes identification and classification of edges/boundaries a difficult task. Speckle noise is multiplicative in nature and is a result of constructive and destructive interference of signals from randomly distributed scatterers in a resolution cell illuminated by a coherent signal. Usually, speckle is reduced by incoherent averaging of high-resolution image pixels that degrade resolution. The principal goal in all speckle-reduction algorithms is to reduce speckle with minimum loss of resolution. In this investigation, we used specially trained and validated artificial neural networks (ANNs) for speckle reduction in images generated with a radar-depth sounder/imager and compared their performance to the conventional adaptive filtering and Speckle Reducing Anisotropic Diffusion (SRAD) algorithm. We show that by training different ANNs to reduce speckle noise at different levels of signal-to-noise ratio (SNR), rather than training one ANN to operate at all levels of SNR, improved performance in speckle reduction can be obtained. Real SAR images and synthetic noise are used in this research to compare the performance of the proposed ANN-based approaches with that obtained from conventional methods. This investigation shows that on combining the results from a set of properly trained and validated neural networks, the SNRs of the output images improve beyond those obtained from conventional approaches when the input SNRs are greater than or equal to 4 dB. For input SNRs greater than 0 dB, however, the ANNs provide better performance in edge preservation compared with conventional methods. We also found that once a set of ANNs is properly trained to reduce speckle from an image, these ANNs can be used in de-speckling other images without any further training. The merits and demerits of different configurations of the ANNs are studied to find useful speckle noise-tolerant ANN architectures.     

Performance analysis of classical, model-based and eigenvector methods: Ophthalmic arterial disorders detection case

In this study, ophthalmic arterial Doppler signals recorded from 214 subjects were processed using classical, model-based, and eigenvector methods. The classical method (fast Fourier transform), two model-based methods (Burg autoregressive, least squares modified Yule-Walker autoregressive moving average methods), and three eigenvector methods (Pisarenko, multiple signal classification, and Minimum-Norm methods) were selected for performing spectral analysis of the ophthalmic arterial Doppler signals. Doppler power spectral density estimates of the ophthalmic arterial Doppler signals were obtained using these spectrum analysis techniques. The variations in the shape of the Doppler power spectra were examined in order to detect variabilities such as stenosis, ocular Behcet disease, and uveitis disease in the physical state of ophthalmic arterial Doppler signals. These power spectra were then used to compare the applied methods in terms of their frequency resolution and the effects in detecting stenosis, Behcet disease and uveitis disease in ophthalmic arteries.     

Automatic detection of ophthalmic artery stenosis using the adaptive neuro-fuzzy inference system

In this study, a new approach based on an adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of ophthalmic artery stenosis. The ANFIS was used to detect ophthalmic artery stenosis when two features, resistivity and pulsatility indices, defining changes of ophthalmic arterial Doppler waveforms were used as inputs. The ophthalmic arterial Doppler signals were recorded from 115 subjects, of whom 52 suffered from ophthalmic artery stenosis and the rest were healthy. 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 ophthalmic artery stenosis were obtained through analysis of the ANFIS. The performances of the classifiers were evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS classifier has potential in detecting the ophthalmic artery stenosis.     

A novel fault diagnosis method of bearing based on improved fuzzy ARTMAP and modified distance discriminant technique

This paper presents a novel intelligent diagnosis method based on multiple domain features, modified distance discrimination technique and improved fuzzy ARTMAP (IFAM). The method consists of three steps. To begin with, time-domain, frequency-domain and wavelet grey moments are extracted from the raw vibration signals to demonstrate the fault-related information. Then through the modified distance discrimination technique some salient features are selected from the original feature set. Finally, the optimal feature set is input into the IFAM incorporated with similarity based on the Yu’s norm in the classification phase to identify the different fault categories. The proposed method is applied to the fault diagnosis of rolling element bearing, and the test results show that the IFAM identify the fault categories of rolling element bearing more accurately and has a better diagnosis performance compared to the FAM. Furthermore, by the application of the bootstrap method to the diagnosis results it can testify that the IFAM has more capacity of reliability and robustness.     

基于视觉显著性检测的图像分类方法

针对传统的图像分类方法对整个图像不分等级处理以及缺乏高层认知的问题,提出了一种基于显著性检测的图像分类方法。首先,利用视觉注意模型进行显著性检测,得到图像的显著区域;然后,利用Gabor滤波方法和脉冲耦合神经网络模型,分别提取该显著区域的纹理特征和时间签名特征;最后,根据提取的纹理特征和时间签名特征,利用支持向量机实现图像分类。实验结果表明,所提方法在SIMPLIcity图像数据集上平均分类正确率达到94.26%,在Caltech数据集上平均分类正确率为95.43%,从而证明,显著性检测与有效的特征提取对图像分类有重要影响。     

An expert system for detection of electrocardiographic changes in patients with partial epilepsy using wavelet-based neural networks

Abstract: In this study a wavelet‐based neural network model, employing the multilayer perceptron, is presented for the detection of electrocardiographic changes in patients with partial epilepsy. Decision making is performed in two stages: feature extraction using the wavelet transform, and multilayer perceptron neural networks (MLPNNs) trained with the backpropagation, delta‐bar‐delta, extended delta‐bar‐delta and quick propagation algorithms as classifiers. The classification results, the values of statistical parameters and performance evaluation parameters of the MLPNNs trained with different algorithms are compared. Two types of electrocardiogram beats (normal and partial epilepsy) obtained from the MIT‐BIH database were classified with accuracy varying from 90.00% to 97.50% by the MLPNNs trained with different algorithms.     

A feature selection technique for generation of classification committees and its application to categorization of laryngeal images

This paper is concerned with a two phase procedure to select salient features (variables) for classification committees. Both filter and wrapper approaches to feature selection are combined in this work. In the first phase, definitely redundant features are eliminated based on the paired t-test. The test compares the saliency of the candidate and the noise features. In the second phase, the genetic search is employed. The search integrates the steps of training, aggregation of committee members, selection of hyper-parameters, and selection of salient features into the same learning process. A small number of genetic iterations needed to find a solution is the characteristic feature of the genetic search procedure developed. The experimental tests performed on five real-world problems have shown that significant improvements in classification accuracy can be obtained in a small number of iterations if compared to the case of using all the features available.     

基于聚类分割和特征点的显著区域检测算法

针对彩色图像中的显著区域检测,对基于聚类分割的特征点检测算法及基于亮度、颜色和梯度多特征的显著区域检测算法进行了研究,提出一种基于特征点和聚类分割的显著区域检测算法,该算法的处理思路是先对目标彩色图像利用高斯低通滤波和局部熵纹理分割去除纹理区,得到R、G、B分量的滤波灰度图,聚类分割自动划分出每个分量的最亮区域、最暗区域和剩余区域这三个区域,每个颜色分量选择最亮或最暗这两个区域与剩余区域亮度差值最大的一个区域,对此选择区域边界进行角点、边缘点检测,将其角点和边缘点作为显著点,然后通过数学形态学将显著点扩展到显著区域.利用公共数据库中的多幅自然图像进行实验对比,实验结果显示本文所提算法不仅提高了检测准确性,同时简化了计算过程,验证了该算法在提取尤其是纹理复杂的图像的显著区域上的有效性.     

Joint salient object detection and existence prediction

Recent advances in supervised salient object detection modeling has resulted in significant performance improvements on benchmark datasets. However, most of the existing salient object detection models assume that at least one salient object exists in the input image. Such an assumption often leads to less appealing saliencymaps on the background images with no salient object at all. Therefore, handling those cases can reduce the false positive rate of a model. In this paper, we propose a supervised learning approach for jointly addressing the salient object detection and existence prediction problems. Given a set of background-only images and images with salient objects, as well as their salient object annotations, we adopt the structural SVM framework and formulate the two problems jointly in a single integrated objective function: saliency labels of superpixels are involved in a classification term conditioned on the salient object existence variable, which in turn depends on both global image and regional saliency features and saliency labels assignments. The loss function also considers both image-level and regionlevel mis-classifications. Extensive evaluation on benchmark datasets validate the effectiveness of our proposed joint approach compared to the baseline and state-of-the-art models.