Automated characterization of different vessel wall tissues including atherosclerotic plaques, branchings and stents from intravascular ultrasound (IVUS) gray-scale images was addressed. The texture features of each frame were firstly detected with local binary pattern (LBP), Haar-like and Gabor filter in the present study. Then, a Gentle Adaboost classifier was designed to classify tissue features. The methods were validated with clinically acquired image data. The manual characterization results obtained by experienced physicians were adopted as the golden standard to evaluate the accuracy. Results indicated that the recognition accuracy of lipidic plaques reached 94.54%, while classification precision of fibrous and calcified plaques reached 93.08%. High recognition accuracy can be reached up to branchings 93.20% and stents 93.50%, respectively.
Cough recognition provides important clinical information for the treatment of many respiratory diseases. A new Mel frequency cepstrum coefficient (MFCC) extracting method has been proposed on the basis of the distributional characteristics of cough spectrum. The whole frequency band was divided into several sub-bands, and the energy coefficient for each band was obtained by method of principle component analysis. Then non-uniform filter-bank in Mel frequency is designed to improve the extracting process of MFCC by distributing filters according to the spectrum energy coefficients. Cough recognition experiment using hidden Markov model was carried out, and the results showed that the proposed method could effectively improve the performance of cough recognition.
Automatic classification of different types of cough plays an important role in clinical. In the previous research of cough classification or cough recognition, traditional Mel frequency cepstrum coefficients (MFCC) which extracts feature mainly from low frequency band is usually used as feature expression. In this paper, by analyzing the distributions of spectral energy of dry/wet cough, it is found that spectral difference of two types of cough exits mainly in middle frequency band and high frequency band. To better reflect the spectral difference of dry cough and wet cough, an improved method of extracting reverse MFCC is proposed. In this method, reverse Mel filter-bank in which filters are allocated in reverse Mel scale is adopted and is improved by placing filters only in the frequency band with high spectral energy. As a result, features are mainly extracted from the frequency band where two types of cough show both high spectral energy and distinguished difference. Detailed process of accessing improved reverse MFCC was introduced and hidden Markov models trained by 60 dry cough and 60 wet cough were used as cough classification model. Classification experiment results for 120 dry cough and 85 wet cough showed that, compared to traditional MFCC, better classification performance was achieved by the proposed method and the total classification accuracy was raised from 89.76% to 93.66%.
Heart sound is one of the common medical signals for diagnosing cardiovascular diseases. This paper studies the binary classification between normal or abnormal heart sounds, and proposes a heart sound classification algorithm based on the joint decision of extreme gradient boosting (XGBoost) and deep neural network, achieving a further improvement in feature extraction and model accuracy. First, the preprocessed heart sound recordings are segmented into four status, and five categories of features are extracted from the signals based on segmentation. The first four categories of features are sieved through recursive feature elimination, which is used as the input of the XGBoost classifier. The last category is the Mel-frequency cepstral coefficient (MFCC), which is used as the input of long short-term memory network (LSTM). Considering the imbalance of the data set, these two classifiers are both improved with weights. Finally, the heterogeneous integrated decision method is adopted to obtain the prediction. The algorithm was applied to the open heart sound database of the PhysioNet Computing in Cardiology(CINC) Challenge in 2016 on the PhysioNet website, to test the sensitivity, specificity, modified accuracy and F score. The results were 93%, 89.4%, 91.2% and 91.3% respectively. Compared with the results of machine learning, convolutional neural networks (CNN) and other methods used by other researchers, the accuracy and sensibility have been obviously improved, which proves that the method in this paper could effectively improve the accuracy of heart sound signal classification, and has great potential in the clinical auxiliary diagnosis application of some cardiovascular diseases.
Medical image fusion realizes advantage integration of functional images and anatomical images. This article discusses the research progress of multi-model medical image fusion at feature level. We firstly describe the principle of medical image fusion at feature level. Then we analyze and summarize fuzzy sets, rough sets, D-S evidence theory, artificial neural network, principal component analysis and other fusion methods' applications in medical image fusion and get summery. Lastly, we in this article indicate present problems and the research direction of multi-model medical images in the future.
Existing arrhythmia classification methods usually use manual selection of electrocardiogram (ECG) signal features, so that the feature selection is subjective, and the feature extraction is complex, leaving the classification accuracy usually affected. Based on this situation, a new method of arrhythmia automatic classification based on discriminative deep belief networks (DDBNs) is proposed. The morphological features of heart beat signals are automatically extracted from the constructed generative restricted Boltzmann machine (GRBM), then the discriminative restricted Boltzmann machine (DRBM) with feature learning and classification ability is introduced, and arrhythmia classification is performed according to the extracted morphological features and RR interval features. In order to further improve the classification performance of DDBNs, DDBNs are converted to deep neural network (DNN) using the Softmax regression layer for supervised classification in this paper, and the network is fine-tuned by backpropagation. Finally, the Massachusetts Institute of Technology and Beth Israel Hospital Arrhythmia Database (MIT-BIH AR) is used for experimental verification. For training sets and test sets with consistent data sources, the overall classification accuracy of the method is up to 99.84% ± 0.04%. For training sets and test sets with inconsistent data sources, a small number of training sets are extended by the active learning (AL) method, and the overall classification accuracy of the method is up to 99.31% ± 0.23%. The experimental results show the effectiveness of the method in arrhythmia automatic feature extraction and classification. It provides a new solution for the automatic extraction of ECG signal features and classification for deep learning.
Emotion plays an important role in people's cognition and communication. By analyzing electroencephalogram (EEG) signals to identify internal emotions and feedback emotional information in an active or passive way, affective brain-computer interactions can effectively promote human-computer interaction. This paper focuses on emotion recognition using EEG. We systematically evaluate the performance of state-of-the-art feature extraction and classification methods with a public-available dataset for emotion analysis using physiological signals (DEAP). The common random split method will lead to high correlation between training and testing samples. Thus, we use block-wise K fold cross validation. Moreover, we compare the accuracy of emotion recognition with different time window length. The experimental results indicate that 4 s time window is appropriate for sampling. Filter-bank long short-term memory networks (FBLSTM) using differential entropy features as input was proposed. The average accuracy of low and high in valance dimension, arousal dimension and combination of the four in valance-arousal plane is 78.8%, 78.4% and 70.3%, respectively. These results demonstrate the advantage of our emotion recognition model over the current studies in terms of classification accuracy. Our model might provide a novel method for emotion recognition in affective brain-computer interactions.
Electroencephalography (EEG) signals are strongly correlated with human emotions. The importance of nodes in the emotional brain network provides an effective means to analyze the emotional brain mechanism. In this paper, a new ranking method of node importance, weighted K-order propagation number method, was used to design and implement a classification algorithm for emotional brain networks. Firstly, based on DEAP emotional EEG data, a cross-sample entropy brain network was constructed, and the importance of nodes in positive and negative emotional brain networks was sorted to obtain the feature matrix under multi-threshold scales. Secondly, feature extraction and support vector machine (SVM) were used to classify emotion. The classification accuracy was 83.6%. The results show that it is effective to use the weighted K-order propagation number method to extract the importance characteristics of brain network nodes for emotion classification, which provides a new means for feature extraction and analysis of complex networks.
The clinical manifestations of patients with schizophrenia and patients with depression not only have a certain similarity, but also change with the patient's mood, and thus lead to misdiagnosis in clinical diagnosis. Electroencephalogram (EEG) analysis provides an important reference and objective basis for accurate differentiation and diagnosis between patients with schizophrenia and patients with depression. In order to solve the problem of misdiagnosis between patients with schizophrenia and patients with depression, and to improve the accuracy of the classification and diagnosis of these two diseases, in this study we extracted the resting-state EEG features from 100 patients with depression and 100 patients with schizophrenia, including information entropy, sample entropy and approximate entropy, statistical properties feature and relative power spectral density (rPSD) of each EEG rhythm (δ, θ, α, β). Then feature vectors were formed to classify these two types of patients using the support vector machine (SVM) and the naive Bayes (NB) classifier. Experimental results indicate that: ① The rPSD feature vector P performs the best in classification, achieving an average accuracy of 84.2% and a highest accuracy of 86.3%; ② The accuracy of SVM is obviously better than that of NB; ③ For the rPSD of each rhythm, the β rhythm performs the best with the highest accuracy of 76%; ④ Electrodes with large feature weight are mainly concentrated in the frontal lobe and parietal lobe. The results of this study indicate that the rPSD feature vector P in conjunction with SVM can effectively distinguish depression and schizophrenia, and can also play an auxiliary role in the relevant clinical diagnosis.
Takayasu arteritis (TA) is a chronic nonspecific inflammation that commonly occurs in the aorta and its main branches. Most patients with TA are lack of clinical manifestations, leading to misdiagnosis. When the TA is correctly diagnosed, the patients may already have stenosis or occlusion in the involved arteries, resulting in arterial ischemia and hypoxia symptoms, and in severe cases it will be life-threatening. Contrast-enhanced ultrasonography (CEUS) is an emerging method for assessing TA, but the assessment relies heavily on experiences of radiologists performing manual and qualitative analyses, so the diagnostic results are often not accurate. To overcome this limitation, this paper presents a computer-assisted quantitative analysis of TA carotid artery lesions based on CEUS. First, the TA lesion was outlined on the carotid wall, and one homogeneous rectangle and one polygon were selected as two reference regions in the carotid lumen. The temporal and spatial features of the lesion region and the reference regions were then calculated. Furthermore, the difference and ratio of the features between the lesion and the reference regions were computed as new features (to eliminate interference factors). Finally, the correlation was analyzed between the CEUS features and inflammation biomarkers consisting of erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP). The data in this paper were collected from 34 TA patients in Zhongshan Hospital undergoing CEUS examination with a total of thirty-seven carotid lesions, where two patients were with two lesions before and after treatment and one patient was with left and right bilateral lesions. Among these patients, 13 were untreated primary patients with a total of 14 lesions, where one patient was with bilateral lesions. The results showed that for all patients, the neovascularization area ratio in the 1/3 inner region of a lesion (ARi1/3) achieved a correlation coefficient (r) of 0.56 (P=0.001) with CRP, and for the primary patients, the neovascularization area ratio in the 1/2 inner region of a lesion (ARi1/2) had an r-value of 0.76 (P=0.001) with CRP. This study indicates that the proposed computer-assisted method can objectively and semi-automatically extract quantitative features from CEUS images, so as to reduce the effect on diagnosis due to subjective experiences of the radiologists, and thus it is expected to be used for clinical diagnosis and severity evaluation of TA carotid lesions.