In order to overcome the shortcomings of high false positive rate and poor generalization in the detection of microcalcification clusters regions, this paper proposes a method combining discriminative deep belief networks (DDBNs) to automatically and quickly locate the regions of microcalcification clusters in mammograms. Firstly, the breast region was extracted and enhanced, and the enhanced breast region was segmented to overlapped sub-blocks. Then the sub-block was subjected to wavelet filtering. After that, DDBNs model for breast sub-block feature extraction and classification was constructed, and the pre-trained DDBNs was converted to deep neural networks (DNN) using a softmax classifier, and the network is fine-tuned by back propagation. Finally, the undetected mammogram was inputted to complete the location of suspicious lesions. By experimentally verifying 105 mammograms with microcalcifications from the Digital Database for Screening Mammography (DDSM), the method obtained a true positive rate of 99.45% and a false positive rate of 1.89%, and it only took about 16 s to detect a 2 888 × 4 680 image. The experimental results showed that the algorithm of this paper effectively reduced the false positive rate while ensuring a high positive rate. The detection of calcification clusters was highly consistent with expert marks, which provides a new research idea for the automatic detection of microcalcification clusters area in mammograms.
Feature extraction is a very crucial step in P300-based brain-computer interface (BCI) and independent component analysis (ICA) is a suitable P300 feature extraction method. But at present the convergence performance of the general ICA iteration methods are not very satisfactory. In this paper, a method based on quantum particle swarm optimizer (QPSO) algorithm and ICA technique is put forward for P300 extraction. In this method, quantum computing is used to impel ICA iteration to globally converge faster. It achieved the purpose of extracting P300 rapidly and efficiently. The method was tested on two public datasets of BCI Competition Ⅱ and Ⅲ, and a simple linear classifier was employed to classify the extracted P300 features. The recognition accuracy reached 94.4% with 15 times averaged. The results showed that the proposed method could extract P300 rapidly and the extraction effect did not reduce. It provides an experimental basis for further study of real-time BCI system.
Due to the sparsity of brain encoding, the neural ensemble signals recorded by microelectrode arrays contain a lot of noise and redundant information, which could reduce the stability and precision of decoding of motion intent. To solve this problem, we proposed a decoding method based on partial least squares (PLS) feature extraction in our study. Firstly, we extracted the features of spike signals using the PLS, and then classified them with support vector machine (SVM) classifier, and decoded them for motion intent. In this study, we decoded neural ensemble signals based on plus-maze test. The results have shown that the proposed method had a better stability and higher decoding accuracy, due to the PLS combined with classification model which overcame the shortcoming of PLS regression that was easily affected by accumulated effect of noise. Meanwhile, the PLS method extracted fewer features with more useful information in comparison with common feature extraction method. The decoding accuracy of real data sets were 93.59%, 84.00% and 83.59%, respectively.
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%.
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.
Neurofeedback, as an alternative treatment method of behavioral medicine, is a technique which translates the electroencephalogram (EEG) signals to styles as sounds or animation to help people understand their own physical status and learn to enhance or suppress certain EEG signals to regulate their own brain functions after several repeated trainings. This paper develops a neurofeedback system on the foundation of brain-computer interface technique. The EEG features are extracted through real-time signal process and then translated to feedback information. Two feedback screens are designed for relaxation training and attention training individually. The veracity and feasibility of the neurofeedback system are validated through system simulation and preliminary experiment.
Brain-computer interaction (BCI) is a transformative human-computer interaction, which aims to bypass the peripheral nerve and muscle system and directly convert the perception, imagery or thinking activities of cranial nerves into actions for further improving the quality of human life. Magnetoencephalogram (MEG) measures the magnetic field generated by the electrical activity of neurons. It has the unique advantages of non-contact measurement, high temporal and spatial resolution, and convenient preparation. It is a new BCI driving signal. MEG-BCI research has important brain science significance and potential application value. So far, few documents have elaborated the key technical issues involved in MEG-BCI. Therefore, this paper focuses on the key technologies of MEG-BCI, and details the signal acquisition technology involved in the practical MEG-BCI system, the design of the MEG-BCI experimental paradigm, the MEG signal analysis and decoding key technology, MEG-BCI neurofeedback technology and its intelligent method. Finally, this paper also discusses the existing problems and future development trends of MEG-BCI. It is hoped that this paper will provide more useful ideas for MEG-BCI innovation research.
Biometrics plays an important role in information society. As a new type of biometrics, electroencephalogram (EEG) signals have special advantages in terms of versatility, durability, and safety. At present, the researches on individual identification approaches based on EEG signals draw lots of attention. Identity feature extraction is an important step to achieve good identification performance. How to combine the characteristics of EEG data to better extract the difference information in EEG signals is a research hotspots in the field of identity identification based on EEG in recent years. This article reviewed the commonly used identity feature extraction methods based on EEG signals, including single-channel features, inter-channel features, deep learning methods and spatial filter-based feature extraction methods, etc. and explained the basic principles application methods and related achievements of various feature extraction methods. Finally, we summarized the current problems and forecast the development trend.
Inferior myocardial infarction is an acute ischemic heart disease with high mortality, which is easy to induce life-threatening complications such as arrhythmia, heart failure and cardiogenic shock. Therefore, it is of great clinical value to carry out accurate and efficient early diagnosis of inferior myocardial infarction. Electrocardiogram is the most sensitive means for early diagnosis of inferior myocardial infarction. This paper proposes a method for detecting inferior myocardial infarction based on densely connected convolutional neural network. The method uses the original electrocardiogram (ECG) signals of serially connected Ⅱ, Ⅲ and aVF leads as the input of the model and extracts the robust features of the ECG signals by using the scale invariance of the convolutional layers. The characteristic transmission of ECG signals is enhanced by the dense connectivity between different layers, so that the network can automatically learn the effective features with strong robustness and high recognition, so as to achieve accurate detection of inferior myocardial infarction. The Physikalisch Technische Bundesanstalt diagnosis public ECG database was used for verification. The accuracy, sensitivity and specificity of the model reached 99.95%, 100% and 99.90%, respectively. The accuracy, sensitivity and specificity of the model are also over 99% even though the noise exists. Based on the results of this study, it is expected that the method can be introduced in the clinical environment to help doctors quickly diagnose inferior myocardial infarction in the future.
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.