Based on bioelectrical impedance theory and pattern recognition algorithm, we in this study measured varieties of people's bioelectrical impedance in hands and identified different people according to their bioelectrical impedance. We designed a bioelectrical impedance collection circuit with AD5933 chip to measure the impedance in different people's hands, and we obtained the bioelectrical impedance spectrum for each person under 1-100 kHz electrical stimulation. We calculated the segmentation slopes of bioelectrical impedance spectrum, and took the slopes as characteristic parameters. In order to promote the recognition rate and prevent the overfitting of the model, we divided the people into the training set and the test set, and designed a 3 layer back propagation neural network model to train and test the samples. The results showed that back propagation neural network model could identify the test set effectively. The recognition rate of the training sets was as high as 97.62%, recognition rate of validation sets was 88.79%, recognition rate of test sets was 86.34%, and the synthetical recognition rate was 94.22%. It gives a clue that the network can perfectly recognize people in the training network as well as strangers that comes from the outside of the tests. Our work can verify the feasibility and reliability of using bioelectrical impedance and pattern recognition algorithm for identification, and can provide a simple and supplementary way to identify people.
Polyvinyl alcohol (PVA) hydrogel was made for simulating human's soft tissue in our experiment. The image acquisition device is composed of an optical platform, a camera and its bracket and a light source. In order to study the law of soft tissue deformation under flexible needle insertion, markers were embedded into the soft tissue and their displacements were recorded. Based on the analysis of displacements of markers in X direction and Y direction, back propagation (BP) neural network was employed to model the displacement of Y direction for the markers. Compared to the experimental data, fitting degree of the neural network model was above 95%, the maximum relative error for valid data was limited to 30%, and the maximum absolute error was 0.8 mm. The BP neural network model was beneficial for predicting soft tissue deformation quantitatively. The results showed that the model could effectively improve the accuracy of flexible needle insertion into soft tissue.
Artifacts produced by chest compression during cardiopulmonary resuscitation (CPR) seriously affect the reliability of shockable rhythm detection algorithms. In this paper, we proposed an adaptive CPR artifacts elimination algorithm without needing any reference channels. The clean electrocardiogram (ECG) signals can be extracted from the corrupted ECG signals by incorporating empirical mode decomposition (EMD) and independent component analysis (ICA). For evaluating the performance of the proposed algorithm, a back propagation neural network was constructed to implement the shockable rhythm detection. A total of 1 484 corrupted ECG samples collected from pigs were included in the analysis. The results of the experiments indicated that this method would greatly reduce the effects of the CPR artifacts and thereby increase the accuracy of the shockable rhythm detection algorithm.
In order to achieve the automatic identification of liver cancer cells in the blood, the present study adopted a principal component analysis (PCA) and back propagation (BP) algorithm of feedforward neural networks to identify white blood cells and red blood cells in mice and human liver cancer cells, HepG2. The present paper shows the process in which PCA was carried out after obtaining spectral data by fiber confocal back-scattering spectrograph, selecting the first two principal components as spectral features, and establishing a neural network pattern recognition model with two input layer nodes, eleven hidden layer nodes and three output nodes. In order to verify whether the model would give accurate identification of cells, we chose 195 object data to train the model with 150 sets of data as training set and 45 sets as test set. According to the results, the overall recognition accuracy of the three cells was above 90% with the average relative deviation only 4.36%. The results showed that PCA+BP algorithm could automatically identify liver cancer cells from erythrocyte and white blood cells, which will provide a useful tool for the study of metastasis and biological metabolism characteristics of liver cancer.
Detection and classification of malignant arrhythmia are key tasks of automated external defibrillators. In this paper, 21 metrics extracted from existing algorithms were studied by retrospective analysis. Based on these metrics, a back propagation neural network optimized by genetic algorithm was constructed. A total of 1,343 electrocardiogram samples were included in the analysis. The results of the experiments indicated that this network had a good performance in classification of sinus rhythm, ventricular fibrillation, ventricular tachycardia and asystole. The balanced accuracy on test dataset reached up to 99.06%. It illustrates that our proposed detection algorithm is obviously superior to existing algorithms. The application of the algorithm in the automated external defibrillators will further improve the reliability of rhythm analysis before defibrillation and ultimately improve the survival rate of cardiac arrest.