ABSTRACT
Atrial fibrillation is a common arrhythmia, and its diagnosis is interfered by many factors. In order to achieve applicability in diagnosis and improve the level of automatic analysis of atrial fibrillation to the level of experts, the automatic detection of atrial fibrillation is very important. This study proposes an automatic detection algorithm for atrial fibrillation based on BP neural network (back propagation network) and support vector machine (SVM). The electrocardiogram (ECG) segments in the MIT-BIH atrial fibrillation database are divided into 10, 32, 64, and 128 heartbeats, respectively, and the Lorentz value, Shannon entropy, K-S test value and exponential moving average value are calculated. These four characteristic parameters are used as the input of SVM and BP neural network for classification and testing, and the label given by experts in the MIT-BIH atrial fibrillation database is used as the reference output. Among them, the use of atrial fibrillation in the MIT-BIH database, the first 18 cases of data are used as the training set, and the last 7 cases of data are used as the test set. The results show that the accuracy rate of 92% is obtained in the classification of 10 heartbeats, and the accuracy rate of 98% is obtained in the latter three categories. The sensitivity and specificity are both above 97.7%, which has certain applicability. Further validation and improvement in clinical ECG data will be done in next study.
Subject(s)
Humans , Atrial Fibrillation/diagnosis , Support Vector Machine , Heart Rate , Algorithms , Neural Networks, Computer , ElectrocardiographyABSTRACT
Objective • To establish a quality control system for serum antibodies and provide a quantitative standard for the quality control of serum autoantibody-based biomarker study. Methods • Sera from 50 healthy people were taken to prepare the samples and aliquoted. The aliquoted sera were stored at-80 ℃, -20 ℃, 4 ℃, 37 ℃, and 95 ℃ for a period of time. Samples were collected at different time points. The samples were incubated with Ph.D.-12, a phage display random peptide library. The phage-IgG complex was enriched through immunoprecipitation with protein G-coated magnetic beads. By sequencing the coding sequences of the displayed peptides, the sequence and frequency of the serum antibody-enriched peptides were determined. Using the frequencies of each enriched peptide, Shannon entropies were calculated for each sample. Shannon entropy was applied as an indicator to evaluate the quality of the serum autoantibodies. Results • With the increase of the treating temperature, the Shannon entropies of the sera gradually decreased, while there was no significant difference between the Shannon entropies of-20 ℃ and-80 ℃. The Shannon entropy reached the lowest value after being treated at 95 ℃ for 10 min. At the same temperature, the Shannon entropies of the sera were inversely proportional to the length of the treating periods. Conclusion • It is practically applicable to decipher the profile of the serum antibody binding peptides, through the combination of a phage display random peptide library and next-generation sequencing. Shannon entropy can be calculated using the frequencies of each enriched peptide, and applied as an indicator to judge the overall quality of the serum antibodies.
ABSTRACT
DNA methylation is an important epigenetic mark that plays a vital role in gene expression and cell differentiation. The average DNA methylation level among a group of cells has been extensively documented. However, the cell-to-cell heterogeneity in DNA methylation, which reflects the differentiation of epigenetic status among cells, remains less investigated. Here we established a gold standard of the cell-to-cell heterogeneity in DNA methylation based on single-cell bisulfite sequencing (BS-seq) data. With that, we optimized a computational pipeline for estimating the heterogeneity in DNA methylation from bulk BS-seq data. We further built HeteroMeth, a database for searching, browsing, visualizing, and downloading the data for heterogeneity in DNA methylation for a total of 141 samples in humans, mice, Arabidopsis, and rice. Three genes are used as examples to illustrate the power of HeteroMeth in the identification of unique features in DNA methylation. The optimization of the computational strategy and the construction of the database in this study complement the recent experimental attempts on single-cell DNA methylomes and will facilitate the understanding of epigenetic mechanisms underlying cell differentiation and embryonic development. HeteroMeth is publicly available at http://qianlab.genetics.ac.cn/HeteroMeth.