SNP double peak methods through chloroplast genome profiling for identifying Gentiana crassicaulis and two closely related species / 药学学报

italic>Gentiana crassicaulis Duthie ex Burk. is one of the plant sources of Gentianae Macrophyllae Radix (QinJiao). Gentiana tibetica King ex Hook. f. and Gentiana robusta King ex Hook. f. are relative species of G. crassicaulis. Due to the large intraspecific morphological variation, G. crassicaulis showed high morphological similarity with G. tibetica and G. robusta. And the distribution area of the three species overlaps to some extent, which makes it difficult to identify them. On the basis of morphological identification, the method of molecular identification of the three species was constructed in this study based on chloroplast genomes. The chloroplast genome of Gentiana tibetica is 148 765bp long, with LSC, SSC and IR 81 163 bp, 17 070 bp and 25 266 bp, respectively. The structure of the three is consistent. The chloroplast genome sequences of G. tibetica and G. crassicaulis are highly similar, and the number of variable sites is 9 (149 267 bp in total). Diagnostic SNP that could effectively identify the three species was screened and verified, and a dual-peak SNP detection method was established for the effective identification of each species and mixed samples. Our study provides basic data for the molecular identification of G. crassicaulis and its related species, and the arrangement of related Tibetan medicine.

Heartbeat-aware convolutional neural network for R-peak detection of wearable device ECG data / 南方医科大学学报

OBJECTIVE@#To develop a method for R-peak detection of ECG data from wearable devices to allow accurate estimation of the physiological parameters including heart rate and heart rate variability.@*METHODS@#A fully convolutional neural network was applied to predict the R-peak heatmap of ECG data and locate the R-peak positions. The heartbeat-aware (HA) module was introduced to enable the model to learn to predict the heartbeat number and R-peak heatmap simultaneously, thereby improving the capability of the model for extraction of the global context. The R-R interval estimated by the predicted heartbeat number was adopted to calculate the minimum horizontal distance for peak positioning. To achieve real-time R-peak detection on mobile devices, the deep separable convolution was adopted to reduce the number of parameters and the computational complexity of the model.@*RESULTS@#The proposed model was trained only with ECG data from wearable devices. At a tolerance window interval of 150 ms, the proposed method achieved R peak detection sensitivities of 100% for both wearable device ECG dataset and a public dataset (i.e. LUDB), and the true positivity rates exceeded 99.9%. As for the ECG signal of a 10 s duration, the CPU time of the proposed method for R-peak detection was about 23.2 ms.@*CONCLUSION@#The proposed method has good performance for R-peak detection of both wearable device ECG data and routine ECG data and also allows real-time R-peak detection of the ECG data.

An improved peak extraction method for heart rate estimation / 生物医学工程学杂志

In order to solve imperfection of heart rate extraction by method of traditional ballistocardiogram (BCG), this paper proposes an improved method for detecting heart rate by BCG. First, weak cardiac activity signals are acquired in real time by embedded sensors. Local BCG beats are obtained by signal filtering and signal conversion. Second, the heart rate is estimated directly from the BCG beat without the use of a heartbeat template. Compared with other methods, the proposed method has strong advantages in heart rate data accuracy and anti-interference, and it also realizes non-contact online detection. Finally, by analyzing the data of more than 20,000 heart rates of 13 subjects, the average beat error was 0.86% and the coverage was 96.71%. It provides a new way to estimate heart rate for hospital clinical and home care.

An algorithm of real-time peak detection for pulse signals based on FPGA / 军事医学

Objective To present a new algorithm of real-time peak detection for pulse signals based on FPGA , which is known as the dynamic threshold with half peak detection method ( DT-HP ) .Methods With Gaussian-shaped pulse signals as the target , the method was improved from conventional methods .The FPGA detection process of the algorithm required no more than three detection parameters: the starting point , the maximum value and the pulse width .Results The algorithm solved the floating baseline and repeating detection that occur in traditional methods .Compared with the results of polynomial fitting method and flow cytometry , the difference was only 3.2% and 9.3%.Conclusion The algorithm takes less time ,RAM, and cache while allowing floating baseline detection , which can be used as an effective method for rapid detection in FPGA .