An intelligent diagnostic method of ECG signal based on Markov transition field and a ResNet.

BACKGROUND AND OBJECTIVE: Heart disease seriously threatens human life and health. It has the character of abruptness and is necessary to accurately monitor and intelligently diagnose electrocardiograph signals in real-time. As part of the automation of heart monitoring, the electrocardiogram (ECG) intelligent diagnosis method based on deep learning not only meets the needs of real-time and accurate but also can abandon relevant professional knowledge, which makes it possible to be promoted in the general population. METHODS: This paper presents an intelligent diagnosis method based on a ResNet. Firstly, ECG signals from MIT-BIH Database are converted into 2-dim matrices by Markov Transition Field. Secondly, the matrices are used as the input of a ResNet. Then, the ResNet is able to extract high abstract features of various diseases and realize intelligent identification of five heartbeat types, including Normal Beat, Left Bundle Branch Block Beat, Right Bundle Branch Block Beat, Premature Ventricular Contraction Beat, and Atrial Premature Contraction Beat. Eventually, the proposed model is used to identify Normal Beat and Atrial Fibrillation(AF) based on the PAF Prediction Challenge Database(the PAFPC Database) to verify its generalization ability. RESULTS: The experiment result shows that the intelligent diagnosis method can reach a high F1-score of 97.7% and a high accuracy upon to 99.2% on MIT-BIH Database, which are higher than the models proposed by other researchers. Its mean sensitivity and mean specificity are 97.42% and 99.54%, respectively. Moreover, the accuracy of the generalization ability verification experiment is 94.57% on the PAFPC Database, which is also higher than the results of other studies. CONCLUSION: The research results show that the method proposed in this paper still achieves higher accuracy and higher F1-score than other methods without any data preprocessing. This method has better classification performance than traditional machine learning methods and other deep learning methods. That is, the method based on Markov Transition Field and a ResNet has good application prospects. At the same time, it has been verified that the model proposed in this paper also has excellent generalization ability.

Combined Effect of NZVI and H2O2 on the Cyanobacterium Microcystis aeruginosa: Performance and Mechanism.

In order to eliminate the harmful cyanobacterium Microcystis aeruginosa and the algal organic matters (AOMs) produced by M. aeruginosa, the combined process of nanoscale zero-valent iron (NZVI) and hydrogen peroxide (H2O2) has been carried out, and the removal mechanism has also been clarified. As the initial cyanobacterial cell concentration is 1.0 (±0.05) × 105 cells·mL-1, all the treatments of NZVI, H2O2, and NZVI/H2O2 have inhibition effects on both the Chl a contents and photosynthetic pigments, with the Chl a removal efficiency of 47.3%, 80.5%, and 90.7% on the 5th day, respectively; moreover, the variation of ζ potential is proportional to that of the Chl a removal efficiency. The malondialdehyde content and superoxide dismutase activity are firstly increased and ultimately decreased to mitigate the oxidative stress under all the treatments. Compared with NZVI treatment alone, the oxidation of the H2O2 and NZVI/H2O2 processes can effectively destroy the antioxidant enzyme system and then inactivate the cyanobacterial cells, which further leads to the release of photosynthetic pigments and intracellular organic matters (IOM); in addition, the IOM removal efficiency (in terms of TOC) is 61.3% and 54.1% for the H2O2 and NZVI/H2O2 processes, respectively. Although NZVI is much more effective for extracellular organic matters (EOM) removal, it is less effective for IOM removal. The results of the three-dimensional EEM fluorescence spectra analysis further confirm that both H2O2 and NZVI/H2O2 have the ability to remove fluorescent substances from EOM and IOM, due to the oxidation mechanism; while NZVI has no removal effect for the fluorescent substances from EOM, it can remove part of fluorescent substances from IOM due to the agglomeration. All the results demonstrate that the NZVI/H2O2 process is a highly effective and applicable technology for the removal of M. aeruginosa and AOMs.

Isolation of axenic cyanobacterium and the promoting effect of associated bacterium on axenic cyanobacterium.

BACKGROUND: Harmful cyanobacterial blooms have attracted wide attention all over the world as they cause water quality deterioration and ecosystem health issues. Microcystis aeruginosa associated with a large number of bacteria is one of the most common and widespread bloom-forming cyanobacteria that secret toxins. These associated bacteria are considered to benefit from organic substrates released by the cyanobacterium. In order to avoid the influence of associated heterotrophic bacteria on the target cyanobacteria for physiological and molecular studies, it is urgent to obtain an axenic M. aeruginosa culture and further investigate the specific interaction between the heterotroph and the cyanobacterium. RESULTS: A traditional and reliable method based on solid-liquid alternate cultivation was carried out to purify the xenic cyanobacterium M. aeruginosa FACHB-905. On the basis of 16S rDNA gene sequences, two associated bacteria named strain B905-1 and strain B905-2, were identified as Pannonibacter sp. and Chryseobacterium sp. with a 99 and 97% similarity value, respectively. The axenic M. aeruginosa FACHB-905A (Microcystis 905A) was not able to form colonies on BG11 agar medium without the addition of strain B905-1, while it grew well in BG11 liquid medium. Although the presence of B905-1 was not indispensable for the growth of Microcystis 905A, B905-1 had a positive effect on promoting the growth of Microcystis 905A. CONCLUSIONS: The associated bacteria were eliminated by solid-liquid alternate cultivation method and the axenic Microcystis 905A was successfully purified. The associated bacterium B905-1 has the potentiality to promote the growth of Microcystis 905A. Moreover, the purification technique for cyanobacteria described in this study is potentially applicable to a wider range of unicellular cyanobacteria.