Biology subjects

Zhong, J.

Publications and source records attributed to Zhong, J..

At least 127 records · Page 7Linked to original sources

bioRxiv2021

Artificial Neural Networks for classification of single cell gene expression

BackgroundSingle-cell transcriptome (SCT) sequencing technology has reached the level of high-throughput technology where gene expression can be measured concurrently from large numbers of cells. The results of gene expression studies are highly reproducible when strict protocols and standard operating procedures (SOP) are followed. However, differences in sample processing conditions result in significant changes in gene expression profiles making direct comparison of different studies difficult. Unsupervised machine learning (ML) uses clustering algorithms combined with semi-automated cell labeling and manual annotation of individual cells. They do not scale up well and a workflow used on a specific dataset will not perform well with other studies. Supervised ML classification shows superior classification accuracy and generalization properties as compared to unsupervised ML methods. We describe a supervised ML method that deploys artificial neural networks (ANN), for 5-class classification of healthy peripheral blood mononuclear cells (PBMC) from multiple diverse studies. ResultsWe used 58 data sets to train ANN incrementally - over ten cycles of training and testing. The sample processing involved four protocols: separation of PBMC, separation of PBMC + enrichment (by negative selection), separation of PBMC + FACS, and separation of PBMC + MACS. The training data set included between 85 and 110 thousand cells, and the test set had approximately 13 thousand cells. Training and testing were done with various combinations of data sets from four principal data sources. The overall accuracy of classification on independent data sets reached 5-class classification accuracy of 94%. Classification accuracy for B cells, monocytes, and T cells exceeded 95%. Classification accuracy of natural killer (NK) cells was 75% because of the similarity between NK cells and T cell subsets. The accuracy of dendritic cells (DC) was low due to very low numbers of DC in the training sets. ConclusionsThe incremental learning ANN model can accurately classify the main types of PBMC. With the inclusion of more DC and resolving ambiguities between T cell and NK cell gene expression profiles, we will enable high accuracy supervised ML classification of PBMC. We assembled a reference data set for healthy PBMC and demonstrated a proof-of-concept for supervised ANN method in classification of previously unseen SCT data. The classification shows high accuracy, that is consistent across different studies and sample processing methods.

bioinformatics↗

bioRxiv2021

TREM2 interacts with TDP-43 and mediates microglial neuroprotection against TDP-43-related neurodegeneration

Triggering receptor expressed on myeloid cell 2 (TREM2) is a surface receptor that, in the central nervous system, is exclusively expressed on microglia. TREM2 variants have been linked to increased risk for neurodegenerative diseases, but the functional effects of microglial TREM2 remain largely unknown. To this end, we investigated TAR-DNA binding protein 43 kDa (TDP-43)-related neurodegenerative disease via viral-mediated expression of human TDP-43 protein (hTDP-43) in neonatal and adult mice or inducible expression of hTDP43 with defective nuclear localization signals in transgenic mice. We found that TREM2 deficiency impaired microglia phagocytic clearance of pathological TDP-43, and enhanced neuronal damage and motor function impairments. Mass cytometry analysis revealed that hTDP-43 induced a TREM2-dependent subpopulation of microglia with high CD11c expression and higher phagocytic ability. Using mass spectrometry and surface plasmon resonance analysis, we further demonstrated an interaction between TDP-43 and TREM2, in vitro and in vivo, in hTDP-43-expressing transgenic mouse brains. We computationally identified the region within hTDP-43 that interacts with TREM2 and observed the potential interaction in ALS patient tissues. Our data reveal the novel interaction between TREM2 and TDP-43, highlighting that TDP-43 is a possible ligand for microglial TREM2 and the interaction mediates neuroprotection of microglial TREM2 in TDP-43-related neurodegeneration.

neuroscience↗

bioRxiv2021

B Cell Receptor Signaling Drives APOBEC3 Expression Via Direct Enhancer Regulation in Chronic Lymphocytic Leukemia B Cells

Constitutively activated B cell receptor (BCR) signaling is a primary biological feature of chronic lymphocytic leukemia (CLL). The biological events controlled by BCR signaling in CLL are not fully understood and need investigation. To make inroads we obtained blood samples from CLL patients before and after Brutons tyrosine kinase inhibitors (BTKi) treatment and used them to study BCR signaling regulated genes. Here, by analysis of the chromatin states and gene expression profiles of CLL B cells from patients before and after BTKi ibrutinib treatment, we show that BTKi treatment leads to a decreased expression of APOBEC3 family genes in an enhancer regulation dependent manner. BTKi treatment reduces enrichment of enhancer markers (H3K4me1, H3K27ac) and chromatin accessibility at putative APOBEC3 enhancers. CRISPR-Cas9 directed deletion or inhibition of the putative APOBEC3 enhancers leads to reduced APOBEC3 expression. We further find that transcription factor NFATc1 couples BCR signaling with the APOBEC3 enhancer activity to control APOBEC3 expression. Importantly, enhancer regulated APOBEC3 expression contributes to replication stress in malignant B cells. We also demonstrate a novel mechanism for BTKi suppression of APOBEC3 expression via direct enhancer regulation in a NFATc1 dependent manner, implicating BCR signaling as a potential regulator of leukemic genomic instability. Key pointsO_LIBCR signaling pathway regulates APOBEC3 expression via direct enhancer regulation. C_LIO_LIAOPEBC3 enhancers are involved in the process of DNA replication stress, implicating a potential role in B cell genomic instability and CLL evolution C_LI

cancer biology↗

bioRxiv2021

Landscape of microenvironment in Randall's plaque by single-cell sequencing

Randalls plaque is significantly associated with the occurrence of nephrolithiasis. However, the microenvironment of Randalls plaque is poorly characterized. To investigate the microenvironment of Randalls plaque, we analyzed single-cell RNA data of 3 Randalls plaque and 3 normal renal papillae tissue and identified 11 different cell types. We screened differentially expressed genes among all cell types between Randalls plaque and normal renal papillae. The microenvironment showed two cell types with multiple stone formation-associated transcriptomic programs. Contrary to previous studies, we did not observe macrophage M1/M2 imbalance. Notably, we detected ossification-associated macrophage is enriched in Randalls plaque and validated GPNMB and ACP5 were potential biomarkers on the ossification-associated macrophage. We also identified an endothelial subset harboring active communication (COL15A1+ PCDH17+ endothelial, DPECs) with other cells. Together with Immunofluorescence, we validated ossification-associated macrophage and DPECs are enriched in Randalls plaque tissue. Finally, cell-to-cell communication revealed that Loop of Henle, DPECs, and osteoblasts-associated macrophages was the main source of SPP1 signaling. Our work will further the understanding of the microenvironment among Randalls plaque tissues and provide deep insight into immune modulation.

bioinformatics↗

bioRxiv2021

Fixel-Based Analysis and Free Water Corrected DTI Evaluation of HIV Associated Neurocognitive Disorders

White matter damage is a consistent finding in HIV infected (HIV+) individuals. Previous studies have evaluated WM fiber tract specific brain regions in HIV-associated neurocognitive disorders using diffusion tensor imaging (DTI). However, DTI might lack an accurate biological interpretation, and the technique suffers from several limitations. Here, we sought to evaluate Fixel-based analysis (FBA) and free water corrected DTI (fwcDTI) metrics between HIV+ and HIV uninfected (HIV-) individuals, and their relationships with blood markers and cognitive scores. We also compared the specificity of both MRI metrics in their ability to distinguish between individuals with and without cognitive impairment using machine learning classifiers. Using 94 age-matched participants, we found that whole brain FBA was significantly reduced (up to 15%) in various fiber bundles. Tract based special statistics (TBSS) of fwcDTI metrics revealed decreased fractional anisotropy FAT (by 1-2%) in HIV+ compared to HIV- individuals in areas consistent with those observed in FBA, but these were not significant. An adaptive boosting classifier reliably distinguished between cognitively normal patients and those with cognitive impairment with 80% precision and 78% recall. Therefore, FBA may serve as a potential in-vivo biomarker for evaluating and monitoring axonal degeneration in HIV+ patients at risk for neurocognitive impairment.

neuroscience↗

bioRxiv2021

Brain Iron Imaging Markers in the Presence of White Matter Hyperintensities

PurposeTo investigate the relationship between pathological brain iron deposition and white matter hyperintensities (WMHs) in cerebral small vessel disease (CSVD), via Monte Carlo simulations of magnetic susceptibility imaging and a novel imaging marker called the Expected Iron Coefficient (EIC). MethodsA synthetic pathological model of a different number of impenetrable spheres at random locations was employed to represent pathological iron deposition. The diffusion process was simulated with a Monte Carlo method with adjustable parameters to manipulate sphere size, distribution, and extracellular properties. Quantitative susceptibility mapping (QSM) was performed in a clinical dataset to study CSVD to derive and evaluate QSM, R2*, the iron microenvironment coefficient (IMC), and EIC in the presence of WMHs. ResultsThe simulations show that QSM signals increase in the presence of increased tissue iron, confirming that the EIC increases with pathology. Clinical results demonstrate that while QSM, R2*, and the IMC do not show differences in brain iron, the EIC does in the context of CSVD. ConclusionThe EIC is more sensitive to subtle changes in brain iron deposition caused by pathology, even when QSM, R2*, and the IMC do not.

pathology↗

bioRxiv2021

Increased Risk for Cerebral Small Vessel Disease is Associated with Quantitative Susceptibility Mapping in HIV Infected and Uninfected Individuals

The aim of this study was to assess in the context of cerebral small vessel disease (CSVD), cardiovascular risk factors and white matter hyperintensities (WMHs) were associated with brain tissue susceptibility as measured by quantitative susceptibility mapping (QSM). Given that CSVD is diagnosed by the presence of lacunar strokes, periventricular and deep WMHs, increased perivascular spaces, and microbleeds, we expected that QSM could capture changes in brain tissue due to underlying CSVD pathology. We compared a cohort of 101 HIV-infected individuals (mean age (SD) = 53.2 (10.9) years) with mild to moderate cardiovascular risk scores, as measured by the Reynolds risk score, to 102 age-matched controls (mean age (SD) = 50.3 (15.7) years) with similar Reynold scores. We performed brain MRI to assess CSVD burden by acquiring 3D T1-MPRAGE, 3D FLAIR, 2D T2-TSE, and mGRE for QSM. We found that signs of CSVD are significantly higher in individuals with HIV-infection compared to controls and that WMH volumes are significantly correlated with age and cardiovascular risk scores. Regional QSM was associated with cardiovascular risk factors, age, sex, and WMH volumes but not HIV status. These results suggest that QSM may be an early imaging marker reflective of alterations in brain microcirculation.

pathology↗

bioRxiv2020

A novel cell culture system modeling the SARS-CoV-2 life cycle

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the global pandemic of COVID-19, and no effective antiviral agents and vaccines are available. SARS-CoV-2 is classified as a biosafety level-3 (BLS-3) agent, impeding the basic research into its biology and the development of effective antivirals. Here, we developed a biosafety level-2 (BSL-2) cell culture system for production of transcription and replication-competent SARS-CoV-2 virus-like-particles (trVLP). This trVLP expresses a reporter gene (GFP) replacing viral nucleocapsid gene (N), which is required for viral genome packaging and virion assembly (SARS-CoV-2-GFP/{Delta}N trVLP). The complete viral life cycle can be achieved and exclusively confined in the cells ectopically expressing SARS-CoV or SARS-CoV-2 N proteins, but not MERS-CoV N. Genetic recombination of N supplied in trans into viral genome was not detected, as evidenced by sequence analysis after one-month serial passages in the N-expressing cells. Moreover, intein-mediated protein trans-splicing approach was utilized to split the viral N gene into two independent vectors, and the ligated viral N protein could function in trans to recapitulate entire viral life cycle, further securing the biosafety of this cell culture model. Based on this BSL-2 SARS-CoV-2 cell culture model, we developed a 96-well format high throughput screening for antivirals discovery. We identified salinomycin, tubeimoside I, monensin sodium, lycorine chloride and nigericin sodium as potent antivirals against SARS-CoV-2 infection. Collectively, we developed a convenient and efficient SARS-CoV-2 reverse genetics tool to dissect the virus life cycle under a BSL-2 condition. This powerful tool should accelerate our understanding of SARS-CoV-2 biology and its antiviral development.

microbiology↗

bioRxiv2020

Integrative Analysis of Phenomic, Genomic, and Transcriptomic to Identify Potential Functional Genes of Yaks in Plain and Plateau

BackgroundThe yak is an important source of livelihood for the people living in the Qinghai-Tibet Plateau. Most genetics detection studies have focused on the comparison between different tissues of different breeds, both living in the Plateau and in the plains. The genetic background and complex regulatory relationship have frequently puzzled researchers. In this study, we divided a population of 10 yaks into two subgroups, namely Plateau (living in the Plateau) and Plain (living in the plains). Phenomic, genomic, and transcriptomic analyses were used to reveal the molecular genetic type in the body weight, slaughter, and beef quality of yaks. ResultsWe found a significant difference (P <0.01) between the third (60 days), fourth (90 days), fifth (120 days), and sixth (150 days) weights of Plateau and plain subpopulations. The difference in body weight was due to differences in kidney weight, meat weight, fur weight, and head weight. However, the beef quality was not significantly different. We identified 540 Differentially Expressed Genes (DEGs). Using weighted gene co-expression network analysis (WGCNA), we have constructed a co-express network, and the modules were strongly related to traits. In the genome-wide association studies (GWAS), we detected significant 156, 52, 33, 15, and 3 signals in the meat weight, head weight, fur weight, liver weight, and the last body weight traits. Based on the epigenome-wide association studies (eWAS) results, we created a link relationship between the DEGs expression level and genotype. ConclusionIn summary, our study demonstrated the effectiveness and representative of multidimensional data from a finite number of yak populations. The study highlights the underlying way, as well as a related network, to yield new information on genome genetics and pathogen-host interactions of both the living Plateau and plain yak populations.

genomics↗

bioRxiv2020

Comparative analysis reveals the species-specific genetic determinants of ACE2 required for SARS-CoV-2 entry

Coronavirus interaction with its viral receptor is a primary genetic determinant of host range and tissue tropism. SARS-CoV-2 utilizes ACE2 as the receptor to enter host cell in a species-specific manner. We and others have previously shown that ACE2 orthologs from New World monkey, koala and mouse cannot interact with SARS-CoV-2 to mediate viral entry, and this defect can be restored by humanization of the restrictive residues in New World monkey ACE2. To better understand the genetic determinants behind the ability of ACE2 orthologs to support viral entry, we compared koala and mouse ACE2 sequences with that of human and identified the key residues in koala and mouse ACE2 that restrict viral receptor activity. Humanization of these critical residues rendered both koala and mouse ACE2 capable of binding the spike protein and facilitating viral entry. The single mutation that allowed for mouse ACE2 to serve as a viral receptor provides a potential avenue for the development of SARS-CoV-2 mouse model.

microbiology↗

bioRxiv2020

MyoD is a structure organizer for emergent property of 3D genome architecture in muscle cells

The genome is not a linear molecule of DNA randomly folded in the nucleus, but exists as an organized, three-dimensional (3D) dynamic architecture. Intriguingly, it is now clear that each cell type has a unique and characteristic 3D genome organization that functions in determining cell identity during development. A currently challenging basic question is how cell-type specific 3D genome structures are established during development. Herein, we analyzed 3D genome structures in primary myoblasts and myocytes from MyoD knockout (MKO) and wild type (WT) mice and discovered that MyoD, a pioneer transcription factor (TF), can function as a "genome organizer" that specifies the proper 3D genome architecture unique to muscle cell development. Importantly, we genetically demonstrate that H3K27ac is insufficient for establishing MyoD-induced chromatin loops in muscle cells. The establishment of MyoDs "architectural role" should have profound impacts on advancing understanding of other pioneer transcription factors in orchestrating lineage specific 3D genome organization during development in a potentially very large number of cell types in diverse organisms.

genomics↗

bioRxiv2020

Surface-Based Connectivity Integration

There has been increasing interest in jointly studying structural connectivity (SC) and functional connectivity (FC) derived from diffusion and functional MRI. However, several fundamental problems are still not well considered when conducting such connectome integration analyses, e.g., "Which structure (e.g., gray matter, white matter, white surface or pial surface) should be used for defining SC and FC and exploring their relationships", "Which brain parcellation should be used", and "How do the SC and FC correlate with each other and how do such correlations vary in different locations of the brain?". In this work, we develop a new framework called surface-based connectivity integration (SBCI) to facilitate the integrative analysis of SC and FC with a re-thinking of these problems. We propose to use the white surface (the interface of white matter and gray matter) to build both SC and FC since diffusion signals are in the white matter while functional signals are more present in the gray matter. SBCI also represents both SC and FC in a continuous manner at very high spatial resolution on the white surface, avoiding the need of pre-specified atlases which may bias the comparison of SC and FC. Using data from the Human Connectome Project, we show that SBCI can create reproducible, high quality SC and FC, in addition to three novel imaging biomarkers reflective of the similarity between SC and FC throughout the brain, called global, local, and discrete SC-FC coupling. Further, we demonstrate the usefulness of these biomarkers in finding group effects due to biological sex throughout the brain.

neuroscience↗

bioRxiv2020

Single-cell transcriptional atlas of the Chinese horseshoe bat (Rhinolophus sinicus) provides insight into the cellular mechanisms which enable bats to be viral reservoirs

Bats are a major "viral reservoir" in nature and there is a great interest in not only the cell biology of their innate and adaptive immune systems, but also in the expression patterns of receptors used for cellular entry by viruses with potential cross-species transmission. To address this and other questions, we created a single-cell transcriptomic atlas of the Chinese horseshoe bat (Rhinolophus sinicus) which comprises 82,924 cells from 19 organs and tissues. This atlas provides a molecular characterization of numerous cell types from a variety of anatomical sites, and we used it to identify clusters of transcription features that define cell types across all of the surveyed organs. Analysis of viral entry receptor genes for known zoonotic viruses showed cell distribution patterns similar to that of humans, with higher expression levels in bat intestine epithelial cells. In terms of the immune system, CD8+ T cells are in high proportion with tissue-resident memory T cells, and long-lived effector memory nature killer (NK) T-like cells (KLRG1, GZMA and ITGA4 genes) are broadly distributed across the organs. Isolated lung primary bat pulmonary fibroblast (BPF) cells were used to evaluate innate immunity, and they showed a weak response to interferon {beta} and tumor necrosis factor- compared to their human counterparts, consistent with our transcriptional analysis. This compendium of transcriptome data provides a molecular foundation for understanding the cell identities, functions and cellular receptor characteristics for viral reservoirs and zoonotic transmission.

cell biology↗

bioRxiv2020

Quantitative occupancy of myriad transcription factors from one DNase experiment enables efficient comparisons across conditions

Over a thousand different transcription factors (TFs) bind with varying occupancy across the human genome. Chromatin immunoprecipitation (ChIP) can assay occupancy genome-wide, but only one TF at a time, limiting our ability to comprehensively observe the TF occupancy landscape, let alone quantify how it changes across conditions. We developed TOP, a Bayesian hierarchical regression framework, to profile genome-wide quantitative occupancy of numerous TFs using data from a single DNase-seq experiment. TOP is supervised, and its hierarchical structure allows it to predict the occupancy of any sequence-specific TF, even those never assayed with ChIP. We used TOP to profile the quantitative occupancy of nearly 1500 human TF motifs, and examined how their occupancies changed genome-wide in multiple contexts: across 178 cell types, over 12 hours of exposure to different hormones, and across the genetic backgrounds of 70 individuals. TOP enables cost-effective exploration of quantitative changes in the landscape of TF binding.

bioinformatics↗

bioRxiv2020

RoboCOP: Jointly computing chromatin occupancy profiles for numerous factors from chromatin accessibility data

Chromatin is the tightly packaged structure of DNA and protein within the nucleus of a cell. The arrangement of different protein complexes along the DNA modulates and is modulated by gene expression. Measuring the binding locations and level of occupancy of different transcription factors (TFs) and nucleosomes is therefore crucial to understanding gene regulation. Antibody-based methods for assaying chromatin occupancy are capable of identifying the binding sites of specific DNA binding factors, but only one factor at a time. On the other hand, epigenomic accessibility data like ATAC-seq, DNase-seq, and MNase-seq provide insight into the chromatin landscape of all factors bound along the genome, but with minimal insight into the identities of those factors. Here, we present RoboCOP, a multivariate state space model that integrates chromatin information from epigenomic accessibility data with nucleotide sequence to compute genome-wide probabilistic scores of nucleosome and TF occupancy, for hundreds of different factors at once. We apply RoboCOP to MNase-seq data to elucidate the protein-binding landscape of nucleosomes and 150 TFs across the yeast genome. Using available protein-binding datasets from the literature, we show that our model predicts the binding of these factors genome-wide more accurately than existing methods.

bioinformatics↗

bioRxiv2020

Massively parallel quantification of CRISPR editing in cells by TRAP-seq enables better design of Cas9, ABE, CBE gRNAs of high efficiency and accuracy

The CRISPR RNA-guided endonucleases Cas9, and Cas9-derived adenine/cytosine base editors (ABE/CBE), have been used in both research and therapeutic applications. However, broader use of this gene editing toolbox is hampered by the great variability of efficiency among different target sites. Here we present TRAP-seq, a versatile and scalable approach in which the CRISPR gRNA expression cassette and the corresponding surrogate site are captured by Targeted Reporter Anchored Positional Sequencing in cells. TRAP-seq can faithfully recapitulate the CRISPR gene editing outcomes introduced to the corresponding endogenous genome site and most importantly enables massively parallel quantification of CRISPR gene editing in cells. We demonstrate the utility of this technology for high-throughput quantification of SpCas9 editing efficiency and indel outcomes for 12,000 gRNAs in human embryonic kidney cells. Using this approach, we also showed that TRAP-seq enables high throughput quantification of both ABE and CBE efficiency at 12,000 sites in cells. This rich amount of ABE/CBE outcome data enable us to reveal several novel nucleotide features (e.g. preference of flanking bases, nucleotide motifs, STOP recoding types) affecting base editing efficiency, as well as designing improved machine learning-based prediction tools for designing SpCas9, ABE and CBE gRNAs of high efficiency and accuracy (>70%). We have integrated all the 12,000 CRISPR gene editing outcomes for SpCas9, ABE and CBE into a CRISPR-centered portal: The Human CRISPR Atlas. This study extends our knowledge on CRISPR gene and base editing, and will facilitate the application and development of CRISPR in both research and therapy.

systems biology↗

bioRxiv2020

Predicting cell fate commitment of embryonic differentiation by single-cell graph entropy

Cell fate commitment occurs during early embryonic development, that is, the embryonic differentiation sometimes undergoes a critical phase transition or "tipping point" of cell fate commitment, at which there is a drastic or qualitative shift of the cell populations. In this study, we presented a novel computational approach, the single-cell graph entropy (SGE), to explore the gene-gene associations among cell populations based on single-cell RNA sequencing (scRNA-seq) data. Specifically, by transforming the sparse and fluctuating gene expression data to the stable local network entropy, the SGE score quantitatively characterizes the criticality of gene regulatory networks among cell populations, and thus can be employed to predict the tipping point of cell fate or lineage commitment at the single cell level. The proposed SGE method was applied to five scRNA-seq datasets. For all these datasets of embryonic differentiation, SGE effectively captures the signal of the impending cell fate transitions, which cannot be detected by gene expressions. Some "dark" genes that are non-differential but sensitive to SGE values were revealed. The successful identification of critical transition for all five datasets demonstrates the effectiveness of our method in analyzing scRNA-seq data from a network perspective, and the potential of SGE to track the dynamics of cell differentiation.

bioinformatics↗

bioRxiv2020

A two-stage ECG signal denoising method based on deep convolutional network

Electrocardiogram (ECG) is an effective and non-invasive indicator for the detection and prevention of arrhythmia. ECG signals are susceptible to noise contamination, which can lead to errors in ECG interpretation. Therefore, ECG pretreatment is important for accurate analysis. In this paper, a method of noise reduction based on deep learning is proposed. The method is divided into two stages, and two corresponding models are formed. In the first stage, a one-dimensional U-net model is designed for ECG signal denoising to eliminate noise as much as possible. The one-dimensional DR-net model in the second stage is used to reconstruct the ECG signal and to correct the waveform distortion caused by noise removal in the first stage. In this paper, the U-net and the DR-net are constructed by the convolution method to achieve end-to-end mapping from noisy ECG signals to clean ECG signals. The ECG data used in this paper are from CPSC2018, and the noise signal is from MIT-BIH Noise Stress Test Database (NSTDB). In the experiment, the improvement in the signal-to-noise ratio SNRimp, the root mean square error decrease RMSEde, and the correlation coefficient P, are used to evaluate the performance of the network. This two-stage method is compared with FCN and U-net alone. The experimental results show that the two-stage noise reduction method can eliminate complex noise in the ECG signal while retaining the characteristic shape of the ECG signal. According to the results, we believe that the proposed method has a good application prospect in clinical practice.

bioengineering↗