yujie zhu ecg
209167790
Progressive cardiac arrhythmias and ECG abnormalities in the Huntington's disease BACHD mouse model.
Huntington's disease (HD) is a dominantly inherited neurodegenerative disease. There is accumulating evidence that HD patients have increased prevalence of conduction abnormalities and compromised sinoatrial node (SAN) function which could lead to increased risk for arrhythmia. We used mutant Huntingtin (mHTT) expressing Bacterial Artificial Chromosome Huntington's disease (BACHD) mice to determine if they exhibit electrocardiogram (ECG) abnormalities involving cardiac conduction that are known to increase risk of sudden arrhythmic death in humans. We obtained surface ECGs and analyzed arrhythmia susceptibility; we observed prolonged QRS duration, increases in PVCs as well as PACs. Abnormal histological and structural changes that could lead to cardiac conduction system dysfunction were seen. Finally, we observed decreases in desmosomal proteins, PKP2 and DSG2, which have been reported to cause cardiac arrhythmias and reduced conduction. Our study indicates that mHTT could cause progressive cardiac conduction system pathology that could increase the susceptibility to arrhythmias and sudden cardiac death in HD patients.
Human molecular genetics
Yujie Zhu Isaac Shamblin Efrain Rodriquez Grace E Salzer Lita M Araysi Katherine A Margolies Ganesh V Halade Silvio H Litovsky Steven M Pogwizd Michelle Gray Sabine Huke
69058273
Thoughts on ECG in cardiovascular disease and accessibility to portable ECG testing
Cardiovascular disease (CVD) continues to be the leading cause of death in the United States. In addition, more than one third of U.S. adults have at least one type of CVD, and about one in three deaths in the U.S. are attributed to CVD. It is not at all unusual to know personally someone suffering from CVD or who experienced sudden death due to a cardiac disorder. Read More
Yujie Zhu
51614497
Arrhythmia Recognition and Classification Using ECG Morphology and Segment Feature Analysis
In this work, arrhythmia appearing with the presence of abnormal heart electrical activity is efficiently recognized and classified. A novel method is proposed for accurate recognition and classification of cardiac arrhythmias. Firstly, P QRS T waves is segmented from ECG waveform; secondly, morphological features are extracted from P QRS T waves, and ECG segment features are extracted from the selected ECG segment by using PCA and dynamic time warping(DTW) finally, SVM is applied to the features and automatic diagnosis results is presented. ECG data set used is derived from the MIT BIH in which ECG signals are divided into the four classes: normal beats(N) supraventricular ectopic beats (SVEBs) ventricular ectopic beats (VEBs) and fusion of ventricular and normal (F) Our proposed method can distinguish N, SVEBs, VEBs and F with an accuracy of 97.80 percent. The sensitivities for the classes N, SVEBs, VEBs and F are 99.27, 87.47, 94.71, and 73.88 percent and the positive predictivities are 98.48, 95.25, 95.22 and 86.09 percent respectively. The detection sensitivity of SVEBs and VEBs has a better performance by combining proposed features than by using the ECG morphology or ECG segment features separately. The proposed method is compared with four selected peer algorithms and delivers solid results.
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Wenliang Zhu Xiaohe Chen Yan Wang Lirong Wang
207372629
A lightweight piecewise linear synthesis method for standard 12 lead ECG signals based on adaptive region segmentation
This paper presents a lightweight synthesis algorithm, named adaptive region segmentation based piecewise linear (ARSPL) algorithm, for reconstructing standard 12 lead electrocardiogram (ECG) signals from a 3 lead subset (I, II and V2) Such a lightweight algorithm is particularly suitable for healthcare mobile devices with limited resources for computing, communication and data storage. After detection of R peaks, the ECGs are segmented by cardiac cycles. Each cycle is further divided into four regions according to different cardiac electrical activity stages. A personalized linear regression algorithm is then applied to these regions respectively for improved ECG synthesis. The proposed ARSPL method has been tested on 39 subjects randomly selected from the PTB diagnostic ECG database and achieved accurate synthesis of remaining leads with an average correlation coefficient of 0.947, an average root mean square error of 55.4mV, and an average runtime performance of 114ms. Overall, these results are significantly better than those of the common linear regression method, the back propagation (BP) neural network and the BP optimized using the genetic algorithm. We have also used the reconstructed ECG signals to evaluate the denivelation of ST segment, which is a potential symptom of intrinsic myocardial disease. After ARSPL, only 10.71% of the synthesized ECG cycles are with a ST level synthesis error larger than 0.1mV, which is also better than those of the three above mentioned methods.
PloS one
Huaiyu Zhu Yun Pan Kwang-Ting Cheng Ruohong Huan
51929807
The 43rd International Society for Computerized Electrocardiology ECG initiative for the automated detection of strict left bundle branch block.
The presence of left bundle branch block (LBBB) is an important predictor of benefit from cardiac resynchronization therapy (CRT) New "strict" electrocardiographic (ECG) criteria for LBBB have been shown to better predict benefit from CRT. The "strict" LBBB criteria include: QRS duration =140 ms (men) or =130 ms (women) QS or rS configurations of the QRS complex in leads V1 and V2, and mid QRS notching or slurring in =2 of leads V1, V2, V5, V6, I and aVL. The "strict" LBBB criteria are not regularly used and most hospital automated ECG systems and physicians still use more conventional LBBB criteria. As part of the 43rd International Society for Computerized Electrocardiology (ISCE) meeting, we conducted an initiative on the automated detection of "strict" LBBB where industry and academic investigators could present their algorithm results on digital 12 lead ECGs with varying QRS morphologies from the MADIT CRT trial (300 training and 302 test set ECGs that were manually adjudicated for "strict" LBBB presence) The results revealed a 64 82% accuracy, 48 76% sensitivity and 46 87% specificity for automated "strict" LBBB detection from 7 participants. Most mismatches were likely attributed to differences in detection and absence of specific definitions for notches and slurs while differences in QRS duration and S waves in leads V1 and V2 were less problematic. The full unblinded training and test datasets including all ECG signals are being made available through the Telemetric and Holter ECG Warehouse (THEW) for further exploration.
Journal of electrocardiology
Robbert Zusterzeel Jose Vicente Roberto Carlos Ochoa-Jimenez Jun Zhu Jean-Philippe Couderc Esther Akinnagbe-Zusterzeel David G Strauss
53099948
QRS Detection and Measurement Method of ECG Paper Based on Convolutional Neural Networks
In this paper, we propose an end to end approach to addressing QRS complex detection and measurement of Electrocardiograph (ECG) paper using convolutional neural networks (CNNs) Unlike conventional detection solutions that convert images to digital data, our method can directly detect QRS complex in images using Faster RCNN, then the R peak can be located and measured through a CNN. Validated by clinical ECG data in the St. Petersburg Institute of Cardiological Technics 12 lead Arrhythmia Database and real ECG paper from Peking University People's Hospital, the proposed method can achieve the recall of 98.32% the precision of 99.01% in detecting and 0.012 mv of mean absolute error in measuring. Experimental results demonstrate the superior performance of our method over conventional solutions, which would pave the way to detect and measure ECG paper using CNNs.
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Runze Yu Yingguo Gao Xiaohui Duan Tiangang Zhu Zhilong Wang Bingli Jiao
49536926
An Energy Efficient ECG Processor With Weak Strong Hybrid Classifier for Arrhythmia Detection
This brief presents an energy efficient electrocardiogram processor for arrhythmia detection with a weak strong hybrid classifier that includes a weak linear classifier (WLC) and a strong support vector machine (SVM) classifier. WLC can only identify the beats with distinct characteristics by performing simple threshold comparisons based on beat interval feature and a novel morphology feature named QRS area ratio. The beats that are unclassified by WLC will activate the more powerful but energy guzzling SVM classifier. Principal component analysis (PCA) is applied for feature dimension reduction to lower the complexity of SVM classifier and a sparse matrix computing architecture is exploited to reduce the computation burden of PCA. Implemented in SMIC 40LL CMOS process, the processor has a total area of 0.12 mm2. It achieves 1.98 uW power consumption in WLC mode and 3.76 uW in SVM mode under 1.1 V voltage supply and 10 KHz operating frequency, with energy dissipation of 6.8/30.3 nJ per beat classification for the two modes, respectively. The overall accuracy for MIT BIH arrhythmia database is 98.2% with energy reduction of 41.7% compared to a single SVM classifier.
IEEE Transactions on Circuits and Systems II: Express Briefs
Zhijian Chen Jiahui Luo Kaiwen Lin Jiaquan Wu Taotao Zhu Xiaoyan Xiang Jianyi Meng
3694412
An FPGA Based Cloud System for Massive ECG Data Analysis
In this brief, we propose a stand alone system on a programmable chip (SOPC) based cloud system to accelerate massive electrocardiogram (ECG) data analysis. The proposed system tightly couples network I/O handling hardware to data processing pipelines in a single field programmable gate array (FPGA) offloading both networking operations and ECG data analysis. In this system, we first propose a massive sessions optimized TCP/IP hardware stack using a macropipeline architecture to accelerate network packet processing. Second, we propose a streaming architecture to accelerate ECG signal processing, including QRS detection, feature extraction, and classification. We verify our design on XC6VLX550T FPGA using real ECG data. Compared to commercial servers, our system shows up to 38x improvement in performance and 142x improvement in energy efficiency.
IEEE Transactions on Circuits and Systems II: Express Briefs
Xu Wang Yongxin Zhu Yajun Ha Meikang Qiu Tian Huang
8836894
A type ECG and EGCG dimers disturb the structure of 3T3 L1 cell membrane and strongly inhibit its differentiation by targeting peroxisome proliferator activated receptor g with miR 27 involved mechanism.
The effects of four proanthocyanidin dimers including epicatechin (4b 8, 2b O 7) epicatechin (A type EC dimer) epicatechin (4b 8) epicatechin (B type EC dimer) epicatechin 3 gallate (4b 8, 2b O 7) epicatechin 3 gallate (A type ECG dimer) and epigallocatechin 3 gallate (4b 8, 2b O 7) epigallocatechin 3 gallate (A type EGCG dimer) on 3T3 L1 preadipocyte cell differentiation and the underlying mechanisms were explored and compared. The results showed that A type ECG dimer and A type EGCG dimer significantly reduced the intracellular lipid accumulation in 3T3 L1 preadipocyte cells by targeting miR 27a and miR 27b as well as peroxisome proliferator activated receptor g (PPARg) in the early stage of differentiation, while A type EC dimer and B type EC dimer showed little effect. In addition, our results revealed that the inhibitory effects of proanthocyanidin dimers on 3T3 L1 preadipocyte differentiation were highly structure dependent and the effects were associated with the dimer membrane interactions. The presence of galloyl moieties and A type linkage within the structure of proanthocyanidins might be crucial for their inhibitory effect on adipogenesis. The strong disturbing effects of A type ECG and A type EGCG dimers on the fluidity, hydrophobicity and permeability of membrane of 3T3 L1 preadipocyte cell were at least, in part, responsible for their distinct inhibitory effects on adipocyte hyperplasia.
The Journal of nutritional biochemistry
Wei Zhu Bo Zou Rongzu Nie Ying Zhang Chun-Mei Li
23961089
A careful look at ECG sampling frequency and R peak interpolation on short term measures of heart rate variability.
As the literature on heart rate variability (HRV) continues to burgeon, so too do the challenges faced with comparing results across studies conducted under different recording conditions and analysis options. Two important methodological considerations are (1) what sampling frequency (SF) to use when digitizing the electrocardiogram (ECG) and (2) whether to interpolate an ECG to enhance the accuracy of R peak detection. Although specific recommendations have been offered on both points, the evidence used to support them can be seen to possess a number of methodological limitations. The present study takes a new and careful look at how SF influences 24 widely used time and frequency domain measures of HRV through the use of a Monte Carlo based analysis of false positive rates (FPRs) associated with two sample tests on independent sets of healthy subjects. HRV values from the first sample were calculated at 1000 Hz, and HRV values from the second sample were calculated at progressively lower SFs (and either with or without R peak interpolation) When R peak interpolation was applied prior to HRV calculation, FPRs for all HRV measures remained very close to 0.05 (i.e. the theoretically expected value) even when the second sample had an SF well below 100 Hz. Without R peak interpolation, all HRV measures held their expected FPR down to 125 Hz (and far lower, in the case of some measures) These results provide concrete insights into the statistical validity of comparing datasets obtained at (potentially) very different SFs; comparisons which are particularly relevant for the domains of meta analysis and mobile health.
Physiological measurement
Robert J Ellis Bilei Zhu Julian Koenig Julian F Thayer Ye Wang