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Impedance cardiography (ICG) is essential in evaluating cardiac function in patients with cardiovascular diseases. Aiming at the problem that the measurement of ICG signal is easily disturbed by motion artifacts, this paper introduces a de-noising method based on two-step spectral ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA). Firstly, the first spectral EEMD-CCA was performed between ICG and motion signals, and electrocardiogram (ECG) and motion signals, respectively. The component with the strongest correlation coefficient was set to zero to suppress the main motion artifacts. Secondly, the obtained ECG and ICG signals were subjected to a second spectral EEMD-CCA for further denoising. Lastly, the ICG signal is reconstructed using these share components. The experiment was tested on 30 subjects, and the results showed that the quality of the ICG signal is greatly improved after using the proposed denoising method, which could support the subsequent diagnosis and analysis of cardiovascular diseases.

Objective: To explore the intrinsic steady-state electrophysiological properties of mouse heart under physiological conditions by high-resolution quantitative analysis., Methods: Twenty-two young adult C57BL/6 mice with a 1:1 male-to-female ratio were used. The limbs of the mice were fixed without anesthesia, and electrocardiographic waveforms, including characteristic P-waves, R-waves, and ST-waves, were recorded using a sensitive 12-lead electrophysiological recorder (ECG sqa ) under spontaneous breathing. LabScribe software was used to extract and quantify high-resolution time course and amplitude parameters within a single cardiac cycle from the V3 precordial lead. Pearson correlation test combined with simple linear regression was used to generate a scatter plot of ECG parameter fitting. The common and unique correlation parameters were separately identified by joint associations for profiling the quantitative association network., Results: ECG sqa analysis identified and quantified 14 characteristic ECG parameters, 28.6% of which showed statistical differences between the groups. Compared to male mice, female mice exhibited higher amplitudes and velocities of R and ST waves. Among the 51 association pairs identified in primary association analysis, 47.1% were positively correlated, including shared (29.2%), male-specific (29.2%), and female-specific (41.7%) association groups. Second-order clustering of the association pairs revealed that the amplituderate association pairs of each waveform voltage in both male and female mouse hearts were strongly correlated. The male mice showed an atrioventricular interconnection pattern, while the female mice showed a unique atrial conduction system quality dependence. The distribution network characteristics of the association groups showed that sex-specific and common correlation sets formed a certain series pattern., Conclusion: We discovered a novel intrinsic correlation network of cardiac electrophysiological traits in male and female mice, which reveals the key internal quantitative characteristics and gender difference of both atrial and ventricular conduction systems.

Atrial fibrillation (AF) is a life-threatening heart condition, and its early detection and treatment have garnered significant attention from physicians in recent years. Traditional methods of detecting AF heavily rely on doctor's diagnosis based on electrocardiograms (ECGs), but prolonged analysis of ECG signals is very time-consuming. This paper designs an AF detection model based on the Inception module, constructing multi-branch detection channels to process raw ECG signals, gradient signals, and frequency signals during AF. The model efficiently extracted QRS complex and RR interval features using gradient signals, extracted P-wave and f-wave features using frequency signals, and used raw signals to supplement missing information. The multi-scale convolutional kernels in the Inception module provided various receptive fields and performed comprehensive analysis of the multi-branch results, enabling early AF detection. Compared to current machine learning algorithms that use only RR interval and heart rate variability features, the proposed algorithm additionally employed frequency features, making fuller use of the information within the signals. For deep learning methods using raw and frequency signals, this paper introduced an enhanced method for the QRS complex, allowing the network to extract features more effectively. By using a multi-branch input mode, the model comprehensively considered irregular RR intervals and P-wave and f-wave features in AF. Testing on the MIT-BIH AF database showed that the inter-patient detection accuracy was 96.89%, sensitivity was 97.72%, and specificity was 95.88%. The proposed model demonstrates excellent performance and can achieve automatic AF detection.

Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life and health. However, clinical records of sudden cardiac death (SCD) electrocardiogram (ECG) data are extremely limited. This paper proposes an early prediction and classification algorithm for SCA based on deep transfer learning. With limited ECG data, it extracts heart rate variability features before the onset of SCA and utilizes a lightweight convolutional neural network model for pre-training and fine-tuning in two stages of deep transfer learning. This achieves early classification, recognition and prediction of high-risk ECG signals for SCA by neural network models. Based on 16 788 30-second heart rate feature segments from 20 SCA patients and 18 sinus rhythm patients in the international publicly available ECG database, the algorithm performance evaluation through ten-fold cross-validation shows that the average accuracy (Acc), sensitivity (Sen), and specificity (Spe) for predicting the onset of SCA in the 30 minutes prior to the event are 91.79%, 87.00%, and 96.63%, respectively. The average estimation accuracy for different patients reaches 96.58%. Compared to traditional machine learning algorithms reported in existing literatures, the method proposed in this paper helps address the requirement of large training datasets for deep learning models and enables early and accurate detection and identification of high-risk ECG signs before the onset of SCA.

Objective: To investugate the unique electrocardiogram (ECG) characteristics of fulminant myocarditis (FM) patients and provide important clues for the diagnosis of FM. Methods: This was a retrospective study. Patients diagnosed with acute myocarditis at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from February 2017 to April 2022 were enrolled and divided into fulminant myocarditis group (FM) and non-fulminant myocarditis group (NFM) according to clinical diagnosis. A total of 246 healthy people who underwent physical examination in the Health examination Center of Tongji Hospital at the same period were selected as the control group. The clinical data and ECG characteristics of the above 3 groups were analyzed and compared. Logistic regression model was used to analyze the influence of ECG parameters on left ventricular ejection fraction in FM patients. Receiver operating curves were constructed to evaluate the predictive value of different ECG parameters for FM. Results: A total of 180 patients were included in this study (FM group: n =123; NFM group: n =57), with an age of (35.0±16.2) years and 106 males (58.89%). Compared with NFM group, ECG was significantly abnormal in FM group, with a higher incidence of sinus tachycardia, ventricular tachycardia or ventricular fibrillation, escape rhythm, right bundle branch block, third degree atrioventricular block, ST-segment elevation, low voltage, prolonged QTc interval, and widened QRS wave in the FM group (all P <0.05). The ECG parameters showed that the amplitude of the full lead QRS wave in FM group was lower than that in NFM group ( P <0.01). The average heart rate and QTc interval of FM group were significantly higher than those of NFM and control groups (all P <0.05). Although ST-segment elevation had a higher incidence in the FM group, ECG parameters showed that except for leads Ⅲ and aVF, the ST segment levels in all leads in the FM group were lower than those in the control group (all P <0.05). There was a statistically significant difference in some ST segment changes between FM and NFM groups, while there was no statistical difference between the NFM and control groups. Multivariate regression analysis showed that widened QRS wave and increased heart rate were the influencing factors for left ventricular systolic dysfunction in FM patients ( OR =16.914, 95% CI : 1.367-209.224, P =0.028; OR =1.026, 95% CI : 1.010-1.042, P =0.001). Receiver operating curve analysis showed that heart rate>86.90 beat/min, QTc>431.50 ms, and RV5+SV1<1.72 mV had certain predictive value for FM diagnosis. Conclusions: FM patients displayed marked and severe ECG abnormalities, and characteristic changes in ECG can provide important first clues for the diagnosis of FM.

Objective: To investigate the value of implantable cardiac monitor (ICM) in the diagnosis and treatment of patients over 60 years old with unexplained syncope. Methods: This was a multi-center, prospective cohort study. Between June 2018 and April 2021, patients over the age of 60 with unexplained syncope at Beijing Hospital, Fuwai Hospital, Beijing Anzhen Hospital and Puren Hospital were enrolled. Patients were divided into 2 groups based on their decision to receive ICM implantation (implantation group and conventional follow-up group). The endpoint was the recurrence of syncope and cardiogenic syncope as determined by positive cardiac arrhythmia events recorded at the ICM or diagnosed during routine follow-up. Kaplan-Meier survival analysis was used to compare the differences of cumulative diagnostic rate between the 2 groups. A multivariate Cox regression analysis was performed to determine independent predictors of diagnosis of cardiogenic syncope in patients with unexplained syncope. Results: A total of 198 patients with unexplained syncope, aged (72.9±8.25) years, were followed for 558.0 (296.0,877.0) d, including 98 males (49.5%). There were 100 (50.5%) patients in the implantation group and 98 (49.5%) in the conventional follow-up group. Compared with conventional follow-up group, patients in the implantation group were older, more likely to have comorbidities, had a higher proportion of first degree atrioventricular block indicated by baseline electrocardiogram, and had a lower body mass index (all P <0.05). During the follow-up period, positive cardiac arrhythmia events were recorded in 58 (58.0%) patients in the ICM group. The diagnosis rate (42.0% (42/100) vs. 4.1% (4/98), P <0.001) and the intervention rate (37.0% (37/100) vs. 2.0% (2/98), P <0.001) of cardiogenic syncope in the implantation group were higher than those in the conventional follow-up group (all P <0.001). Kaplan-Meier survival analysis showed that the cumulative diagnostic rate of cardiogenic syncope was significantly higher in the implantation group than in the traditional follow-up group ( HR =11.66, 95% CI 6.49-20.98, log-rank P <0.001). Multivariate analysis indicated that ICM implantation, previous atrial fibrillation, diabetes mellitus or first degree atrioventricular block in baseline electrocardiogram were independent predictors for cardiogenic syncope (all P <0.05). Conclusions: ICM implantation improves the diagnosis and intervention rates in patients with unexplained syncope, and increases diagnostic efficiency in patients with unexplained syncope.

In the extraction of fetal electrocardiogram (ECG) signal, due to the unicity of the scale of the U-Net same-level convolution encoder, the size and shape difference of the ECG characteristic wave between mother and fetus are ignored, and the time information of ECG signals is not used in the threshold learning process of the encoder's residual shrinkage module. In this paper, a method of extracting fetal ECG signal based on multi-scale residual shrinkage U-Net model is proposed. First, the Inception and time domain attention were introduced into the residual shrinkage module to enhance the multi-scale feature extraction ability of the same level convolution encoder and the utilization of the time domain information of fetal ECG signal. In order to maintain more local details of ECG waveform, the maximum pooling in U-Net was replaced by Softpool. Finally, the decoder composed of the residual module and up-sampling gradually generated fetal ECG signals. In this paper, clinical ECG signals were used for experiments. The final results showed that compared with other fetal ECG extraction algorithms, the method proposed in this paper could extract clearer fetal ECG signals. The sensitivity, positive predictive value, and F1 scores in the 2013 competition data set reached 93.33%, 99.36%, and 96.09%, respectively, indicating that this method can effectively extract fetal ECG signals and has certain application values for perinatal fetal health monitoring.

Objective: To develop an intelligent model for differential diagnosis of atrioventricular nodal re-entrant tachycardia (AVNRT) and atrioventricular re-entrant tachycardia (AVRT) using 12-lead wearable electrocardiogram devices., Methods: A total of 356 samples of 12-lead supraventricular tachycardia (SVT) electrocardiograms recorded by wearable devices were randomly divided into training and validation sets using 5-fold cross validation to establish the intelligent classification model, and 101 patients with the diagnosis of SVT undergoing electrophysiological studies and radiofrequency ablation from October, 2021 to March, 2023 were selected as the testing set. The changes in electrocardiogram parameters before and during induced tachycardia were compared. Based on multiscale deep neural network, an intelligent diagnosis model for classifying SVT mechanisms was constructed and validated. The 3-lead electrocardiogram signals from Ⅱ, Ⅲ, and Ⅴ 1 were extracted to build new classification models, whose diagnostic efficacy was compared with that of the 12-lead model., Results: Of the 101 patients with SVT in the testing set, 68 were diagnosed with AVNRT and 33 were diagnosed with AVRT by electrophysiological study. The pre-trained model achieved a high area under the precision-recall curve (0.9492) and F1 score (0.8195) for identifying AVNRT in the validation set. The total F1 scores of the lead Ⅱ, Ⅲ, Ⅴ 1 , 3-lead and 12-lead intelligent diagnostic models in the testing set were 0.5597, 0.6061, 0.3419, 0.6003 and 0.6136, respectively. Compared with the 12-lead classification model, the lead-Ⅲ model had a net reclassification index improvement of -0.029 ( P =0.878) and an integrated discrimination index improvement of -0.005 ( P =0.965)., Conclusion: The intelligent diagnostic model based on multiscale deep neural network using wearable electrocardiogram devices has an acceptable accuracy for classifying SVT mechanisms.

Cardiac three-dimensional electrophysiological labeling technology is the prerequisite and foundation of atrial fibrillation (AF) ablation surgery, and invasive labeling is the current clinical method, but there are many shortcomings such as large trauma, long procedure duration, and low success rate. In recent years, because of its non-invasive and convenient characteristics, ex vivo labeling has become a new direction for the development of electrophysiological labeling technology. With the rapid development of computer hardware and software as well as the accumulation of clinical database, the application of deep learning technology in electrocardiogram (ECG) data is becoming more extensive and has made great progress, which provides new ideas for the research of ex vivo cardiac mapping and intelligent labeling of AF substrates. This paper reviewed the research progress in the fields of ECG forward problem, ECG inverse problem, and the application of deep learning in AF labeling, discussed the problems of ex vivo intelligent labeling of AF substrates and the possible approaches to solve them, prospected the challenges and future directions for ex vivo cardiac electrophysiology labeling.

ST segment morphology is closely related to cardiovascular disease. It is used not only for characterizing different diseases, but also for predicting the severity of the disease. However, the short duration, low energy, variable morphology and interference from various noises make ST segment morphology classification a difficult task. In this paper, we address the problems of single feature extraction and low classification accuracy of ST segment morphology classification, and use the gradient of ST surface to improve the accuracy of ST segment morphology multi-classification. In this paper, we identify five ST segment morphologies: normal, upward-sloping elevation, arch-back elevation, horizontal depression, and arch-back depression. Firstly, we select an ST segment candidate segment according to the QRS wave group location and medical statistical law. Secondly, we extract ST segment area, mean value, difference with reference baseline, slope, and mean squared error features. In addition, the ST segment is converted into a surface, the gradient features of the ST surface are extracted, and the morphological features are formed into a feature vector. Finally, the support vector machine is used to classify the ST segment, and then the ST segment morphology is multi-classified. The MIT-Beth Israel Hospital Database (MITDB) and the European ST-T database (EDB) were used as data sources to validate the algorithm in this paper, and the results showed that the algorithm in this paper achieved an average recognition rate of 97.79% and 95.60%, respectively, in the process of ST segment recognition. Based on the results of this paper, it is expected that this method can be introduced in the clinical setting in the future to provide morphological guidance for the diagnosis of cardiovascular diseases in the clinic and improve the diagnostic efficiency.

Objective: To investigate the dynamic change of the secundum atrial septal defect (ASD) throughout the cardiac cycle, and assess its impact on occluder selection. Methods: This study retrospectively analyzed 35 patients with ASD who received electrocardiogram-gated coronary CT angiography (CCTA) throughout the cardiac cycle as well as interventional closure therapy in Fuwai Hospital from December 2016 to December 2019. The raw data were reconstructed into 20 phasic images of RR intervals (RRI) ranging from 0 to 95% in an increment of 5% and transmitted to a workstation for postprocessing. For each phase image, CT virtual endoscopy reconstruction technique (CTVE) was used to provide views of ASD. Axial sequence assisted CT volumetric measurement (CTAS) was used to calculate the maximum dimensions in axial planes (D a ) and in superior-inferior direction (D b ). Using a formula for converting circumference to diameter, the equivalent circle dimensions were calculated (D e , D e =minor axis+2 (major axis-minor axis)/3). Taking the data of 75% RRI phase, the patients were divided into D a75%RRI ≥D b75%RRI group and D a75%RRI b75%RR group. According to the postoperative chest X-ray, the waist diameter of the occluder in the left anterior oblique plain film was measured, and its correction value (CR-POD laoc ) was calculated with the correction formula. Scatter plots of the changes of the mean values of D a , D b and D e with the cardiac cycle were presented. The change and ratio of measured values of D a and D e at 35% and 75% RRI was calculated. The ratio of D e change to D a change in D a75%RRI ≥D b75%RRI group and D a75%RRI b75%RR group was calculated, respectively, and compared between groups. Pearson correlation analysis was used to explore the correlation between CR-POD lao c and D e35%RRI and D e75%RRI. Results: A total of 35 patients, aged (42.7±15.0) years, including 10 males, were included. Among 35 patients, 8 cases were divided into group D a75%RRI ≥D b75%RRI and 27 cases into group D a75%RRI b75%RRI . Both D a and D b regularly changed at each phase throughout the cardiac cycle. The D a changed significantly, with a maximum at phase of 35%-45% of RRI, and a minimum between phases of 90% and 0 of RRI. While the D b showed insignificant changes during phases of 10%-90% RRI, and increased at 0-10% of RRI, then reduced in the remaining phases. The change of D a was (6.35±2.46) mm, and the ratio of D a values at 35% and 75% RRI was 0.77±0.08. The change of D e was (2.28±1.32) mm, and the ratio of D e at 35% and 75% RRI was 0.93±0.05. The ratio of D e change to D a change in the D a75%RRI ≥D b75%RRI group was 0.67±0.13; while the ratio was 0.34±0.05 in D a75%RRI b75%RR group, and there was significant difference between the two groups ( P =0.02). CR-POD lao c was positively correlated with D e35% RRI ( r =0.99, P <0.001) and D e75% RRI ( r =0.98, P <0.001). Conclusions: Most secundum ASDs show oval shape with D b >D a . D b is basically constant while D a changes significantly during cardiac cycle (10%-90% RRI). Nonetheless, both values peak and maintain the maximum status at end-systolic phase (35%-45% RRI). For patients with huge ASD, occluder selection should be based on the D e at 35% RRI phase, which is helpful for the successful intervention.

Objective: To explore the feasibility, safety and effectiveness of left bundle branch area pacing (LBBAP) in children aged ≤3 years. Methods: A total of 10 children aged ≤3 years who were diagnosed with brady arrhythmia in the First Hospital of Tsinghua University from September 2020 to September 2021 were retrospectively analyzed. All the children met the indication of permanent pacemaker implantation and underwent LBBAP successfully. The intraoperative data (pacing parameters, electrocardiogram and radiographic imaging), cardiac ultrasound data and clinical data during regular postoperative follow-up were recorded. The preoperative and postoperative data were compared using matched samples t test. Results: Ten children (aged (1.6±0.7) years with weight of (10.3±2.5) kg) underwent LBBAP successfully. The QRS wave duration on the postoperative electrocardiogram was (100±9) ms, and the percentage of ventricular pacing was (97±7)%. The postoperative follow-up period was 6 (6, 12) months. At 1 week after operation, the left ventricular end-diastolic diameter Z scores in these children reduced significantly compared with those before operation (1.3±0.6 vs . 3.6±1.1, t =9.37, P <0.001). During the follow-up period, cardiac function was normal and the last left ventricular ejection fraction was (66±4)% in all children. At the last follow-up, the pacing threshold of the 10 children was smaller than 1.0 V and was acceptable. Compared with the intraoperative baseline values, the pacing threshold was slightly higher ((0.8±0.1) vs . (0.5±0.1) V, t =-5.27, P =0.001). However, no significant difference was found regarding sensing threshold ((16±5) vs . (14±4) mV, t =-0.83, P =0.426) and impedance ((584±88) vs . (652±86) Ω, t =2.26, P =0.050). During follow-up, no electrode related complications were recorded. Conclusions: LBBAP is safe and effective for infants and toddlers. Narrow QRS pacing with stable pacing parameters and normal cardiac function could be achieved postoperatively.

The judgment of the type of arrhythmia is the key to the prevention and diagnosis of early cardiovascular disease. Therefore, electrocardiogram (ECG) analysis has been widely used as an important basis for doctors to diagnose. However, due to the large differences in ECG signal morphology among different patients and the unbalanced distribution of categories, the existing automatic detection algorithms for arrhythmias have certain difficulties in the identification process. This paper designs a variable scale fusion network model for automatic recognition of heart rhythm types. In this study, a variable-scale fusion network model was proposed for automatic identification of heart rhythm types. The improved ECG generation network (EGAN) module was used to solve the imbalance of ECG data, and the ECG signal was reproduced in two dimensions in the form of gray recurrence plot (GRP) and spectrogram. Combined with the branching structure of the model, the automatic classification of variable-length heart beats was realized. The results of the study were verified by the Massachusetts institute of technology and Beth Israel hospital (MIT-BIH) arrhythmia database, which distinguished eight heart rhythm types. The average accuracy rate reached 99.36%, and the sensitivity and specificity were 96.11% and 99.84%, respectively. In conclusion, it is expected that this method can be used for clinical auxiliary diagnosis and smart wearable devices in the future.

Objective: To investigate the efficacy and safety of left bundle branch pacing(LBBP) in patients after transcatheter aortic valve implantation (TAVI). Methods: This is a retrospective study. A total of 35 patients underwent TAVI and received pacemaker implantation from January 2018 to December 2020 in Beijing Fuwai Hospital were enrolled. Patients were divided into LBBP group ( n =12) and right ventricular apex pacing (RVAP) group ( n =23) according to the pacing position. The success rate of operation in LBBP group was calculated, and the occurrence of complications were observed, and the parameters of pacemaker were measured on the 3rd day and 1, 3 and 6 months after operation. The N-terminal pro-B-type natriuretic peptide (NT-proBNP), echocardiographic and ECG indexes were compared between the two groups on the 3rd day and 1, 3, and 6 months after pacemaker implantation. Result: A total of 35 patients were included, The age was (76.4±7.7) years, including 19 males (54.3%). The procedure time ((86.58±17.10)min vs. (68.74±9.18)min, P <0.001) and fluoroscopy duration ((20.08±4.44)min vs. (17.00±2.26)min, P <0.001) were significantly longer in LBBP group compared with RVAP group. The operation success rate of LBBP group was 11/12. There was no serious operation related complications such as pneumothorax, hemothorax, electrode dislocation, infection, and lower limb bleeding. The patients were followed up for 7.43 (5.21, 9.84) months. The programmed parameters of pacemaker were in the ideal range and stable during follow-up. At 3 and 6 months after operation, the left ventricular ejection fraction in LBBP group was higher than that in RVAP Group (at 3 months: (60.75±2.89)% vs. (57.35±3.33)%, P =0.004; at 6 months: (63.17±3.33)% vs. (56.17±3.97)%, P <0.001), NT-proBNP values was lower in LBBP group than that in RVAP Group (at 3 months: 822 (607, 1 150)ng/L vs. 1 052 (902, 1 536)ng/L, P =0.006; at 6 months: 440 (330,679)ng/L vs. 783 (588, 1 023)ng/L, P =0.001). At 1, 3 and 6 months after operation, the QRS duration was shorter in LBBP group than that in RVAP group (1 month: 99 (97, 107)ms vs. 126(124, 130)ms, P <0.001; 3 months: 98(96, 105)ms vs. 129(128, 133)ms, P <0.001; 6 months: 96(94, 104)ms vs. 130(128, 132)ms, P <0.001). Conclusions: For patients with permanent pacemaker indications after TAVI, LBBP is feasible, safe and reliable. It could improve the cardiac function in the short term, the long-term effect of LBBP needs to be further observed.

Objective: To train convolutional networks using multi-lead ECG data and classify new data accurately to provide reliable information for clinical diagnosis., Methods: The data were pre-processed with a bandpass filter, and signal framing was adopted to adjust the data of different lengths to the same size to facilitate network training and prediction. The dataset was expanded by increasing the sample size to improve the detection rate of abnormal samples. A depth-wise separable convolution structure was used for more specific feature extraction for different channels of twelve-lead ECG data. We trained the two classifiers for each label using the improved DenseNet to classify different labels., Results: The propose model showed an accuracy of 80.13% for distinguishing between normal and abnormal ECG with a sensitivity of 80.38%, a specificity of 79.91% and a F1 score of 79.35%., Conclusions: The model proposed herein can rapidly and effectively classify the ECG data. The running time of a single dataset on GPU is 33.59 ms, which allows real-time prediction to meet the clinical requirements.

Arrhythmia is a kind of common cardiac electrical activity abnormalities. Heartbeats classification based on electrocardiogram(ECG) is of great significance for clinical diagnosis of arrhythmia. This paper proposes a feature extraction method based on manifold learning, neighborhood preserving embedding(NPE) algorithm, to achieve the automatic classification of arrhythmia heartbeats. With classification system, we obtained low dimensional manifold structure features of high dimensional ECG signals by NPE algorithm, then we inputted the feature vectors into support vector machine(SVM) classifier for heartbeats diagnosis. Based on MIT-BIH arrhythmia database, we clustered 14 classes of arrhythmia heartbeats in the experiment, which yielded a high overall classification accuracy of 98.51%. Experimental result showed that the proposed method was an effective classification method for arrhythmia heartbeats.

Objective: To investigate the value of deceleration capacity of rate (DC) and heart rate deceleration runs(DRs) in predicting cardiovascular events in patient with acute myocardial infarction (AMI)., Methods: This study included 166 patients with AMI, who underwent ECG with sinus rhythm.These patients were followed-up for major adverse cardiac events (MACE). The receiver operating characteristic curve (ROC) was drawn to determine the best values for estimating the MACE., Results: The mean follow-up time was (20.5±2.8) months, with 13 cases of cardiac death.There was statistically significant difference of DC, DRs and standard diviation of NN intervals(SDNN-24) between the death group and survival group.The area under the curve (AUC) of DC, DR4 and DR8 were larger than SDNN-24 (0.874, 0.804 vs 0.727). The values of DC, DR2, DR4 and root mean square of the successive differences(RMSSD) in the group of patients who underwent cardiac adverse events were smaller than the group of patients who didn't, and the AUC of DC was slightly higher than that of RMSSD., Conclusion: DC and DRs have important predictive value for cardiac death and MACE and can screen high-risk patients in patients with AMI.

Parsed the ECG descriptions in DICOM 3.0 standard and accomplished a DICOM-ECG file which conforms to the DICOM standard by a toolkit DCMTK. The DICOM-ECG file can communicate with systems which support DICOM standard directly.

The diagnosis of sleep apnea syndrome (SAS) has a significant importance in clinic for preventing diseases of hypertention, coronary heart disease, arrhythmia and cerebrovascular disorder, etc. This study presents a novel method for SAS detection based on single-channel electrocardiogram (ECG) signal. The method preprocessed ECG and detected QRS waves to get RR signal and ECG-derived respiratory (EDR) signal. Then 40 time- and spectral-domain features were extracted to normalize the signals. After that support vector machine (SVM) was used to classify the signals as "apnea" or "normal". Finally, the performance of the method was evaluated by the MIT-BIH Apnea-ECG database, and an accuracy of 95% in train sets and an accuracy of 88% in test sets were achieved.

A fast and accurate intelligent identification is the developing trend of electrocardiogram (ECG) research. However, there are few methods by which satisfactory results could be obtained both in speed and in accuracy. A fast identification method of QRS wave was proposed based on moving window operation in this study. An 80 ms wide moving window was employed, in which simple difference and product operations were carried out with simple and less computation, and a very good inhibition of P and T waves and other noises was realized. Then the method was investigated with data from the MIT-BIH arrhythmia database with absence of digital filtering de-noising, and the identification accuracy of QRS complex reached 99.6%. The results showed that a rapid and accurate identification of QRS complex could be realized, which would meet the requirements for studying the real-time ECG monitoring equipment.

To treat the problem of identification performance and the complexity of the algorithm, we proposed a piecewise linear representation and dynamic time warping (PLR-DTW) method for ECG biometric identification. Firstly we detected R peaks to get the heartbeats after denoising preprocessing. Then we used the PLR method to keep important information of an ECG signal segment while reducing the data dimension at the same time. The improved DTW method was used for similarity measurements between the test data and the templates. The performance evaluation was carried out on the two ECG databases: PTB and MIT-BIH. The analystic results showed that compared to the discrete wavelet transform method, the proposed PLR-DTW method achieved a higher accuracy rate which is nearly 8% of rising, and saved about 30% operation time, and this demonstrated that the proposed method could provide a better performance.

The high-pass filter which is designed to overcome the polarization voltage in the ECG measurements can affect the measurement of the ST-segment; but the accuracy of the ST-segment will have an impact on the diagnosis and treatment of heart diseases. This paper used the ECG detection platform to test the effect of the high-pass filter at different frequency and studied the effect of the high-pass filter on the ST segment to ensure proper use of different application modes.

T-wave alternans (TWA) refers to a phenomenon appearing in the surface electrocardiograph (ECG) as a consistent fluctuation in morphology and amplitude of the T wave on an "every-other-beat" basis. Correlation method (CM) has a certain ability to detect the non-stationary TWA, but it is very sensitive to noise. In this paper we propose a modified correlation method to ensure a stable and accurate detection of non-stationary TWA. Compared to the CM, the method modifies the judge condition and uses the linear fitting to limit the noise to gain the ability of detecting of non-stationary TWA. Our simulation and clinical data assessment study demonstrates the improved performance of the proposed algorithm.

This study designed an audio signal acquisition system to achieve real-time detection of R peak for heart rate variability (HRV) analysis, substituting ECG-specific data acquisition board with sound card. It is proved that the R peak with low frequency can be gathered by sound card. The comparison with PowerLab signal acquisition system has shown that the acquisition system of R peak could accurately detect the R peak. The R peak detected by sound card and PowerLab signal acquisition system completely overlap each other, while RR intervals has no difference. The HRV analysis was accurate and reliable with the RR interval from sound card. This low cost, high performance, strong compatibility and easy upgraded acquisition system of R peak compared with the traditional data acquisition board has the value of practical uses and might have broad applications in the future.

Objective: To explore the value of identifying slow conduction zone(SCZ) of idiopathic left ventricular tachycardia(ILVT) by electroanatomic mapping., Methods: Twelve patients with ILVT were mapped by a 3-dimensional electroanatomic (EA) mapping system. Left posterior fascicular potential (PP) and the SCZ with diastolic potential (DP) in LV during sinus rhythm (SR) and ventricular tachycardia (VT) were mapped after a three-dimensional endocardial geometry of the left ventricular was established. Then we investigated the electrophysiological and anatomic characteristics of SCZ., Results: EA mapping was successfully performed in 9 patients during SR and VT, and in 3 patients during VT. The SCZ with DP was located at the inferoposterior septum, and the length of the SCZ was (25.1 ± 2.2) mm with a conduction velocity of (0.08 ± 0.01) m/s. There was no difference in these parameters between patients during SR and VT (P > 0.05). There was one area with PP located at the posterior septum. The areas with both DP and PP were found in 9 patients during SR and VT. In addition, this area was coincided with such area during VT during SR and radiofrequency ablation targeting the site within the area abolished VT in all patients., Conclusions: The ILVT substrate within the junction area of the SCZ and the posterior fascicular can be identified by EA mapping and used to guide the ablation of ILVT.

Non-contact measurement is an effective method of long time measurement of human electrocardiograph (ECG) signal. Because the relative position between measuring electrode and human body is not fixed, this method could result in constant changes of ECG signal collection. It often appears ECG signal distorting and weakened in filtering. This paper, using the principal component analysis (PCA) basic theory, proposes a fast adaptive PCA denoising algorithm which can automatically adjust the parameters according to the changes of ECG signal. The experiment proved that PCA denoising could be barely impacted by signal changes and can disposably remove interference signal on the premise of keeping the main features of ECG signal and can prevent ECG signal from being weakened in filtering at the same time.

This paper presents a myocardial infarction feature extraction algorithm, and whereby using single-cycle signal of electrocardiogram (ECG) lead I, we solved the problem of inconvenience in using multi-lead ECG. Firstly, the noise is eliminated from the signal of ECG lead I, and then, wavelet packet algorithm is introduced to extract the main frequency band of QRS complex and T wave, and reconstruct the QRS complex and T wave shape so as to determine the original and end positions of ST segment. Finally, ST segment is decomposed and the waveform characteristics of myocardial infarction from ECG lead I signal is obtained based on the analysis of wavelet's multi-resolution. Simulation results showed that the algorithm achieved a high recognition rate, which provides fundamental principle for the detection of myocardial infarction by using the ECG lead I signal.

The purpose of this study was to explore the clinical value of ECG-gated dual-source CT angiography (DSCTA) in evaluating coarctation of aorta (CoA). 23 patients suspected with CoA underwent DSCTA and transthoracic echocardiography (TTE). Surgical results were taken as reference standard. 23 patients were diagnosed with CoA, 12 cases had focal stenosis of aorta, while 11 had tubular stenosis. 19 cases were associated with other cardiovascular malformations, including 5 cases of hypoplastic aortic arch, 5 of patent ductus arteriosus, 10 of ventricular septal defect, 2 of bicuspid aortic valve, 4 of collateral arteries, 2 of aberrant left subclavian artery and 2 of persistent left superior vena cava, respectively. The mean diameter of stenotic segment of aorta was (7.0 +/- 3.7) mm. In 9 patients with tubular CoA, the diameter and length of stenotic segment of aorta were (6.2 +/- 7. 3) mm and (29.2 +/- 5.9) mm, respectively. The accuracies of DSCTA and TTE in the diagnosis of CoA were 100% and 91.3% (P > 0.05), respectively. It is well concluded that ECG-gated DSCTA could accurately evaluate CoA and associated cardiovascular malformations, and provide detailed anatomic information before surgery.

Diaphragmatic electromyographic (EMGdi) signal is a weak biological signal, which contains some significant physiological information of our body respiration system and is susceptible to strong electrocardiography (ECG) signal interference. Based on wavelet transform and theory of information entropy, a new wavelet energy entropy threshold algorithm to remove ECG interference is proposed in this paper. On the base of analysis of wavelet coefficients of each scale, the method sees the information of each scale as a single signal source, equalizes it byzones, and then divides the energy entropy into two categories (i. e., high energy entropy and low energy entropy) through the distribution characteristics of energy entropy of each zone to conduct absolute mean value threshold. In addition, the denoised signal is reconstructed by wavelet coefficients processed. The experimental results showed that the method removed the ECG signal in EMGdi effectively and reserved the available characteristics of EMGdi better.

This paper introduces an algorithm for detecting T wave alternans (TWA) using Poincare mapping method, which is a technique for nonlinear dynamic systems to display periodic behavior. Vector angle index (VAI) was used to determine the presence or absence of TWA. Adopting MIT/BIH Arrhythmia database and European ECG ST-T database to simulate, we used VAI via poincare mapping method for correlation analysis with Vtwa by way of the spectral method. The cross-correlation coefficient between Vtwa and VAI is gamma = 0.860 1. The algorithm can identify the absence and presence of TWA accurately and provide idea for further study of TWA.

T wave alternans in ECG is an important prediction factor for a sudden death. It is crucial in clinic to quantify the amplitude of T wave alternans. However, T wave alternans are highly non-stationary, which makes accurate quantification very difficult. In this study, we proposed a new method to improve the amplitude quantification. We identified T wave alternans by principal component analysis (PCA) with statistical test, and processed the principal components with T wave alternans further by imposing sparse constraint. We evaluated and compared our method against other 4 popular solutions on an international benchmark database. The results including rank correlation coefficient and relative error indicate that this sparse principal component (SPC) based method is better than others.

A heart rate variability (HRV)analysis system is presented. The change of the activity of Sympathetic and vagus nerve is evaluated by detecting the parameters of heart rate variability of the tested in different states using the system. The system provides a tool for the study of heart rate variability.

Objective: Therapy effects and cardiac electrical activity comparison of bone marrow stem cells (BMSCs) transplantation and sarcoplasmic reticulum Ca(2+)-ATPase (SERCA2a) gene modified BMSCs transplantation after acute myocardial infarction (AMI) in rats., Methods: Rats with AMI were divided into 4 groups (n = 30) randomly: normal group (n = 6), saline group (control group, n = 8), BMSCs transplantation group (BMSCs group, n = 8) and SERCA2a gene modified BMSCs transplantation group (BMSCs + rAd.SERCA2a group, n = 8). After 14 days, cardiac function was evaluated by echocardiography and heart electrical activity was evaluated by electrocardiogram and microelectrode array (MEA) technology., Results: (1) The transduction ratio of rAd.SERCA2a to BMSCs were 80% to 90%. (2) Left ventricular ejection fraction on 14 days after therapy was significantly higher in BMSCs group and BMSCs + rAd.SERCA2a group than in control group (all P < 0.05). (3) QT duration was significantly shorter [(80.30 ± 6.53) ms vs. (105.31 ± 21.89) ms, P < 0.05] and ventricular premature beats less frequent in BMSCs + rAd. SERCA2a group than in the control group. (4) MEA results suggested that isolated heart beat was significantly slowed down and frequent ventricular arrhythmias and atrioventricular block were recorded in control group. The maximum field potential and field potential duration on infarcted myocardium area in BMSCs group and BMSCs + rAd.SERCA2a group were significantly longer than those in control group[the maximum field potential: (0.51 ± 0.15), (0.55 ± 0.16), (0.23 ± 0.10) mV; field potential duration: (104.5 ± 25.43), (107.67 ± 24.01), (63.00 ± 20.34) ms; all P < 0.05]. (5) The conduction time was the shortest and the cardiac electrical conduction consistency in myocardial infarction tissue was significantly improved in BMSCs + rAd.SERCA2a group., Conclusions: BMSCs and SERCA2a gene modified BMSCs transplantation could significantly improve cardiac function and BMSCs + rAd.SERCA2a could also effectively improve electrical conduction of infarcted myocardium and attenuate the incidence of arrhythmia after myocardial infarction in rats.

To explore the energy proportion of the QRS complexes in the electrocardiogram (ECG), a new method, wavelet analysis, has been presented. Using wavelet transform of ECG, we can calculate the energy proportion of QRS complexes in multiple scales. The electrocardiograms (ECGs) were used as the experimental data, which were collected from a young (21-34 yr) group and an elderly (68-81 yr) group of healthy subjects, as well as from a group of arrhythmia patients (66-81 yr). the data analysis was performed with the energy proportion of the QRS complexes in the ECG using Mexican-Hat as a mother wavelet in multiple scales. Results showed that the energy proportion of the QRS complexes had no changes with ages increasing (P > 0.44), but in the same age group, the arrhythmia patients' energy proportion of the QRS complexes near 17Hz are obviously less than that in the healthy group (P < 0.01), so the energy proportion of the QRS complexes calculated by wavelet analysis can be used as a feature index to judge whether a person is a sinus arrhythmia patient or not.

Objective: To investigate the value of QT hysteresis index during treadmill exercise test (TET) in diagnosing coronary heart disease (CHD)., Methods: One hundred consecutive patients suspected for CHD were referred for TET and selective coronary angiography (CAG). Patients were divided into positive [n = 55, age (56.0 ± 7.9) years] and negative [n = 45, age (53.2 ± 6.7) years] group based on their CAG results. For each TET recording, 50 points were selected for the RR, QTp, and QTe interval measurements. QTp and QTe interval was plotted against corresponding RR interval. QT/RR curve was constructed by connect all point, QT hysteresis index was calculated for each patient., Results: The QTp [(22.4 ± 10.3) ms vs. (6.7 ± 4.6) ms, P < 0.001] and QTe [(27.1 ± 11.1) ms vs. (7.6 ± 4.6) ms, P < 0.001] hysteresis index of patients in positive group were significantly higher than those in negative group. The sensitivity of QTp and QTe hysteresis index for diagnosing CHD was 89.1% (49/55) and 94.5% (52/55), respectively, and the specificity was 82.2% (37/45) and 80.0% (36/45), respectively. If the patient fulfilled both the classical TET and QT hysteresis criteria, the sensitivity for diagnosing CHD increased to 94.3% (33/35, QTp) and 94.6% (35/37, QTe), and the specificity were both 100% (26/26, 26/26). Moreover, QTp (r = -0.399, P < 0.001) and QTe (r = -0.547, P < 0.001) hysteresis index highly correlated to Duke treadmill score., Conclusion: QT hysteresis index is useful parameter for CHD diagnosis and which could improve the diagnostic value of TET for CHD in combination with the classical TET criteria for diagnosis of CHD.

Objective: To examine the efficacy of fresh blood imaging (FBI) in detecting vascular diseases in lower extremity., Methods: Thirty-six patients suspected of having lower extremity vascular diseases were imaged with a 1.5-T MRI system (Toshiba Excelart Vantage). Contrast-enhanced MRA (CEMRA) and FBI technology, with maximum intensity projection (MIP) reconstruction were adopted to visualize lower extremity vascular. Signal to noise ratios (SNR) were measured on the FBI and CEMRA images. Two experienced radiologists assessed the imaging quality of peripheral artery MRA on MIP reconstructed images., Results: All patients successfully underwent both FBI and CEMRA. All arterial segments were obtained in the 36 patients. The SNR values on FBI and CEMRA were 108.39 +/- 9.76 and 87.46 +/- 14.77 (t = - 6.782, P = 0.001), respectively. There were no significant differences in the overall image quality, arterial anatomy and venous overlap (chi2 = 0.004, P = 0.947; chi2 = 0.000, P = 1; chi2 = 0.681, P = 0.409). The CEMRA motion artifacts were less than FBI (chi2 = 8.744, P = 0.03)., Conclusion: The FBI technique, which shows the vascular disease in lower extremity without contrast medium with ECG gating, is considered clinically useful.

A QRS complex detection algorithm based on empirical mode decomposition (EMD) and adaptive windowing technique is proposed in this paper. In this algorithm we mainly used Hilbert-Huang transform to propose EMD method suitable for QRS complex detection, with which the 30th signal in sddb database and the 208th signal in mit-db database could be processed, and then obtained R wave detection results. At the same time, Q and S points' detection technique was analyzed with adaptive windowing technique. The detection results, through proceeding R wave detection on part data of MIT/BIT arrhythmia database, showed that the proposed algorithm in this paper had a very good detection effect, and that its average correct detection rate of R wave reached 99.62%, its average sensitivity of QRS complex was 98.91%, and the corresponding average specificity was 99.35%.

Objective: The prognostic value of corrected QT interval (QTc), corrected Tp-e interval (Tp-ec) and Tp-e/QT ratio on occurrence of malignant arrhythmia events (MAE) in acute ST-segment elevation myocardial infarction (STEMI) patients underwent successful thrombolysis was explored and the potential association of these indices with MAE was analyzed., Methods: Fifty-seven STEMI patients underwent successful thrombolytic therapy within 6 hours after admission and conservative medical treatment were included. QTc, Tp-ec, Tp-e/QT ratio were obtained and calculated in infarct-related electrocardiograph leads and non-infarct-related leads before thrombolysis, (7±1) days and (30±3) days after thrombolysis respectively, and incidence of MAE up to 30 days after thrombolysis was analyzed. Sixty age and gender matched normal subjects served as control group., Results: (1) QTc, Tp-ec, Tp-e/QT in infarct-related and non-infarct-related leads in STEMI group before thrombolysis were significantly higher than those in control group (all P<0.05), and values from the infarct-related leads were significantly higher than those from non-infarct-related leads in STEMI group (all P<0.05). QTc, Tp-ec and Tp-e/QT all significantly and continuously reduced from 7 days and at 30 days post thrombolysis compared the before thrombolysis (P<0.05 vs. before thrombolysis). (2) Tp-ec≥100 ms and Tp-e/QT ratio≥0.25 before thrombolysis in infarct-related leads were linked with higher incidence of MAE within 30 days post thrombolysis in this patient cohort [28.1% (9/32) vs. 40% (1/25), 27.8% (10/36) vs.0, respectively, all P<0.05]., Conclusion: QTc, Tp-ec and Tp-e/QT values decreased post successful thrombolysis in STEMI patients and higher Tp-ec and Tp-e/QT values before thrombolysis in STEMI patients were related with higher MAE incidence up to 30 days post successful thrombolysis in this patient cohort.

Baseline wander (BW) is a common noise contaminating electrocardiogram (ECG). In order to effectively correct baseline of ECG signal and to preserve more latent components of ECG signal, this paper proposes a simple and novel filter based on statistically weighted moving average. Firstly, after the arrange between the maximum and the minimum of these sampling values in a moving window was divided into many sections with equal width, several segments [a(k), b(k)] including most samples were determined. Then, for every sample point in the moving window its weight was decided according to the criterion: its weight was set as 1 if the sampling value belongs to [a(k), b(k)], otherwise, as 0. Lastly, all these ECG sampling points with 1 weight were averaged to estimate the real baseline in the moving window. The algorithm was tested by simulated signal and real signal from www. physionet. org. The results showed that compared to traditional moving average filter and wavelet package (WP) translation, the proposed filter could more effectively correct baseline in ECG signal and result in less distortion to ECG signal.

In this study, ECG signals of normal sinus rhythm (NSR), ventricular tachycardia (VT) and ventricular fibrillation (VF) were taken out to extract the RR interval series. Approximate entropy (ApEn) values of the RR interval series were calculated and compared among the three groups by ANOVA methods. It was found that the values of ApEn were increased significantly from NSR to VT and then to VF. Approximate entropy may be regarded as an index to discriminate the ECG signals of different state.

According to the independent component analysis (ICA) theory, in order to seperate the fetal electrocardiograms (FECG) from the observed data, we must use the larger number of observed signals than that of independent components. This requires that the number of the sentors on the electrocardiograph must be larger than a particular value, which is in practice hard to be satisfied. So we proposed another algorithm using fewer channels of abdominal electrocardiograms, which combines ICA and adaptive noise cancellation to extract the FECG from two leads of abdominal electrocardiograms. The experiment results showed that we could obtain clear FECG by the method we proposed.

Respiratory sinus arrhythmia (RSA) is known as fluctuations of heart rate associated with breathing. It has been increasingly used as a noninvasive index of cardiac vagal tone in psychophysiological research recently. Its analysis is often influenced or distorted by respiratory parameters, posture and action, etc. This paper reviews five methods of quantification, including the root mean square of successive differences (RMSSD), peak valley RSA (pvRSA), cosinor fitting, spectral analysis, and joint timing-frequency analysis (JTFA). Paced breathing, analysis of covariance, residua method and msRSA per liter tidal volume are adjustment strategies of measurement and analysis of RSA in this article as well. At last, some prospects of solutions of the problems of RSA research are given.

According to the frequency overlapping of intrinsic mode function (IMF) based on the temporal and spatial filtering of empirical mode decomposition (EMD), which will lead to the question of useful signals and noises filtered together, we proposed a method that numbers of IMF is determined by energy estimate, temporal and spatial filtering combing wavelet threshold and EMD integrating wavelet local signal characteristics of time and scale domain. This method not only used multi-resolution wavelet transform features, but also combined the EMD and Hilbert decomposition of the adaptive spectral analysis of instantaneous frequency and significance of the relationship between energy, so as to solve the problem of useful signal being weakened. With MIT/BIH ECG database standard data subjects, experimental results showed it was an effective method of data processing for handling this type of physiological signals under strong noise.

ST-segment is the main clinical appearance in myocardial ischemia detection based on electrocardiogram (ECG) signals. However, it is highly sensitive to interferences (baseline wandering, postural changes, electrode interference, etc.), which cause the feature points of ECG ST-segment to be difficult to detect accurately. Currently, the common detection methods of ST-segment are: R+x and J+x, but they are affected badly by T-wave morphological variability and J point location. For these reasons, firstly we proposed a convenient and accurate approach for T-wave onset in this paper. It did not need to locate T-wave peak and was robust to baseline wandering and T-wave morphology. Secondly, we proposed a squeeze approach for ST-segment detection based on R-wave peak and T-wave onset. After the Long-Term ST database (LTST) verification, the proposed method has shown a good timeliness and robustness, and the accuracy of ST-segment detection has reached above 92%.

Objective: To evaluate the role of intracoronary electrocardiogram (IcECG) in examining early myocardial injury during percutaneous coronary intervention (PCI)., Methods: Eight-six patients who had undergone elective PCI for their coronary heart disease were enrolled in the study. The IcECG both at baseline and after procedure were recorded with an incoronary guidewire and the serum levels of cardiac troponin T (cTnT) and creatine kinase-myoglobin were measured at baseline and 8 and 24 hours after intervention. Myocardial damage was defined as serum levels of cTnT increase above the upper normal value after intervention. Cardiac events after intervention was followed up., Results: Of all these 86 patients with normal serum levels of cardiac markers before the procedure, significant shift at ST-segment in IcECG during PCI was observed in 30 patients (35%, abnormal group) and no shift in the remaining 56 patients (65%, control group). All the procedures were successful. Serum levels of cTnT and creatine kinase-myoglobin were significantly higher in abnormal group than in control group after intervention (P < 0.01). The intracoronary ST-segment shift had a sensitivity of 77% and a specificity of 94% in predicting myocardial injury, with positive and negative predictive values of 90% and 86%, respectively. More cardiac events were observed in abnormal group than those in control group at a 4-week follow-up after intervention (P < 0.05) and major coronary event-free survival was significantly lower in those with post-procedural ST-segment shift in the IcECG (P < 0.05)., Conclusion: IcECG may be a useful method for predicting myocardial injuries during PCI.

In this paper, wavelet moudulus maxima based multifractal analysis was used to study the multifractal characteristics of the atrial premature beat (APB) signal, the premature ventricular contraction (PVC) signal and normal ECG signal. By analyzing the multifractal spectrum, it was obtained that three kinds of signals had different multifractal strengths. Normal ECG signals had the strongest singularity strength. The PVC beats had the second stronger singularity strength. And the APB beats had the weakest singularity strength. The T test indicated that above-mentioned analysis could disclose significant differences among these three signals. It has meaningful reference for clinical diagnosing and distinguishing with PVC and APB signals.

The real-time and wireless mobile has become the trend of electrocardiogram (ECG) monitoring system for atrial fibrillation (AF). At present, the ECG with multi-leads (12 leads) is needed by most of AF signal extraction algorithms in order to extract effective AF waves. However, it is not very convenient for patients' movements in a multi-lead ECG monitoring system. Although the traditional template matching method is for single-lead ECG extraction, it is less robust than blind source extraction algorithm, and is affected severely by noise. In view of this,we put forth a new real-time algorithm for extracting AF from the singlelead ECG, using non-stationary heartbeat series during AF to extend dimension (segmentation), and then applying a blind source extraction algorithm to extract the effective AF signal. Experiment results showed that this method could be used to extract AF signal effectively from a single-lead ECG data. Therefore, it is suitable to apply this method to Wireless Monitoring System using single-lead ECG.

Fetal electrocardiogram (FECG) is an objective index of the activities of fetal cardiac electrophysiology. The acquired FECG is interfered by maternal electrocardiogram (MECG). How to extract the fetus ECG quickly and effectively has become an important research topic. During the non-invasive FECG extraction algorithms, independent component analysis(ICA) algorithm is considered as the best method, but the existing algorithms of obtaining the decomposition of the convergence properties of the matrix do not work effectively. Quantum particle swarm optimization (QPSO) is an intelligent optimization algorithm converging in the global. In order to extract the FECG signal effectively and quickly, we propose a method combining ICA and QPSO. The results show that this approach can extract the useful signal more clearly and accurately than other non-invasive methods.