This paper suggests a new approach for cardiac source localization of origin of arrhythmias using only the 12-lead ECG by means of CNN, and may have important applications for future real-time monitoring and localizing origins of cardiac arrhythmias guiding ablation treatment.

The aim of this study is to develop and evaluate a novel imaging method (SSF, Spatial gradient Sparse in Frequency domain) for the reconstruction of activation sequences of ventricular arrhythmia from noninvasive body surface potential map (BSPM) measurements. We formulated and solved the electrocardiographic inverse problem in the frequency domain, and the activation time was encoded in the phase information of the imaging solution. A cellular automaton heart model was used to generate focal ventricular tachycardia (VT). Different levels of Gaussian white noise were added to simulate noise-contaminated BSPM. The performance of SSF was compared with that of WMN (weighted minimum norm) inverse solution. We also evaluated the method in a swine model with simultaneous intracardiac and body surface recordings. Four reentrant VTs were observed in pigs with myocardial infarction generated by left anterior descending artery occlusion. The imaged activation sequences of reentrant VTs were compared with those obtained from intracardiac unipolar electrograms. In focal VT simulation, SSF increased the correlation coefficient (CC) by 5% and decreased localization errors (LEs) by 2.7 mm on average under different noise levels. In the animal validation with reentrant VT, SSF achieved an average CC of 88% and an average LE of 6.3 mm in localizing the earliest and latest activation site in the reentry circuit. Our promising results suggest SSF provides noninvasive imaging capability of detecting and mapping macro-reentrant circuits in 3-dimensional ventricular space. SSF may become a useful imaging tool of identifying and localizing the potential targets for ablation of focal and reentrant ventricular tachycardia.

We propose a new approach to noninvasively image the 3-D myocardial infarction (MI) substrates based on equivalent current density (ECD) distribution that is estimated from the body surface potential maps (BSPMs) during S-T segment. The MI substrates were identified using a predefined threshold of ECD. Computer simulations were performed to assess the performance with respect to: 1) MI locations; 2) MI sizes; 3) measurement noise; 4) numbers of BSPM electrodes; and 5) volume conductor modeling errors. A total of 114 sites of transmural infarctions, 91 sites of epicardial infarctions, and 36 sites of endocardial infarctions were simulated. The simulation results show that: 1) Under 205 electrodes and 10-μV noise, the averaged accuracies of imaging transmural MI are 83.4% for sensitivity, 82.2% for specificity, 65.0% for Dice's coefficient, and 6.5 mm for distances between the centers of gravity (DCG). 2) For epicardial infarction, the averaged imaging accuracies are 81.6% for sensitivity, 75.8% for specificity, 45.3% for Dice's coefficient, and 7.5 mm for DCG; while for endocardial infarction, the imaging accuracies are 80.0% for sensitivity, 77.0% for specificity, 39.2% for Dice's coefficient, and 10.4 mm for DCG. 3) A reasonably good imaging performance was obtained under higher noise levels, fewer BSPM electrodes, and mild volume conductor modeling errors. The present results suggest that this method has the potential to aid in the clinical identification of the MI substrates.

DNA extractionof −80◦C stored leaves by CTAB.

The large number of chemical and pharmaceutical patents has attracted researchers doing biomedical text mining to extract valuable information such as chemicals, genes and gene products. To facilitate gene and gene product annotations in patents, BioCreative V.5 organized a gene- and protein-related object (GPRO) recognition task, in which participants were assigned to identify GPRO mentions and determine whether they could be linked to their unique biological database records. In this paper, we describe the system constructed for this task. Our system is based on two different NER approaches: the statistical-principle-based approach (SPBA) and conditional random fields (CRF). Therefore, we call our system SPBA-CRF. SPBA is an interpretable machine-learning framework for gene mention recognition. The predictions of SPBA are used as features for our CRF-based GPRO recognizer. The recognizer was developed for identifying chemical mentions in patents, and we adapted it for GPRO recognition. In the BioCreative V.5 GPRO recognition task, SPBA-CRF obtained an F-score of 73.73% on the evaluation metric of GPRO type 1 and an F-score of 78.66% on the evaluation metric of combining GPRO types 1 and 2. Our results show that SPBA trained on an external NER dataset can perform reasonably well on the partial match evaluation metric. Furthermore, SPBA can significantly improve performance of the CRF-based recognizer trained on the GPRO dataset.

Objective: To develop an automatic algorithm to detect strict left bundle branch block (LBBB) on electrocardiograms (ECG) and propose a procedure to test the consistency of neural network detections. Approach: The database for the classification of strict LBBB was provided by Telemetric and Holter ECG Warehouse. It contained 10 s ECGs taken from the MADIT-CRT clinical trial. The database was divided into a training dataset (N  =  300, strict LBBB  =  174, non-strict LBBB  =  126) and a test dataset (N  =  302, strict LBBB  =  156, non-strict LBBB  =  146). LBBB-related features were extracted by Philips DXL™ algorithm, selected by a random forest classifier, and fed into a 5-layer neural network (NN) for the classification of strict LBBB on the training dataset. The performance of NN on the test dataset was compared to two random forest classifiers, an algorithm applying strict LBBB criteria, a wavelet-based approach, and a support-vector-machine approach. The consistency of NN’s detection was tested on 549 2 min recordings of the PTB diagnostic ECG database. LBBB annotations are not required to measure consistency. Main results: The performance of NN on the test dataset were sensitivity  =  91. 7%, specificity  =  85.6% and accuracy  =  88.7% (PPV  =  87.2%, NPV  =  90.6%). The consistency score of strict-LBBB and non-strict-LBBB detection was 0.9341 and 0.9973 respectively. Conclusion: NN achieved the highest specificity, accuracy, and PPV. Using random forest for feature selection and NN for classification increased interpretability and reduced computational cost. The consistency test showed that NN achieved high consistency scores in the detection of strict LBBB. Significance: This work proposed an approach for selecting features and training NN for the detection of strict LBBB as well as a consistency test for black-box algorithms.

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