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Myocardial Infarction Detection and Localization with Electrocardiogram Based on Convolutional Neural Network

Chinese Journal of Electronics 2021年第5期30卷 833-842页

作者： LIU Jikui WANG Ruxin WEN Bo LIU Zengding MIAO Fen LI Ye Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen College of Advanced Technology University of Chinese Academy of Sciences

Electrocardiogram(ECG) is widely used in Myocardial infarction(MI) diagnosis. The automatic diagnosis of MI based on the 12-lead ECG needs to consider not only the waveform change features in multi-resolution time series, but also the spatial correlation information between the leads. To this end, this work proposed multiscale spatiotemporal feature extraction method based on Convolutional neural network(CNN) for MI automatic diagnosis. First, the 12-lead ECG is first transformed into an ECG image through wavelet decomposition and 3-dimensional space reconstruction. The MI-CNN model is then constructed to identify MI using 41368 ECG images.Finally, we develop the LL-CNN model, which is utilized only after the ECG signal is identified as an MI event by the MI-CNN model, to localize MI by employing transfer learning to overcome the limited data problem. The proposed method has achieved an accuracy of 99.51% on MI detection, and a macro-F1 of 99.14% on MI localization.Moreover, the features visualization shows that U-wave has significant diagnostic value for MI. The proposed method significantly improves the performance of MI detection and localization compared with other methods. It is promising to be used for MI monitoring and diagnosis.

关键词： Function theory, analysis electrical activity in neurophysiological processes Signal detection Bioelectric signals Digital signal processing Integral transforms Integral transforms electrodiagnostics and other electrical measurement techniques Biology and medical computing Machine learning (artificial intelligence) Electrocardiogram Myocardial infarction Spatiotemporal information Convolutional neural network Transfer learning

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Intracranial Epileptic Seizures Detection Based on an Optimized Neural Network Classifier

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Chinese Journal of Electronics 2021年第3期30卷 419-425页

作者： GONG Chen LIU Jiahui NIU Yunyun School of Information Engineering China University of Geosciences in Beijing Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences

Automatic identification of intracranial electroencephalogram(i EEG) signals has become more and more important in the field of medical diagnostics.In this paper, an optimized neural network classifier is proposed based on an improved feature extraction method for the identification of i EEG epileptic seizures.Four kinds of entropy, Sample entropy, Approximate entropy, Shannon entropy, Log energy entropy are extracted from the database as the feature vectors of Neural network(NN) during the identification process.Four kinds of classification tasks, namely Pre-ictal v Post-ictal(CD), Pre-ictal v Epileptic(CE), Post-ictal v Epileptic(DE), Pre-ictal v Post-ictal v Epileptic(CDE),are used to test the effect of our classification method.The experimental results show that our algorithm achieves higher performance in all tasks than previous algorithms.The effect of hidden layer nodes number is investigated by a constructive approach named growth method. We obtain the optimized number ranges of hidden layer nodes for the binary classification problems CD, CE, DE, and the multitask classification problem CDE, respectively.

关键词： electrical activity in neurophysiological processes Signal detection Bioelectric signals Digital signal processing electrodiagnostics and other electrical measurement techniques Biology and medical computing iEEG Neural network (NN) Entropy Feature extraction Mutual range of coefficient Hidden layer node.

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