A multi-dimensional association information analysis approach to automated detection and localization of myocardial infarction. (January 2021)

• Record Type: Journal Article
• Authors: Zhang, Jieshuo; Liu, Ming; Xiong, Peng; Du, Haiman; Zhang, Hong; Lin, Feng; Hou, Zengguang; Liu, Xiuling
• Is Part Of: Engineering applications of artificial intelligence. Volume 97(2021)
• Publication Date: 2021-01
• Subjects: Myocardial infarction -- Electrocardiograph -- Multi-dimensional association tensor -- Parallel factor analysis -- Bagged decision tree; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.104092
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Developing an accurate and automatic algorithm for detection and localization of myocardial infarction (MI) remains a great challenge for multi-lead electrocardiograph (ECG) signals. The core is a novel technique of multi-dimensional association information analysis for a multi-lead ECG tensor. Tensorization based on Discrete Wavelet Transform is investigated to construct an effective ECG tensor containing multi-dimensional association information from 12-lead ECG signals. The multi-lead feature extraction algorithm based on Parallel Factor Analysis is developed to automatically extract the low-dimensional and highly recognizable lead characteristic features of the tensor. After that a bagged decision tree is constructed to categorize 12 types of heartbeats, healthy controls and 11 kinds of MI, from the lead features. Using the PTB database, we compare with the existing MI diagnosis methods. For MI detection, significant improvement of the accuracy, sensitivity and specificity are achieved; as high as 99.88%, 99.98% and 99.39% respectively. Furthermore, an experiment with 36-dimensional features obtained from the ECG tensor is conducted for the localization of 11 kinds of MI, and our proposed method achieved an accuracy of 99.40%, sensitivity of 99.86%, and specificity of 99.89%. The proposed algorithm can effectually accomplish the localization of 11 categories of MI by using the lead features extracted from the multi-dimensional association ECG tensor, which has not been achieved in literature. The accurate and comprehensive tool development will greatly help cardiologists diagnose 12-lead ECG signals of MI. Highlights: A novel method for automatic diagnosis of MI based on 12-lead ECG signals is proposed. A DWT-based tensorization is proposed to form an multidimensional association tensor. The lead characteristic features is extracted from the ECG tensor via PARAFAC. Realized the classification of 11 types of MI and normal ECG by the lead features.