Patient specific higher order tensor based approach for the detection and localization of myocardial infarction using 12-lead ECG. (May 2023)

• Record Type: Journal Article
• Authors: Chauhan, Chhaviraj; Tripathy, Rajesh Kumar; Agrawal, Monika
• Is Part Of: Biomedical signal processing and control. Volume 83(2023)
• Publication Date: 2023-05
• Subjects: 12-lead ECG -- Myocardial infarction -- Patient specific MI detection -- FA-MVEMD -- Fourth order tensor -- SAE-DNN; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2023.104701
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Myocardial Infarction (MI) is an emergency condition that requires immediate medical treatment. The rapid and accurate diagnosis of MI using a 12-lead electrocardiogram (ECG) is extremely important in a clinical study to save the patient's life. The manual interpretation of MI using a 12-lead ECG is tedious and time-consuming. Therefore, a patient-specific software-based computer-aided diagnosis framework is helpful to detect and localize MI disease accurately. This paper proposes a patient-specific higher-order tensor-based approach to detect and localize MI automatically using 12-lead ECG recordings. The 12-lead ECG recordings are segmented into 12-lead ECG beats using the multi-lead fusion-based QRS detection algorithm. The fast and adaptive multivariate empirical mode decomposition (FA-MVEMD) based multiscale analysis method decomposes 12-lead ECG beat into a third-order tensor containing the information from the samples, beat, and intrinsic mode functions (IMFs). Furthermore, a fourth-order tensor is formulated by considering beats, samples, lead, and IMFs information of 12-lead ECG recording. The multilinear singular value decomposition (MLSVD) extracts features from the fourth-order tensors and third-order tensors of 12-lead ECG. The K-nearest neighbor (KNN), support vector machine (SVM), and stacked autoencoder-based deep neural network (SAE-DNN) models are used for the detection and localization of MI using fourth-order and third-order tensor domain features. The proposed approach is evaluated using 73 healthy control (HC) and 100 different types of MI-based 12-lead ECG recordings from a public database. The proposed approach has obtained the classification accuracy values of (98.84%, 98.27%, 98.27%) and (86.64%, 83.17%, and 81.98%) using (KNN, SVM, and SAE-DNN) models for MI detection, and localization, respectively using 30-min duration of 12-lead ECG recordings. For MI detection and localization, the suggested approach has obtained accuracy values of 96.53% and 93.32%, respectively, using the 4-s duration of 12-lead ECG recordings. Our approach outperformed existing MI detection and localization methods using 12-lead ECG recordings regarding classification performance. Highlights: The 12-lead ECG recordings are segmented into 12-lead ECG beats The FA-MVEMD method decomposes 12-lead ECG beat into 3D tensor (lead x sample x IMF) A 4D tensor of size (lead x sample x beat x IMF) is formulated for 12-lead ECG. The multilinear singular value decomposition extracts features from 4D and 3D tensors The KNN, SVM, and stacked autoencoder-based DNN are used to detect and localize MI