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. 2022 Jun 29:13:912739.
doi: 10.3389/fphys.2022.912739. eCollection 2022.

Using Multi-Task Learning-Based Framework to Detect ST-Segment and J-Point Deviation From Holter

Shuang Wu  1 , Qing Cao  1 , Qiaoran Chen  2 , Qi Jin  1 , Zizhu Liu  1 , Lingfang Zhuang  1 , Jingsheng Lin  3 , Gang Lv  3 , Ruiyan Zhang  1 , Kang Chen  1
Affiliations

Using Multi-Task Learning-Based Framework to Detect ST-Segment and J-Point Deviation From Holter

Shuang Wu et al. Front Physiol. .

Abstract

Artificial intelligence is increasingly being used on the clinical electrocardiogram workflows. Few electrocardiograms based on artificial intelligence algorithms have focused on detecting myocardial ischemia using long-term electrocardiogram data. A main reason for this is that interference signals generated from daily activities while wearing the Holter monitor lowered the ability of artificial intelligence to detect myocardial ischemia. In this study, an automatic system combining denoising and segmentation modules was developed to detect the deviation of the ST-segment and J point. We proposed a ECG Bidirectional Transformer network that applied in both denoising and segmentation tasks. The denoising model achieved RMSEde, SNRimp, and PRD values of 0.074, 10.006, and 16.327, respectively. The segmentation model achieved precision, sensitivity (recall), and F1-score of 96.00, 93.06, and 94.51%, respectively. The system's ability to distinguish the depression and elevation of the ST-segment and J point was also verified by cardiologists as well. From our ECG dataset, 103 patients with ST-segment depression and 10 patients with ST-segment elevation were detected with positive predictive values of 80.6 and 60% respectively. Using Holter ECG and transformer-based deep neural networks, we can detect subtle ST-segment changes in noisy ECG signals. This system has the potential to improve the efficacy of daily medicine and to provide a broader population-level screening for asymptomatic myocardial ischemia.

Keywords: ST-Segment; deep learning; electrocardiogram; holter; multi-task learning.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Schematic workflow of diagnosing ST-segment depression and elevation, and J point elevation from Holter electrocardiogram signal.
FIGURE 2
FIGURE 2
The architecture of the EBTnet.
FIGURE 3
FIGURE 3
Three successive 1D bidirectional SWT blocks. Each SW-MSA is configured with unshifted, forward-shifted, backward-shifted, respectively.
FIGURE 4
FIGURE 4
The illustration of SW-MSA module with (A) unshifted (B) forward-shifted, and (C) backward-shifted.
FIGURE 5
FIGURE 5
The structure of our datasets.
FIGURE 6
FIGURE 6
The inter-analysis denoising results of different methods on multitask inheritance training scheme. (A) Ground-truth ECG. (B) Noise-convolved ECG. (C) Denoised ECG by 1D CNN Unet. (D) Denoised ECG by FCN. (E) Denoised ECG by Unet_LUDB. (F) Denoised ECG by EBTnet.
FIGURE 7
FIGURE 7
The distribution of NQRS and CQRS before and after denoising in R-ECG and E-ECG datasets. Data are expressed as mean ± SD. The difference between un-denoise and denoise groups was analyzed by paired t-test, and the difference between R-ECG and E-ECG was analyzed by independent-samples t-test. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, and ns denoted no significance difference.
FIGURE 8
FIGURE 8
The inter-analysis segmentation results of different methods on multitask inheritance training scheme. (A) Ground-truth ECG. (B) 1D CNN Unet. (C) FCN. (D) Unet_LUDB. (E) EBTnet.
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