A myocardial infarction detection and localization model based on multi-scale field residual blocks fusion with modified channel attention

doi:10.12122/j.issn.1673-4254.2025.08.22

Abstract

Abstract:

Objective We propose a myocardial infarction (MI) detection and localization model for improving the diagnostic accuracy for MI to provide assistance to clinical decision-making. Methods The proposed model was constructed based on multi-scale field residual blocks fusion modified channel attention (MSF-RB-MCA). The model utilizes lead II electrocardiogram (ECG) signals to detect and localize MI, and extracts different levels of feature information through the multi-scale field residual block. A modified channel attention for automatic adjustment of the feature weights was introduced to enhance the model's ability to focus on the MI region, thereby improving the accuracy of MI detection and localization. Results A 5-fold cross-validation test of the model was performed using the publicly available Physikalisch-Technische Bundesanstalt (PTB) dataset. For MI detection, the model achieved an accuracy of 99.96% on the test set with a specificity of 99.84% and a sensitivity of 99.99%. For MI localization, the accuracy, specificity and sensitivity were 99.81%, 99.98% and 99.65%, respectively. The performances of the model for MI detection and localization were superior to those of other comparison models. Conclusion The proposed MSF-RB-MCA model shows excellent performance in AI detection and localization based on lead II ECG signals, demonstrating its great potential for application in wearable devices.

Key words: myocardial infarction, deep learning, multi-scale, residual block, modified channel attention

Qiucen WU, Xueqi LU, Yaoqi WEN, Yong HONG, Yuliang WU, Chaomin CHEN. A myocardial infarction detection and localization model based on multi-scale field residual blocks fusion with modified channel attention[J]. Journal of Southern Medical University, 2025, 45(8): 1777-1790.

Figures/Tables 28

References 37

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Class	Number of subjects
AMI	17
ALMI	16
ASMI	27
IMI	30
ILMI	23
IPMI	1
IPLMI	8
LMI	1
PMI	1
PLMI	2
Unknown	22
Total	148

Class	Number of subjects
AMI	17
ALMI	16
ASMI	27
IMI	30
ILMI	23
IPMI	1
IPLMI	8
LMI	1
PMI	1
PLMI	2
Unknown	22
Total	148

Type of disease	Number of patients	Number of heartbeats	Percentage (%)
MI	148	49179	82.53
HC	52	10409	17.47
Total	200	59588	100

Type of disease	Number of patients	Number of heartbeats	Percentage (%)
MI	148	49179	82.53
HC	52	10409	17.47
Total	200	59588	100

Branch1	Branch2	Branch3
Input (ECG signals)
Conv1d (kernel=7, Number of kernel=64)
BN
ReLU
MaxPool1d (kernel=3, Number of kernel=2)
Backbone Block
RB+MCA (Number of kernel=64)	RB+MCA (Number of kernel=64)	RB+MCA (Number of kernel=64)
RB+MCA (Number of kernel=128)	RB+MCA (Number of kernel=128)	RB+MCA (Number of kernel=128)
RB+MCA ( Number of kernel=256)	RB+MCA (Number of kernel=256)	RB+MCA (Number of kernel=256)
RB+MCA ( Number of kernel=512)	RB+MCA (Number of kernel=512)	RB+MCA (Number of kernel=512)
GAP	GAP	GAP
Fusion
Concatenation
Flatten
Dropout (P=0.2)
FC