ML-Net: Multi-Channel Lightweight Network for Detecting Myocardial Infarction

Cao, Yangjie; Wei, Tingting; Zhang, Bo; Lin, Nan; Rodrigues, Joel J. P. C.; Li, Jie; Zhang, Di

doi:10.1109/jbhi.2021.3060433

Cited by 36 publications

(11 citation statements)

References 44 publications

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“…Considering their results for MI detection, the overall accuracies are only 81.71% and 92.69%, respectively. All the models in [ 24 , 27 , 28 ] employ the conventional MBN skeleton to implement the MI diagnosis without hand-designed feature extraction. The ML-Net in [ 24 ] achieves the best performance for MI detection and localization, according to the experimental results.…”

Section: Discussionmentioning

confidence: 99%

“…All the models in [ 24 , 27 , 28 ] employ the conventional MBN skeleton to implement the MI diagnosis without hand-designed feature extraction. The ML-Net in [ 24 ] achieves the best performance for MI detection and localization, according to the experimental results. However, the ML-Net concentrates on the detection and localization of GAMI, which only includes AMI, ASMI, and ALMI.…”

Section: Discussionmentioning

confidence: 99%

“…For generalized anterior myocardial infarction (GAMI) detection, it utilized lead V2, V3, V5 and aVL to analyze and achieve an accuracy of 96.00%. Based on the same leads, an ML-Net was also developed in [ 24 ] for GAMI detection. Although the models using single lead or multiple leads (<12 leads) can produce accurate MI detection according to the experiment results, limited lead information may prevent these models from extending to a more complex application in the real-world [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

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EvoMBN: Evolving Multi-Branch Networks on Myocardial Infarction Diagnosis Using 12-Lead Electrocardiograms

Liu

Chang

et al. 2021

Biosensors

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Multi-branch Networks (MBNs) have been successfully applied to myocardial infarction (MI) diagnosis using 12-lead electrocardiograms. However, most existing MBNs share a fixed architecture. The absence of architecture optimization has become a significant obstacle to a more accurate diagnosis for these MBNs. In this paper, an evolving neural network named EvoMBN is proposed for MI diagnosis. It utilizes a genetic algorithm (GA) to automatically learn the optimal MBN architectures. A novel fixed-length encoding method is proposed to represent each architecture. In addition, the crossover, mutation, selection, and fitness evaluation of the GA are defined to ensure the architecture can be optimized through evolutional iterations. A novel Lead Squeeze and Excitation (LSE) block is designed to summarize features from all the branch networks. It consists of a fully-connected layer and an LSE mechanism that assigns weights to different leads. Five-fold inter-patient cross validation experiments on MI detection and localization are performed using the PTB diagnostic database. Moreover, the model architecture learned from the PTB database is transferred to the PTB-XL database without any changes. Compared with existing studies, our EvoMBN shows superior generalization and the efficiency of its flexible architecture is suitable for auxiliary MI diagnosis in real-world.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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EvoMBN: Evolving Multi-Branch Networks on Myocardial Infarction Diagnosis Using 12-Lead Electrocardiograms

Liu

Chang

et al. 2021

Biosensors

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“…Lightweight models are also beginning to make their mark in the medical signaling field. Cao et al (2021) proposed a multichannel lightweight model with each channel integrating multiple heterogeneous convolutional layers to obtain multilevel features for classifying myocardial infarction with an accuracy rate of 96.65%. Zheng et al (2021) trained MobileNetV1 and MobileNetV2 models by migration learning for pterygium diagnosis in the eye and compared them with the classical model and found that MobileNetV2 obtained better results with a model size of only 13.5 M. Chen et al (2022) used the lightweight networks MobileNetV1, MobileNetV2, and Xception to classify cervical cancer cells and used knowledge distillation for accuracy improvement.…”

Section: Introductionmentioning

confidence: 99%

A lightweight fetal distress-assisted diagnosis model based on a cross-channel interactive attention mechanism

et al. 2023

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Fetal distress is a symptom of fetal intrauterine hypoxia, which is seriously harmful to both the fetus and the pregnant woman. The current primary clinical tool for the assessment of fetal distress is Cardiotocography (CTG). Due to subjective variability, physicians often interpret CTG results inconsistently, hence the need to develop an auxiliary diagnostic system for fetal distress. Although the deep learning-based fetal distress-assisted diagnosis model has a high classification accuracy, the model not only has a large number of parameters but also requires a large number of computational resources, which is difficult to deploy to practical end-use scenarios. Therefore, this paper proposes a lightweight fetal distress-assisted diagnosis network, LW-FHRNet, based on a cross-channel interactive attention mechanism. The wavelet packet decomposition technique is used to convert the one-dimensional fetal heart rate (FHR) signal into a two-dimensional wavelet packet coefficient matrix map as the network input layer to fully obtain the feature information of the FHR signal. With ShuffleNet-v2 as the core, a local cross-channel interactive attention mechanism is introduced to enhance the model’s ability to extract features and achieve effective fusion of multichannel features without dimensionality reduction. In this paper, the publicly available database CTU-UHB is used for the network performance evaluation. LW-FHRNet achieves 95.24% accuracy, which meets or exceeds the classification results of deep learning-based models. Additionally, the number of model parameters is reduced many times compared with the deep learning model, and the size of the model parameters is only 0.33 M. The results show that the lightweight model proposed in this paper can effectively aid in fetal distress diagnosis.

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“…This is mainly because of the even faster transmission speed (especially the even faster uplink transmission speed) and the ultra-reliable and low latency communications (URLLC) of 5G [1], [2]. Thanks to the 5G technologies, we may provide 24 hours in-home health monitoring, timely e-diagnosis and etreatment services, which are of significant importance to the patient with chronic diseases such as cerebral stoke and myocardial infarction [3]. The 5G e-health systems can also play a significant role on containing the acute infectious diseases.…”

mentioning

confidence: 99%

Design and Implementation of 5G eHealth Systems, Technologies, Use Cases and Future Challenges

Zhang¹,

Rodrigues²,

Zhai³

et al. 2021

Preprint

Self Cite

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Fifth generation (5G) aims to connect massive devices with even higher reliability, lower latency and even faster transmission speed, which are vital for implementing the e-health systems. However, the current efforts on 5G e-health systems are still not enough to accomplish its full blueprint. In this article, we first discuss the related technologies from physical layer, upper layer and cross layer perspectives on designing the 5G e-health systems. We afterwards elaborate two use cases according to our implementations, i.e., 5G e-health systems for remote health and 5G e-health systems for Covid-19 pandemic containment. We finally envision the future research trends and challenges of 5G e-health systems.

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ML-Net: Multi-Channel Lightweight Network for Detecting Myocardial Infarction

Cited by 36 publications

References 44 publications

EvoMBN: Evolving Multi-Branch Networks on Myocardial Infarction Diagnosis Using 12-Lead Electrocardiograms

EvoMBN: Evolving Multi-Branch Networks on Myocardial Infarction Diagnosis Using 12-Lead Electrocardiograms

A lightweight fetal distress-assisted diagnosis model based on a cross-channel interactive attention mechanism

Design and Implementation of 5G eHealth Systems, Technologies, Use Cases and Future Challenges

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