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

Abstract

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.

Myocardial infarction detection method based on the continuous T-wave area feature and multi-lead-fusion deep features

“…The sensitivity is significantly lower than the specificity, which means that the missed diagnosis rate is high, and the sensitivity is significantly higher than the specificity, which means that the misdiagnosis rate is high. Compared with the methods of CNN + active learning (He et al 2021), CNN+LSTM (Feng et al 2019), and evolving MBN (Liu et al 2021), the method proposed in this paper has a better balance of sensitivity and specificity, and will not have a high rate of missed diagnosis or misdiagnosis. Compared with ML-Net (Cao et al 2021) and ResNet (Kachuee et al 2018), the Multi-lead-fusion CNN model in this paper contains fewer parameters, and the model is easy to train and less dependent on hardware.…”

Multi-branch myocardial infarction detection and localization framework based on multi-instance learning and domain knowledge

“…Table 11 shows the relevant literature on MI research on the PTB-XL database. Liu et al (2021) proposed the LSE module to aggregate the features of all branching networks to achieve MI localization on the PTB-XL database, obtaining an accuracy of 75.18% and an F1 score of 0.546. The accuracy and F1-score of our proposed method are superior to that study.…”

Design of an Integrated Myocardial Infarction Detection Model Using ECG Connectivity Features and Multivariate Time Series Classification

“…As discussed earlier, the conventional methods for MI detection [2][3][4][5][6][7][8][9][10][11] typically treat each time series within a multivariate ECG time series as an independent entity, thus overlooking the potentially valuable relational information among them. However, as observed in the detection of some other diseases [24][25][26][27][28], the performance of MI detection can also be improved by harnessing this additional relational In the proposed connectivity-based model, we start by extracting connectivity information from the ECG multivariate time series using four widely recognized connectivity measures: coherence, correlation, phase-lag index, and phase-lag value, which were described in the last section.…”

“…Presently, the diagnosis of MI involves the evaluation of the subjects' electrocardiogram (ECG) signals by a skilled medical professional, thus demanding the timely availability of the same and also introducing the potential for human error and observer bias. To address these challenges, in recent years, a lot of attempts have been made to automate the diagnosis of MI from ECG signals without any human intervention, using various methods ranging from the classical signal processingbased methods, machine learning-based methods to more recent deep learning-based methods [2][3][4][5][6][7][8][9][10][11]. In the realm of signal processing and machine learning-based methods, Dohare et al [2] proposed a model that uses a support vector machine (SVM) classifier and traditional features like the peak-to-peak amplitude, area, mean, etc., and were able to get an accuracy of about 96.66%.…”

“…As far as deep learning-based MI detection is concerned, the majority of the approaches utilize convolutional neural networks (CNNs) or recurrent neural networks (RNNs), which can be observed through a few representative works discussed next. Starting with an interesting work in [7], the authors here proposed a novel deep learning-based MI detection approach using evolutionary multi-branch networks (MBNs) wherein the MBNs were optimized using a genetic algorithm to obtain an overall accuracy of 90.8%. Han et al in [8] proposed an innovative approach for detecting and localizing MI by employing a multi-lead residual neural network (ML-ResNet) structure and obtained an accuracy of 95.37%.…”