The Ecg Signal Classification Based on Ensemble Learning of Pso-Elm Algorithm

Li, Wei; Li, Bin; Guo, Hongli; Fang, Yixian; Qiao, Fengjuan; Zhou, Shuwang

doi:10.14311/nnw.2020.30.018

Cited by 14 publications

(7 citation statements)

References 30 publications

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“…Unlike in conventional neural networks with back propagation (BP), the parameters of the nodes in the hidden layers of ELM are randomly assigned and never tuned. It solves the shortcomings of classic neural networks, such as sluggish training rate, local optimum instability, and sensitivity to learning rate (Li W. et al, 2020). However, the conventional ELM architecture is considered to have drawbacks (Zhang et al, 2022), such as the unpredictability of weights and thresholds, and the uncertainty of network parameters, which make it less effective at processing data and result in overfitting phenomena that reduce the accuracy of the prediction model.…”

Section: Model Building and Predictionmentioning

confidence: 99%

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

Qiu²,

Zhang³

et al. 2022

Front. Plant Sci.

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During the growth season, jujube trees are susceptible to infestation by the leaf mite, which reduces the fruit quality and productivity. Traditional monitoring techniques for mites are time-consuming, difficult, subjective, and result in a time lag. In this study, the method based on a particle swarm optimization (PSO) algorithm extreme learning machine for estimation of leaf chlorophyll content (SPAD) under leaf mite infestation in jujube was proposed. Initially, image data and SPAD values for jujube orchards under four severities of leaf mite infestation were collected for analysis. Six vegetation indices and SPAD value were chosen for correlation analysis to establish the estimation model for SPAD and the vegetation indices. To address the influence of colinearity between spectral bands, the feature band with the highest correlation coefficient was retrieved first using the successive projection algorithm. In the modeling process, the PSO correlation coefficient was initialized with the convergent optimal approximation of the fitness function value; the root mean square error (RMSE) of the predicted and measured values was derived as an indicator of PSO goodness-of-fit to solve the problems of ELM model weights, threshold randomness, and uncertainty of network parameters; and finally, an iterative update method was used to determine the particle fitness value to optimize the minimum error or iteration number. The results reflected that significant differences were observed in the spectral reflectance of the jujube canopy corresponding with the severity of leaf mite infestation, and the infestation severity was negatively correlated with the SPAD value of jujube leaves. The selected vegetation indices NDVI, RVI, PhRI, and MCARI were positively correlated with SPAD, whereas TCARI and GI were negatively correlated with SPAD. The accuracy of the optimized PSO-ELM model (R2 = 0.856, RMSE = 0.796) was superior to that of the ELM model alone (R2 = 0.748, RMSE = 1.689). The PSO-ELM model for remote sensing estimation of relative leaf chlorophyll content of jujube shows high fault tolerance and improved data-processing efficiency. The results provide a reference for the utility of UAV remote sensing for monitoring leaf mite infestation of jujube.

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Section: Model Building and Predictionmentioning

confidence: 99%

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

Qiu²,

Zhang³

et al. 2022

Front. Plant Sci.

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show abstract

“…Therefore, ECG examination is of great significance in confirming the diagnosis of whether a patient is suffering from a heart-related disease. And the presence of abnormal ECG problems can be diagnosed by observing the following indicators [18].…”

Section: Evaluation Indicators Of Ecg Abnormalitiesmentioning

confidence: 99%

Deep Learning-Based ECG Abnormality Identification Prediction and Analysis

Liang

2022

Journal of Sensors

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In recent years, China’s economy has been developing rapidly, and people’s life and quality of life have been improving; more importantly, people’s habits and living habits have also developed from the previous “unhygienic” and “not very careful” to the current “Healthier, more hygienic, greener and more sophisticated” direction. In the process of this development, due to the rapid development of the economy and the industrialization of cities, the incidence of heart disease is also increasing year by year. According to relevant studies, China’s urbanization process has been unprecedented, the number of urbanized people in China has exploded in recent years, and the ratio of urban to rural population has increased from about 20 percent to about 75 percent today. The effects of the population and the urbanization of urban architecture are affecting people’s physical and mental health, both consciously and unconsciously, causing both positive and negative physical and mental effects on the psychological and physical levels. In this paper, the concept of deep learning is fully utilized to train the CNN neural network model and apply it to the ECG abnormality recognition and prediction examination. In order to fully validate the application and significance of deep learning in ECG abnormality identification and prediction, the whole project was completed through subjective and objective experiments. The experimental results show that, from the subjective aspect, the ECG examination has been accepted by most people for different age groups, and the analysis results of the ECG examination with the deep learning model in this paper are more satisfactory; from the objective aspect, the CNN-ECG abnormality recognition prediction network model trained in this paper has high. The accuracy of the model for ECG abnormality recognition prediction can reach 86% when the learning rate is set to 0.0001 and the batch size is set to 120, and the model can reduce the burden of medical personnel to a certain extent.

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“…ELM has three neural network layers with only one hidden layer and an activation function and is always moving forward (Novitasari et al, 2020). This causes ELM to have various advantages over traditional predictive models such as backpropagation neural networks and SVM (Zhang et al, 2020), such as minimized iterations, better results, very fast learning speed (Novitasari et al, 2020), simple network structure (Li et al, 2020), good generalization capabilities, produces the only optimal solution (Zhang et al, 2020), no need to set parameters such as stopping criteria or learning rate, partially overcoming the problem of overfitting and local minimum (Armi et al, 2021), unification of multi-classification, minimal human intervention, ease of implementation and regression (Nagelli et al, 2019).…”

Section: Extreme Learning Machine (Elm) Classificationmentioning

confidence: 99%

Identification of Acne Vulgaris Type in Facial Acne Images Using GLCM Feature Extraction and Extreme Learning Machine Algorithm

2022

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Acne vulgaris or acne is a common inflammatory pilosebaceous condition that affects up to 90% of teenagers, begins during adolescent years, and often persists into adulthood. Acne vulgaris, especially on the face, has a major impact on the emotional, social and psychological health of patients. In treating acne, it is necessary to identify the exact type of acne. The manual method is considered less effective, so it is proposed an automatic method using a computer, which uses image processing techniques. This research was conducted to identify the types of acne on facial acne images. The methods used are K-Means Clustering for segmentation, Gray Level Co-occurrence Matrix (GLCM) for feature extraction, and Extreme Learning Machine (ELM) for classification. The dataset is 100 images and consists of 3 classes, namely Nodules, Papules and Pustules. Testing is done in two stages, namely testing 2 classes (Nodules and Papules), followed by testing 3 classes (Nodules, Papules and Pustules). Testing of 2 classes produces the highest accuracy of 95,24% and testing of 3 classes produces the highest accuracy of 80%.

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The Ecg Signal Classification Based on Ensemble Learning of Pso-Elm Algorithm

Cited by 14 publications

References 30 publications

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

Deep Learning-Based ECG Abnormality Identification Prediction and Analysis

Identification of Acne Vulgaris Type in Facial Acne Images Using GLCM Feature Extraction and Extreme Learning Machine Algorithm

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