Diagnosis of coronary artery occlusion by fitting polynomial curve with the ECG signal based on optimization techniques

Jothiramalingam, Revathi; Anitha, J.; Hemanth, D. Jude

doi:10.1007/s13721-022-00354-6

Cited by 4 publications

(2 citation statements)

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“…Subahni AR employs a racing game to induce stress, collects ECG signals from six subjects, and extracts features of HRV in the time and frequency domains for analysis. It has been verified that HRV can predict psychological stress by detecting changes in the autonomic nervous system [9]. Singh et al quantitatively analyzed the stress of the human body in the state of fatigue, proposed a method for inducing stress based on neural network, and performed statistical and time-frequency domain analysis on it, taking the characteristics of galvanic skin response as the research object.…”

Section: State Of the Artmentioning

confidence: 99%

A Particle Swarm Algorithm-Guided Psychological Stress Analysis to ECG Signal Collecting

Peng

2022

Mobile Information Systems

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In recent years, effective recognition and accurate assessment of psychological stress have been the focus of research. Because of the objectivity and authenticity of physiological signals, psychological stress recognition from physiological signals has become an important research content in the field of psychological stress recognition. As an important physiological signal, electrocardiogram has been proved to contain reliable physiological response to psychological stress. This paper designs a psychological stress analysis algorithm based on particle swarm optimization (PSO). The wavelet transform algorithm was used to filter and detect the ECG signal. RR interval was calculated from the detected R wave to obtain the ECG signal. An improved particle swarm optimization (PSO) algorithm was proposed, which introduced a particle swarm optimization model with contraction factor to eliminate the speed limit and realize the detection of psychological stress. Experimental results show that the recognition rate of the improved particle swarm optimization algorithm is significantly higher than that of the traditional method, which shows the effectiveness of the algorithm. On the one hand, the research of this paper has optimized the algorithm, which has theoretical significance; on the other hand, it can provide reference for the real psychological stress test, which has practical significance.

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Section: State Of the Artmentioning

confidence: 99%

A Particle Swarm Algorithm-Guided Psychological Stress Analysis to ECG Signal Collecting

Peng

2022

Mobile Information Systems

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“…Where Xk represents the jth feature map of the lth layer, f () represents the nonlinear activation function, and the down function is a subsampling function, which means that the bias term is passed to the fully connected layer through the input data of several convolutional layers and pooling layers. , the operation here is the same as the traditional neural network [11][12] .…”

Section: Forward Propagation Of Convolutional Neural Networkmentioning

confidence: 99%

Optimal Analysis of Weather Forecast Supporting Convolutional Neural Networks

2020

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In recent years, with the acceleration of social informatization, people's requirements for weather forecasting have gradually increased. Severe convective weather has attracted the attention of the meteorological department because of its characteristics of strong suddenness and great destructive power. As a forecasting method to prevent severe convective weather, short-term forecasting has important research significance. The purpose of this paper is to study the optimization analysis of meteorological forecasts supported by convolutional neural networks. In this paper, the deep learning method is used to conduct an applied research on the precipitation of short-term and imminent forecasting. Precipitation short-term forecasting is essentially the prediction of future radar echoes from a series of radar echo sequences, which can be regarded as a spatiotemporal sequence prediction problem. Based on the research and summary of commonly used neural networks, this paper refers to ConvLSTM (ConvolutionalLSTM) Structure A ConvGRU model (ConvolutionalGRU) combining Convolution Neural Network (CNN) and GRU (Gated Recurrent Unit) is proposed. Since the structure of GRU is simpler than that of LSTM, the effect is not much different. This model is compared to The ConvLSTM structure has faster training speed and smaller memory requirements. Another work of this paper is to improve the convolutional layer based on VGGNet (VisualGeometryGroupNet), using multiple small convolution kernels to stack instead of large convolution kernels, reducing the number of parameters and improving the feature extraction ability of the network. This model gives full play to the advantages of convolutional neural network and GRU, that is, the spatial feature extraction ability of convolutional structure and the memory ability of GRU to deal with time series problems. Finally, the prediction effects of the model and the optical flow method are compared through experiments to verify the applicability of the model in the short-term precipitation forecasting problem.

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