Multi domain fusion feature extraction and classification of ECG based on PCA-ICA

Zhao, Ling; Li, Juan; Ren, Huilin

doi:10.1109/itnec48623.2020.9084658

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…138 samples of ECG signals and 138 samples of acupoint electrical signals were taken for principal component analysis (PCA), respectively. Similarities and differences of data are classified and explored by using PCA [24][25][26][27][28]. e significance of PCA is to create small variables known as Principal Components (PCs) that work on the variance estimation theory.…”

Section: Discussionmentioning

confidence: 99%

Study on Potential of Meridian Acupoints of Traditional Chinese Medicine

Zhao

Wang

et al. 2021

Journal of Healthcare Engineering

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In recent years, Traditional Chinese Medicine (TCM) has attracted more and more attention due to its good therapeutic effect, low cost, and convenience. This research is also a part of the goal of the modernization of TCM. Based on the meridian electric potential acquisition system independently developed by our project team, in this paper, we designed the human body's meridian electric potential acquisition scheme. We use principal component analysis (PCA) to prove that the meridional potential signal is derived from the ECG signal. Then, Inception ResNet V2 was used to classify acupoints and nonacupoints. Finally, the classification accuracy rate reached 86.59045265, and the F1 score = 0.72161642. This shows that acupoints and nonacupoints can be distinguished by their surface potential.

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Section: Discussionmentioning

confidence: 99%

Study on Potential of Meridian Acupoints of Traditional Chinese Medicine

Zhao

Wang

et al. 2021

Journal of Healthcare Engineering

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“…Shi et al [2], Alim and Islam [3], and Shen et al [4] extracted various manual features such as RR interval, morphology features, average QRS interval, average QTC interval, and STsegment to detect cardiovascular diseases. In addition, Khorrami et al [5], Desai et al [6], and Raj et al [7] used discrete cosine transform (DCT) and discrete wavelet transform (DWT) for feature processing, while Zhao et al [8], Martis et al [9], and Kanaan et al [10] used principal component analysis (PCA) for feature dimensionality reduction, which can further improve the quality of extracted features. As for the classification stage, it is crucial to choose the appropriate classifier.…”

Section: Introductionmentioning

confidence: 99%

Optimized Solutions of Electrocardiogram Lead and Segment Selection for Cardiovascular Disease Diagnostics

Shi

Liu

et al. 2023

Bioengineering

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Most of the existing multi-lead electrocardiogram (ECG) detection methods are based on all 12 leads, which undoubtedly results in a large amount of calculation and is not suitable for the application in portable ECG detection systems. Moreover, the influence of different lead and heartbeat segment lengths on the detection is not clear. In this paper, a novel Genetic Algorithm-based ECG Leads and Segment Length Optimization (GA-LSLO) framework is proposed, aiming to automatically select the appropriate leads and input ECG length to achieve optimized cardiovascular disease detection. GA-LSLO extracts the features of each lead under different heartbeat segment lengths through the convolutional neural network and uses the genetic algorithm to automatically select the optimal combination of ECG leads and segment length. In addition, the lead attention module (LAM) is proposed to weight the features of the selected leads, which improves the accuracy of cardiac disease detection. The algorithm is validated on the ECG data from the Huangpu Branch of Shanghai Ninth People’s Hospital (defined as the SH database) and the open-source Physikalisch-Technische Bundesanstalt diagnostic ECG database (PTB database). The accuracy for detection of arrhythmia and myocardial infarction under the inter-patient paradigm is 99.65% (95% confidence interval: 99.20–99.76%) and 97.62% (95% confidence interval: 96.80–98.16%), respectively. In addition, ECG detection devices are designed using Raspberry Pi, which verifies the convenience of hardware implementation of the algorithm. In conclusion, the proposed method achieves good cardiovascular disease detection performance. It selects the ECG leads and heartbeat segment length with the lowest algorithm complexity while ensuring classification accuracy, which is suitable for portable ECG detection devices.

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“… Zou et al (2020) completed EEG entropy feature extraction based on the fusion method of T-test and KPCA, and realized the identification of drivers’ fatigue driving state. Zhao et al (2020a) simplified the feature matrix by combining PCA and ICA methods to achieve effective feature extraction and classification tasks of ECG signals. DRSN network adds a soft threshold function based on the ResNet, which can effectively remove the influence of noise-related features on the source signal.…”

Section: Introductionmentioning

confidence: 99%

A comparison of contributions of individual muscle and combination muscles to interaction force prediction using KPCA-DRSN model

Lü

Gao

Cao

et al. 2022

Front. Bioeng. Biotechnol.

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Rapid and accurate prediction of interaction force is an effective way to enhance the compliant control performance. However, whether individual muscles or a combination of muscles is more suitable for interaction force prediction under different contraction tasks is of great importance in the compliant control of the wearable assisted robot. In this article, a novel algorithm that is based on sEMG and KPCA-DRSN is proposed to explore the relationship between interaction force prediction and sEMG signals. Furthermore, the contribution of each muscle to the interaction force is assessed based on the predicted results. First of all, the experimental platform for obtaining the sEMG is described. Then, the raw sEMG signal of different muscles is collected from the upper arm during different contractions. Meanwhile, the output force is collected by the force sensor. The Kernel Principal Component Analysis (KPCA) method is adopted to remove the invalid components of the raw sEMG signal. After that, the processed sequence is fed into the Deep Residual Shrinkage Network (DRSN) to predict the interaction force. Finally, based on the prediction results, the contribution of each sEMG signal from different muscles to the interaction force is evaluated by the mean impact value (MIV) indicator. The experimental results demonstrate that our methods can automatically extract the valid features of sEMG signal and provided fast and efficient prediction. In addition, the single muscle with the largest MIV index could predict the interaction force faster and more accurately than the muscle combination in different contraction tasks. The finding of our research provides a solid evidence base for the compliant control of the wearable robot.

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Multi domain fusion feature extraction and classification of ECG based on PCA-ICA

Cited by 7 publications

References 6 publications

Study on Potential of Meridian Acupoints of Traditional Chinese Medicine

Study on Potential of Meridian Acupoints of Traditional Chinese Medicine

Optimized Solutions of Electrocardiogram Lead and Segment Selection for Cardiovascular Disease Diagnostics

A comparison of contributions of individual muscle and combination muscles to interaction force prediction using KPCA-DRSN model

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