Automatic Electrocardiogram Sensing Classifier Based on Improved Backpropagation Neural Network

Mao, Yan-Ming; Chang, Ting-Cheng

doi:10.18494/sam.2020.2804

Cited by 3 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SVM-3 (WT) model uses the traditional WT algorithm for filtering, and the SVM-3 (WT-UKF) model uses the WT-UKF algorithm for filtering. These two models are used in [39] for feature detection to obtain the feature data set, and c = 2 10 , g = 2 −1 according to Tables 3 and 4. In order to verify the influence of parameters c and g on recognition accuracy, SVM-1 (WT-UKF) and SVM-2 (WT-UKF) models were established for comparison with SVM-3 (WT-UKF) models.…”

Section: Identification Resultsmentioning

confidence: 99%

“…After using the WT-UKF algorithm to complete the above-mentioned ECG data denoising, this paper applied the feature point detection method based on the dyadic spline wavelet transform in [39] to locate the QRS complex. At the same time, windowing processing was used to extend this method to the location of P and T waves, so as to extract the sampling time points and amplitudes of QRS, P and T waves.…”

Section: Filtering Algorithm Snr(db) Rmsementioning

confidence: 99%

See 1 more Smart Citation

The Identification of ECG Signals Using WT-UKF and IPSO-SVM

Zhu

et al. 2022

Sensors

View full text Add to dashboard Cite

The biometric identification method is a current research hotspot in the pattern recognition field. Due to the advantages of electrocardiogram (ECG) signals, which are difficult to replicate and easy to obtain, ECG-based identity identification has become a new direction in biometric recognition research. In order to improve the accuracy of ECG signal identification, this paper proposes an ECG identification method based on a multi-scale wavelet transform combined with the unscented Kalman filter (WT-UKF) algorithm and the improved particle swarm optimization-support vector machine (IPSO-SVM). First, the WT-UKF algorithm can effectively eliminate the noise components and preserve the characteristics of ECG signals when denoising the ECG data. Then, the wavelet positioning method is used to detect the feature points of the denoised signals, and the obtained feature points are combined with multiple feature vectors to characterize the ECG signals, thus reducing the data dimension in identity identification. Finally, SVM is used for ECG signal identification, and the improved particle swarm optimization (IPSO) algorithm is used for parameter optimization in SVM. According to the analysis of simulation experiments, compared with the traditional WT denoising, the WT-UKF method proposed in this paper improves the accuracy of feature point detection and increases the final recognition rate by 1.5%. The highest recognition accuracy of a single individual in the entire ECG identification system achieves 100%, and the average recognition accuracy can reach 95.17%.

show abstract

Section: Identification Resultsmentioning

confidence: 99%

Section: Filtering Algorithm Snr(db) Rmsementioning

confidence: 99%

The Identification of ECG Signals Using WT-UKF and IPSO-SVM

Zhu

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…CVANN Achieved a 100% accuracy rate using a 3-level based complex wavelet transform. [345] BPNN Exhibited a steady precision of more than 99% recognition of ECG signal. [349] 2-D CNN Achieved an EER of 3.2% using PTB and an EER of 2.90% using CYBHi [403] CNN, QG-MSVM Achieved an EER of 3.5% with PTB database.…”

Section: ) Ecg Signal Classification Based On Kernel Methodsmentioning

confidence: 97%

“…where D j the desired output for the neuron j and [345] proposed an automated ECG recognition method based on a BPNN, which exhibited a steady precision of more than 99% recognition of ECG signal.…”

Section: Some Of the Major Drawbacks Of Ann Includesmentioning

confidence: 99%

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Uwaechia

Ramli

2021

IEEE Access

View full text Add to dashboard Cite

Electrocardiogram (ECG) has extremely discriminative characteristics in the biometric field and has recently received significant interest as a promising biometric trait. However, ECG signals are susceptible to several types of noises, such as baseline wander, powerline interference, and high/lowfrequency noises, making it challenging to realize biometric identification systems precisely and robustly. Therefore, ECG signal denoising is a major pre-processing step and plays a crucial role in ECG-based biometric human identification. ECG signal analysis for biometric recognition can combine several steps, such as preprocessing, feature extraction, feature selection, feature transformation, and classification which is a very challenging task. Moreover, the selected success measures and proper structure of the ECG signal database play significant roles in biometric system analysis, considering that publicly available databases are essential by the research community to evaluate the performance of their proposed algorithms. In this survey, we review most of the techniques employed for the ECG as biometrics for human authentication. Firstly, we present an overview and discussion on ECG signal preprocessing, feature extraction, feature selection, and feature transformation for ECG-based biometric systems. Secondly, we present a survey of the available ECG databases to evaluate and compare the acquisition protocol, acquisition hardware, and acquisition resolution (bits) for ECG-based biometric systems. Thirdly, we also present a survey on different techniques, including deep learning methods: deep supervised learning, deep semi-supervised learning, and deep unsupervised learning, for ECG signal classification. Lastly, we present the state-of-art approaches of information fusion in multimodal biometric systems.

show abstract

FPGA based analysis of electro cardiography signals utilizing fir channels

Likhitha¹,

Jayanthi²

2022

Industrial, Mechanical and Electrical Engineering

View full text Add to dashboard Cite

Automatic Electrocardiogram Sensing Classifier Based on Improved Backpropagation Neural Network

Cited by 3 publications

References 19 publications

The Identification of ECG Signals Using WT-UKF and IPSO-SVM

The Identification of ECG Signals Using WT-UKF and IPSO-SVM

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

FPGA based analysis of electro cardiography signals utilizing fir channels

Contact Info

Product

Resources

About