ECG features extraction using second-order dynamic system and regeneration using hybrid recurrent network

Abdul-Kadir, Nurul Ashikin; Othman, Mohd Afzan; Safri, Norlaili Mat

doi:10.1109/icspcc.2016.7753648

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Cited by 2 publications

References 16 publications

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Atrial Fibrillation Dynamic Features Recognition using 1-D Convolutional Neural Networks

Ong,

Abdul-Kadir,

Mahmood

et al. 2023

2023 IEEE International Biomedical Instrumentation and Technology Conference (IBITeC)

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Atrial Fibrillation Dynamic Features Recognition using 1-D Convolutional Neural Networks

Ong,

Abdul-Kadir,

Mahmood

et al. 2023

2023 IEEE International Biomedical Instrumentation and Technology Conference (IBITeC)

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Flexible ECG circuit fabrication and application using vinyl cutting technique

2020

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The aim of this study is to prove the capability of vinyl cutting technique to cut the conductive traces of electronic circuit layout which used a copper tape (Copper tape 1181 from 3M) on flexible substrate to replace the method of using nano-scale particle material. A wireless electrocardiography (ECG) circuit was integrated and fabricated on flexible substrate, namely a polyethylene terephthalate (PET) substrate by using vinyl cutting method to produce the conductive line traces. After that, the fabricated circuit is used for acquiring ECG signals from a patient simulator and human subjects to measure the performance differences and compatibility as a wearable device. In the data processing stage, ECG data were denoised using sym20 from Wavelet Transform tool provided by MATLAB. Then, Signal-to-noise-ratio (SNR) was calculated and used as the signal quality indicator. At the end of the study, flexible circuit performance was compared to MIT-BIH Arrhythmia database and it shows that there is no significance difference between both. In conclusion, vinyl cutting method shows a promising fabrication output on PET substrate as the performance of both flexible ECG circuit is comparable with rigid ECG circuit by a previous study.

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ECG features extraction using second-order dynamic system and regeneration using hybrid recurrent network

Cited by 2 publications

References 16 publications

Atrial Fibrillation Dynamic Features Recognition using 1-D Convolutional Neural Networks

Atrial Fibrillation Dynamic Features Recognition using 1-D Convolutional Neural Networks

Flexible ECG circuit fabrication and application using vinyl cutting technique

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