Multi-slice Nested Recurrence Plot (MsNRP): A robust approach for person identification using daily ECG or PPG signals

  • Jeon, YeongJun; 
  • Kang, Soon Ju
Citations

WEB OF SCIENCE

8
Citations

SCOPUS

11

초록

This study presents a novel approach called Multi-slice Nested Recurrence Plot (MsNRP) for person iden-tification using noisy bio-signals. Prior studies in biometrics have predominantly relied on ideal datasets of 5-10 min, which introduces uncertainty in accuracy when dealing with noisy bio-signals. The proposed MsNRP method captures features from one and multiple cycles without the need for preprocessing, making it well-suited for photoplethysmograms(PPG) and electrocardiograms(ECG). By overcoming the limitations of traditional recurrence plots(RP), MsNRP demonstrates robustness to noisy bio-signal datasets, thus offering a reliable solution for identification in practical scenarios. We demonstrate the experiments of MsNRP on both 5-10 min datasets, similar to previous related work and day-long datasets, measured in daily life emphasizing its robustness in handling noisy data.

키워드

Person identification; Recurrence Plot (RP); Convolutional neural network (CNN); Biometrics; Electrocardiogram (ECG); Photoplethysmography (PPG); Internet of Medical Things (IoMT); HEALTH-CARE; SLEEP-APNEA; CLASSIFICATION; SYSTEM
제목
Multi-slice Nested Recurrence Plot (MsNRP): A robust approach for person identification using daily ECG or PPG signals
저자
Jeon, YeongJun; Kang, Soon Ju
DOI
10.1016/j.engappai.2023.106799
발행일
2023-11
유형
Article
저널명
Engineering Applications of Artificial Intelligence
권
126