Application of multiscale Poincaré short-time computation versus multiscale entropy in analyzing fingertip photoplethysmogram amplitudes to differentiate diabetic from non-diabetic subjects
| Autor: | Ming-Xia Xiao, Cheuk-Kwan Sun, Juin J. Liou, Hai-Cheng Wei, Hsien-Tsai Wu, Bagus Haryadi |
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| Rok vydání: | 2018 |
| Předmět: |
Adult
Male Time Factors Entropy 0206 medical engineering Health Informatics Blood Pressure 02 engineering and technology 030204 cardiovascular system & hematology Multiscale entropy 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine Heart Rate Photoplethysmogram Diabetes Mellitus Enhanced sensitivity Humans Fasting blood sugar Diagnosis Computer-Assisted Photoplethysmography Mathematics Aged Models Statistical business.industry Reproducibility of Results Pattern recognition Signal Processing Computer-Assisted Middle Aged 020601 biomedical engineering Computer Science Applications Amplitude chemistry Poincare index Female Glycated hemoglobin Artificial intelligence business Algorithms Software Non diabetic |
| Zdroj: | Computer methods and programs in biomedicine. 166 |
| ISSN: | 1872-7565 |
| Popis: | Multiscale Poincaré (MSP) plots have recently been introduced to facilitate the visualization of time series of physiological signals. This study aimed at investigating the feasibility of MSP application in distinguishing subjects with and without diabetes.Using photoplethysmogram (PPG) waveform amplitudes acquired from unilateral fingertip of non-diabetic (n = 34) and diabetic (n = 30) subjects, MSP indices (MSPI) of the two groups were compared using 1000, 500, 250, 100 data points. Data from Poincaré index (short-term variability/long-term variability [i.e. SD1/SD2] ratio, SSR) and multiscale entropy (MSE) were also obtained with the four corresponding data points for comparison.SSR and MSPI were both negatively related to glycated hemoglobin (HbA1c) and fasting blood sugar levels. Significant negative correlation was also noted between MSPI and pulse pressure. When only 500 and 250 data points were included, significant elevations in the non-diabetic group were only noted in MSPI (both p 0.01). Furthermore, MSPI was significantly higher in non-diabetic than that in diabetic subjects on all scales (i.e., 1-10) but not using MSE when utilizing 1000 data points.The results demonstrated enhanced sensitivity of MSP in differentiating between non-diabetic and diabetic subjects compared to SSR and MSE, highlighting the feasibility of MSP application in biomedical data analysis to reduce computational time and enhance sensitivity. |
| Databáze: | OpenAIRE |
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