PMID- 37337643
OWN - NLM
STAT- MEDLINE
DCOM- 20230621
LR  - 20230621
IS  - 1525-6065 (Electronic)
IS  - 1064-1955 (Linking)
VI  - 42
IP  - 1
DP  - 2023 Dec
TI  - Distinguishing preeclampsia using the falling scaled slope (FSS) --- a novel 
      photoplethysmographic morphological parameter.
PG  - 2225617
LID - 10.1080/10641955.2023.2225617 [doi]
AB  - BACKGROUND: Preeclampsia (PE) presence could lead to hemodynamic changes. 
      Previous research suggested that morphological parameters based on 
      photoplethysmographic pulse waves (PPGW) could help diagnose PE. AIM: To 
      investigate the performance of a novel PPGPW-based parameter, falling scaled 
      slope (FSS), in distinguishing PE. To investigate the advantages of the machine 
      learning algorithm over the conventional statistical methods in the analysis. 
      METHODS: Eighty-one pieces of PPGPW data were acquired for the study (PE, n = 44; 
      normotensive, n = 37). The FSS values were calculated and used to construct a PE 
      classifier using the K-nearest neighbors (KNN) algorithm. A predicted PE state 
      varying from 0 to 1 was also calculated. The classifier's performance in 
      distinguishing PE was evaluated using the ROC and AUC. A comparison was conducted 
      with previously published PPGPW-based models. RESULT: Compared to the previous 
      PPGPW-based parameters, FSS showed a better performance in distinguishing PE with 
      an AUC value of 0.924, the best threshold of 0.498 could predict PE with a 
      sensitivity of 84.1% and a specificity of 89.2%. As for the analysis method, 
      training a classifier using the KNN algorithm had an advantage over the 
      conventional statistical methods with the AUC values of 0.878 and 0.749, 
      respectively. CONCLUSION: The result indicated that FSS might be an effective 
      tool for identifying PE. Moreover, the machine learning algorithm could further 
      help the data analysis and improve performance. [Figure: see text].
FAU - Chen, Hang
AU  - Chen H
AD  - College of Biomedical Engineering and Instrument Science, Zhejiang University, 
      Hangzhou, China.
AD  - Key Lab of Biomedical Engineering of Ministry of Education, Zhejiang University, 
      Hangzhou, China.
AD  - Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection 
      Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 
      China.
AD  - Connected Healthcare Big Data Research Center, Zhejiang Lab, Hangzhou, China.
FAU - Jiang, Feng
AU  - Jiang F
AD  - College of Biomedical Engineering and Instrument Science, Zhejiang University, 
      Hangzhou, China.
AD  - Key Lab of Biomedical Engineering of Ministry of Education, Zhejiang University, 
      Hangzhou, China.
AD  - Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection 
      Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 
      China.
FAU - Chen, Wanlin
AU  - Chen W
AD  - College of Biomedical Engineering and Instrument Science, Zhejiang University, 
      Hangzhou, China.
AD  - Key Lab of Biomedical Engineering of Ministry of Education, Zhejiang University, 
      Hangzhou, China.
AD  - Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection 
      Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 
      China.
FAU - Feng, Ying
AU  - Feng Y
AD  - Department of Anesthesia, Women's Hospital, School of Medicine, Zhejiang 
      University, Hangzhou, China.
FAU - Chen, Shali
AU  - Chen S
AD  - College of Biomedical Engineering and Instrument Science, Zhejiang University, 
      Hangzhou, China.
AD  - Key Lab of Biomedical Engineering of Ministry of Education, Zhejiang University, 
      Hangzhou, China.
AD  - Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection 
      Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 
      China.
FAU - Miao, Jiajun
AU  - Miao J
AD  - College of Biomedical Engineering and Instrument Science, Zhejiang University, 
      Hangzhou, China.
AD  - Key Lab of Biomedical Engineering of Ministry of Education, Zhejiang University, 
      Hangzhou, China.
AD  - Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection 
      Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 
      China.
FAU - Jiao, Cuicui
AU  - Jiao C
AD  - Department of Anesthesia, Women's Hospital, School of Medicine, Zhejiang 
      University, Hangzhou, China.
FAU - Chen, Xinzhong
AU  - Chen X
AD  - Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection 
      Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 
      China.
AD  - Department of Anesthesia, Women's Hospital, School of Medicine, Zhejiang 
      University, Hangzhou, China.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Hypertens Pregnancy
JT  - Hypertension in pregnancy
JID - 9421297
SB  - IM
MH  - Pregnancy
MH  - Female
MH  - Humans
MH  - *Pre-Eclampsia/diagnosis
MH  - Algorithms
OTO - NOTNLM
OT  - characteristics extraction
OT  - machine learning
OT  - morphology
OT  - photoplethysmographic pulse wave
OT  - preeclampsia
EDAT- 2023/06/20 06:41
MHDA- 2023/06/21 06:42
CRDT- 2023/06/20 02:43
PHST- 2023/06/21 06:42 [medline]
PHST- 2023/06/20 06:41 [pubmed]
PHST- 2023/06/20 02:43 [entrez]
AID - 10.1080/10641955.2023.2225617 [doi]
PST - ppublish
SO  - Hypertens Pregnancy. 2023 Dec;42(1):2225617. doi: 10.1080/10641955.2023.2225617.