commit 87752554f2ead159f37ea5564c981e5720390a7e Author: patsyluft53928 Date: Wed Nov 5 10:14:37 2025 +0800 Add HYPE: Predicting Blood Pressure from Photoplethysmograms in A Hypertensive Population diff --git a/HYPE%3A-Predicting-Blood-Pressure-from-Photoplethysmograms-in-A-Hypertensive-Population.md b/HYPE%3A-Predicting-Blood-Pressure-from-Photoplethysmograms-in-A-Hypertensive-Population.md new file mode 100644 index 0000000..1bf08e5 --- /dev/null +++ b/HYPE%3A-Predicting-Blood-Pressure-from-Photoplethysmograms-in-A-Hypertensive-Population.md @@ -0,0 +1,7 @@ +
The original model of this chapter was revised: a brand new reference and a minor change in conclusion part has been up to date. The cutting-edge for monitoring hypertension relies on measuring blood stress (BP) utilizing uncomfortable cuff-based devices. Hence, for increased adherence in monitoring, a greater method of measuring BP is required. That might be achieved by means of comfortable wearables that comprise photoplethysmography (PPG) sensors. There have been several studies exhibiting the opportunity of statistically estimating systolic and diastolic BP (SBP/DBP) from PPG indicators. However, they are both primarily based on measurements of wholesome topics or on patients on (ICUs). Thus, there may be a lack of studies with patients out of the traditional range of BP and with each day life monitoring out of the ICUs. To handle this, we created a dataset (HYPE) composed of data from hypertensive subjects that executed a stress [BloodVitals test](https://files.lab18.net/nickfeint73434/bloodvitals-home-monitor2024/wiki/Do-you-all-the-Time-get-a-Bloody-Nose-With-Altitude-Sickness%3F) and had 24-h monitoring. We then educated and compared machine learning (ML) models to predict BP.
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We evaluated handcrafted feature extraction approaches vs picture representation ones and compared different ML algorithms for both. Moreover, [BloodVitals test](https://hiddenwiki.co/index.php?title=User:RobElder7175369) so as to guage the fashions in a unique situation, we used an brazenly obtainable set from a stress check with healthy topics (EVAL). Although having examined a range of sign processing and ML strategies, [Blood Vitals](https://mqbinfo.com/w/User:TeganSkinner257) we were not able to reproduce the small error ranges claimed within the literature. The mixed results counsel a necessity for more comparative studies with topics out of the intensive care and throughout all ranges of blood pressure. Until then, the clinical relevance of PPG-based mostly predictions in day by day life should remain an open question. A. M. Sasso and S. Datta-The 2 authors contributed equally to this paper. This can be a preview of subscription content material, log in through an establishment to test access. The unique version of this chapter was revised. The conclusion section was corrected and reference was added.
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Challoner, A.V., [BloodVitals SPO2](http://112.124.40.88:5510/maxiefossey584/maxie1991/wiki/What-was-the-Predator-Drone%3F) Ramsay, C.A.: A photoelectric plethysmograph for the measurement of cutaneous blood movement. Elgendi, M., et al.: The usage of photoplethysmography for assessing hypertension. Esmaili, A., Kachuee, M., Shabany, M.: Nonlinear cuffless blood pressure estimation of healthy subjects using pulse transit time and arrival time. IEEE Trans. Instrum. Meas. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ghamari, M.: A assessment on wearable photoplethysmography sensors and their potential future purposes in well being care. Int. J. Biosens. Bioelectron. Gholamhosseini, H., Meintjes, A., [BloodVitals SPO2](https://parentingliteracy.com/wiki/index.php/A_New_Simple_Proxy_To_Measure_Oxygen_Unloading_And_The_Standard_Of_Stored_Blood) Baig, M.M., Lindén, M.: Smartphone-based mostly steady blood pressure measurement using pulse transit time. Goldberger, A.L., et al.: PhysioBank, physioToolkit, and physioNet: parts of a brand new analysis useful resource for complex physiologic indicators. He, K., Zhang, X., [BloodVitals test](http://kcosep.com/2025/bbs/board.php?bo_table=free&wr_id=3072747&wv_checked_wr_id=) Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. He, K., Zhang, X., Ren, [BloodVitals test](https://americanspeedways.net/index.php/Home_Blood_Pressure_Monitoring) S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.
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Ke, G., et al.: LightGBM: a extremely efficient gradient boosting resolution tree. In: Advances in Neural Information Processing Systems, [BloodVitals SPO2](http://youtools.pt/mw/index.php?title=New_At-Dwelling_Monitoring_Program_For_Patients_With_High_Blood_Pressure) pp. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, [BloodVitals wearable](http://222.186.21.32:20000/claudette52b6/blood-vitals1988/wiki/Home+Health+Monitors) pp. Kurylyak, Y., Lamonaca, F., Grimaldi, D.: A neural network-primarily based methodology for continuous blood stress estimation from a PPG sign. In: Conference Record - IEEE Instrumentation and Measurement Technology Conference, pp. Li, Q., Clifford, G.D.: Dynamic time warping and machine studying for sign quality assessment of pulsatile indicators. Liang, Y., Chen, Z., Ward, R., Elgendi, M.: Photoplethysmography and deep learning: enhancing hypertension threat stratification. Liang, Y., Elgendi, M., Chen, Z., Ward, R.: Analysis: an optimal filter for brief photoplethysmogram signals. Luštrek, [BloodVitals test](https://lolipop-pandahouse.ssl-lolipop.jp:443/g5/bbs/board.php?bo_table=aaa&wr_id=2886247) M., Slapničar, G.: Blood stress estimation with a wristband optical sensor. Manamperi, B., Chitraranjan, C.: [BloodVitals test](https://codeforweb.org/mediawiki_tst/index.php?title=Exercise_Hot_Weather) A sturdy neural network-based mostly methodology to estimate arterial blood pressure utilizing photoplethysmography. In: 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), pp.
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