結合機器學習與光體積血容積之連續血壓偵測系統分析

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姓名

郎又諄(Yu-Chun Lang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

結合機器學習與光體積血容積之連續血壓偵測系統分析
(Analysis of continuous blood pressure detection system combining machine learning and photoplethysmography)

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至系統瀏覽論文 (2026-6-30以後開放)

摘要(中)

根據2022年國人十大死因分析指出，國人因心血管相關疾病造成的死亡總人數，已超越癌症死亡率最高的肺癌與肝癌，其中高血壓更是目前已開發國家中引起心臟血管疾病及死亡的重要原因。為了有效預防心血管疾病，連續的血壓偵測尤為重要。
本研究以一次心跳所產生的photoplethysmography(PPG)訊號為單位，預測連續的舒張壓和收縮壓。並探討4種機器學習模型對於血壓預測的結果，比較3種最常見的連續血壓偵測方法，分別為PPG+ Electrocardiography(ECG)、PPG和pulse transit time(PTT)。進一步的分析本文所選的32個特徵對於血壓預測的顯著性，最後完整的比較舒張壓及收縮壓最適合的特徵及方法。
在本次的研究我們發現，Linear regression和Random forest regression模型的彈性大，能夠全面的預測較為擴散的血壓分布。特徵群比較的部分，PTT是三種方法中唯一沒有使用特徵工程的方法，因此準確率相較於PPG+ECG和PPG表現較差。舒張壓最好的結果是採用PPG方法，且刪除後25%重要度較低的特徵，Mean absolute error來到2.21mmHg，標準差為3.6。收縮壓表現最好的方法則是，未刪特徵的PPG+ECG，Mean absolute error為4.2mmHg，標準差為6.96。在特徵方面也發現了，上升高度之寬度適合收縮壓預測、下降高度之寬度適合舒張壓預測、收縮壓相比舒張壓更需要ECG相關特徵。

摘要(英)

According to the 2022 analysis of the top ten causes of death in Taiwan, the total number of deaths caused by cardiovascular-related diseases has surpassed those caused by the most lethal cancers, including lung and liver cancer. Hypertension, in particular, is a leading cause of cardiovascular diseases and mortality in developed countries. Continuous blood pressure monitoring is crucial for the effective prevention of cardiovascular diseases.
This study focuses on predicting continuous diastolic and systolic blood pressure using the photoplethysmography (PPG) signal generated by a single heartbeat. It explores the results of four machine learning models for blood pressure prediction and compares three common continuous blood pressure detection methods: PPG+Electrocardiography (ECG), PPG alone, and pulse transit time (PTT). The study further analyzes the significance of 32 selected features for blood pressure prediction and compares the most suitable features and methods for predicting diastolic and systolic blood pressure.
Our research found that the linear regression and random forest regression models are highly flexible, allowing for comprehensive prediction of more dispersed blood pressure distributions. Among the feature groups, PTT is the only method that does not use feature engineering, resulting in lower accuracy compared to PPG+ECG and PPG methods. The best results for diastolic pressure prediction were obtained using the PPG method, with a mean absolute error (MAE) of 2.21 mmHg and a standard deviation of 3.6, after removing the 25% least significant features. The best method for systolic pressure prediction was PPG+ECG without feature removal, achieving an MAE of 4.2 mmHg and a standard deviation of 6.96. Additionally, we found that the width of the ascending height is suitable for systolic pressure prediction, the width of the descending height is suitable for diastolic pressure prediction, and systolic pressure prediction requires ECG-related features more than diastolic pressure prediction.

關鍵字(中)

★ 連續血壓
★ 光體積血容積

關鍵字(英)

論文目次

中文摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vi
第一章緒論 1
1-1 研究動機與目標 1
1-2 文獻探討 3
第二章研究設計與方法 5
2-1 血壓預測流程 5
2-2 實驗數據來源 6
2-3 訊號預處理 8
2-4 特徵演算法及特徵介紹 9
2-4-1 PPG onset / systolic peak特徵抓取 9
2-4-2 PPG dicrotic notch / diastolic peak特徵抓取 11
2-4-3 ECG R波特徵抓取 13
2-4-4 特徵選取 14
2-5 機器學習模型 17
2-5-1 Linear regression 17
2-5-2 Random forest regression 17
第三章結果與討論 18
3-1 特徵群比較 18
3-1-1 舒張壓特徵群比較 18
3-1-2 收縮壓特徵群比較 19
3-2 特徵重要度分析 20
3-2-1 Linear regression特徵重要度排序 20
3-2-2 Random forest regression特徵重要度排序 22
3-3 特徵與血壓預測 24
3-3-1 Linear regression特徵與血壓預測 24
3-3-2 Random forest regression特徵與血壓預測 26
3-4 Bland-Altman plot和連續血壓預測圖 28
3-4-1 Linear regression Bland-Altman plot 28
3-4-2 Random forest regression Bland-Altman plot 29
3-4-3 連續血壓預測圖 30
第四章結論與總結 31
4-1 特徵群比較 31
4-2 特徵重要度分析 32
4-3 特徵與血壓預測 32
4-4 Bland-Altman plot和連續血壓預測圖 33
4-5 總結 33
第五章未來展望 35
第六章參考文獻 36

參考文獻

〔1〕 https://www.hpa.gov.tw/EngPages/Detail.aspx?nodeid=3999&pid=15562
〔2〕 M. Gurven, A.D. Blackwell, D.E. Rodríguez, J. Stieglitz, H. Kaplan, Does blood
pressure inevitably rise with age? Longitudinal evidence among forager horticulturalists.
60.1 (2012): 25–33.
〔3〕 Allen, John. "Photoplethysmography and its application in clinical physiological
measurement." Physiological measurement 28.3 (2007): R1.
〔4〕 Millasseau, Sandrine C., et al. "Noninvasive assessment of the digital volume pulse:
comparison with the peripheral pressure pulse." Hypertension 36.6 (2000): 952-956.
〔5〕 Kumar, Niranjan, Amogh Agrawal, and Sujay Deb. "Cuffless BP measurement using a
correlation study of pulse transient time and heart rate." 2014 International Conference
on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2014.
〔6〕 Song, Yonghao, et al. "EEG conformer: Convolutional transformer for EEG decoding
and visualization." IEEE Transactions on Neural Systems and Rehabilitation
Engineering 31 (2022): 710-719.
〔7〕 Chen, Yan, et al. "Continuous and noninvasive measurement of systolic and diastolic
blood pressure by one mathematical model with the same model parameters and two
separate pulse wave velocities." Annals of biomedical engineering 40 (2012): 871-882.
〔8〕 Mukkamala, Ramakrishna, et al. "Toward ubiquitous blood pressure monitoring via
pulse transit time: theory and practice." IEEE transactions on biomedical engineering
62.8 (2015): 1879-1901.
〔9〕 Peter, Lukáš, Norbert Noury, and M. Cerny. "A review of methods for non-invasive and
continuous blood pressure monitoring: Pulse transit time method is promising?." Irbm
35.5 (2014): 271-282.
〔10〕 Kachuee, Mohamad, et al. "Cuff-less high-accuracy calibration-free blood pressure
estimation using pulse transit time." 2015 IEEE international symposium on circuits
and systems (ISCAS). IEEE, 2015.
〔11〕 Wang, Ruiping, et al. "Cuff-free blood pressure estimation using pulse transit time and
heart rate." 2014 12th international conference on signal processing (ICSP). IEEE,
2014.
〔12〕 Ruiz-Rodríguez, Juan C., et al. "Innovative continuous non-invasive cuffless blood
pressure monitoring based on photoplethysmography technology." Intensive care
medicine 39 (2013): 1618-1625.
〔13〕 Samria, Rohan, et al. "Noninvasive cuff′less estimation of blood pressure using
Photoplethysmography without electrocardiograph measurement." 2014 IEEE Region 10 Symposium. IEEE, 2014.
〔14〕 Sideris, Costas, et al. "Building continuous arterial blood pressure prediction models
using recurrent networks." 2016 IEEE International Conference on Smart Computing
(SMARTCOMP). IEEE, 2016.
〔15〕 Slapničar, Gašper, Nejc Mlakar, and Mitja Luštrek. "Blood pressure estimation from
photoplethysmogram using a spectro-temporal deep neural network." Sensors 19.15
(2019): 3420.
〔16〕 Zhang, Bing, et al. "An empirical study on predicting blood pressure using
classification and regression trees." IEEE access 6 (2018): 21758-21768.
〔17〕 Miao, Fen, et al. "A novel continuous blood pressure estimation approach based on
data mining techniques." IEEE journal of biomedical and health informatics 21.6
(2017): 1730-1740.
〔18〕 Attarpour, Ahmadreza, et al. "Cuff-less continuous measurement of blood pressure
using wrist and fingertip photo-plethysmograms: Evaluation and feature analysis."
Biomedical Signal Processing and Control 49 (2019): 212-220.
〔19〕 Wang, B., Huang, Z., Wu, J., Liu, Z., Liu, Y., & Zhang, P. (2019). Continuous blood
pressure estimation using PPG and ECG signal. In Advances in Body Area Networks
I: Post-Conference Proceedings of BodyNets (2017): 61-74.
〔20〕 Schafer, Ronald W. "What is a savitzky-golay filter?[lecture notes]." IEEE Signal
processing magazine 28.4 (2011): 111-117.
〔21〕 El-Hajj, Chadi, and Panayiotis A. Kyriacou. "Deep learning models for cuffless blood
pressure monitoring from PPG signals using attention mechanism." Biomedical Signal
Processing and Control 65 (2021): 102301.
〔22〕 Infotek,"Savitzky-Golay 濾波函數",2022.
〔23〕 Hongxing Li,A High-Efficiency and Real-Time Method for Quality Evaluation of
PPG Signals,2020
〔24〕 Asada, H. Harry, et al. "Mobile monitoring with wearable photoplethysmographic
biosensors." IEEE engineering in medicine and biology magazine 22.3 (2003): 28-40.
〔25〕 Chua, Chern Pin, and Conor Heneghan. "Continuous blood pressure monitoring using
ECG and finger photoplethysmogram." 2006 International Conference of the IEEE
Engineering in Medicine and Biology Society. IEEE, 2006.
〔26〕 Murray, Willie Bosseau, and Patrick Anthony Foster. "The peripheral pulse wave:
information overlooked." Journal of clinical monitoring 12 (1996): 365-377.
〔27〕 Dorlas, J. C., and J. A. Nijboer. "Photo-electric plethysmography as a monitoring
device in anaesthesia: application and interpretation." British journal of anaesthesia
57.5 (1985): 524-530.
〔28〕 Chua, Eric Chern-Pin, et al. "Towards using photo-plethysmogram amplitude to
measure blood pressure during sleep." Annals of biomedical engineering 38 (2010): 945-954.
〔29〕 Awad, Aymen A., et al. "The relationship between the photoplethysmographic
waveform and systemic vascular resistance." Journal of clinical monitoring and
computing 21 (2007): 365-372.
〔30〕 Teng, X. F., and Y. T. Zhang. "Continuous and noninvasive estimation of arterial blood
pressure using a photoplethysmographic approach." Proceedings of the 25th Annual
International Conference of the IEEE Engineering in Medicine and Biology Society
(IEEE Cat. No. 03CH37439). Vol. 4. IEEE, 2003.
〔31〕 A.A. Awad, A.S. Haddadin, H. Tantawy, T.M. Badr, R.G. Stout, D.G. Silverman, K. H.
Shelley, The relationship between the photoplethysmographic waveform and systemic
vascular resistance, Journal of clinical monitoring and computing 21 (6) (2007) 365–
372.
〔32〕 Kurylyak, Yuriy, Francesco Lamonaca, and Domenico Grimaldi. "A Neural Networkbased method for continuous blood pressure estimation from a PPG signal." 2013
IEEE International instrumentation and measurement technology conference (I2MTC).
IEEE, 2013.
〔33〕 Poon, Carmen CY, et al. "Changes in the photoplethysmogram waveform after
exercise." 2004 2nd IEEE/EMBS International Summer School on Medical Devices
and Biosensors. IEEE, 2004.
〔34〕 Wang,L.,etal."Noninvasive cardiacoutputestimationusinganovel photoplethysmogram
index." 2009 annual international conference of the IEEE engineering in medicine and
biology society. IEEE, 2009.
〔35〕 Thambiraj, Geerthy, et al. "Investigation on the effect of Womersley number, ECG and
PPG features for cuff less blood pressure estimation using machine learning."
Biomedical Signal Processing and Control 60 (2020): 101942.
〔36〕 N. Kumar, A. Agrawal, S. Deb, Cuffless BP measurement using a correlation study of
pulse transient time and heart rate, in: 2014 International Conference on Advances in
Computing, Communications and Informatics (ICACCI), IEEE, 2014.
〔37〕 Y. Chen, et al., Continuous and noninvasive blood pressure measurement: a novel
modeling methodology of the relationship between blood pressure and pulse wave
velocity, Ann. Biomed. Eng. 37 (11) (2009) 2222–2233.
〔38〕 J. Allen, Photoplethysmography and its application in clinical physiological
measurement, Physiol. Meas. 28 (3) (2007) R1–39

指導教授

李柏磊

審核日期

2024-7-27

推文