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。 ;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.
