| 摘要(英) |
According to statistics from the World Health Organization, cardiovascular diseases are a major cause of death globally. Acute symptoms such as myocardial infarction and stroke are life-threatening emergencies, making daily care and prevention crucial. Early medical intervention can reduce mortality rates and the severity of post-incident complications. Both blood pressure (BP) and pulse wave velocity (PWV) are important indicators of cardiovascular disease. PWV measurements require hospital equipment, while BP is typically measured using a sphygmomanometer, which is uncomfortable and unsuitable for long-term monitoring, particularly during sleep. This study uses a wearable device to collect physiological signals. PWV is estimated using photoplethysmography (PPG) and electrocardiogram (ECG) signals from the wrist, and BP is estimated using only PPG signals. At signal preprocessing stage, 60Hz power line interference and baseline wandering are removed, and poor-quality waveforms are filtered out through pre-screening. Feature extraction and weighted waveform decomposition are then performed to obtain the features. Features extracted from poor-quality waveforms can be dispelled according to signal quality index. In the data processing step, the median of each feature sequence is obtained, and outliers are removed to ensure representativeness. The data is divided into training, validation, and test datasets. To balance the data range, the training dataset is additionally duplicated. The best result for PWV estimation uses a hierarchical regression model combining a random forest classifier and an extreme gradient boosting (XGBoost) regressor. The average root mean square error (RMSE) for males is 145.0 cm/s and 141.4 cm/s for females. Additionally, a calibrated personalized model provides daytime and nighttime BP estimates, including sleep period, in daily life. Using a model-agnostic meta-learning (MAML) algorithm, the concept of few-shot learning allows the model to converge quickly with only a small amount of calibration data, enhancing user convenience. With 5 calibration data, the mean error and standard deviation for SBP across 15 individuals are 0.06 ± 6.90 mmHg, with an RMSE of 6.76 mmHg. For DBP, the mean error and standard deviation are 0.01 ± 6.54 mmHg, with an RMSE of 6.82 mmHg. The mean absolute error for nighttime SBP dip is 4.80 mmHg. When reduced to 3 calibration data, the mean error and standard deviation for SBP are -0.62 ± 7.62 mmHg, with an RMSE of 7.57 mmHg. For DBP, the mean error and standard deviation are -0.32 ± 6.96 mmHg, with an RMSE of 7.00 mmHg. The mean absolute error for nighttime SBP dip is 5.77 mmHg. |
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