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To achieve non-invasive and precise prediction of mean arterial pressure (MAP) based on a fully convolutional neural network (FCNN).
A high-precision blood pressure data acquisition system compliant with international metrological standards was used in conjunction with the ‘gold standard’ auscultation method to collect blood pressure and pulse waveform data from patients. True MAP values were derived via Gaussian fitting of pulse waveform data, constructing a traceable dataset. The FCNN was applied to this dataset to develop a novel MAP prediction method. Additionally, the predictive accuracy of the FCNN was compared with linear regression and conventional empirical formulas.
The mean squared errors (MSE) for MAP prediction using the FCNN, linear regression, and empirical formulas were 19.76, 21.40, and 30.97, respectively. The coefficients of determination (R2) were 0.90, 0.89, and 0.84, and the prediction accuracies were 0.90, 0.89, and 0.85, respectively.
By using systolic blood pressure, diastolic blood pressure, age, and arm circumference as input parameters, the FCNN-based MAP prediction method significantly reduces the bias of empirical formulas. This approach not only improves the accuracy of hemodynamic boundary condition acquisition but also contributes to refining the metrological traceability system of non-invasive blood pressure measurement.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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