| OCR Text |
Show 129 attributes were: sensitivity to dogs noise artifact. and neural the pooled, were increased accuracy and 1) networks performed similarly with respect standard deviations of the diastolic, 2.36 differences diastolic, -0.20 5.07, ± and mean, invasive of the neural blood pressure 5.77 oscillometric intrasubject standard and noninvasive As estimates of -0.08 ± 4.38, noninvasive was of superior to smaller the example, average differences between estimates of mean blood pressure by conventional oscillometric algorithms and neural networks were mmHg and 2.74 mmHg, 3.87 challenged with random noise 8.23 and data (4 pulses) The neural 5.45 mmHg, 6.66 smaller networks changes in a and 3.58 a oscillometric algorithms. In When these values were challenged with sparse respectively. standard deviations more given subject's and when mmHg, intrasubject provided respectively. (120% peak-to-peak) respectively, mean differences between an standard deviation of the and 4.20, The pooled were in terms of the methods. ± of respectively. algorithms deviations 3.52 network performance of the networks mmHg, and measurements differences between the ± of standard were 0.47 invasive measurements the ± respectively. mmHg, systolic conventional intrasubject differences and and However, invasive mean blood pressure and noninvasive invasive measurements five oscillometric algorithm estimates 5.68 ± from all oscillometric estimates standard deviations the ± the data conventional algorithms differences mean systolic 1.71 and 5.15, ± and mean, and differences between conventional noninvasive the decreased differences between simultaneous The pooled of the deviations to and noninvasive invasive measurements blood pressure. When 2) indicate that reliable method for blood pressure addition, when than the identify true did conventional conventional |
