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. 2021 Nov 23;11:22767. doi: 10.1038/s41598-021-01358-4

Table 3.

Evaluation of different models for estimating BP from PAT or PTT.

	A posteriori models					Population-based models
	Model		RMSE	MAE	MAD	Model		RMSE	MAE	MAD
PAT	BP = $\frac{a}{PAT}$ + b	SBP	5.50	3.63	4.14	Poon¹⁶	SBP	11.04	7.10	8.46
	BP = $\frac{a}{PAT}$ + b	DBP	4.49	3.42	2.92	Poon¹⁶	DBP	8.37	6.12	5.71
	BP = $\frac{a}{{PAT}^{2}}$ + b	SBP	5.48	3.61	4.12	Gesche¹⁸	SBP	43.76	21.63	38.06
	BP = $\frac{a}{{PAT}^{2}}$ + b	DBP	4.49	3.42	2.92	Gesche¹⁸	DBP	46.70	22.93	40.71
	BP = a $\times$ ln(PAT) + b	SBP	5.53	3.66	4.15	Fung³⁷	SBP	8.51	5.70	6.33
	BP = a $\times$ ln(PAT) + b	DBP	4.50	3.42	2.92	Fung³⁷	DBP	8.02	5.90	5.43
PTT	BP = $\frac{a}{PTT}$ + b	SBP	3.98	2.82	2.81	Poon¹⁶	SBP	8.49	5.40	6.56
	BP = $\frac{a}{PTT}$ + b	DBP	4.02	3.12	2.54	Poon¹⁶	DBP	7.72	5.64	5.26
	BP = $\frac{a}{{PTT}^{2}}$ + b	SBP	3.91	2.78	2.76	Gesche¹⁸	SBP	4542	1773	4185
	BP = $\frac{a}{{PTT}^{2}}$ + b	DBP	4.01	3.12	2.53	Gesche¹⁸	DBP	4546	1776	4188
	BP = a $\times$ ln(PTT) + b	SBP	4.05	2.86	2.87	Fung³⁷	SBP	30.69	20.77	22.61
	BP = a $\times$ ln(PTT) + b	DBP	4.03	3.12	2.55	Fung³⁷	DBP	33.80	22.45	25.29

All values are given in units of mmHg. Best performing models for PAT and PTT are highlighted in bold.