The document discusses the application of machine learning in biosignal processing, emphasizing the importance of sensitivity and precision in hardware design and digital signal processing. It provides calculations related to true positive rate and positive predictive value based on various parameters. The author, Rafał Samborski, is affiliated with Consonance sp. z o.o. in Kraków, Poland.

1. Machine Learning in
Biosignal processing
Rafał Samborski, CEO
Sensitivity and precision

2. Stay focused on
your business
Hardware design
Research & Development
Digital Signal Processing

3. Stay focused on
your business

7. P = 3
N = 7

9. TP = 2
TN = 5

10. FP = 2
FN =1

11. Sensitivity

12. Sensitivity
True positive rate

13. Sensitivity
TPR =
TP
P

15. P = 3
TP = 2
TPR ≈ 66%

17. P = 3
TP = 3
TPR =100%

18. Precision

19. Precision
Positive predictive value

20. Precision
PPV =
TP
(TP + FP)

22. P = 3
TP = 2
FP = 2
PPV = 50%

24. P = 3
TP = 3
FP = 7
PPV = 30%

25. Sensivity
Precision

26. Sensivity
Precision
RMS error for fetal RR-
interval estimation

27. Consonance sp. z o. o.
ul. Czarnowiejska 50b
30-054 Kraków, Poland
rafal.samborski@consonance.com.pl
@Rafal_Samborski
http://consonance.com.pl