Image Processing Reference
In-Depth Information
Pulses
Heart
(NL)
True
Positives
(PV)
False
Positives
(PF)
False
Negatives
(NF)
Signal
(PF + NF) / NL
R. 1118 - S. 1 2278 2265 4 13 0,5%
R. 118 - S. 2 2278 2263 11 15 1,80%
R. 108 - S. 1 562 538 35 24 10,49%
R. 108 - S. 2 562 524 76 38 20,28%
Table 6. Results obtained with the Holsinger Algorithm Modified Version 2, for some of the
MIT Database records
Pulses
Heart
(NL)
True
Positives
(PV)
False
Positives
(PF)
False
Negatives
(NF)
Signal
(PF + NF) / NL
R. 1118 - S. 1 2278 2265 1 1 0,08%
R. 118 - S. 2 2278 2263 1 2 0,13%
R. 108 - S. 1 562 542 1 15 2,84%
R. 108 - S. 2 562 538 23 21 7,82%
Table 7. Results obtained with the Holsinger Algorithm Modified Version 3, for some of the
MIT Database records
5. Conclusion
The implementation of equipments for the acquisition and processing of bioelectrical human
signals such as the ECG signal is currently a viable task. This chapter is a summary of
previous works with simple equipment to work with the ECG signal. Currently the authors
are working on:
Improvements to the work done:
Increase the number of leads purchased. The A/D converter allows up to 11
simultaneous inputs and supports a sampling rate of 32 KHz. Under certain conditions.
12 simultaneous leads are required for a professional team.
Modify RC filters in the filter stage for more elaborate filters to ensure a better
discrimination of the frequencies that are outside the pass-band.
Include isolation amplifiers to increase levels for the security of patients, isolating the
direct loop with the computer, which is generated with the design proposed in this
chapter. Even with the probability of a catastrophe to occur which are low, but the
possibility exists and such massive use should be avoided, before including these
amplifiers.
Unifying routine readings of A/D converter and display of results.
Certify the technical characteristics of the circuits mounted in order to validate its
massive use.
Future works:
Increase the use of this equipment for capturing other bioelectrical signals such as
electroencephalographic and electromygraphic.
Implement a tool to validate algorithms of detection QRS, based on the MIT DB.
