Image Processing Reference
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In the literature there are several algorithmic approaches for detecting QRS complexes of
ECG signal with pre-filtering of the signal (Thakor et al., 1984)
The implementation of incremental improvements to a classical algorithm to detect QRS
complexes was realized in an experiment as mentioned in (Vidal et al., 2008; Vidal & Gatica,
2010) which in its original form do not have a great performance. The first improvement
based on the first derivative is proposed and analyzed in (Friese at al., 1990). The second
improvement is based on the use of nonlinear transformations proposed in (Pan &
Tompkins, 1985) and analyzed in (Suppappola & Ying, 1994; Hamilton & Tompkins, 1986).
The third is proposed and analyzed in (Vidal & Pavesi, 2004; Vidal et al., 2008), as an
extension and improvement of that is presented in (Friesen et al., 1994) using characteristics
of the algorithm proposed in (Pan & Tompkins, 1985). It should be noted that the three
algorithmic improvements recently mentioned, used classical techniques of DSP (Digital
Signal Processing). It is noteworthy to indicate that the second improvement proposed in
(Pan & Tompkins, 1985) is of great performance in the accurate detection of QRS complexes,
for even the modern technology are not able to provide better results.
To test the algorithms that work on ECG signal, it is not necessary to implement a data
acquisition system. There are specialized databases with ECG records for analyzing the
performance of any algorithm to work with ECG signals (Cuesta, 2001; Vidal & Pavesi,
2004). One of the most important is the MIT DB BIH (database of arrhythmias at
Massachusetts Institute of Technology,) (MIT DB, 2008).
In Tables 4, 5, 6 and 7, respectively, are the results obtained with the application of
incremental improvements made to the first algorithm for detecting QRS complexes in some
records at MIT DB BIH. A good level of performance reached in the final version of
algorithm of detection of QRS complexes implemented in this work could be appreciated,
(Table 7), compared to its original version (Table 4)
Pulses
Heart
(NL)
True
Positives
(PV)
False
Positives
(PF)
False
Negatives
(NF)
Signal
(PF + NF) / NL
R. 1118 - S. 1 2278 2278 79676 0 3497,63%
R. 118 - S. 2 2278 2278 77216 0 3389,64%
R. 108 - S. 1 562 562 8933 0 1589,50%
R. 108 - S. 2 562 562 17299 0 3078,11%
Table 4. Results obtained with the Holsinger Algorithm in its Original version, 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 1558 874 720 69,97%
R. 118 - S. 2 2278 1650 798 628 62,60%
R. 108 - S. 1 562 346 246 216 82,20%
R. 108 - S. 2 562 490 182 72 45,20%
Table 5. Results obtained with the Holsinger Algorithm in its Modified version 1, for some
of the MIT Database records.
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