# QRS Detection Based on Medical Knowledge and Cascades of Moving Average Filters
## 3 Results
In [PERSON]'s approach the QRS detection is  
Figure 8: Detection markers for a strip of channel 2 in record 203 of the MIT-BIH Arrhythmia database. This is an example of a critical instance of signal, were both false positives and false negatives are generated. performed on the signal's energy as well, by checking both the quantity and the dynamic of the QRS complex' energy with adaptive thresholds. A valid heartbeat detection requires both criteria to be satisfied. [PERSON]'s algorithm is structured on two adaptive thresholds that distinguish between signal and noise peaks, a decision logic that corrects multiple detections due to noise or T waves and a backward search procedure. These algorithms were made available by their authors. We did not edit their code, but we did optimize their control parameter(s). Usually, each QRS detector is controlled by one or more control parameters that operate on the decision thresholds. The results we report were calculated using the control parameters that yielded the best performances for each algorithm. Each of these programs was run in MATLAB, on the same calculator (2013 notebook with I7-dual [EMAIL_ADDRESS] GHz, 6 Gb RAM), with the same background conditions, at the same time of day, each one in succession. Table 2 reports the gross and average F1 score achieved by the QRS detectors on all the considered. No record was excluded. Table 3 shows the gross and average Se-A results. The T Wave Alternans database is not feature in this table, as it lacks annotations for non-sinus beats. The highest F1 and Se-A scores for each channel of each database and for the whole dataset are highlighted in bold. Regarding the computational cost of the algorithms, in Table 4 we show that our approach was faster than the other QRS detectors we considered. The comparison was \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Database**} & \multicolumn{5}{c}{**F1 \%**} \\ \cline{2-5}  & **This Work** & **Behar** & **[PERSON]** & **Sedghamiz** \\ \hline MIT-BIH ch1 & 99.84 & 99.08 & **99.88** & 99.36 \\ MIT-BIH ch2 & **98.47** & 96.98 & 97.28 & 95.00 \\ Eu ST-T ch1 & **99.65** & 99.48 & 99.12 & 99.10 \\ Eu ST-T ch2 & **99.46** & 98.21 & 98.69 & 98.34 \\ INCART ch1 & **96.51** & 94.95 & 90.67 & 94.24 \\ INCART ch2 & **99.65** & 99.14 & 97.84 & 98.20 \\ NST ch1 & **91.71** & 89.87 & 91.01 & 88.41 \\ NST ch2 & **94.24** & 93.38 & 78.62 & 92.37 \\ TWA ch1 & **98.20** & 97.60 & 94.72 & 93.94 \\ TWA ch2 & **99.77** & 90.38 & 97.46 & 99.20 \\ QT ch1 & 99.92 & **99.95** & 99.32 & 99.47 \\ QT ch2 & **99.91** & 98.64 & 99.03 & 99.57 \\ \hline Gross & **99.18** & 98.31 & 97.94 & 98.05 \\ Average & **98.11** & 96.47 & 95.30 & 96.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: QRS detection F1 score. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Database**} & \multirow{2}{*}{**Abnormal Beats**} & \multicolumn{5}{c}{**Se-A \%**} \\ \cline{3-5}  & & **This Work** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline MIT-BIH ch1 & 23,456 & 96.47 & 95.77 & **96.56** & 96.08 \\ MIT-BIH ch2 & 23,456 & **94.91** & 93.93 & 93.57 & 90.41 \\ Eu ST-T ch1 & 4822 & **98.18** & 97.53 & 97.86 & 97.93 \\ Eu ST-T ch2 & 4822 & **94.11** & 93.38 & 91.52 & 93.59 \\ INCART ch1 & 23,407 & 86.16 & 82.49 & 76.50 & **91.48** \\ INCART ch2 & 23,407 & **99.20** & 98.44 & 97.03 & 98.77 \\ NST ch1 & 15,756 & **95.69** & 95.17 & 91.45 & 94.28 \\ NST ch2 & 15,756 & 96.22 & **96.91** & 81.87 & 97.51 \\ QT ch1 & 2631 & **96.85** & 95.97 & 96.66 & 95.13 \\ QT ch2 & 2631 & **96.12** & 94.56 & 95.06 & 94.45 \\ \hline Gross & & **94.81** & 93.71 & 90.41 & 94.70 \\ Average & & **95.39** & 94.42 & 91.81 & 94.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: QRS detection Se-A score. made with the same background conditions. Each detector was run on all the databases we introduced, in succession. We report the average execution time.