# QRS Detection Based on Medical Knowledge and Cascades of Moving Average Filters
## 2 Materials and Methods  
### Data  
The algorithms we describe were tested on five public datasets available on the Physionet repository [28]: the MIT-BIH Arrhythmia (here abbreviated as MIT-BIH) database, the European ST-T (Eu ST-T) database, INCART database, the MIT-BIH Noise Stress Test (NST) database, the T Wave Alternans (TWA) database and the QT database. Table 1 is a summary of their features. These databases are often the common ground of performance validation for studies on detection of ECG characteristic waves. Considering multiple databases entails the advantage of testing the algorithms on a wide range of possible scenarios influenced by noise and physiological variability.  
The MIT-BIH Arrhythmia database [24] is a collection of 48 records sampled at 360 Hz. Each record is 30 min long and comprises two channels. The first channel is a modified lead II. The second channel is often akin to lead \(V_{1}\) and occasionally to \(V_{2}\), \(V_{4}\), or \(V_{5}\). We report the performances for QRS detection without excluding any record.  
The European ST-T Database [29] is a collection of ambulatory ECGs developed for the study of ST tract and T wave changes in myocardial ischemia and other abnormalities. The collection consists of 90, 2-h long excerpts, sampled at 250 Hz. The first channel is usually a modified limb lead (I or III), where the QRS complex is evident. The second channel is usually a precordial lead, where the ST episodes are prominent (\(V_{1-5}\)).  
\begin{table}
\begin{tabular}{l c c c} \hline
**Database** & **No. of Records** & **Record Length** & **No. of Beats** \\ \hline MIT-BIH & 48 & 30\({}^{\prime}\) & 109,494 \\ Eu ST-T & 90 & 120\({}^{\prime}\) & 790,565 \\ INCART & 75 & 30\({}^{\prime}\) & 175,906 \\ NST & 12 & 30\({}^{\prime}\) & 25,590 \\ TWA & 100 & \(\sim\)2\({}^{\prime}\) & 18,991 \\ QT & 82 & 15\({}^{\prime}\) & 86,995 \\ \hline \end{tabular}
\end{table}
Table 1: Database features.  
The St. Petersburg INCART 12-lead Arrhythmia Database [28] includes 75 excerpts sampled at 257 Hz, 30 min long each. The excerpts were extracted from 32 Holter recordings of different patients testing for coronary artery disease. The included annotations were first produced by an algorithm and then corrected manually. Each recording in the database is a full 12-lead recording.  
The MIT-BIH Noise Stress test database [30] combines two clean recordings of the MIT-BIH Arrhythmia database with excerpts of noise. Noise sources include baseline wandering, miographic noise and electrode motion artifacts. The resulting collection consists of 12 half-hour ECG records, sampled at 360 Hz, with two channels each.  
The T Wave Alternans database [31] is composed by 100 records, each with a number of channels that can vary from 2 to 12, sampled at 500 Hz. The recordings have been obtained from both healthy patients and patients with risk factors for sudden cardiac death. Additionally, some records are synthesized to display calibrated amounts of T wave alternans. Each record is about 2 min long.  
The QT Database [32] is an anthological database composed by 105 ECG excerpts, extracted from seven other public databases. However, only 82 records are accompanied by the '.att' files containing the beat annotations. Each record is 15 min long, is resampled at 250 Hz and includes two channels. A total of 15 records of the QT Database are derived from the MIT-BIH Arrhythmia Database, while 33 of them are extracted from the Eu ST-T database. In our study, the annotations for the sel232 record of the QT Database were replaced by the corresponding ones from record 232 of the MIT-BIH Arrhythmia Database.