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Automatic detection and classification of cardiac rhythms
Arrhythmia can be defined as either an irregular single heartbeat (arrhythmic beat), or as an irregular group of heartbeats (arrhythmic episode). Arrhythmias can affect the heart rate causing irregular rhythms, such as slow or fast heartbeat. Arrhythmias can take place in a healthy heart and be of minimal consequence (e.g. respiratory sinus arrhythmia which is a natural periodic variation in heart rate, corresponding to respiratory activity), but they may also indicate a serious problem that may lead to stroke or sudden cardiac death. Therefore, automatic arrhythmia detection and classification is critical in clinical cardiology, especially when performed in real time. This is achieved through the analysis of the electrocardiogram (ECG) and its extracted features.

Several researchers have addressed the problem of automatic detection and classification of cardiac rhythms. Some techniques are based on the detection of a single arrhythmia type and its discrimination from normal sinus rhythm, or the discrimination between two different types of arrhythmia. Another field of interest is the ECG beat-by-beat classification, where each beat is classified into several different rhythm types. Methods of this kind classify more arrhythmic beat types. However, they focus on single beat classification and not arrhythmic episode detection. Most of the studies, either for single arrhythmia type detection, detection of different heart rhythms or beat-by-beat classification, are based on the analysis of the ECG signal. In these methods ECG features are extracted and used for the detection and/or classification of arrhythmias. However, this is not always feasible due to: (a) the presence of noise making feature extraction difficult and in some cases impossible (e.g. P wave), and (b) the process being time consuming and ineffective for real time analysis. An alternative would be to use only the RR-interval signal.

Efficient methods for arrhythmia beat classification and arrhythmic episode detection and classification have been developed [1-13]. The methods are based on artificial intelligence field like fuzzy expert systems and data mining.
People: Markos Tsipouras
References:  
[1].
[2].
Tsipouras, M.G., Exarchos, T.P., Fotiadis, D.I. Integration of global and local knowledge for fuzzy expert system creation: application to arrhythmic beat classification. (2007), in Proc. of International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2007, pp. 3840-3843.
[3].
Tsipouras, M.G., Exarchos, T.P., Fotiadis, D.I. Integration of global and local knowledge for fuzzy expert system creation - Application to Arrhythmic Beat Classification (2007), in Proc. of 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07, art. no. 4353170, pp. 3840-3843.
[4].
Tsipouras, M.G., Voglis, C., Fotiadis, D.I. A framework for fuzzy expert system creation - Application to cardiovascular diseases (2007), IEEE Transactions on Biomedical Engineering, 54 (11), pp. 2089-2105.
[5].
Tsipouras, M.G., Exarchos, T.P., Fotiadis, D.I. A comparison of methodologies for fuzzy expert system creation - application to arrhythmic beat classification (2006), in Proc. of Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 2316-2319.
[6].
Tsipouras, M.G., Exarchos, T.P., Fotiadis, D.I. A comparison of methodologies for fuzzy expert system creation--application to arrhythmic beat classification. (2006) , in Proc. of Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 1, pp. 2316-2319.
[7].
Tsipouras, M., Exarchos, T., Papaloukas, C., Bechlioulis, A., Kotsia, A., Nanou, T., Bazios, C., Antoniou, Y., Fotiadis, D., Naka, A., Michalis, L. Automatic creation of Decision Support Systems: Application and results in the cardiovascular diseases domain (2006). Journal on Information Technology in Healthcare, 4 (4), pp. 222-230.
[8].
Tsipouras, M.G., Fotiadis, D.I., Sideris, D. An arrhythmia classification system based on the RR-interval signal (2005). Artificial Intelligence in Medicine, 33 (3), pp. 237- 50.
[9].
Tsipouras, M.G., Goletsis, Y., Fotiadis, D.I. A method for arrhythmic episode classification in ECGs using fuzzy logic and markov models (2004) Computers in Cardiology, 31, pp. 361-364.
[10].
Tsipouras, M.G., Oikonomou, V.P., Fotiadis, D.I., Michails, L.K., Sideris, D. Classification of atrial tachyarrhythmias in electrocardiograms using time frequency analysis (2004) , in Proc. of Computers in Cardiology, 31, pp. 245-248.
[11].
Tsipouras, M.G., Fotiadis, D.I. Automatic arrhythmia detection based on time and time-frequency analysis of heart rate variability (2004). Computer Methods and Programs in Biomedicine, 74 (2), pp. 95-108.
[12].
Tsipouras, M.G., Fotiadis, D.I. An efficient system for the detection of arrhythmic segments in ECG recordings based on non-linear features of the RR interval signal (2003) , in Proc. of Computers in Cardiology, 30, pp. 533-536.
[13].
Tsipouras, M.G., Fotiadis, D.I., Sideris, D. Arrhythmia classification using the RR-interval duration signal (2002) , in Proc. of Computers in Cardiology, 29, pp. 485-488.
 
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