Atrial fibrillation is the most common cardiac arrhythmia and often leads to severe complications such as stroke and other embolic incidents. Areas of complex fractionated atrial activity are in the focus of electrophysiologists and have been used as a target for catheter ablation therapy. The underlying mechanisms of complex fractionated atrial electrograms (CFAEs) are not entirely understood. CFAEs may contain concurrent rhythmic episodes of signals with differing characteristic frequencies (CFs). We propose a new algorithm to detect multiple periodicities in atrial signals.First, we preprocess the signal by applying Teager's non-linear energy operator. Next, the first three characteristic frequencies are detected in the frequency spectrum. Information contained in the harmonics is used to recursively detect the exact frequency. Frequency information is then transformed into the time domain, where repeated occurance of signal activity according to the respective cycle length is found. Further, the detection rate and the mean distance to gravity are calculated as key figures to determine more characteristics of the periodicity.The algorithm performs well in detecting the rhythmic components of atrial signals. It has been tested using real patient data acquired during electrophysiological studies in sinus rhythm, atrial flutter and several forms of atrial fibrillation, as well as with simulated data produced by a cellular automaton at our research group.Its application may provide new insights into atrial signals especially CFAEs and the interpretation of characteristic and dominant frequencies. It can be the foundation of displaying rhythmicity and CF information onto the 3D representation of the patient's atrium and give the physician an impression of the organization and regularity of cardiac electrograms.
Catheter ablation of atrial fibrillation (AF), especially persistent AF, is still challenging. The underlying mechanisms are not yet completely understood and are discussed very controversially. Automated detection and analysis of complex frac- tionated atrial electrograms is essential in supporting the electrophysiologists during ablation therapy. Signal analysis of atrial signals works better the less noise and unwanted signals superimpose the signal to be analysed. As for catheter ablation of persistent AF the atrial signals play the most important role, ventricular activity is unwanted to be seen. For catheter positions in close proximity to the ventricles, i.e. the coronary sinus catheter, those ventricular far fields are taint- ing the atrial signals. For this reason we present a method to cancel the ventricular far field from atrial electrograms. Atrial segments synchronized to the ventricular activity are extracted and the ventricular far field is cancelled by use of a PCA approach. Signal processing of the sole atrial electrogram leads to better results and therefore can better support the abla- tion therapy.
Student Theses (1)
M. Aubreville. Time-frequency-analysis and blind source separation of local atrial electrograms. Institut für Biomedizinische Technik, Karlsruher Institut für Technologie (KIT). Diplomarbeit. 2009
In the scope of this thesis, an algorithm for detection of periodic elements within a complex cardiac signal was developed. It is based on the estimation of characteristic frequencies contained within the signal. For this purpose, it makes use of the harmonic frequencies contained in every repeatedly occurring signal.The algorithm proved to be a valuable filter to analyze atrial flutter signals in order to determine the origin of the focus of the excitation within the heart. Furthermore, it may provide valuable in- formation for the physician, as to what degree complex cardiac signals are composed of reoccurring cardiac events. To date, cardiac signals are often characterized by its most dominant frequency. This work supports the idea of multiple characteristic frequencies that are contained within a cardiac signal. Through the calculation of its time representation, it may provide new insights into the significance of characteristic frequencies.Many atrial signals are tainted with electrical activity originating from the ventricles. Physicians call this effect, that can have a significantly higher amplitude than the atrial signals, the ventricular far field. In this thesis, a method to remove these superimposed signal parts was described. It is based on the Principal Component Analysis (PCA), a method of Blind Source Separation. The method has proven to remove the far fields to a great extent, resulting in correlation rates of over 95 percent to the clean atrial signal.Finally, this thesis examines the use of blind source separation methods on intracardiac signals in general. It has been shown, that there are different approaches utilizing Independent Component Analysis (ICA), that can be valuable for analysis of cardiac signals. Especially the CFICA convolu- tive approach had very promising results for the analysis of multipolar intracardiac electrograms.