Recognition of cardiac arrhythmias by means of beat clustering on ECG-holter records

The follow-up of some cardiac diseases may be achieved by ECG-holter record analysis. A heartbeat clustering method can be used to reduce the usually high computational cost of such holter analysis. This study describes a method aimed at cardiac arrhythmia recognition based on this approach, by means of unsupervised inspection of morphologically similar heartbeat groups. Singular Value Decomposition (SVD) is used as the feature selection method since the complexity increases exponentially with the number of features. A modification of the k-means algorithm was developed for centroid computation, taking into account heartbeat length changes. Experimental set consisted of ECG records from the MIT database. The method yielded a 99.9% clustering accuracy considering pathological versus normal heartbeats. Both clustering error and critical error percentage was 0.01%.