A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals

Abstract

BackgroundThe inter-patient classification schema and the Association for the Advancement of Medical Instrumentation (AAMI) standards are important to the construction and evaluation of automated heartbeat classification systems. The majority of previously proposed methods that take the above two aspects into consideration use the same features and classification method to classify different classes of heartbeats. The performance of the classification system is often unsatisfactory with respect to the ventricular ectopic beat (VEB) and supraventricular ectopic beat (SVEB).MethodsBased on the different characteristics of VEB and SVEB, a novel hierarchical heartbeat classification system was constructed. This was done in order to improve the classification performance of these two classes of heartbeats by using different features and classification methods. First, random projection and support vector machine (SVM) ensemble were used to detect VEB. Then, the ratio of the RR interval was compared to a predetermined threshold to detect SVEB. The optimal parameters for the classification models were selected on the training set and used in the independent testing set to assess the final performance of the classification system. Meanwhile, the effect of different lead configurations on the classification results was evaluated.ResultsResults showed that the performance of this classification system was notably superior to that of other methods. The VEB detection sensitivity was 93.9% with a positive predictive value of 90.9%, and the SVEB detection sensitivity was 91.1% with a positive predictive value of 42.2%. In addition, this classification process was relatively fast.ConclusionsA hierarchical heartbeat classification system was proposed based on the inter-patient data division to detect VEB and SVEB. It demonstrated better classification performance than existing methods. It can be regarded as a promising system for detecting VEB and SVEB of unknown patients in clinical practice.

ECG-based heartbeat classification for arrhythmia detection: A survey

“…As previously mentioned, this heartbeat division protocol is called in the literature intra-patient scheme or paradigm [8,42]. However, in a clinical environment, a fully automatic algorithm/method will find heartbeats of patients different from those they used to learning in the training phase.…”

“…In [42], the signal is subtracted by its mean and then normalized. Escalona-Moran et al [43] used the raw wave i.e., no preprocessing is applied.…”

“…Recently, random projections have also been employed for such aim, as in [86,42]. Huang et al [42] show that features extracted with random projections produced results comparable to the state-of-the-art methods, even when considering the inter-patient paradigm.…”

“…Various approaches with SVM variations have been proposed, such as a combination of the fuzzy theory to refine SVM classification [117], combined with an ensemble of classifiers [42], genetic algorithms combined with restricted fuzzy SVM [118] and least squares SVM [104]. Huang et al [42] used the SVM in a hierarchical manner with a maximum voting strategy and report significantly improvements.…”

“…Huang et al [42] used the SVM in a hierarchical manner with a maximum voting strategy and report significantly improvements. [119] proposed the use of a new kernel function for capturing data from SVM.…”