A Patient-Adapting Heartbeat Classifier Using ECG Morphology and Heartbeat Interval Features

Abstract

Abstract-An adaptive system for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats into one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard is presented. The heartbeat classification system processes an incoming recording with a global-classifier to produce the first set of beat annotations. An expert then validates and if necessary corrects a fraction of the beats of the recording. The system then adapts by first training a local-classifier using the newly annotated beats and combines this with the global-classifier to produce an adapted classification system. The adapted system is then used to update beat annotations. The results of this study show that the performance of a patient adaptable classifier increases with the amount of training of the system on the local record. Crucially, the performance of the system can be significantly boosted with a small amount of adaptation even when all beats used for adaptation are from a single class. This study illustrates the ability to provide highly beneficial automatic arrhythmia monitoring and is an improvement on previously reported results for automated heartbeat classification systems.Index Terms-Adaptive classifier, ECG, heartbeat classifier, linear discriminant analysis, statistical classifier model.

Detecting premature ventricular contractions in ECG signals with Gaussian processes

“…For feeding the classification process, we adopted in this study different representation of the ECG signals, which are the standard temporal signal morphology, the discrete wavelet transform domain, the S-transform characteristics and the high-order statistics. In addition, for each representation, we considered also three temporal features that are the QRS complex duration, the RR interval (i.e., time span between two consecutive R points representing the distance between the QRS peaks of the present and previous beats), and the RR interval averaged over the ten last beats [2]. In order to train the GP classifier and to assess its accuracy, we selected randomly from the 18 records 600 beats for the training set (i.e., 300 samples for both PVC and non-PVC classes, respectively).…”

Real-time premature ventricular contractions detection based on Redundant Discrete Wavelet Transform

“…Hence, in the last years, several PVC detection system have been proposed for this issue: based on Artificial Neural Network (ANN) (Bortolan et al, 1991;Dalvi et al, 2016;Hu et al, 1997;Inan et al, 2006), Heuristic algorithm (Dotsinsky and Stoyanov, 2004), Bayesian framework (Sayadi et al, 2010), Support Vector Machine (SVM) (Shen et al, 2011), morphology ECG features (Chazal and Reilly, 2006;Chazal et al, 2004;Lek-uthai et al, 2014), Fuzzy Neural Network System (FNNS) (Lim, 2009), Wavelet Transform (Inan et al, 2006;Martis et al, 2013;Nazarahari et al, 2015;Orozco-Duque et al, 2013;Shyu et al, 2004;Yochum et al, 2016) and adaptive filter (Nieminaki et al, 1999;Solosenko et al, 2015). The main feature of most detection methods is a real-time analysis, however some methods have high mathematical complexity, which demands a high computational cost.…”

Integrated Information System of Monitoring and Management for Heart Centers

“…This software component is implemented under the form of a desktop application that runs on one or several computers inside the monitoring centre [3][4][5][6][7][8]. By means of this application, the specialist doctor has access to the medical record data, the current ECG investigation as well as to the history of all ECG investigations made for the patient in the past, no matter the place where these were realized (another family doctor).…”