The Classification of Heartbeats from Two-Channel ECG Signals Using Layered Hidden Markov Model

Abstract

Purpose: Cardiac arrhythmia is one of the most common heart diseases that can have serious consequences. Thus, heartbeat arrhythmias classification is very important to help diagnose and treat. To develop the automatic classification of heartbeats, recent advances in signal processing can be employed. The Hidden Markov Model (HMM) is a powerful statistical tool with the ability to learn different dynamics of the real time-series such as cardiac signals.
 Materials and Methods: In this study, a hierarchy of HMMs named Layered HMM (LHMM) was presented to classify heartbeats from the two-channel electrocardiograms. For training in the first layer, the morphology of the heartbeats was used as observations, while observations in the second layer were the inference results of the first layer. The performance of the proposed LHMM was evaluated in classifying three types of heartbeat arrhythmias (Atrial premature beats (A), Escape beats (E), Left bundle branch block beats (L)) using fifteen records of the MIT-BIH arrhythmia database. Furthermore, the obtained results of the proposed model were compared with other HMM generalizations.
 Results: The best average accuracy was achieved 97.10±1.63%. The best sensitivity of 96.8±1.24%, 98.85±0.52%, and 95.64±1.41 were obtained for A, E, and L, respectively. Furthermore, the results of the proposed method were better than other HMM generalizations.
 Conclusion: Extracting information from time-series dynamics by HMM-based methods has good classification results. The proposed model shows that applying a two-layered HMM can lead to better extraction of information from the observations; therefore, the classification performance of cardiac arrhythmias has been improved using LHMM.

Arrhythmia classification for non-experts using infinite impulse response (IIR)-filter-based machine learning and deep learning models of the electrocardiogram

“…Random forest and support vector machine (RF and SVM) ( Bhattacharyya et al, 2021a ), k-nearest neighbors (KNN) ( Sinha, Tripathy & Das, 2022 ), and artificial neural networks with logistic regression are the most common methods (ANN with LR) ( Sanamdikar, Hamde & Asutkar, 2020 ). Different methods, like decision trees ( Mohebbanaaz & Rajani Kumari, 2022 ), hidden Markov models ( Sadoughi, Shamsollahi & Fatemizadeh, 2022 ), and hyperbox classifiers ( Hosseinzadeh et al, 2021 ), are also used to classify arrhythmia. Classifiers such as linear discriminants (LD) ( Krasteva et al, 2015 ), decision trees ( Sultan Qurraie & Ghorbani Afkhami, 2017 ), and as sophisticated as traditional neural networks ( Inan, Giovangrandi & Kovacs, 2006 ; Javadi et al, 2011 ) are some of the methods available.…”