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بخشی از مقاله انگلیسیعنوان انگلیسی:Arrhythmia Identification with Two-Lead Electrocardiograms Using Artificial Neural Networks and Support Vector Machines for a Portable ECG Monitor System~~en~~

Abstract

An automatic configuration that can detect the position of R-waves, classify the normal sinus rhythm (NSR) and other four arrhythmic types from the continuous ECG signals obtained from the MIT-BIH arrhythmia database is proposed. In this configuration, a support vector machine (SVM) was used to detect and mark the ECG heartbeats with raw signals and differential signals of a lead ECG. An algorithm based on the extracted markers segments waveforms of Lead II and V1 of the ECG as the pattern classification features. A self-constructing neural fuzzy inference network (SoNFIN) was used to classify NSR and four arrhythmia types, including premature ventricular contraction (PVC), premature atrium contraction (PAC), left bundle branch block (LBBB), and right bundle branch block (RBBB). In a real scenario, the classification results show the accuracy achieved is 96.4%. This performance is suitable for a portable ECG monitor system for home care purposes.

۱ Introduction

Telemedicine has been widely studied recently. In past research, allowing congestive heart failure patients to monitor their condition at home offered great economic advantages. Electrocardiograms (ECGs) are an important tool that provide useful information about the functional status of the heart. An automated method that accurately diagnoses cardiac diseases through the analysis of ECG signals is critical in healthcare [1], especially for real-time processing. Past research has addressed the problems of heart rate detection and classification of cardiac rhythms. The heart rate signal detects the QRS wave of the ECG and calculates inter-beat intervals [2–۹]. The classification of cardiac rhythms is based on the detection of the different types of arrhythmia from the ECG waveforms [10–۱۳].

However, ECG signals have coupling noises, due to factors such as 50/60 Hz power line signals, the baseline drift caused by patient breathing, bad electrodes, improper electrode location, or electromyograms. These noises result in false QRS wave detections. Thus, some studies have compared the robust performance of different algorithms for QRS wave detection [2]. Widrow et al. applied the adaptive filter to reduce noises that resulted from 60 Hz power lines and baseline drift, and then detect the QRS wave [14]. Pan and Tompkins designed a digital filter to reduce the noise and used a dynamic threshold to detect the QRS wave [4]. Trahanias used the mathematical morphology of the QRS complex to detect heart rates [5]. Chang used the ensemble empirical model decomposition to reduce noises in arrhythmia ECGs [15]. Fan used approximate entropy (ApEn) and Lempel-Ziv complexity as a nonlinear quantification to measure the depth of anaesthesia [16]. In these studies, the normal sinus ECG signal added different noise types and energy was used to evaluate the performance of these algorithms. Several researchers have extracted the features of ECG waveforms to detect the QRS complexes based on the arrhythmia database. Li et al. proposed the wavelet transforms method for detecting the QRS complex from high P or T waves, noise, and baseline drift [6]. Yeh and Wang proposed the difference operation method to detect the QRS complex waves [8]. Mehta and Lingayat used the support vector machine (SVM) method to detect the QRS complexes from a 12-leads ECG [9]. They also used the K-mean algorithm for the detection of QRS complexes in ECG signals [17].

Arrhythmia can be defined as either an irregular single heartbeat or a group of heartbeats. Some classification techniques are based on the ECG beat-by-beat classification with each beat being classified into several different arrhythmic beat types. These include artificial neural networks [11], fuzzy neural networks [18], Hermite functions combined with self-organizing maps [19], and wavelet analysis combined with radial basis function neural networks [20]. In these methods, the ECG waveform of each beat was picked up manually and different features were extracted to classify the arrhythmic types. Tsipouras et al. used the RR-interval signal to classify certain types of arrhythmia based on a group of heartbeats [12]. All the above methods have high classification accuracies that were obtained based on the complete morphology of the ECG or the correct RR-interval that was detected manually.

In this study, we propose an automatic configuration integrating digital signal processing and an artificial intelligence method to detect the position of heartbeats and recognize these heartbeats as belonging to the normal sinus rhythm (NSR) or four arrhythmic types. The four arrhythmic types are premature ventricular contraction (PVC), premature atrium contraction (PAC), left bundle branch block (LBBB), and right bundle branch block (RBBB). ECG signals are provided by the MIT-BIH Arrhythmia Database [21]. This automatic configuration had three steps, as follows:

۱ The Lead II signals were normalized and filtered to reduce the coupled noise (Section 2.2).

۲ The positions of QRS-complexes in Lead II were detected and marked via a well-trained SVM. Two waveforms of each heartbeat in Lead II and V1 were individually extracted according the markers in Lead II (Section 2.3).

۳ The extracted waveform was used as a feature to recognize the arrhythmic type of a heartbeat. In this configuration, a self-constructing neural fuzzy inference network (SoNFIN) was used to recognize the arrhythmic type of the heartbeat using the raw Lead II and V1 signals (Section 2.4).

Moreover, the heartbeat detection accuracy has been increased by the SoNFIN classification results.

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