Abstract
We developed an automated approach to differentiate between different types of arrhythmic episodes in electrocardiogram (ECG) signals, because, in real-life scenarios, a software application does not know in advance the type of arrhythmia a patient experiences. Our approach has four main stages: (1) Classification of ventricular fibrillation (VF) versus non-VF segments—including atrial fibrillation (AF), ventricular tachycardia (VT), normal sinus rhythm (NSR), and sinus arrhythmias, such as bigeminy, trigeminy, quadrigeminy, couplet, triplet—using four image-based phase plot features, one frequency domain feature, and the Shannon entropy index. (2) Classification of AF versus non-AF segments. (3) Premature ventricular contraction (PVC) detection on every non-AF segment, using a time domain feature, a frequency domain feature, and two features that characterize the nonlinearity of the data. (4) Determination of the PVC patterns, if present, to categorize distinct types of sinus arrhythmias and NSR. We used the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, Creighton University’s VT arrhythmia database, the MIT-BIH atrial fibrillation database, and the MIT-BIH malignant ventricular arrhythmia database to test our algorithm. Binary decision tree (BDT) and support vector machine (SVM) classifiers were used in both stage 1 and stage 3. We also compared our proposed algorithm’s performance to other published algorithms. Our VF detection algorithm was accurate, as in balanced datasets (and unbalanced, in parentheses) it provided an accuracy of 95.1% (97.1%), sensitivity of 94.5% (91.1%), and specificity of 94.2% (98.2%). The AF detection was accurate, as the sensitivity and specificity in balanced datasets (and unbalanced, in parentheses) were found to be 97.8% (98.6%) and 97.21% (97.1%), respectively. Our PVC detection algorithm was also robust, as the accuracy, sensitivity, and specificity were found to be 99% (98.1%), 98.0% (96.2%), and 98.4% (99.4%), respectively, for balanced and (unbalanced) datasets.
Highlights
According to the Centers for Disease Control and Prevention, about 735,000 Americans suffer from myocardial infarction every year [1]
We evaluated our algorithm for detection of ventricular fibrillation (VF) against atrial fibrillation (AF) segments
We present a new automated approach for diagnosing and discriminating among various malignant arrhythmias
Summary
According to the Centers for Disease Control and Prevention, about 735,000 Americans suffer from myocardial infarction every year [1]. It is possible that some of these mortalities can be prevented if malignant arrhythmias are detected early and accurately. With the advent of wearable devices, which can facilitate personalized monitoring, continuous monitoring of cardiac health can be realized. To this end, more accurate and automated arrhythmia detection algorithms. The first group of arrhythmias is not imminently fatal but may require properly diagnosed therapy to prevent morphing into the second group or a serious complication. Arrhythmias in the second group need immediate attention and therapy. Real-time monitoring and accurate diagnosis of malignant arrhythmia is crucial so that timely preventive treatments can be performed
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