Key result
AI-enabled models using Hilbert-Huang transform features achieved an AUC of approximately 0.99 for distinguishing premature and fibril-rapid arrhythmia patterns from normal sinus rhythm.
Why the study?
Do AI-enabled models using Hilbert-Huang transform accurately recognize hybrid arrhythmia patterns from ECG signals?
Do AI-enabled models using Hilbert-Huang transform accurately recognize hybrid arrhythmia patterns from ECG signals?
An AI-enabled ECG processing system using Hilbert-Huang transform demonstrated high accuracy (AUC ~0.99) in recognizing various arrhythmia patterns from the MIT-BIH database.
AI ECG platform detects hybrid arrhythmias; leaves open prospective clinical validation before any adoption.
We established a web-based ubiquitous health management (UHM) system, “ECG4UHM”, for processing ECG signals with AI-enabled models to recognize hybrid arrhythmia patterns, including atrial premature atrial complex (APC), atrial fibrillation (AFib), ventricular premature complex (VPC), and ventricular tachycardia (VT), versus normal sinus rhythm (NSR). The analytical model coupled machine learning methods, such as multiple layer perceptron (MLP), random forest (RF), support vector machine (SVM), and naive Bayes (NB), to process the hybrid patterns of four arrhythmia symptoms for AI computation. The data pre-processing used Hilbert–Huang transform (HHT) with empirical mode decomposition to calculate ECGs’ intrinsic mode functions (IMFs). The area centroids of the IMFs’ marginal Hilbert spectrum were suggested as the HHT-based features. We engaged the MATLABTM compiler and runtime server in the ECG4UHM to build the recognition modules for driving AI computation to identify the arrhythmia symptoms. The modeling extracted the crucial data sets from the MIT-BIH arrhythmia open database. The validated models, including the premature pattern (i.e., APC–VPC) and the fibril-rapid pattern (i.e., AFib–VT) against NSR, could reach the best area under the curve (AUC) of the receiver operating characteristic (ROC) of approximately 0.99. The models for all hybrid patterns, without VPC versus AFib and VT, achieved an average accuracy of approximately 90%. With the prediction test, the respective AUCs of the NSR and APC versus the AFib, VPC, and VT were 0.94 and 0.93 for the RF and SVM on average. The average accuracy and the AUC of the MLP, RF, and SVM models for APC–VT reached the value of 0.98. The self-developed system with AI computation modeling can be the backend of the intelligent social-health system that can recognize hybrid arrhythmia patterns in the UHM and home-isolated cares.
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Hsiao et al. (2022) studied Arrhythmia. AI-enabled models (MLP, RF, SVM, NB) vs. Normal sinus rhythm was evaluated on Arrhythmia pattern recognition (AUC and accuracy). AI-enabled models using Hilbert-Huang transform features achieved an AUC of approximately 0.99 for distinguishing premature and fibril-rapid arrhythmia patterns from normal sinus rhythm.
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