A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia

Abstract

Atrial fibrillation (AF) is the most common cardiovascular disease (CVD), and most existing algorithms are usually designed for the diagnosis (i.e., feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the diagnosis of AF electrocardiogram (ECG) and predict the possibility that AF will occur in the future. In this paper, we utilized the MIT-BIH AF Database (AFDB), which is composed of data from normal people and patients with AF and onset characteristics, and the AFPDB database (i.e., PAF Prediction Challenge Database), which consists of data from patients with Paroxysmal AF (PAF; the records contain the ECG preceding an episode of PAF), and subjects who do not have documented AF. We extracted the respective characteristics of the databases and used them in modeling diagnosis and prediction. In the aspect of model construction, we regarded diagnosis and prediction as two classification problems, adopted the traditional support vector machine (SVM) algorithm, and combined them. The improved quantum particle swarm optimization support vector machine (IQPSO-SVM) algorithm was used to speed the training time. During the verification process, the clinical FZU-FPH database created by Fuzhou University and Fujian Provincial Hospital was used for hybrid model testing. The data were obtained from the Holter monitor of the hospital and encrypted. We proposed an algorithm for transforming the PDF ECG waveform images of hospital examination reports into digital data. For the diagnosis model and prediction model trained using the training set of the AFDB and AFPDB databases, the sensitivity, specificity, and accuracy measures were 99.2% and 99.2%, 99.2% and 93.3%, and 91.7% and 92.5% for the test set of the AFDB and AFPDB databases, respectively. Moreover, the sensitivity, specificity, and accuracy were 94.2%, 79.7%, and 87.0%, respectively, when tested using the FZU-FPH database with 138 samples of the ECG composed of two labels. The composite classification and prediction model using a new water-fall ensemble method had a total accuracy of approximately 91% for the test set of the FZU-FPH database with 80 samples with 120 segments of ECG with three labels.

Artificial intelligence in cardiovascular prevention: new ways will open new doors

“…Initially, machine learning was applied to sinus rhythm segments of ambulatory ECGs to predict paroxysmal atrial fibrillation based mainly on premature atrial contractions (PACs). More recently, another approach based on heart rate variability features, including times and frequency domains, is used with an accuracy above 0.9 43–46 …”

“…More recently, another approach based on heart rate variability features, including times and frequency domains, is used with an accuracy above 0.9. [43][44][45][46] Combining machine learning and clinical risk scoring shows potential benefits for reducing the number needed to screen and improving the effectiveness of atrial fibrillation screening. [47][48][49] Although results are encouraging, these have yet to be tested within prospective studies.…”

Acoustic Signal NLOS Identification Method Based on Swarm Intelligence Optimization SVM for Indoor Acoustic Localization

“…SVM Implementation. The SVM model is very suitable for our indoor localization system [4,26,27], as it has strong generalization ability and involves fewer parameters. At this time, it is imperative to use reasonable methods to build our own data set based on the MATLAB toolbox developed by scholar Chih-Jen Lin [28], and relevant codes are completed.…”

Development of a Convolutional Neural Network Model to Predict Coronary Artery Disease Based on Single-Lead and Twelve-Lead ECG Signals

“…CAD is commonly identified and diagnosed based on different tests, such as ECG, treadmill ECG, echocardiogram (ECHO), and angiography. The intelligent systems use different neural architectures, such as the convolutional neural network CNN [11], recurrent neural network (RNN) [12], CNN with RNN [13,14], deep belief network (DBN) [3,15], and the fully-connected neural network (FC) [11] to predict electrocardiogram (ECG)-related issues, such as arrhythmia [16][17][18][19][20], atrial fibrillation (AF) [21][22][23], myocardial infarction (MI) [24,25], ST elevation [21], CAD [10,[26][27][28], etc. Tan, J.H.…”