scholarly journals Atrial fibrillation detection using support vector machine and electrocardiographic descriptive statistics

2017 ◽  
Vol 24 (3) ◽  
pp. 225 ◽  
Author(s):  
N.A. Nuryani ◽  
Bambang Harjito ◽  
Iwan Yahya ◽  
Maratus Solikhah ◽  
Rifai Chai ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Support Vector Machine ◽  
Descriptive Statistics ◽  
Support Vector ◽  
Atrial Fibrillation Detection

scholarly journals IDENTIFIKASI FIBRILASI ATRIUM PADA ISYARAT ELEKTROKARDIOGRAM (EKG) MENGGUNAKAN SUPPORT VECTOR MACHINE (SVM)

2018 ◽  
Vol 9 (1) ◽  
pp. 231-240
Author(s):  
Mohammad Rofii
Keyword(s):  
Atrial Fibrillation ◽  
Support Vector Machine ◽  
Standard Deviation ◽  
Sinus Rhythm ◽  
Naive Bayes ◽  
Normal Sinus Rhythm ◽  
Naïve Bayes ◽  
Support Vector ◽  
Bayes Classifier ◽  
Normal Sinus

Jantung merupakan salah satu organ penting yang terdapat pada tubuh manusia. Fungsi vital yang diperankan oleh organ jantung berpengaruh besar terhadap kondisi seseorang yang dapat dilihat dari isyarat fisiologi yang dihasilkan oleh aktivitas kelistrikan jantung yang dapat diukur dan direkam berupa electrocardiogram (EKG). Tujuan dari penelitian ini adalah untuk mengidentifikasi kelainan jantung atau aritmia berupa atrial fibrillation (AF) pada isyarat EKG. Data penelitian yang digunakan berasal dari Rumah Sakit  Umum Daerah Tugurejo Semarang yang  terdiri dari data  pasien dengan kasus  atrial fibrillation (AF) dan data ECG normal atau normal sinus rhythm (NSR). Data yang diambil dalam bentuk data cetak, selanjutnya di lakukan scanning   untuk mendapatkan data citra digital agar dapat diproses dengan komputer. Pada penelitian ini terdapat beberapa tahapan, diantaranya adalah pra-pengolahan, ekstraksi ciri, dan klasifikasi. Proses ekstraksi ciri berdasarkan ciri statistik (mean, standard deviation, kurtosis, variance, skewness) isyarat periodogram dari EKG, selanjutnya diklasifikasi menggunakan algoritma Support Vector Machine (SVM) dan Naive bayes Classifier (NBC) sebagai algoritma pembanding. Hasil yang didapatkan pada penelitian ini, SVM memiliki kinerja yang lebih baik dengan nilai akurasi sebesar sebesar 84,0%, sensitivitas 80,5%, dan spesifisitas 92,8%.

scholarly journals Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 765 ◽  
Author(s):  
Robert Czabanski ◽  
Krzysztof Horoba ◽  
Janusz Wrobel ◽  
Adam Matonia ◽  
Radek Martinek ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Support Vector Machine ◽  
Heart Rhythm ◽  
Training Data ◽  
Support Vector ◽  
Automated Classification ◽  
Perinatal Medicine ◽  
Rate Analysis

Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.

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

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5222
Author(s):  
Liang-Hung Wang ◽  
Ze-Hong Yan ◽  
Yi-Ting Yang ◽  
Jun-Ying Chen ◽  
Tao Yang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Hybrid Model ◽  
Model Construction ◽  
Digital Data ◽  
Support Vector ◽  
Test Set ◽  
Svm Algorithm ◽  
Sensitivity Specificity

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.

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