Atrial fibrillation detection using support vector machine and electrocardiographic descriptive statistics

Nuryani, N; Harjito, Bambang; Yahya, Iwan; Solikhah, M.Pd Imroatus; Chai, Rifai; Lestari, Anik

doi:10.1504/ijbet.2017.085140

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…where n is a lot of data. The maximum separation of two classes from training data with an optimal hyperplane is expressed in the following equation [27][28] [37]:…”

Section: Classificationmentioning

confidence: 99%

Gray Level Co-Occurrence Matrices and Support Vector Machine for Improved Lung Cancer Detection

Yunianto¹,

Suparmi

Cari

et al. 2023

Int. J. Onl. Eng.

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A detection system based on digital image processing and machine learning classification was developed to detect normal and cancerous lung conditions. 340 data from LIDC –IDRI were processed through several stages. The first stage is pre-processing using three filter variations and contrast stretching, which reduce noise and increase image contrast. The image segmentation process uses Otsu Thresholding to clarify the ROI of the image. The texture feature extraction with GLCM was applied using 21 feature variations. Data extraction is used as a label value learned by the classification system in the form of SVM. The results of the training data classification are processed with a confusion matrix which shows that the high pass filter has higher accuracy than the other two variations. The proposed method was assessed in terms of accuracy, precision and recall. The model provided an accuracy of 99.67 % training data and 97.50 % testing data.

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“…where n is a lot of data. The maximum separation of two classes from training data with an optimal hyperplane is expressed in the following equation [27][28] [37]:…”

Section: Classificationmentioning

confidence: 99%

Gray Level Co-Occurrence Matrices and Support Vector Machine for Improved Lung Cancer Detection

Yunianto¹,

Suparmi

Cari

et al. 2023

Int. J. Onl. Eng.

View full text Add to dashboard Cite

show abstract

“…The final sensitivity, specificity, and accuracy were 95.81%, 98.44%, and 97.50%, respectively. In general, SVM has a better performance compared to other traditional machine learning methods [ 28 , 29 ] and can be generalized well even with small samples [ 30 ]. The related works are listed in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Yan

Yang

et al. 2021

Sensors

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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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Detecting atrial fibrillation using statistical features of electrocardiographic interbeat interval

Nuryani

Nugroho

2019

J. Phys.: Conf. Ser.

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Atrial fibrillation detection using support vector machine and electrocardiographic descriptive statistics

Cited by 5 publications

References 16 publications

Gray Level Co-Occurrence Matrices and Support Vector Machine for Improved Lung Cancer Detection

Gray Level Co-Occurrence Matrices and Support Vector Machine for Improved Lung Cancer Detection

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

Detecting atrial fibrillation using statistical features of electrocardiographic interbeat interval

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