Detection of Atrial Fibrillation Disease Based on Electrocardiogram Signal Classification Using RR Interval and K-Nearest Neighbor

Resiandi, Kartika; Adiwijaya, Adiwijaya; Utama, Dody Qori

doi:10.1109/icoict.2018.8528737

Cited by 15 publications

(10 citation statements)

References 4 publications

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“…The RR Interval that divides AF and normal ECG signals can be indicated by heart rhythms. The signal is considered normal if there is a gap between the peak and the regular rhythm [27]. If there is no gap between them, the signal is regarded as AF.…”

Section: Rr Interval Detectionmentioning

confidence: 99%

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The Sensitivity of The Heart Rate Detection for Atrial Electrograms Signal

Kong¹,

Ahmad²,

Hassan³

et al. 2021

Elektrika

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Atrial Fibrillation (AF) is the most familiar example of arrhythmia that will occur health problems such as stroke, heart failure and other complications. Globally, the number of AF patients will more than triple by 2050 worldwide. Current methods involve performing large-area ablation without knowing the exact location of key parts. The reliability of the technology can be used as a target for atrial fibrillation’s catheter ablation. The factors that leading to the onset of atrial fibrillation include the triggering factors that induce arrhythmia and the substrate that maintains the arrhythmia. The project’s aim is to create a method for identifying AF that can be used as screening tool in medical practice. The primary goals for the detection method’s design are to develop a MATLAB software program that can compare the complexity of a normal ECG signal and an AF ECG signal. Currently, this can be achieved by the ECG Signal’s R peaks and RR Interval. For AF detection, there are more R peaks and RR Intervals and it is irregular. In this research, the detection of AF is based on the heart rate (RR Intervals). For the ECG preprocessing, Pan-Tompkins Algorithm and Discrete Wavelet Transform is used to detect the sensitivity on the R peaks and RR Intervals. As a result, Discrete Wavelet Transform algorithm gives 100% sensitivity for the dataset obtained from MIT-BIH Atrial Fibrillation and MIT-BIH Arrhythmia Database.

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Section: Rr Interval Detectionmentioning

confidence: 99%

“…K-Nearest Neighbour (KNN) has been used in a variety of applications including data analysis, empirical pattern classification and image recognition [27], [31]. The KNN classifier determines the subspace principle that is closest to the training dataset.…”

Section: K-nearest Neighbourmentioning

confidence: 99%

The Sensitivity of The Heart Rate Detection for Atrial Electrograms Signal

Kong¹,

Ahmad²,

Hassan³

et al. 2021

Elektrika

View full text Add to dashboard Cite

show abstract

“…Although several recent methods have utilized a window of RRI tachogram as a multidimensional feature vector [19], [31], [34], they have failed to achieve the desirable performance for the AF vs non-AF classification. This can be attributed to the complex nonlinear decision boundaries in higher dimensional space.…”

Section: A Nca Transformationmentioning

confidence: 99%

“…For the AF vs NSR classification, it is assumed that an RRI tachogram window mainly contains any of these two types of rhythms. Most of existing methods fall in this category [24], [29], [31], including methods utilizing machinelearning [30], [32] and deep-learning [19], [33], [34] based classification techniques. Many of these methods have been evaluated with a Physionet database [35], known as MIT Atrial Fibrillation Database (AFDB), containing mostly AF and NSR rhythms.…”

Section: Introductionmentioning

confidence: 99%

Robust Detection of Atrial Fibrillation Using Classification of a Linearly-Transformed Window of R-R Intervals Tachogram

et al. 2019

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Atrial fibrillation (AF) is the most common cardiac arrhythmia. It increases the risk of stroke, dementia, and death; therefore, its timely diagnosis at an initial stage is crucial. Often wearable mobile devices are recommended for the primary detection of this life-threatening arrhythmia. Irregularity of the heartbeat duration, often measured through R-R intervals (RRI), has been intensively investigated during the past four decades for automatic detection of AF. However, little improvement has been made when the input signal (RRI tachogram) contains different types of arrhythmic rhythms. In this paper, we propose a neighborhood component analysis (NCA) based linear transformation of a window of RRI tachogram to improve the robustness of AF detection. Several state-of-the-art classification models are trained and tested using transformed signals, and AF detection performance are evaluated using the challenging MIT-BIH Arrhythmia Database containing various types of arrhythmic rhythms. The experimental results show significant improvement in AF detection performance using the transformed signals compared to those for signals in the original space and after linear-discriminant-analysis-based transformation. In particular, for the Naïve Bayesian classification of the transformed signals, we obtained 98.59% sensitivity, 99.91% specificity, 99.16% positive predictive value, and 99.79% accuracy. The proposed AF detection method outperforms the existing methods reported in the past four decades. Owing to the use of a short window of RRI tachogram (15 consecutive RRIs), the proposed method can be incorporated into a deployable mobile screening device for robust detection of AF.

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“…It is estimated that the geriatric population (elderly) will reach 11.39% in Indonesia or 28 million people in Indonesia by 2020. Increasingly aging, the percentage of arrhythmia incidence is increasing, is 70% at age 65-85 years and 84% over 85 years [7,12] Arrhythmias are heart problems that occur when the organ is beating too fast, too slow, or irregularly. Rapid heartbeats are grouped into the type of tachycardia, while slow heartbeats fall into the bradycardia type.…”

Section: Introductionmentioning

confidence: 99%

Classification of Electrocardiogram Signals using Principal Component Analysis and Levenberg Marquardt Backpropagation for Detection Ventricular Tachyarrhythmia

Manik¹,

Adiwijaya²,

Utama³

2019

J. Data Sci. Appl.

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Abstract Ventricular Tachyarrhythmia (VT) are the primary arrhythmias which are cause of sudden death. For someone who already has symptoms of VT should immediately perform an examination of one of them by using an electrocardiogram (ECG). An electrocardiogram is a recording of the heart's electrical results in a waveform. However, limited ability in analysis and diagnosis of ECG reading is still difficult to do. Therefore, the classification of ECG signals is needed to detect a person, especially those with VT or not. In this research focuses on the classification of VT heartbeats from ECG signals by using median filter method in preprocessing, Principal Component Analysis (PCA) as feature extraction and modified Backpropagation (MBP) as classification. This research used machine learning method that is a neural network with backpropagation modification that is Levenberg Marquardt to speed up network training process. The best VT detection performance results were based on the average accuracy of the overall scheme of 91.67% with the best parameters that principal component=10 and 20, hidden neuron=4, and µ value=0.001 as well training time 1 seconds with a comparison of train data and test data that is 80:20 percent. Keywords: Electrocardiogram, Levenberg Marquardt Backpropagation, Median filter, Principal Component Analysis, and Ventricular Tachyarrhythmia

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Detection of Atrial Fibrillation Disease Based on Electrocardiogram Signal Classification Using RR Interval and K-Nearest Neighbor

Cited by 15 publications

References 4 publications

The Sensitivity of The Heart Rate Detection for Atrial Electrograms Signal

The Sensitivity of The Heart Rate Detection for Atrial Electrograms Signal

Robust Detection of Atrial Fibrillation Using Classification of a Linearly-Transformed Window of R-R Intervals Tachogram

Classification of Electrocardiogram Signals using Principal Component Analysis and Levenberg Marquardt Backpropagation for Detection Ventricular Tachyarrhythmia

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