Computer aided ECG analysis &amp;#x2014; State of the art and upcoming challenges

Velic, Marko; Padavic, Ivan; Car, Siniša

doi:10.1109/eurocon.2013.6625218

Cited by 20 publications

(15 citation statements)

References 30 publications

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“…Sejak saat itu ECG menjadi salah satu isyarat yang penting dan paling sering digunakan untuk melakukan analisis terhadap aktivitas jantung [4][5] [6] dan telah menjadi golden standard untuk diagnosis aritmia jantung [7]. Penelitian terhadap isyarat yang dihasilkan oleh jantung dengan menggunakan bantuan teknologi komputer sudah banyak dilakukan [8]. Sehingga hal tersebut dapat dimanfaatkan oleh dokter ataupun tenaga medis dalam membaca dan menganalisis isyarat jantung yang dapat menampilkan abnormalitas aktivitas jantung.…”

Section: Pendahuluanunclassified

Identifikasi Fibrilasi Atrium Pada Isyarat Elektrokardiogram (Ekg) Menggunakan Support Vector Machine (Svm)

Rofii¹

2018

Simet

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ABSTRAKJantung 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%.Kata kunci: fibrilasi atrium, elektrokardiogram, SVM, NBC, periodogram. ABSTRACTThe heart is one of the important organs in the human body. The vital function of the heart organ affects the person's condition which can be seen from the physiological signal resulting from the electrical activity of the heart and can be measured, recorded on an electrocardiogram (ECG) PENDAHULUANPenyakit kardiovaskuler adalah penyakit yang disebabkan gangguan fungsi jantung dan pembuluh darah. Berdasarkan data dari badan kesehatan dunia (WHO) menempatkan abnormalitas jantung sebagai urutan teratas daftar penyebab utama kematian di seluruh dunia [1]. Jantung merupakan suatu organ otot berongga yang terletak di pusat dada. Fungsi utama jantung adalah menyediakan oksigen ke seluruh tubuh dan membersihkan tubuh dari hasil metabolism (karbon dioksida). Jantung melaksanakan fungsi tersebut dengan mengumpulkan darah yang kekurangan oksigen dari seluruh tubuh dan memompanya kedalam paru-paru, dimana darah akan mengambil oksigen dan membuang karbon dioksida, jantung kemudian mengumpulkan darah yang kaya oksigen dari paru-paru dan memompanya ke jaringan di seluruh tubuh [2][3]. Fungsi vital yang diperankan oleh organ yang satu ini berpengaruh besar terhadap kondisi seseorang yang dapat dilihat dari isyarat yang dihasilkan oleh aktivitas kelistrikan jantung. Electrocardiogram merupakan salah satu rekaman isyarat fisiologi utama yang dihasilkan dari aktivitas kelistrikan jantung baik ketika fase repolarisation maupun depolarization [4]. Electrocardiogram (ECG)

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Section: Pendahuluanunclassified

Identifikasi Fibrilasi Atrium Pada Isyarat Elektrokardiogram (Ekg) Menggunakan Support Vector Machine (Svm)

Rofii¹

2018

Simet

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“…Since the new algorithms are increasingly more powerful and precise, gaps between the use of recent algorithms and the standard analysis methodology of the available evidence have begun to emerge [5–7]. Nazarahari et al [8] used a neural network (NN) to classify six relevant types of heartbeats from a set of features created by new wavelet functions along with different distances and principal component analysis (PCA) to reduce the dimensionality.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Classification Models for Premature Ventricular Contraction Detection on ECG Traces

Casas

Avitia

González-Navarro

et al. 2018

Journal of Healthcare Engineering

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According to the American Heart Association, in its latest commission about Ventricular Arrhythmias and Sudden Death 2006, the epidemiology of the ventricular arrhythmias ranges from a series of risk descriptors and clinical markers that go from ventricular premature complexes and nonsustained ventricular tachycardia to sudden cardiac death due to ventricular tachycardia in patients with or without clinical history. The premature ventricular complexes (PVCs) are known to be associated with malignant ventricular arrhythmias and sudden cardiac death (SCD) cases. Detecting this kind of arrhythmia has been crucial in clinical applications. The electrocardiogram (ECG) is a clinical test used to measure the heart electrical activity for inferences and diagnosis. Analyzing large ECG traces from several thousands of beats has brought the necessity to develop mathematical models that can automatically make assumptions about the heart condition. In this work, 80 different features from 108,653 ECG classified beats of the gold-standard MIT-BIH database were extracted in order to classify the Normal, PVC, and other kind of ECG beats. Three well-known Bayesian classification algorithms were trained and tested using these extracted features. Experimental results show that the F1 scores for each class were above 0.95, giving almost the perfect value for the PVC class. This gave us a promising path in the development of automated mechanisms for the detection of PVC complexes.

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“…Despite the fact that automated ECG analysis has been an area of interest for a while, a 2013 literature review by Velic, Padavic, and Car suggests that there is still much to be done in strengthening algorithms for clinical use [5]. This claim is supported, for example, by the need for monitoring systems that are designed for specific populations, such as pediatric patients with parallel circulation [6].…”

Section: Introductionmentioning

confidence: 99%

Finding representative electrocardiogram beat morphologies with CUR

Hendryx

Rivière

Sorensen

et al. 2018

Journal of Biomedical Informatics

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In this paper, we use the CUR matrix factorization as a means of dimension reduction to identify important subsequences in electrocardiogram (ECG) time series. As opposed to other factorizations typically used in dimension reduction that characterize data in terms of abstract representatives (for example, an orthogonal basis), the CUR factorization describes the data in terms of actual instances within the original data set. Therefore, the CUR characterization can be directly related back to the clinical setting. We apply CUR to a synthetic ECG data set as well as to data from the MIT-BIH Arrhythmia, MGH-MF, and Incart databases using the discrete empirical interpolation method (DEIM) and an incremental QR factorization. In doing so, we demonstrate that CUR is able to identify beat morphologies that are representative of the data set, including rare-occurring beat events, providing a robust summarization of the ECG data. We also see that using CUR-selected beats to label the remaining unselected beats via 1-nearest neighbor classification produces results comparable to those presented in other works. While the electrocardiogram is of particular interest here, this work demonstrates the utility of CUR in detecting representative subsequences in quasiperiodic physiological time series.

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Computer aided ECG analysis — State of the art and upcoming challenges

Cited by 20 publications

References 30 publications

Identifikasi Fibrilasi Atrium Pada Isyarat Elektrokardiogram (Ekg) Menggunakan Support Vector Machine (Svm)

Identifikasi Fibrilasi Atrium Pada Isyarat Elektrokardiogram (Ekg) Menggunakan Support Vector Machine (Svm)

Bayesian Classification Models for Premature Ventricular Contraction Detection on ECG Traces

Finding representative electrocardiogram beat morphologies with CUR

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