Domain adaptation methods for ECG classification

Bazi, Yakoub; Alajlan, Naif; Alhichri, Haikel; Malek, Salim

doi:10.1109/iccma.2013.6506156

Cited by 39 publications

(17 citation statements)

References 17 publications

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“…Ye et al [38] used a wavelet-based approach to remove baseline wander [39] and then a band-pass filter at 0.5 -12Hz is applied to maximize QRS complex energy. Bazi et al [40] proposed the use of high pass filter for noise artifacts and a notch filter for power network noise.…”

Section: Preprocessingmentioning

confidence: 99%

“…The four most popular algorithms employed for this task and found in the literature are: support vector machines (SVM) [40,38,66], artificial neural networks (ANN) [34,116,69] and linear discriminant (LD) [7,37,17], and Reservoir Computing With Logistic Regression (RC) [43]. Note that the state-of-the-art method aiming heartbeat classification uses RC algorithm.…”

Section: Learning Algorithmsmentioning

confidence: 99%

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ECG-based heartbeat classification for arrhythmia detection: A survey

Luz

Schwartz

Cámara-Chávez

et al. 2016

Computer Methods and Programs in Biomedicine

734

340

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An electrocardiogram (ECG) measures the electric activity of the heart and has been widely used for detecting heart diseases due to its simplicity and non-invasive nature. By analyzing the electrical signal of each heartbeat, i.e., the combination of action impulse waveforms produced by different specialized cardiac tissues found in the heart, it is possible to detect some of its abnormalities. In the last decades, several works were developed to produce automatic ECG-based heartbeat classification methods. In this work, we survey the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used. In addition, we describe some of the databases used for evaluation of methods indicated by a well-known standard developed by the Association for the Advancement of Medical Instrumentation (AAMI) and described in ANSI/AAMI EC57:1998/(R)2008 (ANSI/AAMI, 2008). Finally, we discuss limitations and drawbacks of the methods in the literature presenting concluding remarks and future challenges, and also we propose an evaluation process workflow to guide authors in future works.

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Section: Preprocessingmentioning

confidence: 99%

Section: Learning Algorithmsmentioning

confidence: 99%

ECG-based heartbeat classification for arrhythmia detection: A survey

Luz

Schwartz

Cámara-Chávez

et al. 2016

Computer Methods and Programs in Biomedicine

734

340

View full text Add to dashboard Cite

show abstract

“…Some authors used FIR filters for same purpose [16], [17], [18], [19], [20], [21], [22]. Bazi et al [23] introduced high pass filter to remove high frequency noise and notch filter to remove power line interference. Lin and young proposed second order low pass filter and median filter [24].…”

Section: Fig 3 10 Electrodes Configurationmentioning

confidence: 99%

“…Here one set of features is used for machine learning and rest of set are for testing. The three most popular algorithms used for classification and got in literature survey are: support vector machine (SVM) [15], [55], [23], artificial neural network (ANN) [19], [56], [30] and linear discriminate (LD) [31], [57]. Most of papers are based on these three algorithms.…”

Section: Classification Techniquesmentioning

confidence: 99%

ECG Arrhythmia Classification Algorithms

Nainwal¹,

Kumar²,

Jha³

2019

IJRTE

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In last two decades lot of work is introduced on ECG classification. Authors took different database, features of ECG, class of data, learning and training algorithms to classify ECG signal. Normally class of data mentioned in source of database. Mainly three classification techniques which are discussed in this paper, these are support vectored machine (SVM), artificial neural network (ANN) and linear discriminate (LD). In this paper all the ECG classification based papers are analyzed and try to find out loophole and future challenges. This paper also discusses the different database of ECG signal

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“…(2) Preprocessing the ECG signals; (3) Detecting the QRS structure (the region around the periodic peaks in ECG signal); (4) Performing signal segmentation; (5) Extracting features; (6) Training the classifier; (7) Testing the classifier; and (8) Evaluating and analyzing the results.…”

Section: Introductionmentioning

confidence: 99%

Classifying cardiac rhythms by means of digital signal processing and machine learning

Pinho

Gomes

Santos

2020

JCIS

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Electrocardiogram (ECG) measures the electrical activity of the heart, which can be used in the diagnosis of different heart diseases. In the scientific literature there are many studies that have been applied machine learning for recognizing ECG patterns, where most of them attempt to classify heart beats. This paper presents a novel methodology for automatically classifying seventeen cardiac rhythms by means of digital signal processing and machine learning. The steps before the classification include the mapping of ECG signal to the frequency domain through power spectrum density, class balance with Adaptive Synthetic Sampling algorithm, and statistical normalization. The classifiers employed were Support Vector Machine, Multilayer Perceptron Neural Network, k-Nearest Neighbors, and Random Forest. The results showed accuracy, sensitivity, specificity, and Fleiss' kappa of up to 98.86%, 99.93%, 98.85%, and 89.68%, respectively, which are relatively better than the performance observed in the state-of-the-art works. In addition, this study highlighted that when the class balance procedure is applied, the classification step becomes less complex and can increase in terms of performance.

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Domain adaptation methods for ECG classification

Cited by 39 publications

References 17 publications

ECG-based heartbeat classification for arrhythmia detection: A survey

ECG-based heartbeat classification for arrhythmia detection: A survey

ECG Arrhythmia Classification Algorithms

Classifying cardiac rhythms by means of digital signal processing and machine learning

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