A novel machine learning framework for automated detection of arrhythmias in ECG segments

Pham, The‐Hanh; Sree, Vinitha; Mapes, John; Dua, Sumeet; Lih, Oh Shu; Koh, Joel En Wei; Ciaccio, Edward J.; Acharya, U. Rajendra

doi:10.1007/s12652-020-02779-1

Cited by 32 publications

(13 citation statements)

References 24 publications

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“…In the feld of machine learning, the use of artifcial features is essential. Based on a large number of previous studies, this study used four types of features, that is, waveform features, interval features, frequency-domain features, and nonlinear feature without discarding prior knowledge, and 24 specifc features were calculated [4][5][6][7][8]. Table 2 shows the artifcial features used in this study.…”

Section: Artifcial Featuresmentioning

confidence: 99%

“…Finally, the ECG recordings were divided into four categories, that is, normal, AF, other, and noisy ECG recordings. Pham et al [ 7 ] first generated third-order cumulant images from four categories of ECG recordings and extracted 18 features including entropy features and other texture-based features. They used multiple classifiers to classify the recordings into four categories, that is, N sr , V fib , A fl , and A fib .…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al [ 19 ] calculated a multiscale entropy of the ECG recordings for signal quality assessment and further studied the sensitivity of multiscale entropy on the ECG recordings with noise. Pham et al [ 7 ] extracted a large number of entropy features to train classifiers. Fu et al [ 20 ] extracted different entropy features, that is, approximate entropy, sample entropy, and fuzzy entropy to feed into machine learning, that is., support vector machine (SVM), least-squares SVM (LS-SVM), and long short-term memory (LSTM) for assessing the quality of the ECG recordings.…”

Section: Introductionmentioning

confidence: 99%

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Atrial Fibrillation Detection with Low Signal-to-Noise Ratio Data Using Artificial Features and Abstract Features

Bao

Shou-fen

et al. 2023

Journal of Healthcare Engineering

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Detecting atrial fibrillation (AF) of short single-lead electrocardiogram (ECG) with low signal-to-noise ratio (SNR) is a key of the wearable heart monitoring system. This study proposed an AF detection method based on feature fusion to identify AF rhythm (A) from other three categories of ECG recordings, that is, normal rhythm (N), other rhythm (O), and noisy (∼) ECG recordings. So, the four categories, that is, N, A, O, and ∼ were identified from the database provided by PhysioNet/CinC Challenge 2017. The proposed method first unified the 9 to 60 seconds unbalanced ECG recordings into 30 s segments by copying, cutting, and symmetry. Then, 24 artificial features including waveform features, interval features, frequency-domain features, and nonlinear feature were extracted relying on prior knowledge. Meanwhile, a 13-layer one-dimensional convolutional neural network (1-D CNN) was constructed to yield 38 abstract features. Finally, 24 artificial features and 38 abstract features were fused to yield the feature matrix. Random forest was employed to classify the ECG recordings. In this study, the mean accuracy (Acc) of the four categories reached 0.857. The F1 of N, A, and O reached 0.837. The results exhibited the proposed method had relatively satisfactory performance for identifying AF from short single-lead ECG recordings with low SNR.

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Section: Artifcial Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Atrial Fibrillation Detection with Low Signal-to-Noise Ratio Data Using Artificial Features and Abstract Features

Bao

Shou-fen

et al. 2023

Journal of Healthcare Engineering

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“…Furthermore, the complexity of beat localization itself and possible errors often have a negative impact on the subsequent ECG analysis. Therefore, random slices have been directly used to segment ECG signals [12] . Thus, the random slicing method reduces the coupling between preprocessing and subsequent tasks, making it easier to operate.…”

Section: Segmentationmentioning

confidence: 99%

Intelligent ECG Analysis in Medicine: Data, Methods and Applications

Guan¹,

Yushkov²,

Guo³

et al. 2019

Chinese Medical Sciences Journal

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Electrocardiogram (ECG) is a low-cost, simple, fast, and non-invasive test. It can reflect the heart's electrical activity and provide valuable diagnostic clues about the health of the entire body.Therefore, ECG has been widely used in various biomedical applications such as arrhythmia detection, disease-specific detection, mortality prediction, and biometric recognition. In recent years, ECG-related studies have been carried out using a variety of publicly available datasets, with many differences in the datasets used, data preprocessing methods, targeted challenges, and modeling and analysis techniques. Here we systematically summarize and analyze the ECG-based automatic analysis methods and applications. Specifically, we first reviewed 16 commonly used ECG public datasets and provided an overview of data preprocessing processes. Then we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these applications. Finally, we elucidated some of the challenges in ECG analysis and provided suggestions for further research.

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“…Frameworks are applied in different domains. For instance, [30] applies it to the Automated Detection of Arrhythmias in ECG Segments, while [26] is a framework application in the health domain for smart patient monitoring and recommendation. The work of [25] [3] present and compares explainable and interpretable frameworks.…”

Section: Introductionmentioning

confidence: 99%

AI2: The next leap toward native language based and explainable machine learning framework

Dessureault¹,

Massicotte²

2023

Preprint

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The machine learning frameworks flourished in the last decades, allowing artificial intelligence to get out of academic circles to be applied to enterprise domains. This field has significantly advanced, but there is still some meaningful improvement to reach the subsequent expectations. The proposed framework, named AI 2 , uses a natural language interface that allows a non-specialist to benefit from machine learning algorithms without necessarily knowing how to program with a programming language. The primary contribution of the AI 2 framework allows a user to call the machine learning algorithms in English, making its interface usage easier. The second contribution is greenhouse gas (GHG) awareness. It has some strategies to evaluate the GHG generated by the algorithm to be called and to propose alternatives to find a solution without executing the energy-intensive algorithm. Another contribution is a preprocessing module that helps to describe and to load data properly. Using an English text-based chatbot, this module guides the user to define every dataset so that it can be described, normalized, loaded and divided appropriately. The last contribution of this paper is about explainability. For decades, the scientific community has known that machine learning algorithms imply the famous black-box problem. Traditional machine learning methods convert an input into an output without being able to justify this result. The proposed framework explains the algorithm's process with the proper texts, graphics and tables. The results, declined in five cases, present usage applications from the user's English command to the explained output. Ultimately, the AI 2 framework represents the next leap toward native language-based, human-oriented concerns about machine learning framework.

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A novel machine learning framework for automated detection of arrhythmias in ECG segments

Cited by 32 publications

References 24 publications

Atrial Fibrillation Detection with Low Signal-to-Noise Ratio Data Using Artificial Features and Abstract Features

Atrial Fibrillation Detection with Low Signal-to-Noise Ratio Data Using Artificial Features and Abstract Features

Intelligent ECG Analysis in Medicine: Data, Methods and Applications

AI2: The next leap toward native language based and explainable machine learning framework

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