Fusing QRS Detection, Waveform Features, and Robust Interval Estimation with a Random Forest to Classify Atrial Fibrillation

Antink, Christoph Hoog; Leonhardt, Steffen; Walter, Marian

doi:10.22489/cinc.2017.167-163

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…After feature extraction, feature selection is required; here, several useful techniques have been used: wrappers ( 154 ) as a recursive feature elimination method ( 155 ), filters ( 156 ) as information gain ( 157 , 158 ), and embedded, such as least absolute shrinkage and selection operator LASSO ( 159 ). It is sometimes necessary to reduce the number of selected features from the last stage by selecting the principal variables that best represent the signals and suppressing the ones with redundant information (dimensionality reduction).…”

Section: Discussionmentioning

confidence: 99%

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Front. Cardiovasc. Med.

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Electrocardiographic signals (ECG) and heart rate viability measurements (HRV) provide information in a range of specialist fields, extending to musical perception. The ECG signal records heart electrical activity, while HRV reflects the state or condition of the autonomic nervous system. HRV has been studied as a marker of diverse psychological and physical diseases including coronary heart disease, myocardial infarction, and stroke. HRV has also been used to observe the effects of medicines, the impact of exercise and the analysis of emotional responses and evaluation of effects of various quantifiable elements of sound and music on the human body. Variations in blood pressure, levels of stress or anxiety, subjective sensations and even changes in emotions constitute multiple aspects that may well-react or respond to musical stimuli. Although both ECG and HRV continue to feature extensively in research in health and perception, methodologies vary substantially. This makes it difficult to compare studies, with researchers making recommendations to improve experiment planning and the analysis and reporting of data. The present work provides a methodological framework to examine the effect of sound on ECG and HRV with the aim of associating musical structures and noise to the signals by means of artificial intelligence (AI); it first presents a way to select experimental study subjects in light of the research aims and then offers possibilities for selecting and producing suitable sound stimuli; once sounds have been selected, a guide is proposed for optimal experimental design. Finally, a framework is introduced for analysis of data and signals, based on both conventional as well as data-driven AI tools. AI is able to study big data at a single stroke, can be applied to different types of data, and is capable of generalisation and so is considered the main tool in the analysis.

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Section: Discussionmentioning

confidence: 99%

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Front. Cardiovasc. Med.

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“…classified ECG using wavelet coefficients, optimizing classification performance using automatic feature selection and classification combining multiple ECG information. Antik et al (Antink et al 2017) used random forests to fuse the QRS complex features of the ECG signal with the RR interval to detect the occurrence of atrial fibrillation. The Asgari (Asgari et al 2015) team proposed the use of peak power ratio and logarithmic energy entropy for automatic detection of AF, which eliminated the dependence of P and R waves information.…”

Section: Introductionmentioning

confidence: 99%

Visualization deep learning model for automatic arrhythmias classification

Jiang¹,

Qiu²,

Zhang³

et al. 2022

Physiol. Meas.

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With the improvement of living standards, heart disease has become one of the common diseases that threaten human health. As a widely used reliable, non-invasive technology, the Electrocardiography (ECG) data has been increasingly used for diagnosing cardiovascular diseases. With the rapid growth of ECG examinations and the shortage of cardiologists, accurate and automatic recognition of ECG patterns has become a research hotspot. In order to improve the accuracy in detecting abnormal ECG patterns, this paper proposes a hybrid 1D Resnet-GRU consisting of the Resnet and gated recurrent unit (GRU) modules to implement classification of arrhythmias from 12-lead ECG recordings. In addition, the focal Loss function is used to solve the problem of unbalanced datasets. Based on the proposed 1D Resnet-GRU model, the Grad-CAM++ mechanism, one of class-discriminative visualization methods, has been employed to the trained network model and generate thermal images superimposed on raw signals, so as to improve interpretability and transparency for arrhythmia classification. Experimental results show the proposed Resnet-GRU method can improve the F_1 score and accuracy effectively, and Grad-CAM++ can provide interpretability and clinical information for arrhythmia classification.

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“…Regular P-wave is not present; instead, socalled f-waves are present in the whole ECG signal. In the past, several approaches to detect atrial fibrillation has been proposed, ranging from analysis of ventricular response [1]- [3] to more complex signal analysis [4] implementing machine-learning techniques including convolutional neural networks [5], [6]. Although this description might lead to the impression that detecting AF is already solved the issue, the CinC/Physionet Challenge 2017 [7] shown that this task is still challenging, especially under specific circumstances.…”

Section: Introductionmentioning

confidence: 99%

Clustered Standard Deviation and Its Benefit to Identify Atrial Fibrillation

Plešinger¹,

Viščor²,

Nejedlý³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

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Background: Atrial fibrillation (AF) is a dysfunction of heart atriums shown as irregular heart activity leading to a higher risk of heart failure. Since AF may occur episodically, it is usually diagnosed using ECG Holter recordings. However, the presence of other pathologies and noise makes the automated processing of ECG Holter recordings complicated. Here, we present a new feature to distinguish AF from sinus rhythm as well as from other pathologies: Clustered Standard Deviation (CSTD).Method: QRS complexes are extracted from the ECG signal, and inter-beat intervals (RR) are ordered by their length. Then, RR clusters are found and the mean RR value is computed for each RR cluster. CSTD is computed using a formula for standard deviation using cluster-specific mean values instead of a global mean.Results: CSTD was evaluated for 7,254 ECG segments from a private dataset (MDT company, Brno, Czechia), 60 seconds length, 1-lead, 250 Hz sampling frequency. CSTD showed high values for AF while remaining low for other pathologies and sinus rhythm. CSTD between AF and other classes showed AUC 0.95. For comparison, a standard deviation of RR intervals leads to AUC 0.65 due to its sensitivity to other pathologies. Test on public respectively.

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Fusing QRS Detection, Waveform Features, and Robust Interval Estimation with a Random Forest to Classify Atrial Fibrillation

Cited by 10 publications

References 7 publications

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Visualization deep learning model for automatic arrhythmias classification

Clustered Standard Deviation and Its Benefit to Identify Atrial Fibrillation

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