ECG arrhythmia Discrimination using SVM and Nonlinear and Non-stationary Decomposition

Abdalla, Fakheraldin Y. O.; Wu, Longwen; Ullah, Hikmat; Mkindu, Hassan; Nie, Yuting; Zhao, Yaqin

doi:10.1109/isspit47144.2019.9001889

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…It is then time to train the algorithm using the training set or k-fold cross-validation method. Performance evaluation with the test set follows, measured using such elements as the confusion matrix ( 209 ), and derived metrics such as F1 score, accuracy ( 208 ), and receiver operation characteristic (ROC) curve ( 210 ). Although these metrics are used widely, most have drawbacks ( 13 ).…”

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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“…According to how the information is exploited, these approaches can be divided into two categories: methods based on convolutional neural network (CNN) and methods based on sequence module. The CNN-based methods aim to exploit morphological features of ECG signals either in one dimensional time series [2,3] or in two dimensional image [4], and are shown to outperform the traditional machine learning methods such as support vector machines (SVM) [8,9,10,11]. However, these methods focus on the morphological features in the area centred around a certain peak data point or in the wavelets decomposed from original signal via DWT [3], and they are ineffective in exploiting the latent feature of the temporal signal in either time or frequency domain.…”

Section: Introductionmentioning

confidence: 99%

Low-Dimensional Denoising Embedding Transformer for ECG Classification

Guan

Wang

Feng³

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The transformer based model (e.g., FusingTF) has been employed recently for Electrocardiogram (ECG) signal classification. However, the high-dimensional embedding obtained via 1-D convolution and positional encoding can lead to the loss of the signal's own temporal information and a large amount of training parameters. In this paper, we propose a new method for ECG classification, called low-dimensional denoising embedding transformer (LDTF), which contains two components, i.e., low-dimensional denoising embedding (LDE) and transformer learning. In the LDE component, a low-dimensional representation of the signal is obtained in the time-frequency domain while preserving its own temporal information. And with the low-dimensional embedding, the transformer learning is then used to obtain a deeper and narrower structure with fewer training parameters than that of the FusingTF. Experiments conducted on the MIT-BIH dataset demonstrates the effectiveness and the superior performance of our proposed method, as compared with state-of-the-art methods.

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