A deep learning approach to cardiovascular disease classification using empirical mode decomposition for ECG feature extraction

Li, Ya; Luo, Jing-hao; Dai, Qingyun; Eshraghian, Jason K.; Ling, Bingo Wing‐Kuen; Zheng, Ci-yan; Wang, Xiao-li

doi:10.1016/j.bspc.2022.104188

Cited by 19 publications

(2 citation statements)

References 48 publications

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“…Statisticalbased approaches rely on time-domain properties of the modes such as skewness, kurtosis, Root-Mean-Square (RMS), and crest factor for feature extraction. 2) Obtaining a low-redundancy (highly informative) version of the composite signal [240], [248], [255], [256], [258], [259], [268]- [271], [276]- [279], [281]: This is accomplished by decomposing the signal into elementary modes and applying further processing to examine these modes based on predefined ranking criteria. The highest-ranked mode is thus selected and various feature extraction techniques can be applied to the selected mode accordingly.…”

Section: Signal Decomposition-based Methodsmentioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between signal processing and Machine Learning (ML) to improve both performance and reliability. This fusion represents a critical point in the evolution of signal-based systems, highlighting the need to bridge the existing knowledge gap between these two interdisciplinary fields. Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing. This assumption creates a significant obstacle for a wide range of readers. To address these challenges, this paper takes an integrated article approach. It begins with a detailed tutorial on the fundamentals of signal processing, providing the reader with the necessary background knowledge. Following this, it explores the key stages of a standard signal processing-based ML pipeline, offering an in-depth review of feature extraction techniques, their inherent challenges, and solutions. Differing from existing literature, this work offers an application-independent review and introduces a novel classification taxonomy for feature extraction techniques. Furthermore, it aims at linking theoretical concepts with practical applications, and demonstrates this through two specific use cases: a spectral-based method for condition monitoring of rolling bearings and a wavelet energy analysis for epilepsy detection using EEG signals. In addition to theoretical contributions, this work promotes a collaborative research culture by providing a public repository of relevant Python and MATLAB signal processing codes. This effort is intended to support collaborative research efforts and ensure the reproducibility of the results presented.

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Section: Signal Decomposition-based Methodsmentioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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“…Inik and Turan, 2018) and shorten the early diagnosis process by inferring from images in the diagnosis of multi-class diseases, such as brain tumors. Deep learning methods are widely used in many medical classification problems, such as the classification of dermatological diseases (Zhou et al, 2022), cardiovascular diseases (Li et al, 2023), Alzheimer's disease (Hu et al, 2022), Parkinson's disease (Rezaee et al, 2022), chest diseases (Ibrahim et al, 2021), colon cancer and diseases (Pacal et al, 2020;Pacal and Karaboga, 2021) breast cancer (İ. Pacal, 2022), and brain tumors (Jia and Chen, 2020).…”

Section: Introductionmentioning

confidence: 99%

Detection of Monkeypox Among Different Pox Diseases with Different Pre-Trained Deep Learning Models

ÇELİK

İni̇k

2023

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Monkeypox is a viral disease that has recently rapidly spread. Experts have trouble diagnosing the disease because it is similar to other smallpox diseases. For this reason, researchers are working on artificial intelligence-based computer vision systems for the diagnosis of monkeypox to make it easier for experts, but a professional dataset has not yet been created. Instead, studies have been carried out on datasets obtained by collecting informal images from the Internet. The accuracy of state-of-the-art deep learning models on these datasets is unknown. Therefore, in this study, monkeypox disease was detected in cowpox, smallpox, and chickenpox diseases using the pre-trained deep learning models VGG-19, VGG-16, MobileNet V2, GoogLeNet, and EfficientNet-B0. In experimental studies on the original and augmented datasets, MobileNet V2 achieved the highest classification accuracy of 99.25% on the augmented dataset. In contrast, the VGG-19 model achieved the highest classification accuracy with 78.82% of the original data. Considering these results, the shallow model yielded better results for the datasets with fewer images. When the amount of data increased, the success of deep networks was better because the weights of the deep models were updated at the desired level.

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Integrating Advanced Combined Numerical Filters for ECG Denoising and Cardiovascular Disease Classification Using Deep Learning

Khatar,

Bentaleb,

El Mansouri

2024

Lecture Notes in Networks and Systems

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A deep learning approach to cardiovascular disease classification using empirical mode decomposition for ECG feature extraction

Cited by 19 publications

References 48 publications

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Detection of Monkeypox Among Different Pox Diseases with Different Pre-Trained Deep Learning Models

Integrating Advanced Combined Numerical Filters for ECG Denoising and Cardiovascular Disease Classification Using Deep Learning

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