ECG based Atrial Fibrillation detection using Sequency Ordered Complex Hadamard Transform and Hybrid Firefly Algorithm

Kora, Padmavathi; Annavarapu, Ambika; Yadlapalli, Priyanka; Krishna, K. Sri Rama; Viswanadharaju, S.

doi:10.1016/j.jestch.2017.02.002

Cited by 17 publications

(8 citation statements)

References 13 publications

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“…From the futuristic perspective, low computational cost and robustness to noise are key requirement for the feature extraction techniques, to support low energy devices for AF autodiagnosis. Authors in [106] share a smart modified version of Sequency Ordered Complex Hadamard Transform (SCHT) method, called Conjugate Symmetric Sequency Ordered Complex Hadamard Transform (CS-SCHT) for the efficient and effective feature extraction. This method outperforms other popular conventional and advance methods like adamard transform (NCHT), and natural-ordered complex Hadamard transform (NCHT) when applied with different classification algorithms like KNN, SVM and Levenberg Marquardt Neural Network (LMNN).…”

Section: Features Extractionmentioning

confidence: 99%

A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning

Rizwan

Zoha

Mabrouk

et al. 2021

IEEE Rev. Biomed. Eng.

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Atrial Fibrillation (AF) the most commonly occurring type of cardiac arrhythmia is one of the main causes of morbidity and mortality worldwide. The timely diagnosis of AF is an equally important and challenging task because of its asymptomatic and episodic nature. n this paper, state-ofthe-art ECG data-based machine learning models and signal processing techniques applied for auto diagnosis of AF are reviewed. Moreover, key biomarkers of AF on ECG and the common methods and equipment used for the collection of ECG data are discussed. Besides that, the modern wearable and implantable ECG sensing technologies used for gathering AF data are presented briefly. In the end, key challenges associated with the development of auto diagnosis solutions of AF are also highlighted. It is the first review paper of its kind that comprehensively presents a discussion on all these aspects related to AF auto-diagnosis at one place. It is observed that there is dire need of low energy, low cost but accurate auto diagnosis solutions for the proactive management of AF.

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Section: Features Extractionmentioning

confidence: 99%

A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning

Rizwan

Zoha

Mabrouk

et al. 2021

IEEE Rev. Biomed. Eng.

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“…As a result, they obtained a sensitivity of 98% compared to the threshold level algorithm. P. Kora et al (2017) have made feature extraction for R waves of AFR and NSR with Hadamard Transform. By using SVM and SVM like classifiers, have obtained accuracy rate.…”

Section: Scientific Literature Scanningmentioning

confidence: 99%

Atri̇yal Fi̇bri̇lasyonun Rr Araliği İle Otomati̇k Tespi̇ti̇

Güzeler

Bilgin

2019

Mühendislik Bilimleri Ve Tasarım Dergisi

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Heart diseases are rapidly increasing worldwide and in our country. This increase causes difficulties in the diagnosis processes of heart diseases. Considering these problems, the studies of engineering applications related to medical science give effective results in terms of solutions. By means of engineering devices and algorithms, positive contributions are made to medical applications. These contributions assist physicians especially in the diagnosis stages and speed up these processes. In this study, a new algorithm is developped so that Atrial Fibrillation (AF), which is the most common type of arrhythmia encountered, can be automatically detected at a high success rate. Electrocardiogram (ECG) data used in this study were obtained from physiobank ATM database. 31 samples of Atrial Fibrillation Rhythm (AFR) and 31 samples of Normal Sinus Rhythm (NSR) were obtained from this database. RR Interval (RRI) sequences being 12 hours long are used in the study. The change of the RRI sequences is an important parameter for AF. The RRI sequences are re-sampled using signal pre-processing techniques. The Discrete Wavelet Transform (DWT) was then applied to the resampled signals. In this way, feature extraction process is performed and the wavelet energies of these signals are visually examined with boxplot. The wavelet energies of the RRI sequences are classified by the Support Vector Machine (SVM). Finally, AFR and NSR are successfully separated as 99.60% achievement.

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“…Reported AI based AF detection algorithms generally utilizes machine learning or deep learning techniques. Machine learning based AF detection algorithms employ features, which are measured or calculated by original ECG signal [9][10][11][12][13][14][15][16][17] . This feature extraction step is important for the machine learning based AF detection algorithms.…”

Section: Introductionmentioning

confidence: 99%

Study On ECG Data Dependency For Atrial Fibrillation Detection Based On Residual Networks

Seo¹,

Oh²,

Joo³

2021

Preprint

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Atrial fibrillation (AF) is an arrhythmia that can cause blood clot and may lead to stroke and heart failure. To detect AF, deep learning-based detection algorithms have recently been developed. However, deep learning models were often trained with limited datasets and were evaluated within the same datasets, which makes their performance generally drops on the external datasets, known as data dependency. For this study, three different databases from PhysioNet were used to investigate the data dependency of deep learning-based AF detection algorithm using the residual neural network (Resnet). Resnet 18, 34, 50 and 152 model were trained with raw electrocardiogram (ECG) signal extracted from independent database. The highest accuracy was about 98–99% which is evaluation results of test dataset from the own database. On the other hand, the lowest accuracy was about 53–92% which was evaluation results of the external dataset extracted from different source. There are data dependency according to the train dataset and the test dataset. However, the data dependency decreased as a large amount of train data.

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ECG based Atrial Fibrillation detection using Sequency Ordered Complex Hadamard Transform and Hybrid Firefly Algorithm

Cited by 17 publications

References 13 publications

A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning

A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning

Atri̇yal Fi̇bri̇lasyonun Rr Araliği İle Otomati̇k Tespi̇ti̇

Study On ECG Data Dependency For Atrial Fibrillation Detection Based On Residual Networks

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