Design of a Low-Complexity Real-Time Arrhythmia Detection System

Chang, Robert Chen-Hao; Chen, Hung-Lieh; Lin, Chih-Hung; Lin, Kuang-Hao

doi:10.1007/s11265-017-1221-2

Cited by 10 publications

(3 citation statements)

References 25 publications

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“…A low complexity system has been proposed by Chang et al which use simple features to classify ECG signals. The scores for this system are above 98 percent for both sensitivity and specificity; however, the PVC scores are not available in the presented results (Chang, et al 2017). Regarding QRS detection Andrysiak et al used a sparse ECG signal representation based on dictionaries and they used neural networks to detect these (Andrysiak 2016).…”

Section: Related Workmentioning

confidence: 80%

“…One arrhythmia that belongs to these groups is the Premature Ventricular Contraction (PVC) or its synonym Ventricular Ectopic Beat (VEB). This arrhythmia is very difficult to detect why it is subjected to intensive researched (Luz, et al 2016), (Jambukia, Dabhi og Prajapati 2015), (Chang, et al 2017). This paper discusses, elaborates, and designs a model for a novel PVC classifier that focuses on classifying PVC beats.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Premature Ventricular Contraction by Using Regulated Discriminant Analysis with Very Sparse Training Data

Lynggaard

2018

Applied Artificial Intelligence

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Pathological electrocardiogram is often used to diagnose abnormal cardiac disorders where accurate classification of the cardiac beat types is crucial for timely diagnosis of dangerous conditions. However, accurate, timely, and precise detection of arrhythmia-types like premature ventricular contraction is very challenging as these signals are multiform, i.e. a reliable detection of these requires expert annotations. In this paper, a multivariate statistical classifier that is able to detect premature ventricular contraction beats is presented. This novel classifier can be trained with a very sparse amount of expert annotated data. To enable this, the dimensionality of the feature vector is kept very low, it uses strong designed features, and it uses a regularization mechanism. This approach is compared to other classifiers by using the MIT-BIH arrhythmia database. It has been found that the average accuracy, specificity and sensitivity are above 96 percent, which is superior given the sparse amount of training data.

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Section: Related Workmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Detecting Premature Ventricular Contraction by Using Regulated Discriminant Analysis with Very Sparse Training Data

Lynggaard

2018

Applied Artificial Intelligence

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“…This study uses datasets from AFDB MIT-BIH [ 46 ] and MITDB-MIT-BIH [ 63 ]. In the MITDB data, this study extracted two types of arrhythmia (PAC and PVC, with symbols’ A’ and’ V’ respectively) and normal data with symbols’ N.’ As for AFDB data, this study extracts AF data with the symbol’ AFIB’ and normal data’ N.’ According to each record’s label, MITDB and AFDB data are read.…”

Section: Section 5: Resultsmentioning

confidence: 99%

An improved method to detect arrhythmia using ensemble learning-based model in multi lead electrocardiogram (ECG)

Mandala,

Rizal,

Adiwijaya

et al. 2024

PLoS ONE

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Arrhythmia is a life-threatening cardiac condition characterized by irregular heart rhythm. Early and accurate detection is crucial for effective treatment. However, single-lead electrocardiogram (ECG) methods have limited sensitivity and specificity. This study propose an improved ensemble learning approach for arrhythmia detection using multi-lead ECG data. Proposed method, based on a boosting algorithm, namely Fine Tuned Boosting (FTBO) model detects multiple arrhythmia classes. For the feature extraction, introduce a new technique that utilizes a sliding window with a window size of 5 R-peaks. This study compared it with other models, including bagging and stacking, and assessed the impact of parameter tuning. Rigorous experiments on the MIT-BIH arrhythmia database focused on Premature Ventricular Contraction (PVC), Atrial Premature Contraction (PAC), and Atrial Fibrillation (AF) have been performed. The results showed that the proposed method achieved high sensitivity, specificity, and accuracy for all three classes of arrhythmia. It accurately detected Atrial Fibrillation (AF) with 100% sensitivity and specificity. For Premature Ventricular Contraction (PVC) detection, it achieved 99% sensitivity and specificity in both leads. Similarly, for Atrial Premature Contraction (PAC) detection, proposed method achieved almost 96% sensitivity and specificity in both leads. The proposed method shows great potential for early arrhythmia detection using multi-lead ECG data.

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Comparison Between J48 and MLP on QRS Classification Through Complexity Measures

Hübner

Kauati

2022

XXVII Brazilian Congress on Biomedical Engineering

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Design of a Low-Complexity Real-Time Arrhythmia Detection System

Cited by 10 publications

References 25 publications

Detecting Premature Ventricular Contraction by Using Regulated Discriminant Analysis with Very Sparse Training Data

Detecting Premature Ventricular Contraction by Using Regulated Discriminant Analysis with Very Sparse Training Data

An improved method to detect arrhythmia using ensemble learning-based model in multi lead electrocardiogram (ECG)

Comparison Between J48 and MLP on QRS Classification Through Complexity Measures

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