An improved method to detect arrhythmia using ensemble learning-based model in multi lead electrocardiogram (ECG) Article Swipe
Satria Mandala
,
Ardian Rizal
,
Adiwijaya Adiwijaya
,
Siti Nurmaini
,
Sabilla Suci Amini
,
Gabriel Almayda Sudarisman
,
Yuan Wen Hau
,
Abdul Hanan Abdullah
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0297551
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0297551
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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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0297551
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0297551&type=printable
- OA Status
- gold
- Cited By
- 33
- References
- 73
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394596698
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4394596698Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1371/journal.pone.0297551Digital Object Identifier
- Title
-
An improved method to detect arrhythmia using ensemble learning-based model in multi lead electrocardiogram (ECG)Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-09Full publication date if available
- Authors
-
Satria Mandala, Ardian Rizal, Adiwijaya Adiwijaya, Siti Nurmaini, Sabilla Suci Amini, Gabriel Almayda Sudarisman, Yuan Wen Hau, Abdul Hanan AbdullahList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pone.0297551Publisher landing page
- PDF URL
-
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0297551&type=printableDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0297551&type=printableDirect OA link when available
- Concepts
-
Atrial fibrillation, Premature atrial contraction, Cardiac arrhythmia, Electrocardiography, Boosting (machine learning), Cardiology, Internal medicine, Sensitivity (control systems), Artificial intelligence, Pattern recognition (psychology), Computer science, Medicine, Engineering, Electronic engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
33Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 26, 2024: 7Per-year citation counts (last 5 years)
- References (count)
-
73Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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