Ensemble of Feature:based and Deep learning:based Classifiers for Detection of Abnormal Heart Sounds Article Swipe
Cristhian Potes
,
Saman Parvaneh
,
Asif Rahman
,
Bryan Conroy
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.22489/cinc.2016.182-399
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.22489/cinc.2016.182-399
The goal of the 2016 PhysioNet/CinC Challenge is the development of an algorithm to classify normal/abnormal heart sounds.A total of 124 time-frequency features were extracted from the phonocardiogram (PCG) and input to a variant of the AdaBoost classifier.A second classifier using convolutional neural network (CNN) was trained using PCGs cardiac cycles decomposed into four frequency bands.The final decision rule to classify normal/abnormal heart sounds was based on an ensemble of classifiers combining the outputs of AdaBoost and the CNN.The algorithm was trained on a training dataset (normal= 2575, abnormal= 665) and evaluated on a blind test dataset.Our classifier ensemble approach obtained the highest score of the competition with a sensitivity, specificity, and overall score of 0.9424, 0.7781, and 0.8602, respectively.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.22489/cinc.2016.182-399
- https://doi.org/10.22489/cinc.2016.182-399
- OA Status
- hybrid
- Cited By
- 305
- References
- 6
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- OpenAlex ID
- https://openalex.org/W2593628220
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https://openalex.org/W2593628220Canonical identifier for this work in OpenAlex
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https://doi.org/10.22489/cinc.2016.182-399Digital Object Identifier
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Ensemble of Feature:based and Deep learning:based Classifiers for Detection of Abnormal Heart SoundsWork title
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articleOpenAlex work type
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enPrimary language
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2016Year of publication
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2016-09-14Full publication date if available
- Authors
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Cristhian Potes, Saman Parvaneh, Asif Rahman, Bryan ConroyList of authors in order
- Landing page
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https://doi.org/10.22489/cinc.2016.182-399Publisher landing page
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https://doi.org/10.22489/cinc.2016.182-399Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.22489/cinc.2016.182-399Direct OA link when available
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Computer science, Ensemble learning, Artificial intelligence, Feature (linguistics), Pattern recognition (psychology), Feature extraction, Speech recognition, Machine learning, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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305Total citation count in OpenAlex
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2025: 21, 2024: 32, 2023: 37, 2022: 35, 2021: 57Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.specificity, | 110 |
| abstract_inverted_index.convolutional | 41 |
| abstract_inverted_index.respectively. | 119 |
| abstract_inverted_index.PhysioNet/CinC | 5 |
| abstract_inverted_index.time-frequency | 21 |
| abstract_inverted_index.normal/abnormal | 15, 61 |
| abstract_inverted_index.phonocardiogram | 27 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.699999988079071 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.99426431 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |