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Heart sound anomaly and quality detection using ensemble of neural networks without segmentation

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Heart sound anomaly and quality detection using ensemble of neural networks without segmentation. / Zabihi, Morteza; Rad, Ali Bahrami; Kiranyaz, Serkan; Gabbouj, Moncef; Katsaggelos, Aggelos K.

Computing in Cardiology Conference, CinC 2016. IEEE, 2017. p. 613-616.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Zabihi, M, Rad, AB, Kiranyaz, S, Gabbouj, M & Katsaggelos, AK 2017, Heart sound anomaly and quality detection using ensemble of neural networks without segmentation. in Computing in Cardiology Conference, CinC 2016. IEEE, pp. 613-616, Computing in cardiology conference, 1/01/00. https://doi.org/10.23919/CIC.2016.7868817

APA

Zabihi, M., Rad, A. B., Kiranyaz, S., Gabbouj, M., & Katsaggelos, A. K. (2017). Heart sound anomaly and quality detection using ensemble of neural networks without segmentation. In Computing in Cardiology Conference, CinC 2016 (pp. 613-616). IEEE. https://doi.org/10.23919/CIC.2016.7868817

Vancouver

Zabihi M, Rad AB, Kiranyaz S, Gabbouj M, Katsaggelos AK. Heart sound anomaly and quality detection using ensemble of neural networks without segmentation. In Computing in Cardiology Conference, CinC 2016. IEEE. 2017. p. 613-616 https://doi.org/10.23919/CIC.2016.7868817

Author

Zabihi, Morteza ; Rad, Ali Bahrami ; Kiranyaz, Serkan ; Gabbouj, Moncef ; Katsaggelos, Aggelos K. / Heart sound anomaly and quality detection using ensemble of neural networks without segmentation. Computing in Cardiology Conference, CinC 2016. IEEE, 2017. pp. 613-616

Bibtex - Download

@inproceedings{b654fa9fb3754bee97303f3cab5cd066,
title = "Heart sound anomaly and quality detection using ensemble of neural networks without segmentation",
abstract = "Phonocardiogram (PCG) signal is used as a diagnostic test in ambulatory monitoring in order to evaluate the heart hemodynamic status and to detect a cardiovascular disease. The objective of this study is to develop an automatic classification method for anomaly (normal vs. abnormal) and quality (good vs. bad) detection of PCG recordings without segmentation. For this purpose, a subset of 18 features is selected among 40 features based on a wrapper feature selection scheme. These features are extracted from time, frequency, and time-frequency domains without any segmentation. The selected features are fed into an ensemble of 20 feedforward neural networks for classification task. The proposed algorithm achieved the overall score of 91.50{\%} (94.23{\%} sensitivity and 88.76{\%} specificity) and 85.90{\%} (86.91{\%} sensitivity and 84.90{\%} specificity) on the train and unseen test datasets, respectively. The proposed method got the second best score in the PhysioNet/CinC Challenge 2016.",
author = "Morteza Zabihi and Rad, {Ali Bahrami} and Serkan Kiranyaz and Moncef Gabbouj and Katsaggelos, {Aggelos K.}",
note = "EXT={"}Rad, Ali Bahrami{"} EXT={"}Kiranyaz, Serkan{"}",
year = "2017",
month = "3",
day = "1",
doi = "10.23919/CIC.2016.7868817",
language = "English",
publisher = "IEEE",
pages = "613--616",
booktitle = "Computing in Cardiology Conference, CinC 2016",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Heart sound anomaly and quality detection using ensemble of neural networks without segmentation

AU - Zabihi, Morteza

AU - Rad, Ali Bahrami

AU - Kiranyaz, Serkan

AU - Gabbouj, Moncef

AU - Katsaggelos, Aggelos K.

N1 - EXT="Rad, Ali Bahrami" EXT="Kiranyaz, Serkan"

PY - 2017/3/1

Y1 - 2017/3/1

N2 - Phonocardiogram (PCG) signal is used as a diagnostic test in ambulatory monitoring in order to evaluate the heart hemodynamic status and to detect a cardiovascular disease. The objective of this study is to develop an automatic classification method for anomaly (normal vs. abnormal) and quality (good vs. bad) detection of PCG recordings without segmentation. For this purpose, a subset of 18 features is selected among 40 features based on a wrapper feature selection scheme. These features are extracted from time, frequency, and time-frequency domains without any segmentation. The selected features are fed into an ensemble of 20 feedforward neural networks for classification task. The proposed algorithm achieved the overall score of 91.50% (94.23% sensitivity and 88.76% specificity) and 85.90% (86.91% sensitivity and 84.90% specificity) on the train and unseen test datasets, respectively. The proposed method got the second best score in the PhysioNet/CinC Challenge 2016.

AB - Phonocardiogram (PCG) signal is used as a diagnostic test in ambulatory monitoring in order to evaluate the heart hemodynamic status and to detect a cardiovascular disease. The objective of this study is to develop an automatic classification method for anomaly (normal vs. abnormal) and quality (good vs. bad) detection of PCG recordings without segmentation. For this purpose, a subset of 18 features is selected among 40 features based on a wrapper feature selection scheme. These features are extracted from time, frequency, and time-frequency domains without any segmentation. The selected features are fed into an ensemble of 20 feedforward neural networks for classification task. The proposed algorithm achieved the overall score of 91.50% (94.23% sensitivity and 88.76% specificity) and 85.90% (86.91% sensitivity and 84.90% specificity) on the train and unseen test datasets, respectively. The proposed method got the second best score in the PhysioNet/CinC Challenge 2016.

U2 - 10.23919/CIC.2016.7868817

DO - 10.23919/CIC.2016.7868817

M3 - Conference contribution

SP - 613

EP - 616

BT - Computing in Cardiology Conference, CinC 2016

PB - IEEE

ER -