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Automated Arrhythmia Detection Based on RR Intervals.
Diagnostics (Basel). 2021 Aug 10;11(8):1446. doi: 10.3390/diagnostics11081446.
Diagnostics (Basel). 2021.
PMID: 34441380
Free PMC article.
Having such a cost-effective solution might lead to widespread long-term monitoring, which can help detecting arrhythmia earlier. Detection can lead to treatment, which improves outcomes for patients....
Having such a cost-effective solution might lead to widespread long-term monitoring, which can help detecting arrhythmia earlier. Det …
Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records.
Yildirim O, Talo M, Ciaccio EJ, Tan RS, Acharya UR.
Yildirim O, et al. Among authors: ciaccio ej.
Comput Methods Programs Biomed. 2020 Dec;197:105740. doi: 10.1016/j.cmpb.2020.105740. Epub 2020 Sep 8.
Comput Methods Programs Biomed. 2020.
PMID: 32932129
Free PMC article.
Convolutional layers and sub-sampling layers were used in the representation learning phase. The sequence learning part involved a long short-term memory (LSTM) unit after representation of learning layers. ...
Convolutional layers and sub-sampling layers were used in the representation learning phase. The sequence learning part involved a long shor …
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Comprehensive electrocardiographic diagnosis based on deep learning.
Lih OS, Jahmunah V, San TR, Ciaccio EJ, Yamakawa T, Tanabe M, Kobayashi M, Faust O, Acharya UR.
Lih OS, et al. Among authors: ciaccio ej.
Artif Intell Med. 2020 Mar;103:101789. doi: 10.1016/j.artmed.2019.101789. Epub 2020 Jan 20.
Artif Intell Med. 2020.
PMID: 32143796
The Convolutional Neural Network (CNN), followed by combined CNN and Long Short-Term Memory (LSTM) models, appear to be the most useful architectures for classification. ...
The Convolutional Neural Network (CNN), followed by combined CNN and Long Short-Term Memory (LSTM) models, appear to be the most usef …
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A new approach for arrhythmia classification using deep coded features and LSTM networks.
Yildirim O, Baloglu UB, Tan RS, Ciaccio EJ, Acharya UR.
Yildirim O, et al. Among authors: ciaccio ej.
Comput Methods Programs Biomed. 2019 Jul;176:121-133. doi: 10.1016/j.cmpb.2019.05.004. Epub 2019 May 10.
Comput Methods Programs Biomed. 2019.
PMID: 31200900
BACKGROUND AND OBJECTIVE: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. ...METHODS: A convolutional auto-encoder (CAE) based nonlinea …
BACKGROUND AND OBJECTIVE: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The …
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Development of gradient descent adaptive algorithms to remove common mode artifact for improvement of cardiovascular signal quality.
Ciaccio EJ, Micheli-Tzanakou E.
Ciaccio EJ, et al.
Ann Biomed Eng. 2007 Jul;35(7):1146-55. doi: 10.1007/s10439-007-9294-x. Epub 2007 Mar 31.
Ann Biomed Eng. 2007.
PMID: 17401690
METHOD: Two adaptive noise canceling (ANC) algorithms were tested to adjust weighted reference signals for optimal subtraction from a primary signal. Update of weight w was based upon the gradient term of the steepest descent equation: [see text], where the error epsilon i …
METHOD: Two adaptive noise canceling (ANC) algorithms were tested to adjust weighted reference signals for optimal subtraction from a primar …
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