Predicting Electrical Storm Using Episodes’ Parameters from ICD Recorded Data*
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Standard
Predicting Electrical Storm Using Episodes’ Parameters from ICD Recorded Data*. / Shakibfar, Saeed; Yazdchi, Mohammadreza; Aliakbaryhosseinabadi, Susan.
41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2019. p. 4885-4888.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - GEN
T1 - Predicting Electrical Storm Using Episodes’ Parameters from ICD Recorded Data*
AU - Shakibfar, Saeed
AU - Yazdchi, Mohammadreza
AU - Aliakbaryhosseinabadi, Susan
PY - 2019
Y1 - 2019
N2 - Electrical storm (ES) is a life-threatening heart condition for patients with implantable cardioverter defibrillators (ICDs). ICD patients experienced episodes are at higher risk for ES. However, predicting ES using previous episodes' parameters recorded by ICDs have never been developed. This study aims to predict ES using machine learning models based on ICD remote monitoring-summaries during episodes in the anonymized large number of patients. Episode ICD-summaries from 16,022 patients were used to construct and evaluate two models, logistic regression and random forest, for predicting the short-term risk of ES. Episode parameters in this study included the total number of sustained episodes, shocks delivered and the cycle length parameters. The models evaluated on the data sections not used for model development. Random forest performed significantly better than logistic regression (P <; 0.01), achieving a test accuracy of 0.99 and an Area Under an ROC Curve (AUC) of 0.93 (vs. an accuracy of 0.98 and an AUC of 0.90). The total number of previous sustained episodes was the most relevant variables in the both models
AB - Electrical storm (ES) is a life-threatening heart condition for patients with implantable cardioverter defibrillators (ICDs). ICD patients experienced episodes are at higher risk for ES. However, predicting ES using previous episodes' parameters recorded by ICDs have never been developed. This study aims to predict ES using machine learning models based on ICD remote monitoring-summaries during episodes in the anonymized large number of patients. Episode ICD-summaries from 16,022 patients were used to construct and evaluate two models, logistic regression and random forest, for predicting the short-term risk of ES. Episode parameters in this study included the total number of sustained episodes, shocks delivered and the cycle length parameters. The models evaluated on the data sections not used for model development. Random forest performed significantly better than logistic regression (P <; 0.01), achieving a test accuracy of 0.99 and an Area Under an ROC Curve (AUC) of 0.93 (vs. an accuracy of 0.98 and an AUC of 0.90). The total number of previous sustained episodes was the most relevant variables in the both models
U2 - 10.1109/EMBC.2019.8857720
DO - 10.1109/EMBC.2019.8857720
M3 - Article in proceedings
C2 - 31946955
SN - 978-1-5386-1312-2
SP - 4885
EP - 4888
BT - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - IEEE
T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Y2 - 23 July 2019 through 27 July 2019
ER -
ID: 231252086