Epileptic Seizure Prediction Using Heart Rate Variability Signal analysis

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

POUDINEH M.; MOHEBBI M.; Gharagozli K.

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF ADVANCED SIGNAL PROCESSING Year:2019 | Volume:3 | Issue:1 (3) Page(s): 25-33

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

1,315

Download:

Cites:

Information Journal Paper

Title

Epileptic Seizure Prediction Using Heart Rate Variability Signal analysis

Author(s)

POUDINEH M. | MOHEBBI M. | Gharagozli K. | Issue Writer Certificate

Keywords

Heart rate variability

prediction

epilepsy

multivariate statistical process control

Abstract

epilepsy is a neural disorder with unknown nature and epileptic patients suffer from the consequences of unexpected seizures. In this paper, we proposed a new method to predict epileptic seizures using Heart rate variability (HRV) signal analysis. During preictal period of epilepsy, increasing in nervous activities of neurons affects the autonomic nervous system that disturbs heart rates. Therefore, epileptic seizures can be predicted through HRV monitoring. In our method, we extracted 12 features of HRV signal from different domains: time, frequency, time-frequency and non-linear domain. We used multivariate statistical process control (MSPC) algorithm for anomaly detection which is able to detect anomalies that cannot be detected by monitoring each variable independently. This algorithm has been applied to the clinical data collected from 17 patients. The obtained results demonstrated that the proposed method can predict seizure onset with an accuracy of 88. 2%. The proposed HRV-based seizure prediction algorithm is more promising than the conventional EEG-based methods from the viewpoint of practical use.

Cites

No record.

References

No record.

Cite

APA: Copy

POUDINEH, M., MOHEBBI, M., & Gharagozli, K.. (2019). Epileptic Seizure Prediction Using Heart Rate Variability Signal analysis. JOURNAL OF ADVANCED SIGNAL PROCESSING, 3(1 (3) ), 25-33. SID. https://sid.ir/paper/268951/en

Vancouver: Copy

POUDINEH M., MOHEBBI M., Gharagozli K.. Epileptic Seizure Prediction Using Heart Rate Variability Signal analysis. JOURNAL OF ADVANCED SIGNAL PROCESSING[Internet]. 2019;3(1 (3) ):25-33. Available from: https://sid.ir/paper/268951/en

IEEE: Copy

M. POUDINEH, M. MOHEBBI, and K. Gharagozli, “Epileptic Seizure Prediction Using Heart Rate Variability Signal analysis,” JOURNAL OF ADVANCED SIGNAL PROCESSING, vol. 3, no. 1 (3) , pp. 25–33, 2019, [Online]. Available: https://sid.ir/paper/268951/en

Related Journal Papers