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Wavelet Transform-Statistical Time Features-Based Methodology for Epileptic Seizure Prediction Using Electrocardiogram Signals

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  • Andrea V. Perez-Sanchez

    (ENAP-Research Group, CA-Sistemas Dinámicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, San Juan del Río 76807, Mexico)

  • Carlos A. Perez-Ramirez

    (ENAP-Research Group, Facultad de Ingeniería, Universidad Autónoma de Querétaro (UAQ), Campus Aeropuerto, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico)

  • Martin Valtierra-Rodriguez

    (ENAP-Research Group, CA-Sistemas Dinámicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, San Juan del Río 76807, Mexico)

  • Aurelio Dominguez-Gonzalez

    (ENAP-Research Group, CA-Sistemas Dinámicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, San Juan del Río 76807, Mexico)

  • Juan P. Amezquita-Sanchez

    (ENAP-Research Group, CA-Sistemas Dinámicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, San Juan del Río 76807, Mexico)

Abstract

Epilepsy is a brain disorder that affects about 50 million persons around the world and is characterized by generating recurrent seizures, which can put patients in permanent because of falls, drowning, burns, and prolonged seizures that they can suffer. Hence, it is of vital importance to propose a methodology with the capability of predicting a seizure with several minutes before the onset, allowing that the patients take their precautions against injuries. In this regard, a methodology based on the wavelet packet transform (WPT), statistical time features (STFs), and a decision tree classifier (DTC) for predicting an epileptic seizure using electrocardiogram (ECG) signals is presented. Seventeen STFs were analyzed to measure changes in the properties of ECG signals and find characteristics capable of differentiating between healthy and 15 min prior to seizure signals. The effectiveness of the proposed methodology for predicting an epileptic event is demonstrated using a database of seven patients with 10 epileptic seizures, which was provided by the Massachusetts Institute of Technology–Beth Israel Hospital (MIT–BIH). The results show that the proposed methodology is capable of predicting an epileptic seizure 15 min before with an accuracy of 100%. Our results suggest that the use of STFs at frequency bands related to heart activity to find parameters for the prediction of epileptic seizures is suitable.

Suggested Citation

  • Andrea V. Perez-Sanchez & Carlos A. Perez-Ramirez & Martin Valtierra-Rodriguez & Aurelio Dominguez-Gonzalez & Juan P. Amezquita-Sanchez, 2020. "Wavelet Transform-Statistical Time Features-Based Methodology for Epileptic Seizure Prediction Using Electrocardiogram Signals," Mathematics, MDPI, vol. 8(12), pages 1-17, November.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:12:p:2125-:d:452238
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    References listed on IDEAS

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    1. Lucia Billeci & Daniela Marino & Laura Insana & Giampaolo Vatti & Maurizio Varanini, 2018. "Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-21, September.
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    Cited by:

    1. Afshin Shoeibi & Marjane Khodatars & Navid Ghassemi & Mahboobeh Jafari & Parisa Moridian & Roohallah Alizadehsani & Maryam Panahiazar & Fahime Khozeimeh & Assef Zare & Hossein Hosseini-Nejad & Abbas K, 2021. "Epileptic Seizures Detection Using Deep Learning Techniques: A Review," IJERPH, MDPI, vol. 18(11), pages 1-33, May.
    2. Apostolos Karasmanoglou & Marios Antonakakis & Michalis Zervakis, 2023. "ECG-Based Semi-Supervised Anomaly Detection for Early Detection and Monitoring of Epileptic Seizures," IJERPH, MDPI, vol. 20(6), pages 1-20, March.

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