To propose a seizure prediction method through heart rate variability.To improve the quality of life of the patient through a simple electronic circuit.To incorporate GPS to trace out the exact location of the patient

1. ASSISTIVE SYSTEM FOR
EPILEPSY

2. ABSTRACT
 To propose a seizure prediction method through heart rate
variability.
 To improve the quality of life of the patient through a simple
electronic circuit.
 To incorporate GPS to trace out the exact location of the patient.

3. DESIGN
 Heart beat sensor
 Micro Electro Mechanical Sensor
 Microcontroller
 GSM Module
 GPS Tracking System

4. HEART BEAT SENSOR
 Change in blood volume through optical density.
 Signal pulse = heart beat pulse
 DC signal to AC signal.
Courtesy : Google Images

5. ECG WAVEFORMS
Courtesy : Google Images

6. WORKING
 Acquire the sensor signals.
 Process the signals using PIC18F4620.
 Detection of abnormality through a capable algorithm.
 Trigger the wireless transceiver.
 Track the location.

7. BLOCK DIAGRAM

8. PROS & CONS
 Non usage of EEG.
 Comparatively low cost.
 Non restrictive, portable.
 Treatment on time.
 Noise and temperature effect on the sensor outputs.
 Need to be patient specific.

9. IMPROVEMENTS SUGGESTED
 Internet of Things
 Wearable device
 Multi parameter acquisition
 Vehicle controlled device
 Improved prediction accuracy
 Self treatment technique

10. FUTURE SCOPE
Courtesy : Google Images

11. BIBLIOGRAPHY
 Ali MOTIE NASRABADI, Antonio DOURADO, Nader JAFARNIA DABANLOO,
SOROOR BEHBAHANI, ‘Prediction of Epileptic Seizures Based on Heart Rate
Variability’, Technology and Healthcare 24 Journal, Volume 24, February 2016.
 FARHADI H., MORIDANI M.K.,‘HEART Rate Variability as a Biomarker for
Epilepsy Seizure Prediction’, Bratislava Medical Journal, Volume 118, January
2017.
 Koichi Fujiwara, Miho MIYAJIMA, TOSHITAKA YAMAKAWA, Erika Abe, Yoko
Suzuki, Yuriko Sawada, Manabu Kano, Taketoshi MAEHARA, KATSUYA OHTA,
TAEKO SASAI-SAKUMA, ‘Epileptic Seizure Prediction Based on Multivariate
Statistical Process Control of Heart Rate Variability Features’, Volume 63, June
2016

12. CONCLUSION
 Optimized threshold, high dimensional time series analysis improve
the performance of algorithm.
 The clinical reliability depends upon the sensitivity and false
positive rate.
 The developing system has the potential for improving QoL of
epileptic patients through real-time seizure warning.

13. THANK
YOU