A Comparison of Energy-Efficient Seizure Detectors for Implantable Neurostimulation Devices

Farrokh Manzouri; Marc Zöllin; Simon Schillinger; Matthias Dümpelmann; Ralf Mikut; Peter Woias; Laura Maria Comella; Andreas Schulze-Bonhage

doi:10.3389/fneur.2021.703797

A Comparison of Energy-Efficient Seizure Detectors for Implantable Neurostimulation Devices

Front Neurol. 2022 Mar 4:12:703797. doi: 10.3389/fneur.2021.703797. eCollection 2021.

Authors

Farrokh Manzouri¹, Marc Zöllin², Simon Schillinger^{2

3}, Matthias Dümpelmann¹, Ralf Mikut³, Peter Woias², Laura Maria Comella², Andreas Schulze-Bonhage^{1

4}

Affiliations

¹ Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
² Laboratory for Design of Microsystems, Department of Microsystems Engineering - IMTEK, University of Freiburg, Freiburg, Germany.
³ Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany.
⁴ Faculty of Medicine, Center for Basics in NeuroModulation, University of Freiburg, Freiburg, Germany.

Abstract

Introduction: About 30% of epilepsy patients are resistant to treatment with antiepileptic drugs, and only a minority of these are surgical candidates. A recent therapeutic approach is the application of electrical stimulation in the early phases of a seizure to interrupt its spread across the brain. To accomplish this, energy-efficient seizure detectors are required that are able to detect a seizure in its early stages.

Methods: Three patient-specific, energy-efficient seizure detectors are proposed in this study: (i) random forest (RF); (ii) long short-term memory (LSTM) recurrent neural network (RNN); and (iii) convolutional neural network (CNN). Performance evaluation was based on EEG data (n = 40 patients) derived from a selected set of surface EEG electrodes, which mimic the electrode layout of an implantable neurostimulation system. As for the RF input, 16 features in the time- and frequency-domains were selected. Raw EEG data were used for both CNN and RNN. Energy consumption was estimated by a platform-independent model based on the number of arithmetic operations (AOs) and memory accesses (MAs). To validate the estimated energy consumption, the RNN classifier was implemented on an ultra-low-power microcontroller.

Results: The RNN seizure detector achieved a slightly better level of performance, with a median area under the precision-recall curve score of 0.49, compared to 0.47 for CNN and 0.46 for RF. In terms of energy consumption, RF was the most efficient algorithm, with a total of 67k AOs and 67k MAs per classification. This was followed by CNN (488k AOs and 963k MAs) and RNN (772k AOs and 978k MAs), whereby MAs contributed more to total energy consumption. Measurements derived from the hardware implementation of the RNN algorithm demonstrated a significant correlation between estimations and actual measurements.

Discussion: All three proposed seizure detection algorithms were shown to be suitable for application in implantable devices. The applied methodology for a platform-independent energy estimation was proven to be accurate by way of hardware implementation of the RNN algorithm. These findings show that seizure detection can be achieved using just a few channels with limited spatial distribution. The methodology proposed in this study can therefore be applied when designing new models for responsive neurostimulation.

Keywords: convolutional neural network; low-power hardware implementation; random forest; recurrent neural network; responsive neurostimulation; seizure detection.