Epileptic Seizure Detection With a Reduced Montage: A Way Forward for Ambulatory EEG Devices

Abstract

Electroencephalogram (EEG) is one of the fundamental tools for analyzing the behavior of brain and particularly helpful for treatment of epilepsy and detection of associated seizures. For longterm recording of EEG signals, current research is heading towards simple, unobtrusive and ambulatory devices with a small number of channels. The primary contribution of this paper is to assess the performance difference between the seizure detection results using features from all channels versus only the channels in/around the temporal region. For this purpose, we develop a supervised seizure detection algorithm that uses time domain features extracted sequentially for every 1-second epoch. By using this algorithm, we obtained sensitivity values of 0.95 and 0.92, specificity values of 0.99 and 0.99 and false positive per hour values as 0.16 and 0.21 for all 23 channels and 10 temporal region channels, respectively. These results show that restricting the EEG analysis to temporal region results only in a graceful and gradual degradation of classifier performance. We conclude that EEG ambulatory devices with a montage local to the temporal region could demonstrate satisfactory performance. This presents a promising way forward for the use of ambulatory devices with compact wearable design.

Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices

“…Firstly, traditional EEG caps have 16-32 channels, providing full coverage of the scalp. Hence, it must be understood if it is possible to effectively detect seizures with a reduced electrode set, to be placed into minimally intrusive devices, such as glasses or behind-the-ear systems [3]- [6]. The second challenge is linked to the performance of the system, especially for what concerns specificity.…”

Single-channel seizure detection with clinical confirmation of seizure locations using CHB-MIT dataset

“…Numerous studies have been suggested patient-specific deep learning-based automated seizure detection approaches using the CHB-MIT dataset ( 21 , 34 , 35 , 37–48 ). In terms of seizure detection with a full montage setting, Xu et al ( 44 ) proposed seizure detectors based on a three-dimensional (3D) CNN using time-frequency matrices by multiscale short-time Fourier transform with an average sensitivity, FAR, and latency of 94.95%, 0.08/h, and 2.3 s, respectively.…”

Efficient EEG motion artifact elimination framework for ambulatory epileptic seizure detection application

“…[3], has shown great potential in capturing elusive seizures outside clinical settings. However, the presence of motion artifacts, resulting from the patient's movement during recording, poses a significant challenge to reliable seizure detection using ambulatory EEG [4]. Recent progress in low-energy wireless transmission, sensor design and cost-effective hardware design has facilitated the creation of innovative wireless headsets, ideal for mobile EEG recording.…”