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

被引:15
作者
Asif, Raheel [1 ]
Saleem, Sand [1 ,2 ]
Hassan, Syed Ali [1 ]
Alharbi, Soltan Abed [2 ]
Kamboh, Awais Mehmood [1 ,2 ]
机构
[1] Natl Univ Sci & Technol NUST, Sch Elect Engn & Comp Sci SEECS, Islamabad 44000, Pakistan
[2] Univ Jeddah, Coll Comp Sci & Engn CCSE, Dept Comp & Network Engn, Jeddah 21589, Saudi Arabia
关键词
Electroencephalography; seizure; epilepsy; classification; learning; RUSBoost; SMOTEboost; temporal region; EEG; WAVELET TRANSFORM; CHANNEL SELECTION; CLASSIFICATION; SIGNALS; ALGORITHMS; PREDICTION; MACHINES; NETWORKS;
D O I
10.1109/ACCESS.2020.2983917
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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 long-term 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.
引用
收藏
页码:65880 / 65890
页数:11
相关论文
共 51 条
  • [1] American Clinical Neurophysiology Society Guideline 2: Guidelines for Standard Electrode Position Nomenclature
    Acharya, Jayant N.
    Hani, Abeer
    Cheek, Janna
    Thirumala, Partha
    Tsuchida, Tammy N.
    [J]. JOURNAL OF CLINICAL NEUROPHYSIOLOGY, 2016, 33 (04) : 308 - 311
  • [2] Analysis of EEG records in an epileptic patient using wavelet transform
    Adeli, H
    Zhou, Z
    Dadmehr, N
    [J]. JOURNAL OF NEUROSCIENCE METHODS, 2003, 123 (01) : 69 - 87
  • [3] Mallat's Scattering Transform Based Anomaly Sensing for Detection of Seizures in Scalp EEG
    Ahmad, Muhammad Zubair
    Kamboh, Awais Mehmood
    Saleem, Sajid
    Khan, Amir Ali
    [J]. IEEE ACCESS, 2017, 5 : 16919 - 16929
  • [4] Comparison of wavelet transform and FFT methods in the analysis of EEG signals
    Akin M.
    [J]. Journal of Medical Systems, 2002, 26 (3) : 241 - 247
  • [5] A review of channel selection algorithms for EEG signal processing
    Alotaiby, Turky
    Abd El-Samie, Fathi E.
    Alshebeili, Saleh A.
    Ahmad, Ishtiaq
    [J]. EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, 2015,
  • [6] Amin S., 2016, P IEEE 26 INT WORKSH, P1, DOI DOI 10.1109/MLSP.2016.7738825
  • [7] [Anonymous], P VIS MOD VIS C VMV
  • [8] [Anonymous], UNDERSTANDING PSYCHO
  • [9] The urban brain: analysing outdoor physical activity with mobile EEG
    Aspinall, Peter
    Mavros, Panagiotis
    Coyne, Richard
    Roe, Jenny
    [J]. BRITISH JOURNAL OF SPORTS MEDICINE, 2015, 49 (04) : 272 - U91
  • [10] Automatic Seizure Detection Using Multi-Resolution Dynamic Mode Decomposition
    Bilal, Muhammad
    Rizwan, Muhammad
    Saleem, Sajid
    Khan, Muhammad Murtaza
    Alkatheiri, Mohammed Saeed
    Alqarni, Mohammed
    [J]. IEEE ACCESS, 2019, 7 : 61180 - 61194