Faculty of Engineering & IT, Foundation University Islamabad, Islamabad 46000, Pakistan.
Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan.
Sensors (Basel). 2022 Apr 15;22(8):3036. doi: 10.3390/s22083036.
The timely detection of seizure activity in the case of newborns can help save lives. Clinical signs of seizures in newborns are difficult to observe, so, in this study, we propose an automated method of detecting seizures in newborns using multi-channel electroencephalogram (EEG) recording acquired from 36 newborns admitted to Royal Women's Hospital, Brisbane, Australia. A novel set of time-frequency marginal features are defined to detect seizure activity in newborns. The proposed set is based on the observation that EEG seizure signals appear either as a train of spikes or as a summation of frequency-modulated chirps with slow variation in the instantaneous frequency curve. The proposed set of features is obtained by extracting the time-frequency (TF) signature of seizure spikes and frequency-modulated chirps by exploiting the direction of ridges in the TF plane. Based on extracted TF signature of spikes, the modified time-marginal is computed whereas based on the extracted TF signature of frequency-modulated chirps, the modified frequency-marginal is computed. It is demonstrated that features extracted from the modified time-domain marginal and frequency-domain marginal in combination with TF statistical and frequency-related features lead to better accuracy than the existing TF signal classification method, i.e., the proposed method achieves an F1 score of 70.93% which is 5% greater than the existing method.
及时检测新生儿的癫痫发作活动有助于拯救生命。新生儿癫痫发作的临床体征难以观察，因此，在这项研究中，我们提出了一种使用多通道脑电图（EEG）记录从澳大利亚布里斯班皇家妇女医院收治的 36 名新生儿中自动检测新生儿癫痫发作的方法。定义了一组新的时频边缘特征来检测新生儿的癫痫发作活动。该组特征基于这样的观察结果，即 EEG 癫痫发作信号要么表现为一连串尖峰，要么表现为频率调制啁啾的总和，其瞬时频率曲线的变化缓慢。通过利用 TF 平面上脊线的方向来提取癫痫发作尖峰和频率调制啁啾的时频（TF）特征，可以获得所提出的特征集。基于提取的尖峰的 TF 特征，计算修改后的时间边缘，而基于提取的频率调制啁啾的 TF 特征，计算修改后的频率边缘。结果表明，与现有 TF 信号分类方法相比，从修改后的时域边缘和频域边缘提取的特征与 TF 统计和频率相关特征相结合，可以提高准确性，即，所提出的方法实现了 70.93%的 F1 分数，比现有方法高 5%。
