Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:2933-2936. doi: 10.1109/EMBC48229.2022.9871579.
Seizure termination has received significantly less attention than initiation and propagation and consequently, remains a poorly understood phase of seizure evolution. Yet, its study may have a significant impact on the development of efficient interventional approaches, i.e., it may be critical for the design of treatments that induce or reproduce termination mechanisms that are triggered in self-terminating seizures. In this work, we aim to study temporal and spectral features of intracranial EEG (iEEG) during epileptic seizures to find time-frequency signatures that can predict the termination patterns. We propose a deep learning model for classification of multi channel iEEG epileptic seizure termination pattern into burst suppression and continuous bursting. We decompose the raw time series seizure data into time-frequency maps using Morlet Wavelet Transform. A Convolution Neural Network (CNN) is then trained on cross-patient time-frequency maps to classify the seizure termination patterns. For evaluation of classification performance, we compared the proposed method with k-Nearest Neighbour (k-NN). The CNN is shown to achieve an accuracy of 90 % and precision of 92 % as compared to 70% and 72% accuracy and precision achieved with the k-NN respectively. The proposed model is thus able to capture the temporal and spatial patterns which results in high performance of the classifier. This method of classification can be used to predict how a particular seizure will end and can potentially inform seizure management and treatment. Clinical relevance- This method establishes a model that can be used to classify seizure termination patterns with an accuracy of 90 % which can assist in better treatment of epilepsy patients.
癫痫发作的终止比起始和传播受到的关注要少得多，因此仍然是一个理解不充分的癫痫发作演变阶段。然而，它的研究可能对高效介入方法的发展产生重大影响，即对于设计能够诱导或重现自终止癫痫发作中触发的终止机制的治疗方法可能至关重要。在这项工作中，我们旨在研究颅内 EEG（iEEG）在癫痫发作期间的时频特征，以找到可以预测终止模式的时频特征。我们提出了一种深度学习模型，用于将多通道 iEEG 癫痫发作终止模式分类为爆发抑制和连续爆发。我们使用 Morlet 小波变换将原始时间序列发作数据分解为时频图。然后，在跨患者时频图上训练卷积神经网络（CNN）以对发作终止模式进行分类。为了评估分类性能，我们将提出的方法与 k-最近邻（k-NN）进行了比较。与 k-NN 分别达到的 70%和 72%的准确性和精度相比，CNN 达到了 90%的准确性和 92%的精度。因此，所提出的模型能够捕捉到时间和空间模式，从而实现分类器的高性能。这种分类方法可用于预测特定发作将如何结束，并可能为癫痫发作的管理和治疗提供信息。临床相关性-该方法建立了一个可以用于分类发作终止模式的模型，准确性达到 90%，可以帮助更好地治疗癫痫患者。
