Data_Sheet_1_A High Accuracy Electrographic Seizure Classifier Trained Using Semi-Supervised Labeling Applied to a Large Spectrogram Dataset.docx

The objective of this study was to explore using ECoG spectrogram images for training reliable cross-patient electrographic seizure classifiers, and to characterize the classifiers’ test accuracy as a function of amount of training data. ECoG channels in ∼138,000 time-series ECoG records from 113 patients were converted to RGB spectrogram images. Using an unsupervised spectrogram image clustering technique, manual labeling of 138,000 ECoG records (each with up to 4 ECoG channels) was completed in 320 h, which is an estimated 5 times faster than manual labeling without ECoG clustering. For training supervised classifier models, five random folds of data were created; with each fold containing 72, 18, and 23 patients’ data for model training, validation and testing respectively. Five convolutional neural network (CNN) architectures, including two with residual connections, were trained. Cross-patient classification accuracies and F1 scores improved with model complexity, with the shallowest 6-layer model (with ∼1.5 million trainable parameters) producing a class-balanced seizure/non-seizure classification accuracy of 87.9% on ECoG channels and the deepest ResNet50-based model (with ∼23.5 million trainable parameters) producing a classification accuracy of 95.7%. The trained ResNet50-based model additionally had 93.5% agreement in scores with an independent expert labeller. Visual inspection of gradient-based saliency maps confirmed that the models’ classifications were based on relevant portions of the spectrogram images. Further, by repeating training experiments with data from varying number of patients, it was found that ECoG spectrogram images from just 10 patients were sufficient to train ResNet50-based models with 88% cross-patient accuracy, while at least 30 patients’ data was required to produce cross-patient classification accuracies of >90%.

本研究旨在探索利用皮层脑电图（ECoG）频谱图图像训练可靠的跨患者癫痫发作分类器，并表征分类器的测试精度随训练数据量变化的规律。从113例患者的约13.8万份时序ECoG记录中提取的ECoG通道被转换为RGB频谱图图像。采用无监督频谱图图像聚类技术，仅耗时320小时即完成了13.8万份ECoG记录（每份最多包含4个ECoG通道）的人工标注，该效率较未使用ECoG聚类的人工标注提升约5倍。为训练有监督分类器模型，我们构建了5组随机折数据集，每组折分别包含72、18和23例患者的数据，分别用于模型训练、验证与测试。共训练了5种卷积神经网络（Convolutional Neural Network，CNN）架构，其中2种带有残差连接。跨患者分类精度与F1分数随模型复杂度提升而改善：最浅层的6层模型（约含150万可训练参数）在ECoG通道上实现了87.9%的类平衡癫痫发作/非发作分类准确率；而最深的基于ResNet50的模型（约含2350万可训练参数）分类准确率达95.7%。经训练的基于ResNet50的模型与独立专家标注者的评分一致性达93.5%。对基于梯度的显著图（Saliency Map）进行可视化检查证实，模型的分类决策基于频谱图图像的相关区域。此外，通过使用不同数量患者的数据重复训练实验，研究发现仅需10例患者的ECoG频谱图图像即可训练出跨患者准确率达88%的基于ResNet50的模型；而要实现跨患者分类准确率超过90%，则至少需要30例患者的数据。