Preventing early progression of epilepsy and so the severity of seizures requires an effective diagnosis. Epileptic transients indicate the ability to develop seizures but humans overlook such brief events in an electroencephalogram (EEG) what compromises patient treatment. Traditionally, training of the EEG event detection algorithms has relied on ground truth labels, obtained from the consensus of the majority of labelers. In this work, we go beyond labeler consensus on EEG data. Our event descriptor integrates EEG signal features with one-hot encoded labeler category that is a key to improved generalization performance. Notably, boosted decision trees take advantage of singly-labeled but more varied training sets. Our quantitative experiments show the proposed labeler-hot epileptic event detector consistently outperforms a consensus-trained detector and maintains confidence bounds of the detection. The results on our infant EEG recordings suggest datasets can gain higher event variety faster and thus better performance by shifting available human effort from consensus-oriented to separate labeling when labels include both, the event and the labeler category.
For algorithm developers, this review details recent approaches to the problem, compares the accuracy of various algorithms, identifies common testing issues and proposes some solutions. For the algorithm user, e.g. electroencephalograph (EEG) technician or neurologist, this review provides an estimate of algorithm accuracy and comparison to that of human experts. Manuscripts dated from 1975 are reviewed. Progress since Frost's 1985 review of the state of the art is discussed. Twenty-five manuscripts are reviewed. Many novel methods have been proposed including neural networks and high-resolution frequency methods. Algorithm accuracy is less than that of experts, but the accuracy of experts is probably less than what is commonly believed. Larger record sets will be required for expert-level detection algorithms. Abstract Objective Compare the spike detection performance of three skilled humans and three computer algorithms. Methods 40 prolonged EEGs, 35 containing reported spikes, were evaluated. Spikes and sharp waves were marked by the humans and algorithms. Pairwise sensitivity and false positive rates were calculated for each human–human and algorithm-human pair. Differences in human pairwise performance were calculated and compared to the range of algorithm versus human performance differences as a type of statistical Turing test. Results 5474 individual spike events were marked by the humans. Mean, pairwise human sensitivities and false positive rates were 40.0 , 42.1 , and 51.5 , and 0.80, 0.97, and 1.99 min. Only the Persyst 13 (P13) algorithm was comparable to humans – 43.9 and 1.65 min. Evaluation of pairwise differences in sensitivity and false positive rate demonstrated that P13 met statistical noninferiority criteria compared to the humans. Conclusion Humans had only a fair level of agreement in spike marking. The P13 algorithm was statistically noninferior to the humans. Significance This was the first time that a spike detection algorithm and humans performed similarly. The performance comparison methodology utilized here is generally applicable to problems in which skilled human performance is the desired standard and no external gold standard exists. The EEG of epileptic patients often contains sharp waveforms called "spikes", occurring between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we develop a convolutional neural network (CNN) for detecting spikes in EEG of epileptic patients in an automated fashion. The CNN has a convolutional architecture with filters of various sizes applied to the input layer, leaky ReLUs as activation functions, and a sigmoid output layer. Balanced mini-batches were applied to handle the imbalance in the data set. Leave-one-patient-out cross-validation was carried out to test the CNN and benchmark models on EEG data of five epilepsy patients. We achieved 0.947 AUC for the CNN, while the best performing benchmark model, Support Vector Machines with Gaussian kernel, achieved an AUC of 0.912. 