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The modeling of the brain as a three-dimensional spatial object, similar to a geographical landscape, has the paved way for the successful application of Kriging methods in solving the seizure detection problem with good performance but in cubic computational time complexity. The Deep Neural Network (DNN) has been widely used for seizure detection due to its effectiveness in classification tasks, although at the cost of a protracted training time. While Kriging exploits the spatial correlation between data locations, DNN relies on its capacity to learn intrinsic representations within the dataset from the basest unit parts. This paper presents a Distributed Kriging-Bootstrapped Deep Neural Network (DNN) model as a twofold solution for fast and accurate seizure detection using brain signals collected with the electroencephalogram (EEG) from healthy subjects and patients of epilepsy. The proposed model parallelizes the Kriging computation into different cores in a machine and then produces a strongly correlated, unified quasi-output data which serves as an input to the Deep Neural Network. Experimental results validate the proposed model as superior to conventional Kriging methods and DNN by training in 91% less time than the basic DNN and about three times as fast as the ordinary Kriging-Bootstrapped Deep Neural Network (DNN) model while maintaining good performance in terms of sensitivity, specificity and testing accuracy compared to other models and existing works.
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Krig-Detect: Exploring Alternative Kriging Methods for Real-Time Seizure Detection from EEG Signal
Epileptic seizures are dangerous. They render patients unconscious and can lead to death within seconds of onset. There is, therefore, the need for a very fast and accurate seizure detection mechanism. Kriging methods have been used extensively in geostatistics for spatial prediction and are known for very high accuracy. By modeling the brain as a spatial map, we demonstrate the effectiveness of Kriging Methods for efficient seizure detection in an edge computing paradigm. We explore three different types of Kriging - Simple Kriging, Ordinary Kriging and Universal Kriging. Results from various experiments with electroencephalogram (EEG) signals of both healthy and diseased patients show that all three Kriging methods have good performance in terms of accuracy, sensitivity and detection latency. However, Simple Kriging emerged as the slight favorite for seizure detection with a mean detection latency of 0.81 sec, an accuracy of 97.50%, a sensitivity of 94.74% and a perfect specificity. Simple Kriging is at least 5% better than Ordinary Kriging and Universal Kriging when evaluated at 68.2% confidence interval. The results obtained in this paper compare favorably with other seizure detection models in the literature.
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- Award ID(s):
- 1924112
- PAR ID:
- 10158118
- Date Published:
- Journal Name:
- Proceedings of the 6th IEEE World Forum on Internet of Things (WF-IoT)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The Deep Neural Network (DNN) model is known for its high accuracy in classification tasks due to its intrinsic ability to learn the underlying patterns existing in a set of data. Hence it has gained momentum in seizure detection research, as in many other fields. However, its high performance is at the expense of an extensive training time. This is not appropriate for a real-time application such as seizure detection in which a swift reaction is required to save the life of the patient. This paper presents a novel Kriging-Bootstrapped Deep Neural Network hierarchical model for early seizure detection in which Kriging is first used to generate a well-correlated intermediate data set from the original input. The correlated data is then fed into the DNN for the final training. Experiments were carried out using electroencephalogram (EEG) data from both normal and epileptic patients. Results show that, with the same architecture and data size, the cumulative training time of the Krigging-Bootstrapped DNN is about 75% lower than that of the ordinary DNN without a compromise in performance as the proposed hybrid model shows a slightly better accuracy than the baseline DNN model.more » « less
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Obeid, Iyad Selesnick (Ed.)Scalp electroencephalogram (EEG) signals inherently have a low signal-to-noise ratio due to the way the signal is electrically transduced. Temporal and spatial information must be exploited to achieve accurate detection of seizure events. Most popular approaches to seizure detection using deep learning do not jointly model this information or require multiple passes over the signal, which makes the systems inherently non-causal. In this paper, we exploit both simultaneously by converting the multichannel signal to a grayscale image and using transfer learning to achieve high performance. The proposed system is trained end-to-end with only very simple pre- and post-processing operations which are computationally lightweight and have low latency, making them conducive to clinical applications that require real-time processing. We have achieved a performance of 42.05% sensitivity with 5.78 false alarm per 24 hours on the development dataset of v1.5.2 of the Temple University Hospital Seizure Detection Corpus. On a single core CPU operating at 1.7 GHz, the system runs faster than real-time (0.58 xRT), uses 16 Gbytes of memory, and has a latency of 300 msec.more » « less
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Introduction Intracerebral hemorrhage represents 15 % of all strokes and it is associated with a high risk of post-stroke epilepsy. However, there are no reliable methods to accurately predict those at higher risk for developing seizures despite their importance in planning treatments, allocating resources, and advancing post-stroke seizure research. Existing risk models have limitations and have not taken advantage of readily available real-world data and artificial intelligence. This study aims to evaluate the performance of Machine-learning-based models to predict post-stroke seizures at 1 year and 5 years after an intracerebral hemorrhage in unselected patients across multiple healthcare organizations. Design/methods We identified patients with intracerebral hemorrhage (ICH) without a prior diagnosis of seizures from 2015 until inception (11/01/22) in the TriNetX Diamond Network, using the International Classification of Diseases, Tenth Revision (ICD-10) I61 (I61.0, I61.1, I61.2, I61.3, I61.4, I61.5, I61.6, I61.8, and I61.9). The outcome of interest was any ICD-10 diagnosis of seizures (G40/G41) at 1 year and 5 years following the first occurrence of the diagnosis of intracerebral hemorrhage. We applied a conventional logistic regression and a Light Gradient Boosted Machine (LGBM) algorithm, and the performance of the model was assessed using the area under the receiver operating characteristics (AUROC), the area under the precision-recall curve (AUPRC), the F1 statistic, model accuracy, balanced-accuracy, precision, and recall, with and without seizure medication use in the models. Results A total of 85,679 patients had an ICD-10 code of intracerebral hemorrhage and no prior diagnosis of seizures, constituting our study cohort. Seizures were present in 4.57 % and 6.27 % of patients within 1 and 5 years after ICH, respectively. At 1-year, the AUROC, AUPRC, F1 statistic, accuracy, balanced-accuracy, precision, and recall were respectively 0.7051 (standard error: 0.0132), 0.1143 (0.0068), 0.1479 (0.0055), 0.6708 (0.0076), 0.6491 (0.0114), 0.0839 (0.0032), and 0.6253 (0.0216). Corresponding metrics at 5 years were 0.694 (0.009), 0.1431 (0.0039), 0.1859 (0.0064), 0.6603 (0.0059), 0.6408 (0.0119), 0.1094 (0.0037) and 0.6186 (0.0264). These numerical values indicate that the statistical models fit the data very well.more » « less
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