Journal: F1000Research

Article Title: AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software

doi: 10.12688/f1000research.10569.2

Figure Lengend Snippet: The AR2 method automatically separates independent components containing myogenic from neurogenic potentials in the beta and gamma band on the basis of spatial topography and explained variance. A. Unprocessed scalp ictal EEG recording that was deemed uninterpretable. B. The same epoch after applying a low pass (<16 Hz) filter demonstrating a lack of a convincing ictal rhythm. C. The ictal epoch after applying a high pass (> 16 Hz) filter demonstrating dense muscle artifact. D. An example of a mutual information adjacency matrix calculated during an epoch of artifact in the high pass (> 16 Hz) filtered scalp EEG recording. Three scalp electrode recordings exhibited relatively low mutual information with all other electrodes and were designated poor quality and excluded from further processing to optimize INFO-MAX ICA based artifact reduction. E. The normalized inverse weight matrix of all independent components across scalp electrode recordings for the seizure in panel A. Independent components 1-13 exhibited strong focality and were designated as containing myogenic potentials, while independent components 14 and above were designated neurogenic.

Article Snippet: Minimal system requirements for operating AR2 include Matlab v8.4 or above, an Intel Xeon CPU, 2 GB of memory, a NVIDIA GPU, which is CUDA compatible, and CUDAICA.

Techniques:

Journal: F1000Research

Article Title: AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software

doi: 10.12688/f1000research.10569.2

Figure Lengend Snippet: Ictal scalp EEG recording from seizure 18 prior to artifact reduction processing (top), after processing with artifact reduction methodology 1 (AR1, middle), and after processing with artifact reduction methology 2 (AR2, bottom). Only processing with AR2 reveals a right hemispheric onset followed by clear spread to right frontal regions.

Article Snippet: Minimal system requirements for operating AR2 include Matlab v8.4 or above, an Intel Xeon CPU, 2 GB of memory, a NVIDIA GPU, which is CUDA compatible, and CUDAICA.

Techniques:

Journal: F1000Research

Article Title: AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software

doi: 10.12688/f1000research.10569.2

Figure Lengend Snippet: Clinical description of patients. Clinical description of patients and ictal EEG laterality and focus assignments with AR1 and AR2. Abbreviations (L:left, R:right), PET findings refer to hypometabolism, SPECT findings to hyperperfusion. The focus was determined on a majority basis across all the assignments made by the readers for a subject’s seizure(s).

Article Snippet: Minimal system requirements for operating AR2 include Matlab v8.4 or above, an Intel Xeon CPU, 2 GB of memory, a NVIDIA GPU, which is CUDA compatible, and CUDAICA.

Techniques: Single Photon Emission Computed Tomography

Journal: F1000Research

Article Title: AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software

doi: 10.12688/f1000research.10569.2

Figure Lengend Snippet: More readers could visualize the time of seizure onset, and assign laterality to seizure onset utilizing AR2 as compared to AR1, and the assigned laterality of seizure onset sometimes differed between the two methods. A. Bar plot of the number of readers whom visualized the time of onset for each seizure utilizing AR1 (blue) or AR2 (red). Across seizures more readers visualized seizure onset utilizing AR2 compared with AR1 (p<0.01). Asterisks indicate statistically significant differences between the two methods in individual seizures (McNemar, p<0.05, Bonferroni-Holm corrected). B. Stacked bar plot of the number of readers selecting a left- or right-sided seizure onset utilizing AR1 (light blue, left; light yellow, right) or AR2 (dark blue, left; yellow, right). Across seizures more readers lateralized seizure onset utilizing AR2 compared with AR1 (p<0.01). Asterisks indicate statistically significant differences in individual seizures (McNemar, p<0.05, Bonferroni-Holm corrected), number sign indicates a significant change in the determination of laterality utilizing AR2 compared to AR1 (McNemar, p<0.05, Bonferroni-Holm corrected).

Article Snippet: Minimal system requirements for operating AR2 include Matlab v8.4 or above, an Intel Xeon CPU, 2 GB of memory, a NVIDIA GPU, which is CUDA compatible, and CUDAICA.

Techniques:

Journal: F1000Research

Article Title: AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software

doi: 10.12688/f1000research.10569.2

Figure Lengend Snippet: A. Bar plot of the mean confidence scale values for visualizing the time of seizure onset for the 23 seizures interpreted utilizing AR1 (blue), and AR2 (red). Across seizures, confidence scale values were greater when AR2 was utilized as compared with AR1 (p<0.01). Asterisks indicate differences in confidence values in individual seizures (p<0.05, Bonferroni-Holm corrected). Error bars are calculated as s.e.m. B. The respective mean confidence scale values for seizure onset lateralization. C. The respective mean confidence scale values for seizure focus localization. Across seizures, confidence scale values for lateralizing seizure onset, and identifying the seizure focus were greater when AR2 was utilized as compared with AR1 (p<0.05).

Article Snippet: Minimal system requirements for operating AR2 include Matlab v8.4 or above, an Intel Xeon CPU, 2 GB of memory, a NVIDIA GPU, which is CUDA compatible, and CUDAICA.

Techniques:

Journal: F1000Research

Article Title: AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software

doi: 10.12688/f1000research.10569.2

Figure Lengend Snippet: Stacked bar plot of the ictal onset region assignments using either AR1 (lighter colors) or AR2 (darker colors) for all 23 seizures. Overall, across seizures, more readers were able to render an assignment using AR2 as compared to AR1 (p<0.05). Inter-reader agreement using for assigning the ictal onset region was marginal using either AR1 or AR2.

Article Snippet: Minimal system requirements for operating AR2 include Matlab v8.4 or above, an Intel Xeon CPU, 2 GB of memory, a NVIDIA GPU, which is CUDA compatible, and CUDAICA.

Techniques:

Journal: F1000Research

Article Title: AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software

doi: 10.12688/f1000research.10569.2

Figure Lengend Snippet: Contingency table of agreement between assigned seizure onset laterality and other clinical findings. Contingency table of the agreement between seizure-onset laterality using AR1 (left), and AR2 (right) and the laterality of seizure-onset assigned on the basis of other clinical data for all the study patients and seizures. Note that clinical seizure-onset lateralization was not available for all patients, and when readers rendered a laterality decision that matched the laterality based on other clinical data, the assignments “agreed”.

Article Snippet: Minimal system requirements for operating AR2 include Matlab v8.4 or above, an Intel Xeon CPU, 2 GB of memory, a NVIDIA GPU, which is CUDA compatible, and CUDAICA.

Techniques:

Journal: F1000Research

Article Title: AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software

doi: 10.12688/f1000research.10569.2

Figure Lengend Snippet: Agreement between seizure-onset laterality and other clinical findings. Agreement between seizure-onset laterality assignments using either AR1 or AR2 and the suspected laterality of the SOZ assigned on the basis of other clinical data. Parentheses indicate the 95% confidence interval. “n” refers to the number of subjects.

Article Snippet: Minimal system requirements for operating AR2 include Matlab v8.4 or above, an Intel Xeon CPU, 2 GB of memory, a NVIDIA GPU, which is CUDA compatible, and CUDAICA.

Techniques: