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Rapidly learned identification of epileptic seizures from sonified EEG.

Loui P, Koplin-Green M, Frick M, Massone M - Front Hum Neurosci (2014)

Bottom Line: However, sonification of the EEG data provides certain advantages due to the nature of human auditory perception.After training, subjects successfully distinguished seizures from non-seizures using the auditory modality alone.Further analyses using signal detection theory demonstrated improvement in sensitivity and reduction in response bias as a result of training.

View Article: PubMed Central - PubMed

Affiliation: Program in Neuroscience and Behavior, Music, Imaging, and Neural Dynamics Laboratory, Department of Psychology, Wesleyan University , Middletown, CT , USA.

ABSTRACT
Sonification refers to a process by which data are converted into sound, providing an auditory alternative to visual display. Currently, the prevalent method for diagnosing seizures in epilepsy is by visually reading a patient's electroencephalogram (EEG). However, sonification of the EEG data provides certain advantages due to the nature of human auditory perception. We hypothesized that human listeners will be able to identify seizures from EEGs using the auditory modality alone, and that accuracy of seizure identification will increase after a short training session. Here, we describe an algorithm that we have used to sonify EEGs of both seizure and non-seizure activity, followed by a training study in which subjects listened to short clips of sonified EEGs and determined whether each clip was of seizure or normal activity, both before and after a short training session. Results show that before training subjects performed at chance level in differentiating seizures from non-seizures, but there was a significant improvement of accuracy after the training session. After training, subjects successfully distinguished seizures from non-seizures using the auditory modality alone. Further analyses using signal detection theory demonstrated improvement in sensitivity and reduction in response bias as a result of training. This study demonstrates the potential of sonified EEGs to be used for the detection of seizures. Future studies will attempt to increase accuracy using novel training and sonification modifications, with the goals of managing, predicting, and ultimately controlling seizures using sonification as a possible biofeedback-based intervention for epilepsy.

No MeSH data available.


Related in: MedlinePlus

Results of brief training on seizure identification from sonified EEGs. (A) Proportion correct of seizure identification pre- and post-training, showing improvement after training. (B)d-Prime values of the same responses, showing improvement in sensitivity. (C) Criterion values of the same data, showing reduction in bias. *p < 0.05; **p < 0.01. Error bars reflect between-subject standard error.
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Figure 2: Results of brief training on seizure identification from sonified EEGs. (A) Proportion correct of seizure identification pre- and post-training, showing improvement after training. (B)d-Prime values of the same responses, showing improvement in sensitivity. (C) Criterion values of the same data, showing reduction in bias. *p < 0.05; **p < 0.01. Error bars reflect between-subject standard error.

Mentions: Before training, mean accuracy in correctly categorized sonifications was 53.1% (SD = 0.17). This was not significantly higher than chance level [t(42) = 1.177, p = 0.25, one-sample t-test against chance level of 50%]. After training, subjects’ mean accuracy was 63.4% (SD = 0.13). This performance was significantly above chance [t(42) = 6.607, p < 0.001, one-sample t-test against chance level of 50%]. In addition, the difference in average accuracy before and after training was highly significant [t(42) = 3.553, p < 0.001, two-sample t-test; Cohen’s d = 0.963] (Figure 2).


Rapidly learned identification of epileptic seizures from sonified EEG.

Loui P, Koplin-Green M, Frick M, Massone M - Front Hum Neurosci (2014)

Results of brief training on seizure identification from sonified EEGs. (A) Proportion correct of seizure identification pre- and post-training, showing improvement after training. (B)d-Prime values of the same responses, showing improvement in sensitivity. (C) Criterion values of the same data, showing reduction in bias. *p < 0.05; **p < 0.01. Error bars reflect between-subject standard error.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4195310&req=5

Figure 2: Results of brief training on seizure identification from sonified EEGs. (A) Proportion correct of seizure identification pre- and post-training, showing improvement after training. (B)d-Prime values of the same responses, showing improvement in sensitivity. (C) Criterion values of the same data, showing reduction in bias. *p < 0.05; **p < 0.01. Error bars reflect between-subject standard error.
Mentions: Before training, mean accuracy in correctly categorized sonifications was 53.1% (SD = 0.17). This was not significantly higher than chance level [t(42) = 1.177, p = 0.25, one-sample t-test against chance level of 50%]. After training, subjects’ mean accuracy was 63.4% (SD = 0.13). This performance was significantly above chance [t(42) = 6.607, p < 0.001, one-sample t-test against chance level of 50%]. In addition, the difference in average accuracy before and after training was highly significant [t(42) = 3.553, p < 0.001, two-sample t-test; Cohen’s d = 0.963] (Figure 2).

Bottom Line: However, sonification of the EEG data provides certain advantages due to the nature of human auditory perception.After training, subjects successfully distinguished seizures from non-seizures using the auditory modality alone.Further analyses using signal detection theory demonstrated improvement in sensitivity and reduction in response bias as a result of training.

View Article: PubMed Central - PubMed

Affiliation: Program in Neuroscience and Behavior, Music, Imaging, and Neural Dynamics Laboratory, Department of Psychology, Wesleyan University , Middletown, CT , USA.

ABSTRACT
Sonification refers to a process by which data are converted into sound, providing an auditory alternative to visual display. Currently, the prevalent method for diagnosing seizures in epilepsy is by visually reading a patient's electroencephalogram (EEG). However, sonification of the EEG data provides certain advantages due to the nature of human auditory perception. We hypothesized that human listeners will be able to identify seizures from EEGs using the auditory modality alone, and that accuracy of seizure identification will increase after a short training session. Here, we describe an algorithm that we have used to sonify EEGs of both seizure and non-seizure activity, followed by a training study in which subjects listened to short clips of sonified EEGs and determined whether each clip was of seizure or normal activity, both before and after a short training session. Results show that before training subjects performed at chance level in differentiating seizures from non-seizures, but there was a significant improvement of accuracy after the training session. After training, subjects successfully distinguished seizures from non-seizures using the auditory modality alone. Further analyses using signal detection theory demonstrated improvement in sensitivity and reduction in response bias as a result of training. This study demonstrates the potential of sonified EEGs to be used for the detection of seizures. Future studies will attempt to increase accuracy using novel training and sonification modifications, with the goals of managing, predicting, and ultimately controlling seizures using sonification as a possible biofeedback-based intervention for epilepsy.

No MeSH data available.


Related in: MedlinePlus