Loui Psyche, Koplin-Green Matan, Frick Mark, Massone Michael
Program in Neuroscience and Behavior, Music, Imaging, and Neural Dynamics Laboratory, Department of Psychology, Wesleyan University , Middletown, CT , USA.
Front Hum Neurosci. 2014 Oct 13;8:820. doi: 10.3389/fnhum.2014.00820. eCollection 2014.
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.
声波化是指将数据转换为声音的过程，它为视觉显示提供了一种听觉替代方式。目前，癫痫发作诊断的普遍方法是通过目视读取患者的脑电图（EEG）。然而，由于人类听觉感知的特性，EEG数据的声波化具有一定优势。我们假设人类听众仅通过听觉模态就能从EEG中识别出癫痫发作，并且在经过短暂训练后，癫痫发作识别的准确性将会提高。在此，我们描述了一种用于将癫痫发作和非癫痫发作活动的EEG进行声波化的算法，随后进行了一项训练研究，在该研究中，受试者在短暂训练前后听取了声波化EEG的短片，并确定每个短片是癫痫发作还是正常活动。结果表明，在训练前，受试者在区分癫痫发作和非癫痫发作方面表现为随机水平，但在训练后准确性有了显著提高。训练后，受试者仅通过听觉模态就成功地区分了癫痫发作和非癫痫发作。使用信号检测理论进行的进一步分析表明，训练后敏感性提高，反应偏差降低。这项研究证明了声波化EEG用于癫痫发作检测的潜力。未来的研究将尝试通过新颖的训练和声波化修改来提高准确性，目标是使用声波化作为一种可能的基于生物反馈的癫痫干预措施来管理、预测并最终控制癫痫发作。
