Sparse Multi-way Digital Signal Processing Approach for Detection of Deep Medial Temporal Discharges from Scalp EEG

用于检测头皮脑电图深层内侧颞放电的稀疏多路数字信号处理方法

• 批准号: EP/K005510/1
• 负责人: Saeid Sanei
• 金额: $ 40.33万
• 依托单位: University of Surrey
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FK005510%2F1
• 关键词: Sparse; Multi; way; Digital; Signal

Detection of deep brain medial temporal discharges is extremely crucial for early detection of epilepsy and plan for a surgical operation to remove very small regions of the brain to avoid recurrent or progress of such very common neurological condition. Currently, this affects more than 1% of the UK population. In addition to clinical history, the scalp EEG is the most popular and accepted test to support the diagnosis of epilepsy. Unfortunately, such discharges (spikes) cannot be seen from the scalp EEG due to their relatively low sensitivity. 45% of awake EEGs and 20% of sleep EEGs recorded from people with epilepsy do not show clear epileptiform abnormalities. This leaves a significant proportion with uncertain diagnosis and delayed treatment. However, although many epileptiform discharges cannot be detected on the scalp EEG, they can be recorded using intracranial EEG electrodes implanted in deep brain structures. A non-invasive method for increasing the probability of detection of epileptiform discharges will be therefore vary crucial and valuable to increase the diagnostic power of scalp EEG.Detection of deep brain discharges using scalp EEG recordings using advanced digital signal processing (DSP) hasn't been much explored in the literature. In this proposal new algorithms will be developed to initially use a set of previously recorded data (in their so called training phase) to best model the neural pathways from deep medial temporal source to scalp potential patterns. Solving this problem, we can then perform separation of the weak spikes from noise-like scalp signals, and localize the sources. In this direction, the major problems are nonlinearity of the medium and interference of the cortical potentials which are usually recognised as the scalp EEG of a normal brain. A large set of simultaneous scalp and intracranial EEG data using Foramen Ovale (FO) electrodes was collected from more than twenty patients have been recorded and analysed. Using some simple methods the signal-to-noise ratio (SNR) was increased by averaging the data over a number of trials synchronized on discharges using intracranial recording. It has been reported by providing many evidences that: a) Before averaging only 9% of the discharges were detectable when only scalp recordings were used.b) The majority of the spikes (up to 72.3%) could be detected by using both intracranial and scalp EEGs particularly after averaging.c) In 18.7% of discharges no scalp signal was observed even after averaging. From this unique and clinically important setup and the outcome of their analyses it is evident that interictal medial temporal epileptiform discharges, originating from deep medial temporal structure (MTS), can hardly be detected by visual inspection of the scalp signals. On the other hand, intracranial recording is a very inconvenient process and costly to the patients requiring hazardous and timely surgical operations. From signal processing point of view, the deep sources are sparse, the medium is nonlinear, and the interfering signals are correlated and nonstationary. On the other hand, the number of sources is potentially larger than the number of electrodes which makes the overall system underdetermined.In this proposal, to identify the underdetermined system when the sources are sparse and nonstationary, we will develop a sparse tensor factorization algorithm. The statistical (such as sparsity), geometrical (such as approximate locations), and physiological a priories (nature/shape of sources and artefacts) will be incorporated into the formulation as constraint terms. Finally, such a multiple constraint problem will be solved by developing a global optimization method.

脑深部内侧颞叶放电的检测对于癫痫的早期检测和手术计划至关重要，以去除大脑的非常小的区域，以避免这种非常常见的神经系统疾病的复发或进展。目前，这影响了英国1%以上的人口。除了临床病史外，头皮脑电图是最受欢迎和接受的测试，以支持癫痫的诊断。不幸的是，这种放电（尖峰）由于其相对低的灵敏度而不能从头皮EEG看到。45%的清醒脑电图和20%的睡眠脑电图记录癫痫患者没有显示出明确的癫痫样异常。这使得很大一部分人的诊断不确定，治疗延误。然而，尽管许多癫痫样放电不能在头皮EEG上检测到，但它们可以使用植入脑深部结构的颅内EEG电极记录。因此，一种非侵入性的方法，用于增加癫痫样放电的检测概率将是至关重要的和有价值的，以增加头皮EEG的诊断能力。使用先进的数字信号处理（DSP）的头皮EEG记录检测脑深部放电还没有太多的文献中探讨。在该提案中，将开发新的算法，以最初使用一组先前记录的数据（在其所谓的训练阶段）来最好地模拟从深内侧颞源到头皮电位模式的神经通路。解决这个问题，我们就可以从类似噪声的头皮信号中分离出微弱的尖峰信号，并定位源。在这个方向上，主要的问题是介质的非线性和通常被认为是正常大脑的头皮EEG的皮层电位的干扰。本文对20多例病人的头皮和颅内脑电图（EEG）进行了同步记录和分析。使用一些简单的方法，信噪比（SNR）的增加，平均数的试验同步放电使用颅内记录的数据。通过提供许多证据，已经报道了：a）在平均之前，仅使用头皮记录时，仅9%的放电是可检测的。B）大多数尖峰（高达72.3%）可以通过使用颅内和头皮EEG来检测，特别是在平均之后。c）在18.7%的放电中，即使在平均之后也没有观察到头皮信号。从这个独特的和临床上重要的设置和他们的分析结果，很明显，发作间期内侧颞叶癫痫样放电，起源于深内侧颞叶结构（MTS），很难检测到的头皮信号的视觉检查。另一方面，颅内记录对于需要危险和及时的外科手术的患者来说是非常不方便的过程和昂贵的。从信号处理的角度来看，深部源是稀疏的，介质是非线性的，干扰信号是相关的和非平稳的。另一方面，源的数量可能大于电极的数量，这使得整个系统欠定。在这个建议中，识别欠定系统时，源是稀疏和非平稳的，我们将开发一个稀疏张量分解算法。统计（如稀疏性），几何（如近似位置），和生理优先级（源和伪影的性质/形状）将被纳入公式作为约束条件。最后，这种多约束问题将通过开发全局优化方法来解决。

项目成果

Incorporating Uncertainty In Data Labeling Into Detection of Brain Interictal Epileptiform Discharges From EEG Using Weighted optimization

使用加权优化将数据标记中的不确定性纳入脑电图发作间期癫痫样放电的检测中

• DOI: 10.1109/icassp39728.2021.9414463
• 发表时间: 2021
• 作者: Abdi-Sargezeh B

Deep Neural Architectures for Mapping Scalp to Intracranial EEG

• DOI: 10.1142/s0129065718500090
• 发表时间: 2018-10-01
• 期刊: INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
• 影响因子: 8
• 作者: Antoniades, Andreas;Spyrou, Loukianos;Took, Clive Cheong
• 通讯作者: Took, Clive Cheong

Deep learning for epileptic EEG data

癫痫脑电图数据的深度学习

• 作者: Antoniades A

Incorporating Uncertainty in Data Labeling into Automatic Detection of Interictal Epileptiform Discharges from Concurrent Scalp-EEG via Multi-way Analysis.

通过多路分析将数据标记中的不确定性纳入同步头皮脑电图发作间期癫痫样放电的自动检测中。

• DOI: 10.1142/s0129065721500192
• 发表时间: 2021
• 期刊: International journal of neural systems
• 影响因子: 8
• 作者: Abdi-Sargezeh B

Detection of Brain Interictal Epileptiform Discharges from Intracranial EEG by Exploiting their Morphology in the Tensor Structure

通过利用张量结构中的形态来检测颅内脑电图的大脑发作间期癫痫样放电

• DOI: 10.23919/eusipco54536.2021.9616233
• 发表时间: 2021
• 作者: Abdi-Sargezeh B