Memristive Neural Networks for Predicting Seizure Activity.

IF 1.1 Q4 MEDICINE, RESEARCH & EXPERIMENTAL
Sovremennye Tehnologii v Medicine Pub Date : 2023-01-01 Epub Date: 2023-07-28 DOI:10.17691/stm2023.15.4.03
S A Gerasimova, A V Lebedeva, N V Gromov, A E Malkov, А А Fedulina, T A Levanova, A N Pisarchik
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引用次数: 0

Abstract

The aim of the study is to assess the possibilities of predicting epileptiform activity using the neuronal activity data recorded from the hippocampus and medial entorhinal cortex of mice with chronic epileptiform activity. To reach this goal, a deep artificial neural network (ANN) has been developed and its implementation based on memristive devices has been demonstrated.

Materials and methods: The biological part of the investigation. Young healthy outbred CD1 mice were used in our study. They were divided into two groups: control (n=6) and the group with induced chronic epileptiform activity (n=6). Local field potentials (LFP) were recorded from the hippocampus and medial entorhinal cortex of the mice of both groups to register neuronal activity. The LFP recordings were used for deep ANN training. Epileptiform activity in mice was modeled by intraperitoneal injection of pilocarpine (280 mg/kg). LFP were recorded in the awake mice a month after the induction of epileptiform activity.Mathematical part of the investigation. A deep long short-term memory (LSTM) ANN capable of predicting biological signals of neuronal activity in mice has been developed. The ANN implementation is based on memristive devices, which are described by the equations of the redox processes running in the memristive thin metal-oxide-metal films, e.g., Au/ZrO2(Y)/TiN/Ti and Au/SiO2(Y)/TiN/Ti. In order to train the developed ANN to predict epileptiform activity, a supervised learning algorithm was used, which allowed us to adjust the network parameters and train LSTM on the described recordings of neuronal activity.

Results: After training on the LFP recordings from the hippocampus and medial entorhinal cortex of the mice with chronic epileptiform activity, the proposed deep ANN has demonstrated high values of evaluation metric (root-mean-square error, RMSE) and successfully predicted epileptiform activity shortly before its occurrence (40 ms). The results of the numerical experiments have shown that the RMSE value of 0.019 was reached, which indicates the efficacy of proposed approach. The accuracy of epileptiform activity prediction 40 ms before its occurrence is a significant result and shows the potential of the developed neural network architecture.

Conclusion: The proposed deep ANN can be used to predict pathological neuronal activity including epileptic seizure (focal) activity in mice before its actual occurrence. Besides, it can be applied for building a long-term prognosis of the disease course based on the LFP data. Thus, the proposed ANN based on memristive devices represents a novel approach to the prediction and analysis of pathological neuronal activity possessing a potential for improving the diagnosis and prognostication of epileptic seizures and other diseases associated with neuronal activity.

预测癫痫活动的记忆神经网络
本研究的目的是利用从患有慢性癫痫样活动的小鼠海马和内侧内视网膜皮层记录到的神经元活动数据,评估预测癫痫样活动的可能性。为实现这一目标,我们开发了一种深度人工神经网络(ANN),并演示了基于记忆设备的实现方法:研究的生物部分。我们的研究使用了年轻健康的外交 CD1 小鼠。它们被分为两组:对照组(n=6)和诱发慢性癫痫样活动组(n=6)。从两组小鼠的海马和内侧内顶叶皮层记录局部场电位(LFP),以记录神经元活动。LFP 记录用于深度 ANN 训练。通过腹腔注射皮洛卡品(280 毫克/千克)来模拟小鼠的癫痫样活动。在诱导癫痫样活动一个月后,对清醒小鼠的 LFP 进行记录。研究开发了一种深度长短期记忆(LSTM)方差网络,能够预测小鼠神经元活动的生物信号。该方差网络的实现基于忆阻器,忆阻器由忆阻器金属-氧化物-金属薄膜(如 Au/ZrO2(Y)/TiN/Ti 和 Au/SiO2(Y)/TiN/Ti)中运行的氧化还原过程方程描述。为了训练所开发的预测癫痫样活动的方差网络,我们使用了一种监督学习算法,该算法允许我们调整网络参数,并在所述神经元活动记录上训练 LSTM:在对慢性癫痫样活动小鼠海马和内侧内耳皮层的 LFP 记录进行训练后,所提出的深度 ANN 显示出较高的评估指标值(均方根误差,RMSE),并在癫痫样活动发生前不久(40 毫秒)成功预测了癫痫样活动。数值实验结果表明,均方根误差值达到了 0.019,这表明所提议的方法非常有效。在癫痫发生前 40 毫秒预测癫痫样活动的准确性是一个重要结果,显示了所开发的神经网络架构的潜力:结论:所提出的深度神经网络可用于在小鼠癫痫发作(病灶)实际发生前预测病理神经元活动,包括癫痫发作(病灶)活动。此外,它还可用于根据 LFP 数据建立病程的长期预后。因此,所提出的基于记忆器件的方差网络是预测和分析病理神经元活动的一种新方法,具有改善癫痫发作和其他与神经元活动相关疾病的诊断和预后的潜力。
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来源期刊
Sovremennye Tehnologii v Medicine
Sovremennye Tehnologii v Medicine MEDICINE, RESEARCH & EXPERIMENTAL-
CiteScore
1.80
自引率
0.00%
发文量
38
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