PSO-LSTM优化的癫痫预测和分类研究

Epilepsy prediction and classification based on PSO-LSTM optimization algorithm

针对癫痫的传统药物治疗方法可能产生耐药性和手术治疗的非通用性等问题,提出一种基于计算模型的癫痫预测和分类算法。该算法从目前公认的癫痫发作机制的研究出发,采用计算模型生成具有特异性的癫痫数据,采用粒子群优化(particle swarm optimization,PSO)-长短期记忆网络(long short-term memory networks,LSTM)对癫痫进行分类和预测。PSO-LSTM算法突破参数搜索和时序特征捕捉的限制,在分析模拟癫痫不同数据特点的基础上,利用LSTM模型预测癫痫发作进程,并采用PSO算法优化LSTM模型参数,达到提高模型精度的目的。采用PSO-LSTM算法对癫痫发作进行分类和预测,并与传统算法进行对比,结果表明:该算法比传统算法在预测和分类癫痫方面具有更高的准确性和鲁棒性。

In response to issues such as drug resistance and the limited applicability of surgical interventions in traditional epilepsy treatment,this paper proposes a computational model-based approach for epilepsy prediction and classification.Grounded in current research on established seizure mechanisms,this algorithm generates specific epilepsy data using computational models and employs particle swarm optimization(PSO)-long short-term memory networks(LSTM)for epilepsy prediction and classification.The PSO-LSTM approach overcomes limitations in parameter search and temporal feature capture.By analyzing the characteristics of various simulated epilepsy data,it utilizes LSTM models to predict seizure progression and employs PSO algorithm to optimize LSTM model parameters,thereby enhancing the overall model accuracy.Experimental results indicate that this method exhibits higher accuracy and robustness in the prediction and classification of epilepsy compared to traditional algorithms.