机器学习在听性脑干诱发电位数据分析中的应用研究

近年来,许多学者将机器学习算法应用到肌电信号(EMG)数据分析中,取得了良好的效果,但是主要针对手势识别等应用研究,较少有学者对辅助临床诊断进行研究。针对模型训练所需数据较大和机器学习在听性脑干诱发电位(ABR)数据分析中的应用较少两种问题。研究了机器学习方法在基于小型ABR数据集数据的计算机辅助诊断中的应用。收集了四川省中医医院的2 352份肌电图检查报告,通过设计纳入标准并进行数据清洗,构建了包含233份ABR报告的数据集。之后,使用数据标准化方法对数据进行数据预处理,再使用随机森林、线性回归、Logistic回归和人工神经网络4种机器学习算法对数据集进行分析处理。4种算法的性能对比表明随机森林算法性能最优,其准确率、召回率、精确率分别达到了0.995 7、0.989 7、0.950 0。此外还对每种算法在数据标准化前后的性能进行了比较,表明标准化处理对准确率的提高有一定的提升效果。随机森林算法输出的特征重要性表明,ABR检查中最重要的指标是L_latency_5、L_latency_A和L_Interval_35,其次是L_latency_b和L_latency_4。这些指标重要性融入上位机软件有助于提高临床诊断效率,在临床应用中具有较高的临床判读潜力。

Application of machine learning in auditory brainstem response data analysis

In recent years, many scholars have applied machine learning algorithm to electromyogram (EMG) data analysis and achieved good results, but the main direction is gesture recognition, few scholars have applied machine learning to EMG clinical diagnosis. There are two problems:The amount of data needed is large; Machine learning is rarely used in auditory brainstem response (ABR) data analysis. Aiming at these two problems, this paper studies the application of machine learning method in computer-aided diagnosis of ABR data based on small data set. In this paper, 2 352 EMG examination reports of Sichuan Traditional Chinese Medicine Hospital were collected. A data set containing 233 ABR reports data was created by inclusion criteria and data cleaning. Then, four machine learning algorithms, linear regression, logistic regression, random forest and Artificial neural network, are used to analyze and process this data set. According to the performance comparison, the random forest is considered to be the best one, the accuracy, recall and precision of this algorithm is 0. 995 7, 0. 989 7 and 0. 950 0 respectively. In addition, this paper also compares the effect of each algorithm with and without data standardization, this experiment shows that data standardization can improve the accuracy to some extent. The random forest model outputs the importance of each indicator, the most important indicator in ABR are L_latency_5, L_latency_A and L_Interval_35, followed by L_latency_b and L_latency_4. The integration of the importance of these indicators into the upper computer software helps to improve the efficiency of clinical diagnosis and has certain diagnostic evaluation potential in clinical application.