基于复杂域分析算法的脑电图图像分析儿童癫痫发作的神经状态和不良预后因素。
Electroencephalogram Image under Complex Domain Analysis Algorithm to Analyze Neurological Status Epilepticus and Poor Prognostic Factors of Children.
机构信息
Department of Pediatrics, Jinan Maternity and Child Care Hospital, Jinan 250001, Shandong, China.
出版信息
J Healthc Eng. 2021 Dec 15;2021:3109061. doi: 10.1155/2021/3109061. eCollection 2021.
This study was to adopt the electroencephalogram (EEG) image to analyze the neurological status epilepticus (SE) and adverse prognostic factors of children using the complex domain analysis algorithm, aiming at providing a theoretical basis for the clinical treatment of children with SE. 24-hour EEG was adopted to diagnose 197 children with SE. The patients were divided into an experimental group (100 cases) and a control group (97 cases) using a random number table method. The EEGs of children in the experimental group were analyzed using the compound domain analysis algorithm, and those in the control group were diagnosed by a professional doctor. The indicators of children in two groups were compared to analyze the effect of the compound domain analysis algorithm in diagnosing diseases through EEG. The prognostic scores of 197 children were scored one month after they were diagnosed, treated, and discharged, and the adverse prognostic factors were analyzed. As a result, EEG can accurately and effectively analyze the brain diseases in children. The sensitivity and specificity of the complex domain analysis algorithm for the detection of epilepsy EEG were much higher than those of the EEG automatic detection algorithm based on time-domain waveform similarity and the EEG automatic detection algorithm based on convolutional neural network (CNN), and the average running time was opposite, showing obvious difference ( < 0.05).The average accuracy, sensitivity, and specificity of children in the experimental group were 96.11%, 97.10%, and 95.19%, respectively; and those in the control group were 88.83%, 90.14%, and 87.82%, respectively, so there was an obvious difference in accuracy between two groups ( < 0.05). There were 57 cases with good prognosis and 140 cases with poor prognosis; there were 70 males with good prognosis and 19 poor prognoses and 69 women with good prognosis and 19 poor prognoses. Among 121 patients with infections, 84 cases had good prognosis and 37 cases had poor prognosis; 39 cases of irregular medication had good prognosis in 31 cases and a poor prognosis in 8 cases; and 37 cases had no obvious cause, including 25 cases with good prognosis and 12 cases with poor prognosis. In short, the EEG diagnosis and treatment effect of the compound domain analysis algorithm were better than those of professional doctors; the gender of the patient had no effect on the poor prognosis, and the pathogenic factors had an impact on the poor prognosis of the patient.
本研究采用脑电图（EEG）图像，通过复杂域分析算法分析儿童神经状态癫痫（SE）及不良预后因素，旨在为儿童 SE 的临床治疗提供理论依据。采用 24 小时 EEG 诊断 197 例 SE 患儿。采用随机数字表法将患儿分为实验组（100 例）和对照组（97 例）。采用复合域分析算法对实验组患儿的 EEG 进行分析，对照组由专业医生进行诊断。比较两组患儿的指标，分析 EEG 对疾病的诊断效果。对 197 例患儿进行诊断、治疗和出院后 1 个月的预后评分，分析不良预后因素。结果 EEG 可准确、有效地分析儿童脑部疾病。复杂域分析算法对癫痫 EEG 的检测敏感性和特异性明显高于基于时域波形相似性的 EEG 自动检测算法和基于卷积神经网络（CNN）的 EEG 自动检测算法，平均运行时间相反，差异明显（<0.05）。实验组患儿的平均准确率、灵敏度、特异度分别为 96.11%、97.10%、95.19%；对照组分别为 88.83%、90.14%、87.82%，两组准确率差异明显（<0.05）。预后良好 57 例，预后不良 140 例；预后良好男性 70 例，预后不良 19 例；预后良好女性 69 例，预后不良 19 例。121 例感染患者中，预后良好 84 例，预后不良 37 例；不规则用药 39 例，预后良好 31 例，预后不良 8 例；无明显原因 37 例，其中预后良好 25 例，预后不良 12 例。总之，复合域分析算法的 EEG 诊断和治疗效果优于专业医生；患者性别对预后不良无影响，致病因素对患者预后不良有影响。
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