Extraction of fetal electrocardiogram (FECG) from the abdominal region of the mother’s skin is challenge task due to the high overlapping of maternal and fetal signals in this area. To overcome the problem, this paper proposes the utilization of extreme learning model (ELM) as the prediction algorithm to train on the FECG signal extracted by least mean square approach from the input abdominal and thoracic signals. The trained ELM model is used to model the FECG signal for the testing samples. Also, this paper investigates the firefly algorithm (FA) to tune the parameters of ELM and improve its performance. Due to the high complexity and too many parameters of FA, this paper embeds the evolutionary scheme into the FA algorithm which benefits from adaptive crossover and mutation probabilities. Evaluation of the proposed method on both synthetic and actual datasets proves its qualification in FECG extraction and modeling.

中文翻译：

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基于混合进化萤火虫算法和极限学习机的胎儿心电图建模

由于母体和胎儿信号在该区域的高度重叠，从母亲皮肤的腹部区域提取胎儿心电图 (FECG) 是一项具有挑战性的任务。为了克服这个问题，本文提出利用极限学习模型（ELM）作为预测算法，对输入的腹部和胸部信号通过最小均方法提取的FECG信号进行训练。经过训练的 ELM 模型用于对测试样本的 FECG 信号进行建模。此外，本文研究了萤火虫算法（FA）来调整 ELM 的参数并提高其性能。由于FA算法复杂度高，参数过多，本文将进化方案嵌入到FA算法中，利用自适应交叉和变异概率。