Atal Dinesh Kumar, Singh Mukhtiar
Department of Electrical Engineering, Delhi Technological University, Bawana Road, Delhi-110042, India.
Department of Electrical Engineering, Delhi Technological University, Bawana Road, Delhi-110042, India.
Comput Methods Programs Biomed. 2020 Nov;196:105607. doi: 10.1016/j.cmpb.2020.105607. Epub 2020 Jun 18.
Arrhythmia classification is the need of the hour as the world is reporting a higher death troll as a cause of cardiac diseases. Most of the existing methods developed for arrhythmia classification face a hectic challenge of classification accuracy and they raised the challenge of automatic monitoring and classification methods. Accordingly, the paper proposes the automatic arrhythmia classification strategy using the optimization-based deep convolutional neural network (deep CNN). The optimization algorithm named, Bat-Rider optimization algorithm (BaROA) is newly developed using the multi-objective bat algorithm (MOBA) and Rider Optimization Algorithm (ROA).At first, the wave and gabor features are extracted from the ECG signals in such a way that these features represent the individual ECG features. Finally, the signals are provided to the BaROA-based DCNN classifier that identifies conditions of the individual as arrhythmia and no-arrhythmia from the ECG signals. The methods are analyzed using the MIT-BIH Arrhythmia Database and the analysis is performed based on the evaluation parameters, like accuracy, specificity, and sensitivity, which are found to be 93.19 %, 95 %, and 93.98 %, respectively.
心律失常分类是当务之急，因为全球因心脏病导致的死亡人数在不断上升。现有的大多数心律失常分类方法都面临着分类准确性方面的艰巨挑战，并且它们还提出了自动监测和分类方法的挑战。因此，本文提出了一种基于优化的深度卷积神经网络（深度卷积神经网络）的自动心律失常分类策略。一种名为蝙蝠骑手优化算法（BaROA）的优化算法是新开发的，它使用了多目标蝙蝠算法（MOBA）和骑手优化算法（ROA）。首先，从心电图信号中提取波形和伽柏特征，使这些特征能够代表个体心电图特征。最后，将信号提供给基于BaROA的深度卷积神经网络分类器，该分类器从心电图信号中识别个体的心律失常和无心律失常情况。使用麻省理工学院 - 贝斯以色列女执事医疗中心心律失常数据库对这些方法进行了分析，并且基于准确性、特异性和敏感性等评估参数进行了分析，发现这些参数分别为93.19%、95%和93.98%。
