基于一维卷积特征与手工特征融合的集成超限学习机心跳分类方法

Ensemble extreme learning machine approach for heartbeat classification by fusing 1d convolutional and handcrafted features

融合手工特征和深度特征,提出了一种集成超限学习机心跳分类方法.手工提取的特征明确地表征了心电信号的特定特性,如相邻心跳时间间隔反映了心跳信号的时域特性,小波系数反映了心跳信号的时频特性.同时设计了一维卷积神经网络对心跳信号特征进行自动提取.基于超限学习机(Extreme leaning machine,ELM),将上述特征融合进行心跳分类.由于ELM初始参数的随机给定可能导致其性能不稳定,进一步提出了一种基于袋装(Bagging)策略的多个ELM集成方法,使分类结果更加稳定且模型泛化能力更强.利用麻省理工心律失常公开数据集对所提方法进行了验证,分类准确率达到了99.02%,实验结果也表明基于融合特征的分类准确率高于基于单独特征的分类准确率.

Arrhythmia is a common cardiovascular disease whose occurrence is mainly related to two factors:cardiac pacing and conduction.Some severe arrhythmias can even threaten human life.An electrocardiogram(ECG)records the changes in electrical activity generated during each cardiac cycle of the heart,which can reflect the human cardiac health status and help diagnose arrhythmias.However,because of the brevity of conventional ECGs,arrhythmias,which occasionally occur in daily life,cannot be detected easily.Automatic ECG analysis-based long-term heartbeat monitoring is of great significance for the effective detection of accidental arrhythmias and then for taking indispensable measures to prevent cardiovascular diseases in time.An ensemble extreme learning machine(ELM)approach for heartbeat classification that fuses handcrafted features and deep features was proposed.The manually extracted features clearly characterize the heartbeat signal,where RR interval features reflect the time-domain characteristic,and the wavelet coefficient features reflect the time–frequency characteristic.A 1D convolutional neural network(1D CNN)was designed to automatically extract deep features for heartbeat signals.These features were fused by an ELM for heartbeat classification.Because of the instability caused by the random assignment of ELM hidden layer parameters,the bagging ensemble strategy was introduced to integrate multiple ELMs to achieve stable classification performance and good generalization ability.The proposed approach was validated on the MIT-BIH arrhythmia public dataset.The classification accuracy reaches 99.02%,and the experimental results show that the performance of the proposed approach with fused features is better than those with only deep features and only handcrafted features.