基于深度残差卷积神经网络的心电信号心律不齐识别

心电图(ECG)信号在采集过程中容易受内部和外部噪声干扰,而且不同患者的ECG信号形态特征差异较大,即使同一患者在不同时间和环境下其ECG信号也会有差异,因此ECG信号特征检测与识别在心脏病远程实时监测与智能诊断中具有一定难度。基于此,本研究提出将小波自适应阈值去噪和深度残差卷积神经网络算法用于多种心律不齐的信号识别过程中。其中,使用小波自适应阈值技术完成ECG信号滤波,并设计了包含多个残差块(residual block)结构的20层卷积神经网络(CNN),即深度残差卷积神经网络(DR-CNN),对5大类心律不齐ECG信号进行了识别。然后,本文采用残差块局部神经网络结构单元构建DR-CNN,缓解了深层网络的收敛难、调优难等问题,克服了CNN随着网络层数增加而导致的退化问题;进一步引入批标准化(batch normalization)技术,保证了网络的平滑收敛。按照美国医疗器械促进协会(AAMI)的心搏分类标准,使用麻省理工学院和波士顿贝丝以色列医院(MIT-BIH)心律不齐数据库中94 091个ECG心搏信号(2个导联),完成了心律不齐多分类、室性异位搏动(Veb)和室上性异位搏动(Sveb)等分类识别实验。实验结果表明,本文所提出的方法在ECG信号多分类、Veb和Sveb识别中的准确率分别达到了99.034 9%、99.498 0%和99.334 7%。在相同的数据集和实验平台下,DR-CNN在分类准确率、特异性和灵敏度上均优于相同结构复杂度的CNN、深度多层感知机等传统算法。DR-CNN算法提高了心律不齐智能诊断的精度,该方法与可穿戴设备、物联网和无线通信技术相结合,可以将心脏病的预防、监测和诊断延伸到家庭、养老院等院外场景,从而提高心脏病患者的救治率,并且有效地节约医疗资源。

Deep residual convolutional neural network for recognition of electrocardiogram signal arrhythmias

Electrocardiogram(ECG)signals are easily disturbed by internal and external noise,and its morphological characteristics show significant variations for different patients.Even for the same patient,its characteristics are variable under different temporal and physical conditions.Therefore,ECG signal detection and recognition for the heart disease real-time monitoring and diagnosis are still difficult.Based on this,a wavelet self-adaptive threshold denoising combined with deep residual convolutional neural network algorithm was proposed for multiclass arrhythmias recognition.ECG signal filtering was implemented using wavelet adaptive threshold technology.A 20-layer convolutional neural network(CNN)containing multiple residual blocks,namely deep residual convolutional neural network(DR-CNN),was designed for recognition of five types of arrhythmia signals.The DR-CNN constructed by residual block local neural network units alleviated the difficulty of deep network convergence,the difficulty in tuning and so on.It also overcame the degradation problem of the traditional CNN when the network depth was increasing.Furthermore,the batch normalization of each convolution layer improved its convergence.Following the recommendations of the Association for the Advancements of Medical Instrumentation(AAMI),experimental results based on 94 091 2-lead heart beats from the MIT-BIH arrhythmia benchmark database demonstrated that our proposed method achieved the average detection accuracy of 99.034 9%,99.498 0%and 99.334 7%for multiclass classification,ventricular ectopic beat(Veb)and supra-Veb(Sveb)recognition,respectively.Using the same platform and database,experimental results showed that under the comparable network complexity,our proposed method significantly improved the recognition accuracy,sensitivity and specificity compared to the traditional deep learning networks,such as deep Multilayer Perceptron(MLP),CNN,etc.The DR-CNN algorithm improves the accuracy of the arrhythmia intelligent diagnosis.If it is combined with wearable equipment,internet of things and wireless communication technology,the prevention,monitoring and diagnosis of heart disease can be extended to out-of-hospital scenarios,such as families and nursing homes.Therefore,it will improve the cure rate,and effectively save the medical resources.