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Received：25 May 2020，
Revised：2020-7-21，
Accepted：28 July 2020，
Published：2020-10
移动端阅览
目的
2
可穿戴设备能够长时间实时监测人体心脏状况，其在心电信号监测领域应用广泛。但目前仍没有公开的来自可穿戴设备的心电数据集，大部分心电信号分析算法都是针对医院设备所采集的心电数据。因此，本文使用IREALCARE 2.0柔性远程心电贴作为心电信号监测和采集设备制作了可穿戴设备的心电数据集。针对可穿戴心电数据干扰多、数据量大等特点，本文提出了一种针对可穿戴设备获得的心电信号进行自动分类的深层卷积神经网络，称之为时空卷积神经网络（time-spatial convolutional neural networks，TSCNN）。
方法
2
将原始的长时间心电信号分割为单个的心搏并与滤波后不同频段的心搏数据组合成十通道的数据输入到TSCNN中。TSCNN对每个心搏使用时间卷积和空间滤波来提取丰富的特征。采用小卷积核级联卷积的方式提高分类性能，并降低网络的参数量和计算量。
结果
2
在本文制作的心电数据集上进行了测试，并与其他4种心电分类算法：CNN（convolutional neural networks）、RNN（recurrent neural networks）、1-DCNN（1-dimensional convolution neural networks）和DCN（dense convolutional networks）进行了比较。实验结果显示，本文方法的分类准确率达到91.16%，优于其他4种方法。
结论
2
本文方法面向可穿戴心电数据，获得了较好的分类性能，可以有效监控穿戴者是否出现了心电异常情况。
Objective
2
Wearable devices are expanding in terms of electrocardiograms (ECG) because they can portably monitor the heart condition of a human for a long time. The key for wearable devices to monitor heart conditions in real time is to be able to process the collected ECG data automatically. Therefore
an efficient and reliable classification algorithm for ECG data from wearable devices should be designed. Many ECG classification algorithms have been proposed in recent years. They are mainly divided into two categories: one is based on handcrafted features
and the other is based on deep learning. Classification methods based on artificial features need to design various features manually
and many useful features are often ignored. The deep learning method can automatically extract features
and it has achieved good performance in image classification
object recognition
and natural language processing. However
no ECG data set from wearable devices is publicly available. Most ECG analysis algorithms are aimed at ECG data collected using hospital equipment. The ECG signal collected using wearable devices is susceptible to various interferences due to the body movement of people and changes in the surrounding environment
resulting in various noises in the signal. Therefore
many methods that perform well in ECG data sets collected using hospital equipment cannot achieve accurate classification of ECG data from wearable devices. In this study
we used IREALCARE 2.0 flexible cardiac monitor patch as the wearable device to collect ECG signals and make ECG data sets. It has the characteristics of small size
lightweight
and accurate and reliable measurement. The ECG data set came from 38 subjects and mainly included five types of heartbeats: normal
ventricular premature beat (VPB)
supraventricular premature beat (SPB)
atrial fibrillation (AF)
and interference. In accordance with the characteristics of ECG data from wearable devices
such as considerable interference and large amount of data
this study proposed a deep convolutional neural network (CNN) named time-spatial CNN (TSCNN) for the automatic classification and analysis of ECG signals from wearable devices.
Method
2
TSCNN consisted of seven convolutional layers
four pooling layers
and one fully connected layer. It also comprised a convolution-pooling block
three convolution-convolution-pooling blocks
and a classification layer. First
the original long-term ECG signals were divided into separate heartbeats. We regarded the 600 data points around R-wave coordinates as one heartbeat
combined them with filtered heartbeat data of different frequency bands into 10 channels of data
and input them into TSCNN. Second
we applied convolution over time and spatial filtering for each heartbeat
which could effectively expand the receptive field of the network to extract abundant features. Lastly
cascaded convolution with a small convolution kernel was applied to improve classification performance and reduce the amount of network parameters and computation. In accordance with the input data size of each layer
we adopted three convolution kernels of different sizes. The convolution kernels in the three convolution-convolution-pooling blocks were 10×1
5×1
and 3×1 in sequence with stride 1×1. The fully connected layer was used as the classification layer. Some regularized methods
such as dropout and batch normalization
were adopted to avoid overfitting. The batch normalization layer was placed after the convolutional layer and before the nonlinear layer
which made the input value of the nonlinear transformation function fall into a region sensitive to the input
to avoid the problem of gradient disappearance. Dropout randomly set some hidden layer nodes to zero during each iteration
which could reduce the interaction among hidden layer nodes.
Result
2
The proposed method was evaluated on the ECG data set produced in this paper and compared with four other ECG classification algorithms: CNN
recurrent neural network
1D CNN (1-DCNN)
and dense convolutional network (DCN). We used 10 records containing 138 853 heartbeats as the training set
and the test data were collected from 28 subjects
which contained 241 896 heartbeats. Results showed that the method in this paper achieved an overall accuracy of 91.16%
and the accuracies of normal
VPB
SPB
AF
and interference were 89.17%
92.99%
62.03%
91.56%
and 94.89%
respectively. Comparative experiments indicated that our method was better than the four other methods in overall accuracy and the accuracies of normal
VPB
SPB
and AF and achieved a great improvement. Compared with 1-DCNN
the proposed method demonstrated increased overall accuracy by 7.25% and increased accuracies of normal
VPB
SPB
and AF by 18.13%
12.67%
9.29%
and 23.96%
respectively. Compared with DCN
the proposed method exhibited increased overall accuracy by 5.88% and increased accuracies of normal
VPB
SPB
and AF by 16.88%
8.41%
11.73%
and 20.32%
respectively. In addition
the classification results of our method were balanced
and the recognition capability for each class was similar. We also conducted a comparative experiment with and without spatial filtering. The experimental results demonstrated that spatial filtering could improve the classification performance of the network
and the overall accuracy increased from 88.17% to 91.16%. We further trained and evaluated deep networks that use cascaded convolution with a small convolution kernel and shallow networks that do not use it. The comparative experiment results showed that using cascaded convolution with a small convolution kernel could improve the classification capability of the network
in which the overall accuracy was increased by 1.56%; it also effectively reduced the amount of network parameters and calculations
thus increasing the processing speed.
Conclusion
2
In this study
we proposed TSCNN to classify ECG signals from wearable devices. The experimental results indicated that the proposed method can achieve good classification performance for ECG data from wearable devices and effectively monitor whether the wearer has abnormal ECG.
Al Rahhal M M, Bazi Y, Al Zuair M, Othman E and BenJdira B. 2018. Convolutional neural networks for electrocardiogram classification. Journal of Medical and Biological Engineering, 38(6):1014-1025[DOI:10.1007/s40846-018-0389-7]
Al Rahhal M M, Bazi Y, Almubarak H, Alajlan N and Al Zuair M. 2019. Dense convolutional networks with focal loss and image generation for electrocardiogram classification. IEEE Access, 7:182225-182237[DOI:10.1109/ACCESS.2019.2960116]
Bouaziz F, Boutana D and Benidir M. 2014. Multiresolution wavelet-based QRS complex detection algorithm suited to several abnormal morphologies. IET Signal Processing, 8(7):774-782[DOI:10.1049/iet-spr.2013.0391]
Carrera D, Rossi B, Fragneto P and Boracchi G. 2019. Online anomaly detection for long-term ECG monitoring using wearable devices. Pattern Recognition, 88:482-492[DOI:10.1016/j.patcog.2018.11.019]
Castells F, Laguna P, Sörnmo L, Bollmann A and Roig J M. 2007. Principal component analysis in ECG signal processing. EURASIP Journal on Advances in Signal Processing, 2007:074580[DOI:10.1155/2007/74580]
Chen S S, Hua W, Li Z, Li J and Gao X J. 2017. Heartbeat classification using projected and dynamic features of ECG signal. Biomedical Signal Processing and Control, 31:165-173[DOI:10.1016/j.bspc.2016.07.010]
Clevert D A, Unterthiner T and Hochreiter S. 2016. Fast and accurate deep network learning by exponential linear units (ELUs)[EB/OL].[2020-03-10] . https://arxiv.org/pdf/1511.07289.pdf https://arxiv.org/pdf/1511.07289.pdf
Ergin S, Uysal A K, Gunal E S, Gunal S and Gulmezoglu M B. 2014. ECG based biometric authentication using ensemble of features//Proceedings of the 2014 9th Iberian Conference on Information Systems and Technologies (CISTI). Barcelona: IEEE: 1-6[ DOI:10.1109/CISTI.2014.6877089 http://dx.doi.org/10.1109/CISTI.2014.6877089 ]
Fayn J. 2011. A classification tree approach for cardiac ischemia detection using spatiotemporal information from three standard ECG leads. IEEE Transactions on Biomedical Engineering, 58(1):95-102[DOI:10.1109/TBME.2010.2071872]
Giménez M, Palanca J and Botti V. 2020. Semantic-based padding in convolutional neural networks for improving the performance in natural language processing. A case of study in sentiment analysis. Neurocomputing, 378:315-323[DOI:10.1016/j.neucom.2019.08.096]
He P, Huang W L, Qiao Y, Loy C C and Tang X O. 2016. Reading scene text in deep convolutional sequences//Proceedings of the 30th AAAI Conference on Artificial Intelligence. Phoenix: AAAI Press: 3501-3508
Islam M S and Alajlan N. 2013. A morphology alignment method for resampled heartbeat signals. Biomedical Signal Processing and Control, 8(3):315-324[DOI:10.1016/j.bspc.2012.11.006]
Ji Y S, Zhang S and Xiao W D. 2019. Electrocardiogram classification based on faster regions with convolutional neural network. Sensors, 19(11):2558[DOI:10.3390/s19112558]
Karthikeyan P, Murugappan M and Yaacob S. 2012. ECG signal denoising using wavelet thresholding techniques in human stress assessment. International Journal on Electrical Engineering and Informatics, 4(2):306-319[DOI:10.15676/ijeei.2012.4.2.9]
Kiranyaz S, Ince T and Gabbouj M. 2016. Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Transactions on Biomedical Engineering, 63(3):664-675[DOI:10.1109/TBME.2015.2468589]
Korürek M and Doǧan B. 2010. ECG beat classification using particle swarm optimization and radial basis function neural network. Expert Systems with Applications, 37(12):7563-7569[DOI:10.1016/j.eswa.2010.04.087]
Kutlu Y and Kuntalp D. 2011. A multi-stage automatic arrhythmia recognition and classification system. Computers in Biology and Medicine, 41(1):37-45[DOI:10.1016/j.compbiomed.2010.11.003]
Li H Q and Wang X F. 2013. Detection of electrocardiogram characteristic points using lifting wavelet transform and Hilbert transform. Transactions of the Institute of Measurement and Control, 35(5):574-582[DOI:10.1177/0142331212460720]
Li H Q, Wang X F, Chen L and Li E B. 2014. Denoising and R-Peak detection of electrocardiogram signal based on EMD and improved approximate envelope. Circuits, Systems, and Signal Processing, 33(4):1261-1276[DOI:10.1007/s00034-013-9691-3]
Li H Q, Yuan D Y, Ma X D, Cui D Y and Cao L. 2017. Genetic algorithm for the optimization of features and neural networks in ECG signals classification. Scientific Reports, 7:41011[DOI:10.1038/srep41011]
Li Y Z, Pang Y W, Wang J and Li X L. 2018. Patient-specific ECG classification by deeper CNN from generic to dedicated. Neurocomputing, 314:336-346[DOI:10.1016/j.neucom.2018.06.068]
Lin C C and Yang C M. 2014. Heartbeat classification using normalized RR intervals and morphological features. Mathematical Problems in Engineering, 2014:712474[DOI:10.1155/2014/712474]
Lopes A T, De Aguiar E, De Souza A F and Oliveira-Santos T. 2017. Facial expression recognition with convolutional neural networks:coping with few data and the training sample order. Pattern Recognition, 61:610-628[DOI:10.1016/j.patcog.2016.07.026]
Martis R J, Acharya U R, Prasad H, Chua C K and Lim C M. 2013. Automated detection of atrial fibrillation using Bayesian paradigm. Knowledge-Based Systems, 54:269-275[DOI:10.1016/j.knosys.2013.09.016]
Mazomenos E B, Chen T, Acharyya A, Bhattacharya A, Rosengarten J and Maharatna K. 2012. A time-domain morphology and gradient based algorithm for ECG feature extraction//Proceedings of 2012 IEEE International Conference on Industrial Technology. Athens: IEEE: 117-122[ DOI:10.1109/ICIT.2012.6209924 http://dx.doi.org/10.1109/ICIT.2012.6209924 ]
Mondéjar-Guerra V, Novo J, Rouco J, Penedo M G and Ortega M. 2019. Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers. Biomedical Signal Processing and Control, 47:41-48[DOI:10.1016/j.bspc.2018.08.007]
Pourbabaee B, Roshtkhari M J and Khorasani K. 2018. Deep convolutional neural networks and learning ECG features for screening paroxysmal atrial fibrillation patients. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 48(12):2095-2104[DOI:10.1109/TSMC.2017.2705582]
Pyakillya B, Kazachenko N and Mikhailovsky N. 2017. Deep learning for ECG classification. Journal of Physics:Conference Series, 913:012004[DOI:10.1088/1742-6596/913/1/012004]
Raj S and Ray K C. 2017. ECG signal analysis using DCT-based DOST and PSO optimized SVM. IEEE Transactions on Instrumentation and Measurement, 66(3):470-478[DOI:10.1109/TIM.2016.2642758]
Schirrmeister R T, Springenberg J T, Fiederer L D J, Glasstetter M, Eggensperger K, Tangermann M, Hutter F, Burgard W and Ball T. 2017. Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping, 38(11):5391-5420[DOI:10.1002/hbm.23730]
Shadmand S and Mashoufi B. 2016. A new personalized ECG signal classification algorithm using block-based neural network and particle swarm optimization. Biomedical Signal Processing and Control, 25:12-23[DOI:10.1016/j.bspc.2015.10.008]
Sopic D, Aminifar A, Aminifar A and Atienza D. 2018. Real-time event-driven classification technique for early detection and prevention of myocardial infarction on wearable systems. IEEE Transactions on Biomedical Circuits and Systems, 12(5):982-992[DOI:10.1109/TBCAS.2018.2848477]
Tantawi M M, Revett K, Salem A and Tolba M F. 2013. Fiducial feature reduction analysis for electrocardiogram (ECG) based biometric recognition. Journal of Intelligent Information Systems, 40(1):17-39[DOI:10.1007/s10844-012-0214-7]
Wang G J, Zhang C S, Liu Y P, Yang H Z, Fu D P, Wang H Q and Zhang P. 2019. A global and updatable ECG beat classification system based on recurrent neural networks and active learning. Information Sciences, 501:523-542[DOI:10.1016/j.ins.2018.06.062]
Yeh Y C and Wang W J. 2008. QRS complexes detection for ECG signal:the difference operation method. Computer Methods and Programs in Biomedicine, 91(3):245-254[DOI:10.1016/j.cmpb.2008.04.006]
Yeh Y C, Wang W J and Chiou C W. 2009. Cardiac arrhythmia diagnosis method using linear discriminant analysis on ECG signals. Measurement, 42(5):778-789[DOI:10.1016/j.measurement.2009.01.004]
Yildirim O, Baloglu U B, Tan R S, Ciaccio E J and Acharya U R. 2019. A new approach for arrhythmia classification using deep coded features and LSTM networks. Computer Methods and Programs in Biomedicine, 176:121-133[DOI:10.1016/j.cmpb.2019.05.004]
Zhang C S, Wang G J, Zhao J W, Gao P F, Lin J P and Yang H Z. 2017. Patient-specific ECG classification based on recurrent neural networks and clustering technique//Proceedings of the 2017 13th IASTED International Conference on Biomedical Engineering (BioMed). Innsbruck: IEEE: 63-67[ DOI:10.2316/P.2017.852-029 http://dx.doi.org/10.2316/P.2017.852-029 ]
Zuo Z, Wang G, Shuai B, Zhao L F and Yang Q X. 2015. Exemplar based deep discriminative and shareable feature learning for scene image classification. Pattern Recognition, 48(10):3004-3015[DOI:10.1016/j.patcog.2015.02.003]
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