Energy efficient ECG classification with spiking neural network

Biomedical Signal Processing and Control - Tập 63 - Trang 102170 - 2021
Zhanglu Yan1, Jun Zhou1, Weng-Fai Wong1
1Department of Computer Science, National University of Singapore, Singapore

Tài liệu tham khảo

for the Advancement of Medical Instrumentation, 1998, Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms, ANSI/AAMI EC38, 1998

Luz, 2016, ECG-Based heartbeat classification for arrhythmia detection: A survey, Comput. Methods Programs Biomed., 127, 144, 10.1016/j.cmpb.2015.12.008

Takalo-Mattila, 2018, Inter-patient ECG classification using deep convolutional neural networks, 421

Lin, 2014, Heartbeat classification using normalized RR intervals and morphological features, Math. Probl. Eng., 2014, 10.1155/2014/712474

Osowski, 2001, ECG Beat recognition using fuzzy hybrid neural network, IEEE Trans. Biomed. Eng., 48, 1265, 10.1109/10.959322

Mar, 2011, Optimization of ECG classification by means of feature selection, IEEE Trans. Biomed. Eng., 58, 2168, 10.1109/TBME.2011.2113395

Liu, 2017, A survey of deep neural network architectures and their applications, Neurocomputing, 234, 11, 10.1016/j.neucom.2016.12.038

K. He, J. Sun, Convolutional neural networks at constrained time cost, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 5353–5360.

Maass, 1995, On the computational complexity of networks of spiking neurons, 183

Moody, 2001, The impact of the MIT-bih arrhythmia database, IEEE Eng. Med. Biol. Mag., 20, 45, 10.1109/51.932724

De Chazal, 2004, Automatic classification of heartbeats using ECG morphology and heartbeat interval features, IEEE Trans. Biomed. Eng., 51, 1196, 10.1109/TBME.2004.827359

Faziludeen, 2013, ECG Beat classification using wavelets and SVM, 815

Diehl, 2015, Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing, 1

Mondéjar-Guerra, 2019, Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers, Biomed. Signal Process. Control, 47, 41, 10.1016/j.bspc.2018.08.007

Chawla, 2002, SMOTE: Synthetic Minority over-sampling technique, J. Artif. Intell. Res., 16, 321, 10.1613/jair.953

Abadi, 2015

Wang, 2020, Shenjing: A low power reconfigurable neuromorphic accelerator with partial-sum and spike networks-on-chip

Altman, 1994, Statistics notes: Diagnostic tests 2: predictive values, Bmj, 309, 102, 10.1136/bmj.309.6947.102

He, 2019, Temporal convolutional networks for anomaly detection in time series, J. Phys.: Conf. Ser., 1213

Willems, 1987, Comparison of multigroup logistic and linear discriminant ECG and VCG classification, J. Electrocardiol., 20, 83, 10.1016/S0022-0736(87)80096-1

Rajesh, 2018, Classification of imbalanced ECG beats using re-sampling techniques and adaboost ensemble classifier, Biomed. Signal Process. Control, 41, 242, 10.1016/j.bspc.2017.12.004

Li, 2017, Genetic algorithm for the optimization of features and neural networks in ECG signals classification, Sci. Rep., 7, 41011, 10.1038/srep41011

Yildirim, 2018, A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification, Comput. Biol. Med., 96, 189, 10.1016/j.compbiomed.2018.03.016

Acharya, 2017, Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals, Inform. Sci., 415, 190, 10.1016/j.ins.2017.06.027

Kiranyaz, 2015, Real-time patient-specific ECG classification by 1-D convolutional neural networks, IEEE Trans. Biomed. Eng., 63, 664, 10.1109/TBME.2015.2468589

Amirshahi, 2019, Ecg classification algorithm based on STDP and R-STDP neural networks for real-time monitoring on ultra low-power personal wearable devices, IEEE Trans. Biomed. Circuits Syst., 13, 1483, 10.1109/TBCAS.2019.2948920

Garcia, 2017, Inter-patient ecg heartbeat classification with temporal vcg optimized by pso, Sci. Rep., 7, 10543, 10.1038/s41598-017-09837-3

Zhang, 2014, Heartbeat classification using disease-specific feature selection, Comput. Biol. Med., 46, 79, 10.1016/j.compbiomed.2013.11.019

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Biomedical Signal Processing and Control

Tập 63

102170

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