DENS-ECG: A Deep Learning Approach for ECG Signal Delineation

Objectives: With the technological advancements in the field of tele-health monitoring, it is now possible to gather huge amounts of electro-physiological signals such as electrocardiogram (ECG). It is therefore necessary to develop models/algorithms that are capable of analysing these massive amounts of data in real-time. This paper proposes a deep learning model for real-time segmentation of heartbeats. Methods: The proposed algorithm, named as the DENS-ECG algorithm, combines convolutional neural network (CNN) and long short-term memory (LSTM) model to detect onset, peak, and offset of different heartbeat waveforms such as the P-wave, QRS complex, T-wave, and No wave (NW). Using ECG as the inputs, the model learns to extract high level features through the training process, which, unlike other classical machine learning based methods, eliminates the feature engineering step. Results: The proposed DENS-ECG model was trained and validated on a dataset with 105 ECGs of length 15 minutes each and achieved an average sensitivity and precision of 97.95% and 95.68%, respectively, using a 5-fold cross validation. Additionally, the model was evaluated on an unseen dataset to examine its robustness in QRS detection, which resulted in a sensitivity of 99.61% and precision of 99.52%. Conclusion: The empirical results show the flexibility and accuracy of the combined CNN-LSTM model for ECG signal delineation. Significance: This paper proposes an efficient and easy to use approach using deep learning for heartbeat segmentation, which could potentially be used in real-time tele-health monitoring systems.

[1]  Khashayar Khorasani,et al.  Deep Convolutional Neural Networks and Learning ECG Features for Screening Paroxysmal Atrial Fibrillation Patients , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[2]  R. Poli,et al.  Genetic design of optimum linear and nonlinear QRS detectors , 1995, IEEE Transactions on Biomedical Engineering.

[3]  Xiang Bai,et al.  An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  M Fukunami,et al.  Detection of Patients at Risk for Paroxysmal Atrial Fibrillation During Sinus Rhythm by P Wave‐Triggered Signal‐Averaged Electrocardiogram , 1991, Circulation.

[5]  U. Rajendra Acharya,et al.  Computer-aided diagnosis of atrial fibrillation based on ECG Signals: A review , 2018, Inf. Sci..

[6]  Pablo Laguna,et al.  A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG , 1997, Computers in Cardiology 1997.

[7]  Sadasivan Puthusserypady,et al.  Ensemble Learning for Detection of Short Episodes of Atrial Fibrillation , 2018, 2018 26th European Signal Processing Conference (EUSIPCO).

[8]  Lorenzo Chiari,et al.  A wavelet-based ECG delineation algorithm for 32-bit integer online processing , 2011, Biomedical engineering online.

[9]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

[10]  Guy Amit,et al.  Supraventricular Tachycardia Classification in the 12-Lead ECG Using Atrial Waves Detection and a Clinically Based Tree Scheme , 2016, IEEE Journal of Biomedical and Health Informatics.

[11]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[12]  Chandan Chakraborty,et al.  Application of principal component analysis to ECG signals for automated diagnosis of cardiac health , 2012, Expert Syst. Appl..

[13]  M. I. Gabriel Khan,et al.  Rapid ECG Interpretation , 1997 .

[14]  C. Li,et al.  Detection of ECG characteristic points using wavelet transforms. , 1995, IEEE transactions on bio-medical engineering.

[15]  Paulo Carvalho,et al.  Detection of Atrial Fibrillation using model-based ECG analysis , 2008, 2008 19th International Conference on Pattern Recognition.

[16]  Sadasivan Puthusserypady,et al.  An end-to-end deep learning approach to MI-EEG signal classification for BCIs , 2018, Expert Syst. Appl..

[17]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[18]  Sadasivan Puthusserypady,et al.  An Ensemble of Deep Recurrent Neural Networks for P-wave Detection in Electrocardiogram , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[19]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[20]  Antonio Pescapè,et al.  Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG , 2020, Scientific Reports.

[21]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[22]  Pablo Laguna,et al.  A wavelet-based ECG delineator: evaluation on standard databases , 2004, IEEE Transactions on Biomedical Engineering.

[23]  Tara N. Sainath,et al.  Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[24]  Matin Hashemi,et al.  LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices , 2018, IEEE Journal of Biomedical and Health Informatics.

[25]  Matthew Richardson,et al.  Blending LSTMs into CNNs , 2015, ICLR 2016.

[26]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[27]  Pawe Pawiak,et al.  Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system , 2018 .

[28]  Sabir Jacquir,et al.  Automatic detection of P, QRS and T patterns in 12 leads ECG signal based on CWT , 2016, Biomed. Signal Process. Control..

[29]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[30]  Surya Ganguli,et al.  Identifying and attacking the saddle point problem in high-dimensional non-convex optimization , 2014, NIPS.

[31]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[32]  Hangsik Shin,et al.  Simple and Robust Realtime QRS Detection Algorithm Based on Spatiotemporal Characteristic of the QRS Complex , 2016, PloS one.

[33]  Philip de Chazal,et al.  Automatic classification of heartbeats using ECG morphology and heartbeat interval features , 2004, IEEE Transactions on Biomedical Engineering.

[34]  Vias Markides,et al.  Atrial fibrillation: classification, pathophysiology, mechanisms and drug treatment , 2003, Heart.

[35]  David Atienza,et al.  A Modular Low-Complexity ECG Delineation Algorithm for Real-Time Embedded Systems , 2018, IEEE Journal of Biomedical and Health Informatics.

[36]  David Menotti,et al.  ECG arrhythmia classification based on optimum-path forest , 2013, Expert Syst. Appl..

[37]  L Glass,et al.  Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and ΔRR intervals , 2001, Medical and Biological Engineering and Computing.

[38]  Navdeep Jaitly,et al.  Towards End-To-End Speech Recognition with Recurrent Neural Networks , 2014, ICML.

[39]  Gérard Dreyfus,et al.  Automatic ECG wave extraction in long-term recordings using Gaussian mesa function models and nonlinear probability estimators , 2007, Comput. Methods Programs Biomed..

[40]  Stanley Nattel,et al.  Atrial fibrillation pathophysiology: implications for management. , 2011, Circulation.

[41]  Ghanahshyam B Kshirsagar,et al.  Improving Performance of Devanagari Script Input-Based P300 Speller Using Deep Learning , 2019, IEEE Transactions on Biomedical Engineering.

[42]  Charu C. Aggarwal,et al.  Neural Networks and Deep Learning , 2018, Springer International Publishing.

[43]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[44]  Sadasivan Puthusserypady,et al.  A deep learning approach for real-time detection of atrial fibrillation , 2019, Expert Syst. Appl..

[45]  Yüksel Özbay,et al.  A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network , 2009, Expert Syst. Appl..

[46]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[47]  Francisco C. Pereira,et al.  Multi-output bus travel time prediction with convolutional LSTM neural network , 2019, Expert Syst. Appl..

[48]  Nigel H. Lovell,et al.  Detection of Atrial Fibrillation from RR Intervals and PQRST Morphology using a Neural Network Ensemble , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[49]  Senén Barro,et al.  A new approach for TU complex characterization , 2000, IEEE Transactions on Biomedical Engineering.

[50]  B. V. K. Vijaya Kumar,et al.  An Automatic Subject-Adaptable Heartbeat Classifier Based on Multiview Learning , 2016, IEEE Journal of Biomedical and Health Informatics.

[51]  Majid Moavenian,et al.  A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification , 2010, Expert Syst. Appl..

[52]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[53]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[54]  U. Rajendra Acharya,et al.  Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals , 2017, Inf. Sci..

[55]  José Luis Rojo-Álvarez,et al.  Feature selection using support vector machines and bootstrap methods for ventricular fibrillation detection , 2012, Expert Syst. Appl..

[56]  Jean-Yves Tourneret,et al.  P- and T-Wave Delineation in ECG Signals Using a Bayesian Approach and a Partially Collapsed Gibbs Sampler , 2010, IEEE Transactions on Biomedical Engineering.

[57]  Feng Wan,et al.  A 0.83-$\mu {\rm W}$ QRS Detection Processor Using Quadratic Spline Wavelet Transform for Wireless ECG Acquisition in 0.35- $\mu{\rm m}$ CMOS , 2012, IEEE Transactions on Biomedical Circuits and Systems.

[58]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[59]  M. I. Owis,et al.  A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machines , 2015, Expert systems with applications.

[60]  Özal Yildirim,et al.  A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification , 2018, Comput. Biol. Medicine.

[61]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[62]  Sebastian Zaunseder,et al.  Optimization of ECG Classification by Means of Feature Selection , 2011, IEEE Transactions on Biomedical Engineering.

[63]  Chao Huang,et al.  A Novel Method for Detection of the Transition Between Atrial Fibrillation and Sinus Rhythm , 2011, IEEE Transactions on Biomedical Engineering.

[64]  Ali Ghaffari,et al.  ECG arrhythmia recognition via a neuro-SVM-KNN hybrid classifier with virtual QRS image-based geometrical features , 2012, Expert Syst. Appl..