A multimodal parallel method for left ventricular dysfunction identification based on phonocardiogram and electrocardiogram signals synchronous analysis

Yajing Zeng; Siyu Yang; Xiongkai Yu; Wenting Lin; Wei Wang; Jijun Tong; Shudong Xia; Yajing Zeng; Siyu Yang; Xiongkai Yu; Wenting Lin; Wei Wang; Jijun Tong; Shudong Xia

doi:10.3934/mbe.2022447

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 9: 9612-9635. doi: 10.3934/mbe.2022447

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Research article

A multimodal parallel method for left ventricular dysfunction identification based on phonocardiogram and electrocardiogram signals synchronous analysis

1.
The Fourth Affiliated Hospital Zhejiang University School of Medicine, Jinhua 321000, China
2.
School of Information and Technology, Zhejiang Sci-Tech University, Hangzhou 310000, China

Academic Editor: Yang Kuang

Received: 11 January 2022 Revised: 07 June 2022 Accepted: 04 July 2022 Published: 04 July 2022

Heart failure (HF) is widely acknowledged as the terminal stage of cardiac disease and represents a global clinical and public health problem. Left ventricular ejection fraction (LVEF) measured by echocardiography is an important indicator of HF diagnosis and treatment. Early identification of LVEF reduction and early treatment is of great significance to improve LVEF and the prognosis of HF. This research aims to introduce a new method for left ventricular dysfunction (LVD) identification based on phonocardiogram (ECG) and electrocardiogram (PCG) signals synchronous analysis. In the present study, we established a database called Synchronized ECG and PCG Database for Patients with Left Ventricular Dysfunction (SEP-LVDb) consisting of 1046 synchronous ECG and PCG recordings from patients with reduced (n = 107) and normal (n = 699) LVEF. 173 and 873 recordings were available from the reduced and normal LVEF group, respectively. Then, we proposed a parallel multimodal method for LVD identification based on synchronous analysis of PCG and ECG signals. Two-layer bidirectional gate recurrent unit (Bi-GRU) was used to extract features in the time domain, and the data were classified using residual network 18 (ResNet-18). This research confirmed that fused ECG and PCG signals yielded better performance than ECG or PCG signals alone, with an accuracy of 93.27%, precision of 93.34%, recall of 93.27%, and F1-score of 93.27%. Verification of the model's performance with an independent dataset achieved an accuracy of 80.00%, precision of 79.38%, recall of 80.00% and F1-score of 78.67%. The Bi-GRU model outperformed Bi-directional long short-term memory (Bi-LSTM) and recurrent neural network (RNN) models with a best selection frame length of 3.2 s. The Saliency Maps showed that SEP-LVDPN could effectively learn features from the data.
- multi-modal,
- phonocardiogram,
- electrocardiogram,
- neural network,
- left ventricular dysfunction
Citation: Yajing Zeng, Siyu Yang, Xiongkai Yu, Wenting Lin, Wei Wang, Jijun Tong, Shudong Xia. A multimodal parallel method for left ventricular dysfunction identification based on phonocardiogram and electrocardiogram signals synchronous analysis[J]. Mathematical Biosciences and Engineering, 2022, 19(9): 9612-9635. doi: 10.3934/mbe.2022447

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Abstract

Heart failure (HF) is widely acknowledged as the terminal stage of cardiac disease and represents a global clinical and public health problem. Left ventricular ejection fraction (LVEF) measured by echocardiography is an important indicator of HF diagnosis and treatment. Early identification of LVEF reduction and early treatment is of great significance to improve LVEF and the prognosis of HF. This research aims to introduce a new method for left ventricular dysfunction (LVD) identification based on phonocardiogram (ECG) and electrocardiogram (PCG) signals synchronous analysis. In the present study, we established a database called Synchronized ECG and PCG Database for Patients with Left Ventricular Dysfunction (SEP-LVDb) consisting of 1046 synchronous ECG and PCG recordings from patients with reduced (n = 107) and normal (n = 699) LVEF. 173 and 873 recordings were available from the reduced and normal LVEF group, respectively. Then, we proposed a parallel multimodal method for LVD identification based on synchronous analysis of PCG and ECG signals. Two-layer bidirectional gate recurrent unit (Bi-GRU) was used to extract features in the time domain, and the data were classified using residual network 18 (ResNet-18). This research confirmed that fused ECG and PCG signals yielded better performance than ECG or PCG signals alone, with an accuracy of 93.27%, precision of 93.34%, recall of 93.27%, and F1-score of 93.27%. Verification of the model's performance with an independent dataset achieved an accuracy of 80.00%, precision of 79.38%, recall of 80.00% and F1-score of 78.67%. The Bi-GRU model outperformed Bi-directional long short-term memory (Bi-LSTM) and recurrent neural network (RNN) models with a best selection frame length of 3.2 s. The Saliency Maps showed that SEP-LVDPN could effectively learn features from the data.

References

[1]	G. Savarese, D. Stolfo, G. Sinagra, L. H. Lund, Heart failure with mid-range or mildly reduced ejection fraction, Nat. Rev. Cardiol., 19 (2021), 100-116. https://doi.org/10.1038/s41569-021-00605-5 doi: 10.1038/s41569-021-00605-5
[2]	V. L. Roger, Epidemiology of Heart Failure: A Contemporary Perspective, Circ. Res., 128 (2021), 1421-1434. https://doi.org/10.1161/CIRCRESAHA.121.318172 doi: 10.1161/CIRCRESAHA.121.318172
[3]	A. Groenewegen, F. H. Rutten, A. Mosterd, A. W. Hoes, Epidemiology of heart failure, Eur. J. Heart. Fail., 22 (2020), 1342-1356. https://doi.org/10.1002/ejhf.1858 doi: 10.1002/ejhf.1858
[4]	S. L. James, D. Abate, K. H. Abate, S. M. Abay, C. Abbafati, N. Abbasi, et al., Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017, Lancet, 392 (2018), 1789-1858. https://doi.org/10.1016/s0140-6736(18)32279-7 doi: 10.1016/s0140-6736(18)32279-7
[5]	X. Zhang, Y. Sun, Y. Zhang, F. Chen, S. Zhang, H. He, et al., Heart failure with midrange ejection fraction: Prior left ventricular ejection fraction and prognosis, Front. Cardiovasc. Med., 8 (2021), 697221. https://doi.org/10.3389/fcvm.2021.697221 doi: 10.3389/fcvm.2021.697221
[6]	J. N. Njoroge, J. R. Teerlink, Systolic time intervals in patients with heart failure: time to teach new dogs old tricks, Eur. J. Heart Fail., 22 (2020): 1183-1185. https://doi.org/10.1002/ejhf.1725
[7]	P. Ponikowski, A. A. Voors, S. D. Anker, H. Bueno, J. G. Cleland, A. J. Coats, et al., 2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur. J. Heart Fail., 18 (2016), 891-975. https://doi.org/10.1002/ejhf.592
[8]	O. Chioncel, M. Lainscak, P. M. Seferovic, S. D. Anker, M. G. Crespo-Leiro, V. P. Harjola, et al., Epidemiology and one-year outcomes in patients with chronic heart failure and preserved, mid-range and reduced ejection fraction: an analysis of the ESC Heart Failure Long-Term Registry, Eur. J. Heart Fail., 19 (2017), 1574-1585. https://doi.org/10.1002/ejhf.813 doi: 10.1002/ejhf.813
[9]	Meta-analysis Global Group in Chronic Heart, The survival of patients with heart failure with preserved or reduced left ventricular ejection fraction: an individual patient data meta-analysis, Eur. Heart J., 33 (2012), 1750-1757. https://doi.org/10.1093/eurheartj/ehr254 doi: 10.1093/eurheartj/ehr254
[10]	J. Lupón, G. Gavidia-Bovadilla, E. Ferrer, M. de Antonio, A. Perera-Lluna, J. López-Ayerbe, et al., Dynamic trajectories of left ventricular ejection fraction in heart failure, J. Am. Coll. Cardiol., 72 (2018), 591-601. https://doi.org/10.1016/j.jacc.2018.05.042 doi: 10.1016/j.jacc.2018.05.042
[11]	J. Butler, S. D. Anker, M. Packer, Redefining heart failure with a reduced ejection fraction, JAMA, 322 (2019), 1761-1762. https://doi.org/10.1001/jama.2019.15600 doi: 10.1001/jama.2019.15600
[12]	J. M. T. Wu, M. H. Tsai, Y. Z. Huang, S. K. H. Islam, M. M. Hassan, A. Alelaiwi, et al., Applying an ensemble convolutional neural network with Savitzky-Golay filter to construct a phonocardiogram prediction model, Appl. Soft Comput., 78 (2019), 29-40. https://doi.org/10.1016/j.asoc.2019.01.019 doi: 10.1016/j.asoc.2019.01.019
[13]	M. Deng, T. Meng, J. Cao, S. Wang, J. Zhang, H. Fan, Heart sound classification based on improved MFCC features and convolutional recurrent neural networks, Neural Netw., 130 (2020), 22-32. https://doi.org/10.1016/j.neunet.2020.06.015 doi: 10.1016/j.neunet.2020.06.015
[14]	H. Li, X. Wang, C. Liu, Q. Zeng, Y. Zheng, X. Chu, et al., A fusion framework based on multi-domain features and deep learning features of phonocardiogram for coronary artery disease detection, Comput. Biol. Med., 120 (2020), 103733. https://doi.org/10.1016/j.compbiomed.2020.103733 doi: 10.1016/j.compbiomed.2020.103733
[15]	J. S. Chorba, A. M. Shapiro, L. Le, J. Maidens, J. Prince, S. Pham, et al., Deep learning algorithm for automated cardiac murmur detection via a digital stethoscope platform, J. Am. Heart Assoc., 10 (2021), e019905. https://doi.org/10.1161/JAHA.120.019905 doi: 10.1161/JAHA.120.019905
[16]	S. Dami, M. Yahaghizadeh, Predicting cardiovascular events with deep learning approach in the context of the internet of things, Neural Comput. Appl., 33 (2021), 7979-7996. https://doi.org/10.1007/s00521-020-05542-x doi: 10.1007/s00521-020-05542-x
[17]	N. Gumpfer, D. Grun, J. Hannig, T. Keller, M. Guckert, Detecting myocardial scar using electrocardiogram data and deep neural networks, Biol. Chem., 402 (2021), 911-923. https://doi.org/10.1515/hsz-2020-0169 doi: 10.1515/hsz-2020-0169
[18]	Y. Liu, X. Guo, Y. Zheng, An automatic approach using ELM classifier for HFpEF identification based on heart sound characteristics, J. Med. Syst., 43 (2019), 285. https://doi.org/10.1007/s10916-019-1415-1 doi: 10.1007/s10916-019-1415-1
[19]	S. Gao, Y. Zheng, X. Guo, Gated recurrent unit-based heart sound analysis for heart failure screening, Biomed. Eng. Online., 19 (2020), 3. https://doi.org/10.1186/s12938-020-0747-x doi: 10.1186/s12938-020-0747-x
[20]	M. Gjoreski, A. Gradisek, B. Budna, M. Gams, G. Poglajen, Machine learning and end-to-end deep learning for the detection of chronic heart failure from heart sounds. IEEE Access., 8 (2020), 20313-20324. https://doi.org/10.1109/access.2020.2968900 doi: 10.1109/access.2020.2968900
[21]	J. Cho, B. Lee, J. M. Kwon, Y. Lee, H. Park, B. H. Oh, et al., Artificial intelligence algorithm for screening heart failure with reduced ejection fraction using electrocardiography, ASAIO J., 67 (2021), 314-321. https://doi.org/10.1097/MAT.0000000000001218 doi: 10.1097/MAT.0000000000001218
[22]	D. Li, X. Li, J. Zhao, X. Bai Automatic staging model of heart failure based on deep learning. Biomed. Signal Process. Control, 52 (2019), 77-83. https://doi.org/10.1016/j.bspc.2019.03.009 doi: 10.1016/j.bspc.2019.03.009
[23]	A. S. Eltrass, M. B. Tayel, A. I. Ammar, A new automated CNN deep learning approach for identification of ECG congestive heart failure and arrhythmia using constant-Q non-stationary Gabor transform, Biomed. Signal Process. Control, 65 (2021), 102326. https://doi.org/10.1016/j.bspc.2020.102326 doi: 10.1016/j.bspc.2020.102326
[24]	X. C. Li, X. H. Liu, L. B. Liu, S. M. Li, Y. Q. Wang, R. H. Mead, Evaluation of left ventricular systolic function using synchronized analysis of heart sounds and the electrocardiogram, Heart Rhythm., 17 (2020), 876-880. https://doi.org/10.1016/j.hrthm.2020.01.025 doi: 10.1016/j.hrthm.2020.01.025
[25]	S. Wang, F. Fang, M. Liu, Y. Y. Lam, J. Wang, Q. Shang, et al., Rapid bedside identification of high-risk population in heart failure with reduced ejection fraction by acoustic cardiography, Int. J. Cardiol., 168 (2013), 1881-1886. https://doi.org/10.1016/j.ijcard.2012.12.064 doi: 10.1016/j.ijcard.2012.12.064
[26]	B. Moyers, M. Shapiro, G. M. Marcus, I. L. Gerber, B. H. McKeown, J. C. Vessey, et al., Performance of phonoelectrocardiographic left ventricular systolic time intervals and B-type natriuretic peptide levels in the diagnosis of left ventricular dysfunction, Ann. Noninvasive Electrocardiol., 12 (2007), 89-97. https://doi.org/10.1111/j.1542-474X.2007.00146.x doi: 10.1111/j.1542-474X.2007.00146.x
[27]	S. Efstratiadis, A. D. Michaels, Computerized acoustic cardiographic electromechanical activation time correlates with invasive and echocardiographic parameters of left ventricular contractility, J. Card. Fail., 14 (2008), 577-582. https://doi.org/10.1016/j.cardfail.2008.03.011 doi: 10.1016/j.cardfail.2008.03.011
[28]	Y. N. Wen, A. P. Lee, F. Fang, C. N. Jin, C. M. Yu, Beyond auscultation: acoustic cardiography in clinical practice, Int. J. Cardiol., 172 (2014), 548-560. https://doi.org/10.1016/j.ijcard.2013.12.298 doi: 10.1016/j.ijcard.2013.12.298
[29]	P. Li, Y. Hu, Z. P. Liu, Prediction of cardiovascular diseases by integrating multi-modal features with machine learning methods, Biomed. Signal Process. Control., 66 (2021), 102474. https://doi.org/10.1016/j.bspc.2021.102474 doi: 10.1016/j.bspc.2021.102474
[30]	J. Y. Sun, Y. Qiu, H. C. Guo, Y. Hua, B. Shao, Y. C. Qiao, et al., A method to screen left ventricular dysfunction through ECG based on convolutional neural network, J. Cardiovasc. Electrophysiol., 32 (2021), 1095-1102. https://doi.org/10.1111/jce.14936 doi: 10.1111/jce.14936
[31]	C. Liu, D. Springer, Q. Li, B. Moody, R. A. Juan, F. J. Chorro, et al., An open access database for the evaluation of heart sound algorithms, Physiol. Meas., 37 (2016), 2181-2213. https://doi.org/10.1088/0967-3334/37/12/2181 doi: 10.1088/0967-3334/37/12/2181
[32]	C. C. Lang, D. M. Mancini, Non-cardiac comorbidities in chronic heart failure, Heart, 93 (2007), 665-671. https://doi.org/10.1136/hrt.2005.068296 doi: 10.1136/hrt.2005.068296
[33]	M. Metra, V. Zaca, G. Parati, P. Agostoni, M. Bonadies, M. Ciccone, et al., Cardiovascular and noncardiovascular comorbidities in patients with chronic heart failure, J. Cardiovasc. Med., 12 (2011), 76-84. https://doi.org/10.2459/JCM.0b013e32834058d1 doi: 10.2459/JCM.0b013e32834058d1
[34]	E. Sze, J. P. Daubert, Left bundle branch block-induced left ventricular remodeling and its potential for reverse remodeling, J. Interv. Card. Electrophysiol., 52 (2018), 343-352. https://doi.org/10.1007/s10840-018-0407-2 doi: 10.1007/s10840-018-0407-2
[35]	W. Zaremba, I. Sutskever, O. Vinyals, Recurrent neural network regularization, preprint, arXiv: 1409.2329.
[36]	S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Comput., 9 (1997), 1735-1780. https://doi.org/DOI10.1162/neco.1997.9.8.1735
[37]	J. Chung, Ç. Gülçehre, K. Cho, Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling, preprint, arXiv: 1412.3555.
[38]	K. Cho, B. v. Merrienboer, D. Bahdanau, Y. Bengio, On the properties of neural machine translation: Encoder-decoder approaches, preprint, arXiv: 1409.1259.
[39]	N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting, J. Machine Learn. Res., 15 (2014), 1929-1958.
[40]	S. Ioffe, C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, preprint, arXiv: 1502.03167
[41]	K. M. He, X. Y. Zhang, S. Q. Ren, J. Sun, Deep residual learning for image recognition, in 2016 Ieee Conference on Computer Vision and Pattern Recognition (Cvpr), (2016), 770-778. https://doi.org/10.1109/Cvpr.2016.90
[42]	C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, et al., Going deeper with convolutions, in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2015), 1-9. https://doi.org/10.1109/CVPR.2015.7298594
[43]	D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, preprint, arXiv: 1412.6980.
[44]	L. N. Smith, Cyclical Learning Rates for Training Neural Networks, in 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), (2017), 464-472. https://doi.org/10.1109/WACV.2017.58
[45]	I. Loshchilov, F. Hutter, SGDR: Stochastic gradient descent with restarts, preprint, arXiv: 1608.03983.
[46]	Y. Lecun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, in Proceedings of the IEEE, 86 (1998), 2278-2324. https://doi.org/10.1109/5.726791

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