Abstract
Medical technologies in the form of wearable devices are an integral part of our daily lives. These devices are devoted to acquire physiological data to provide personal analytics and to assess the physical status of assisted individuals. Nowadays, thanks to the research effort and to the continuously evolving technologies, telemedecine plays a crucial role in healthcare. Electrocardiogram (ECG) is one of the source signal that has been widely involved in telemedicine and therefore the need for a quick and precise screening of ECG pathological conditions has become a priority for the scientific community. Based on the above motivation, we present a study aimed at evaluating the applicability of an highly accurate detector of arrhythmia conditions to be used in combination of a compressed version of the ECG signal. The advantage of using a technique of Compressed Sensing (CS) relies on a faster detection of the approach, due to the lower complexity of the method’s workflow. We conducted an experimental study to determine if such a detector, working on compressed ECG signal, can achieve comparable results with the original approach applied to the uncompressed signal. The results demonstrated that with a Compression Ratio equal to 16 it is possible to achieve classification metrics around 99\(\%\), therefore showing a high suitability of the approach to be involved in contexts of Compressed ECG.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amft, O.: How wearable computing is shaping digital health. IEEE Pervasive Comput. 17(1), 92–98 (2018). https://doi.org/10.1109/MPRV.2018.011591067
Amir, M., Mappangara, I., Setiadji, R., Zam, S.M.: Characteristics and prevalence of premature ventricular complex: a telemedicine study. Cardiol. Res. 10(5), 285 (2019)
Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms. Standard, Association for the Advancement of Medical Instrumentation, Arlington, VA (1998)
Atkins, J.M., Leshin, S.J., Blomqvist, G., Mullins, C.B.: Ventricular conduction blocks and sudden death in acute myocardial infarction: potential indications for pacing. N. Engl. J. Med. 288(6), 281–284 (1973)
Baldasseroni, S., et al.: Left bundle-branch block is associated with increased 1-year sudden and total mortality rate in 5517 outpatients with congestive heart failure: a report from the Italian network on congestive heart failure. Am. Heart J. 143(3), 398–405 (2002)
Balestrieri, E., et al.: Research challenges in measurement for Internet of Things systems. ACTA IMEKO 7, 82–94 (2018). http://dx.doi.org/10.21014/acta_imeko.v7i4.675
Balestrieri, E., et al.: The architecture of an innovative smart T-shirt based on the internet of medical things paradigm. In: 2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–6. IEEE (2019)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intel. Res. 16, 321–357 (2002)
Cheng, Y., Hu, Y., Hou, M., Pan, T., He, W., Ye, Y.: Atrial fibrillation detection directly from compressed ECG with the prior of measurement matrix. Information 11(9) (2020). https://doi.org/10.3390/info11090436, https://www.mdpi.com/2078-2489/11/9/436
Clark, A.L., Goode, K., Cleland, J.G.: The prevalence and incidence of left bundle branch block in ambulant patients with chronic heart failure. Eur. J. Heart Fail. 10(7), 696–702 (2008)
Col, J.J., Weinberg, S.L.: The incidence and mortality of intraventricular conduction defects in acute myocardial infarction. Am. J. Cardiol. 29(3), 344–350 (1972)
Cosoli, G., Spinsante, S., Scalise, L.: Wearable devices and diagnostic apps: beyond the borders of traditional medicine, but what about their accuracy and reliability? IEEE Instrum. Meas. Mag. 24(6), 89–94 (2021). https://doi.org/10.1109/MIM.2021.9513636
Curone, D., et al.: Smart garments for emergency operators: the ProeTEX project. IEEE Trans. Inf Technol. Biomed. 14(3), 694–701 (2010)
De Vito, L., et al.: An undershirt for monitoring of multi-lead ECG and respiration wave signals. In: 2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4. 0 &IoT), pp. 550–555. IEEE (2021)
Dias, D., Paulo Silva Cunha, J.: Wearable health devices-vital sign monitoring, systems and technologies. Sensors 18(8), 2414 (2018). https://doi.org/10.3390/s18082414
Elhaj, F.A., Salim, N., Harris, A.R., Swee, T.T., Ahmed, T.: Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Comput. Methods Programs Biomed. 127, 52–63 (2016)
Evans, A., Perez, I., Yu, G., Kalra, L.: Secondary stroke prevention in atrial fibrillation: lessons from clinical practice. Stroke 31(9), 2106–2111 (2000)
Fahy, G.J., et al.: Natural history of isolated bundle branch block. Am. J. Cardiol. 77(14), 1185–1190 (1996)
Figueroa-Triana, J.F., et al.: Acute myocardial infarction with right bundle branch block at presentation: prevalence and mortality. J. Electrocardiol. 66, 38–42 (2021)
Franklin, R.G., Muthukumar, B.: Survey of heart disease prediction and identification using machine learning approaches. In: 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), pp. 553–557. IEEE (2020)
Friedlander, B., Porat, B.: The modified Yule-Walker method of ARMA spectral estimation. IEEE Trans. Aerosp. Electron. Syst. 2, 158–173 (1984)
Ghaemi, A., Rezaie-Balf, M., Adamowski, J., Kisi, O., Quilty, J.: On the applicability of maximum overlap discrete wavelet transform integrated with mars and M5 model tree for monthly pan evaporation prediction. Agric. For. Meteorol. 278, 107647 (2019)
Goldberger, A.L., et al.: Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)
Haque, A., Ali, M.H., Kiber, M.A., Hasan, M.T., et al.: Detection of small variations of ECG features using wavelet. ARPN J. Eng. Appl. Sci. 4(6), 27–30 (2009)
Hart, R.G.: Atrial fibrillation and stroke prevention. N. Engl. J. Med. 349(11), 1015–1016 (2003)
Hart, R.G., et al.: Lessons from the stroke prevention in atrial fibrillation trials. Ann. Intern. Med. 138(10), 831–838 (2003)
Huarng, K.H., Yu, T.H.K., fang Lee, C.: Adoption model of healthcare wearable devices. Technol. Forecast. Soc. Chang. 174, 121286 (2022). https://doi.org/10.1016/j.techfore.2021.121286, https://www.sciencedirect.com/science/article/pii/S0040162521007204
Imanishi, R., Seto, S., Ichimaru, S., Nakashima, E., Yano, K., Akahoshi, M.: Prognostic significance of incident complete left bundle branch block observed over a 40-year period. Am. J. Cardiol. 98(5), 644–648 (2006)
Ip, J.E., Lerman, B.B.: Idiopathic malignant premature ventricular contractions. Trends Cardiovasc. Med. 28(4), 295–302 (2018)
Isin, A., Ozdalili, S.: Cardiac arrhythmia detection using deep learning. Procedia Comput. Sci. 120, 268–275 (2017)
Jaffard, S., Lashermes, B., Abry, P.: Wavelet leaders in multifractal analysis. In: Qian, T., Vai, M.I., Xu, Y. (eds.) Wavelet Analysis and Applications. Applied and Numerical Harmonic Analysis. Birkhäuser Basel, pp. 201–246. Springer (2006). https://doi.org/10.1007/978-3-7643-7778-6_17
Julian, D.G., Valentine, P.A., Miller, G.G.: Disturbances of rate, rhythm and conduction in acute myocardial infarction: a prospective study of 100 consecutive unselected patients with the aid of electrocardiographic monitoring. Am. J. Med. 37(6), 915–927 (1964)
Kleemann, T., et al.: Incidence and clinical impact of right bundle branch block in patients with acute myocardial infarction: ST elevation myocardial infarction versus non-ST elevation myocardial infarction. Am. Heart J. 156(2), 256–261 (2008)
Kones, R., Phillips, J.: Bundle branch block in acute myocardial infarction. current concepts and indications. Acta Cardiol. 35(6), 469–478 (1980)
Lashermes, B., Jaffard, S., Abry, P.: Wavelet leader based multifractal analysis. In: 2005 Proceedings (ICASSP’05). IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 4, pp. iv–161. IEEE (2005)
Laudato, G., et al.: ATTICUS: ambient-intelligent tele-monitoring and telemetry for incepting and catering over hUman sustainability. Front. Hum. Dyn. 3 (2021). https://doi.org/10.3389/fhumd.2021.614309, https://www.frontiersin.org/article/10.3389/fhumd.2021.614309
Laudato, G., et al.: Identification of R-peak occurrences in compressed ECG signals. In: 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–6. IEEE (2020)
Laudato, G., Picariello, F., Scalabrino, S., Tudosa, I., De Vito, L., Oliveto, R.: Morphological classification of heartbeats in compressed ECG. SciTePress (2021)
Laudato, G., et al.: MIPHAS: military performances and health analysis system. In: 2020 13th International Conference on Health Informatics, HEALTHINF 2020-Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC, pp. 198–207. SciTePress (2020)
Leonarduzzi, R.F., Schlotthauer, G., Torres, M.E.: Wavelet leader based multifractal analysis of heart rate variability during myocardial ischaemia. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 110–113. IEEE (2010)
Li, T., Zhou, M.: ECG classification using wavelet packet entropy and random forests. Entropy 18(8), 285 (2016)
Li, Z., Zhou, D., Wan, L., Li, J., Mou, W.: Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram. J. Electrocardiol. 58, 105–112 (2020)
Lin, C.Y., et al.: An observational study on the effect of premature ventricular complex burden on long-term outcome. Medicine 96(1), e5476 (2017)
Matias, I., Pombo, N., Garcia, N.M.: Towards a fully automated bracelet for health emergency solution. In: IoTBDS, pp. 307–314 (2018)
Melgarejo-Moreno, A., et al.: Incidence, clinical characteristics, and prognostic significance of right bundle-branch block in acute myocardial infarction: a study in the thrombolytic era. Circulation 96(4), 1139–1144 (1997)
Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45–50 (2001)
Mullins, C.B., Atkins, J.M.: Prognoses and management of venticular conduction blocks in acute myocardial infarction. Mod. Concepts Cardiovasc. Dis. 45(10), 129–133 (1976)
Newby, K.H., Pisano, E., Krucoff, M.W., Green, C., Natale, A.: Incidence and clinical relevance of the occurrence of bundle-branch block in patients treated with thrombolytic therapy. Circulation 94(10), 2424–2428 (1996)
Osowski, S., Hoai, L.T., Markiewicz, T.: Support vector machine-based expert system for reliable heartbeat recognition. IEEE Trans. Biomed. Eng. 51(4), 582–589 (2004)
Pal, A., Srivastva, R., Singh, Y.N.: CardioNET: An efficient ECG arrhythmia classification system using transfer learning. Big Data Res. 26, 100271 (2021)
Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 3, 230–236 (1985)
Pandey, S.K., Janghel, R.R.: Automatic arrhythmia recognition from electrocardiogram signals using different feature methods with long short-term memory network model. Sign. Image Video Process. 14(6), 1255–1263 (2020). https://doi.org/10.1007/s11760-020-01666-8
Picariello, F., Iadarola, G., Balestrieri, E., Tudosa, I., De Vito, L.: A novel compressive sampling method for ECG wearable measurement systems. Measurement 167, 108259 (2021). https://doi.org/10.1016/j.measurement.2020.108259, https://www.sciencedirect.com/science/article/pii/S0263224120307983
Ravelomanantsoa, A., Rabah, H., Rouane, A.: Compressed sensing: a simple deterministic measurement matrix and a fast recovery algorithm. IEEE Trans. Instrum. Meas. 64(12), 3405–3413 (2015). https://doi.org/10.1109/TIM.2015.2459471
Rizzon, P., Di Biase, M., Baissus, C.: Intraventricular conduction defects in acute myocardial infarction. Br. Heart J. 36(7), 660 (1974)
Rosa, G., Laudato, G., Colavita, A.R., Scalabrino, S., Oliveto, R.: Automatic real-time beat-to-beat detection of arrhythmia conditions. In: HEALTHINF, pp. 212–222 (2021)
Sahoo, S., Kanungo, B., Behera, S., Sabut, S.: Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities. Measurement 108, 55–66 (2017)
Scalise, L., Cosoli, G.: Wearables for health and fitness: measurement characteristics and accuracy. In: 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1–6 (2018). https://doi.org/10.1109/I2MTC.2018.8409635
Shenkman, H.J., et al.: Congestive heart failure and QRS duration: establishing prognosis study. Chest 122(2), 528–534 (2002)
Shindler, D.M., Kostis, J.B.: Electrocardiographic technology of cardiac arrhythmias. In: Sleep Disorders Medicine, pp. 182–187. Elsevier (2009)
Simpson, R.J., Jr., Cascio, W.E., Schreiner, P.J., Crow, R.S., Rautaharju, P.M., Heiss, G.: Prevalence of premature ventricular contractions in a population of African American and white men and women: the atherosclerosis risk in communities (ARIC) study. Am. Heart J. 143(3), 535–540 (2002)
Surantha, N., Atmaja, P., David, Wicaksono, M.: A review of wearable internet-of-things device for healthcare. Procedia Comput. Sci. 179, 936–943 (2021). https://doi.org/10.1016/j.procs.2021.01.083, https://www.sciencedirect.com/science/article/pii/S1877050921001149,. 5th International Conference on Computer Science and Computational Intelligence 2020
Villar, R., Beltrame, T., Hughson, R.L.: Validation of the hexoskin wearable vest during lying, sitting, standing, and walking activities. Appl. Physiol. Nutr. Metab. 40(10), 1019–1024 (2015)
Wallmann, D., Tüller, D., Kucher, N., Fuhrer, J., Arnold, M., Delacretaz, E.: Frequent atrial premature contractions as a surrogate marker for paroxysmal atrial fibrillation in patients with acute ischaemic stroke. Heart 89(10), 1247–1248 (2003)
Wallmann, D., et al.: Frequent atrial premature beats predict paroxysmal atrial fibrillation in stroke patients: an opportunity for a new diagnostic strategy. Stroke 38(8), 2292–2294 (2007)
van Walraven, C., Hart, R.G., Singer, D.E., Koudstaal, P.J., Connolly, S.: Oral anticoagulants vs. aspirin for stroke prevention in patients with non-valvular atrial fibrillation: the verdict is in. Card. Electrophysiol. Rev. 7(4), 374–378 (2003). https://doi.org/10.1023/B:CEPR.0000023143.98705.ee
Xu, S.S., Mak, M.W., Cheung, C.C.: Towards end-to-end ECG classification with raw signal extraction and deep neural networks. IEEE J. Biomed. Health Inform. 23(4), 1574–1584 (2018)
Yildirim, Ö.: A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Comput. Biol. Med. 96, 189–202 (2018)
Yildirim, O., Baloglu, U.B., Tan, R.S., Ciaccio, E.J., Acharya, U.R.: A new approach for arrhythmia classification using deep coded features and LSTM networks. Comput. Methods Programs Biomed. 176, 121–133 (2019)
Zhao, Q., Zhang, L.: ECG feature extraction and classification using wavelet transform and support vector machines. In: 2005 International Conference on Neural Networks and Brain, vol. 2, pp. 1089–1092. IEEE (2005)
Zheng, Z., Chen, Z., Hu, F., Zhu, J., Tang, Q., Liang, Y.: An automatic diagnosis of arrhythmias using a combination of CNN and LSTM technology. Electronics 9(1), 121 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rosa, G. et al. (2022). Multi-class Detection of Arrhythmia Conditions Through the Combination of Compressed Sensing and Machine Learning. In: Gehin, C., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2021. Communications in Computer and Information Science, vol 1710. Springer, Cham. https://doi.org/10.1007/978-3-031-20664-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-20664-1_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20663-4
Online ISBN: 978-3-031-20664-1
eBook Packages: Computer ScienceComputer Science (R0)