Cardiovascular disease (CVD) is the leading cause of death. The transition in cardiovascular disease threatens the economies of the less developed world. An electrocardiogram (ECG) machine is a device that checks the patient’s heart rhythm and electrical activity. ECG signals give crucial information about the heart and numerous cardiac problems, such as coronary artery disease, myocardial infarction, and hypertension, which can be detected with an ECG report. The success rate for cardiac disease diagnosis will rise if ECG signals can be adequately recognized and interpreted. Classic signal processing and machine learning algorithms are utilized to evaluate the ECG signal and detect distinct types of arrhythmia for early treatment and prevention of cardiovascular diseases. To provide a sustainable solution for developing countries, we need to make an accurate diagnosis device that is portable and low-cost. This research aims to create a new low-cost ECG device and interface patients with HealthyPi v3 which is a miniature raspberry pi-based vital sign monitor to record raw ECG signals. We proposed an integrated environment with classical ECG acquisition and classification techniques to obtain the preferable outcome. Also, we allowed us to assimilate with a mobile remote monitoring system to create a dynamic healthcare monitoring environment for the patients. This work implies the acquisition of real-time ECG data via HealthyPi V3 integrated with peripheral capillary oxygen saturation (SpO2) sensor and temperature sensor. The software is designed to read and analyze the hardware system-driven real-time ECG data, heart rate, blood pressure, respiratory rate, and temperature. To categorize the QRS complex of ECG data obtained and analyzed by the hardware-software system for heart disease prediction, Support Vector Machine (SVM) classifier, Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) is applied where CNN has achieved the highest accuracy while processing the signal.

1. P. K. Jain, A. K.P. K. Jain, A. K. Tiwari, “Heart monitoring systems—A review”, Computers in Biology and Medicine, Volume 54,2014, Pages 1-13. https://doi.org/10.1016/j.compbiomed.2014.08.014.
2. A. Gacek, “An Introduction to ECG Signal Processing and Analysis”, In: Gacek, A., Pedrycz, W. (eds), ECG Signal Processing, Classification and Interpretation, pp. 21-46, 2011. https://doi.org/10.1007/978-0-85729-868-3_2
3. Z. Sankari, H. Adeli, “HeartSaver: a mobile cardiac monitoring system for auto-detection of atrial fibrillation, myocardial infarction, and atrioventricular block,” Computers in biology and medicine vol. Vol. 41, no. 4, pp. 211-214, 2011. https://doi.org/10.1016/j.compbiomed.2011.02.002
4. N. Stojanovic, Y. Xu, A. Stojadinovic, L. Stojanovic, “Using Mobile-based Complex Event Processing to realise Collaborative Remote Person Monitoring. DEBS ’14,” 8th ACM International Conference on Distributed Event-Based Systems. 2014, page 225. https://doi.org/10.1145/2611286.2611306
5. M. A. Mahamdy, H. B. Riley, “Performance Study of Different Denoising Methods for ECG Signals,” Procedia Computer Science, Volume 37, 2014, Pages 325-332. https://doi.org/10.1016/j.procs.2014.08.04800
6. L. R. Yeh, W.C. Chen,H. Y. Chan,N. H. Lu, C. Y. Wang, W. C. Du, Y. H. Huang, S. Y. Hsu,T. B. Chen, “Integrating ECG Monitoring and Classification via IoT and Deep Neural Networks,” Biosensors (Basel). 2021 Jun 8;11(6):188. doi: 10.3390/bios11060188. 
7. J.J. Huang, B. Chen, B. Yao and W. He, “ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network,” in IEEE Access, vol. 7, pp. 92871-92880, 2019, doi: 10.1109/ACCESS.2019.2928017.
8. L. Alzubaidi, J. Zhang, A. J. Humaidi et al., ”Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” J. Big Data 8, 53 (2021).
9. R. Thilagavathy, R. Srivatsan, S. Sreekarun, D. Sudeshna, P. L. Priya and B. Venkataramani, “Real-Time ECG Signal Feature Extraction and Classification using Support Vector Machine,” 2020 International Conference on Contemporary Computing and Applications (IC3A), 2020, pp. 44-48, doi: 10.1109/IC3A48958.2020.233266.
10. Guijin Wang, Chenshuang Zhang, Yongpan Liu, Huazhong Yang, Dapeng Fu, Haiqing Wang, Ping Zhang, “A global and updatable ECG beat classification system based on recurrent neural networks and active learning,” Information Sciences, Volume 501, pp. 523 – 542,2019. https://doi.org/10.1016/j.ins.2018.06.062.
11. F. T. Johura, S. M. R. Islam, M. Maniruzzaman and M. Hasan, “ECG signal for artrial fibrillation detection,” 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE), 2017, pp. 928-934, doi: 10.1109/ECACE.2017.7913036.
12. J. R. Mou, S. M. Rabiul Islam, X. Huang and K. L. Ou, “A new approach of noise elimination methodology for ECG signal,” 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 921 – 927, 2017 doi: 10.1109/ECACE.2017.7913035.
13. H. Khorrami and M. Moavenian, “A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification,” Expert Systems with Applications, vol. 37, no. 8, pp. 5751–5757, 2010. https://doi.org/10.1016/j.eswa.2010.02.033
14. N. Tabassum, S. M. R. Islam, X. Huang, “Novel Multirate Digital Filter for EEG on FPGA”, 2nd International Conference on Electrical &Electronic Engineering (ICEEE), RUET, Rajshahi, Bangladesh, 19-21 December 2017. doi: 10.1109/CEEE.2017.8412848.
15. Mishra et al., “ECG Data Analysis with Denoising Approach and Customized CNNs”, Sensors, vol. 22, no. 5, p. 1928, 2022. https://doi.org/10.3390/s22051928
16. E. D. Übeyli, “Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents,” Computer Methods and Programs in Biomedicine, Volume 93, Issue 3, 2009, pp. 313-321. https://doi.org/10.1016/j.cmpb.2008.10.012
17. Tomasini, Marco, Simone Benatti, Bojan Milosevic, Elisabetta Farella, and Luca Benini. “Power line interference removal for high-quality continuous biosignal monitoring with low-power wearable devices.” IEEE Sensors, vol. 16, no. 10, pp. 3887-3895, 2016.
18. K. Tanji, M.A.G de Brito,M.G. Alves,R. C. Garcia, G.L. Chen, N. R. N. Ama, “Improved Noise Cancelling Algorithm for Electrocardiogram Based on Moving Average Adaptive Filter,” Electronics vol. 10, no. 19, pp. 1 – 18, 2021, . https://doi.org/10.3390/electronics1019236
19. N. Wang and S. Sun, “Event-triggered sequential fusion filters based on estimators of observation noises for multi-sensor systems with correlated noises”, Digital Signal Processing, vol. 111, p. 102960, 2021. https://doi.org/10.1016/j.dsp.2020.102960
20. S. M. Anwar , M. Gul, M. Majid, M. Alnowami, “Arrhythmia Classification of ECG Signals Using Hybrid Features,” Comput Math Methods Med. 2018 Nov 12;2018:1380348. doi: 10.1155/2018/1380348. PMID: 30538768; PMCID: PMC6260536.
21. C. Jha and M. Kolekar, “Cardiac arrhythmia classification using tunable Q-wavelet transform based features and support vector machine classifier”, Biomedical Signal Processing and Control, vol. 59, p. 101875, 2020. https://doi.org/10.1016/j.bspc.2020.101875
22. A. Darmawahyuni et al., “Deep Learning with a Recurrent Network Structure in the Sequence Modeling of Imbalanced Data for ECG-Rhythm Classifier”, Algorithms, vol. 12, no. 6, p. 118, 2019. https://doi.org/10.3390/a12060118
23. Z. He, Y. Chen, D. Zhang, W. Yin, and H. R. Karimi. “A new intelligent ECG recognition approach based on CNN and improved ALO-SVM.” Signal, Image and Video Processing (2022): 1-8. https://doi.org/10.1007/s11760-022-02300-5

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