Strong Implementation of Huffman Coding on Energy Savings in Electrocardiogram (ECG) Wireless Sensor Device

Strong Implementation of Huffman Coding on Energy Savings in Electrocardiogram (ECG) Wireless Sensor Device

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© 2024 by IJETT Journal
Volume-72 Issue-2
Year of Publication : 2024
Author : Guillermo Wenceslao Zarate Segura, Luis Mateo Huapaya, Julio cesar Arenas
DOI : 10.14445/22315381/IJETT-V72I2P121

How to Cite?

Guillermo Wenceslao Zarate Segura, Luis Mateo Huapaya, Julio cesar Arenas, "Strong Implementation of Huffman Coding on Energy Savings in Electrocardiogram (ECG) Wireless Sensor Device," International Journal of Engineering Trends and Technology, vol. 72, no. 2, pp. 197-202, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I2P121

Abstract
The present research aims to enhance energy efficiency saved by implementing Huffman coding in the data obtained through the transmission of an ECG device and a receiver. The data is first compressed and later transmitted by a wireless module. The heartbeat signal is acquired through an ADC (ESP 32 module). The compressed data is scaled into 8 bits, followed by a package method using the Huffman coding. Posterior, the data is sent wirelessly from the transmitter module to the receiver. The beats per minute are calculated by using the received data. In the present method, compressed 400-byte packets and a continuous uncompressed 1-byte packet were tested under energy efficiency scope. One of the outcomes is that the saved energy was roughly 20%. Furthermore, the current consumption is analyzed under multiple package size transmission.

Keywords
Energy savings, Heart rate, Wireless transmission, Electrocardiogram (ECG).

References
[1] Juan José Garza-Saldaña, Arturo Díaz-Pérez, and Arturo Medina Puente, “Energy Saving for Body Sensor Node Networks through Physiological Data Compression,” Magazine National Congress of Engineering and Technologies for Sustainable Development, vol. 1, no. 1, pp. 35–41, 2015.
[Google Scholar]
[2] Christopher M. Sadler, and Margaret Martonosi, “Data Compression Algorithms for Energy-Constrained Devices in Delay Tolerant Networks,” SenSy '06: Proceedings of the 4 th International Conference on Embedded Networked Sensor Systems, pp. 265–278, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Dora María Ballesteros Larrotta, Camilo Andrés Lemus Bernal, and Carlos Alberto Suárez López, “Compression of ECG signals using DWT and Huffman coding,” Science and Technology, vol. 1, no. 41, pp. 340–345, 2009.
[Google Scholar] [Publisher Link]
[4] Khalida Shaaban Rijab, and Mohammed Abdul Redha Hussien, “Efficient Electrocardiogram Signal Compression Algorithm Using Dual Encoding Technique,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 25, no. 3, pp. 1529–1538, 2022.
[CrossRef] [Publisher Link]
[5] Bharat Garg, Sameer Yadav, and G.K. Sharma, “An Area and Performance Aware ECG Encoder Design for Wireless Healthcare Services,” 2016 20th International Symposium on VLSI Design and Test (VDAT), Guwahati, India, pp. 1-6, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Miguel Mora González et al., “Digital Noise Reduction in ECG Signals Using Convolution Filtering,” Research and Science, vol. 16, no. 40, pp. 26–32, 2008.
[Google Scholar] [Publisher Link]
[7] F.J. Alvarado Rodríguez et al., “Development of a Computational Tool for the Analysis of Galvanic Skin Conductance, ECG and Respiratory Rate due to Respiratory Sinus Arrhythmia (SCRATER),” Mexican Journal of Biomedical Engineering, vol. 38, no. 1, pp. 7– 18, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[8] John Bustamante Osorno et al., “Development of the Hardware Component of a Device for Tele-Monitor Wireless of Cardiac Events,” Research and Science, no. 39, pp. 445–448, 2008.
[Google Scholar] [Publisher Link]
[9] Rubén Cobo Alea, Rafael E. Smith Colás, and Carlos R. Vázquez Seisdedos, “Design of a Wireless Electrocardiography Monitoring System for Android Devices,” Electronic, Automatic and Communications Engineering, vol. 41, no. 2, pp. 63–79, 2020.
[Google Scholar] [Publisher Link]
[10] Pablo Jacome Ruiz, and Christian Baquerizo Cardenas, “Portable Heart Monitor with a Bluetooth Interface “CARDIO UEES”,” Unemi Science, vol. 9, no. 20, pp. 36–49, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Ahmed Aboalseoud et al., “Wireless ECG Monitoring System for Telemedicine Application,” 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt, pp. 300–305, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] J.L. Varela Benítez et al., “High Sensitivity Capacitive Electrode for the Detection of Electrical Biopotentials,” Mexican Journal of Biomedical Engineering, vol. 36, no. 2, pp. 131–142, 2015.
[Google Scholar] [Publisher Link]
[13] Assim Boukhayma, Antonino Caizzone, and Christian Enz, “An Ultra-Low Power PPG and mm-Resolution ToF PPD-Based CMOS Chip towards All-in-One Photonic Sensors,” IEEE Sensors Journal, vol. 19, no. 24, pp. 11858–11866, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Erick Alexánderson Rosas, and Gerardo Gamba Ayala, Cardiovascular, Renal and Respiratory physiology, 1 st ed., Editorial El Manual Moderno, pp. 1-277, 2014.
[Google Scholar] [Publisher Link]
[15] Thomas H. Cormen et al., Introduction to Algorithms, 3 rd ed., London, England: The MIT Press, 2009.
[Google Scholar] [Publisher Link]
[16] SadiManna/Compression, Data Compression Techniques in C and Matlab, 2018. [Online]. Available: https://github.com/sadimanna/compression