ECG Wavelet Compression for Transmission over IoT Networks | ||||
| Engineering Research Journal (Shoubra) | ||||
| Volume 46, Issue 1, October 2020, Page 53-62 PDF (1.4 MB) | ||||
| Document Type: Research articles | ||||
| DOI: 10.21608/erjsh.2021.405356 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Ramez Hosny1; Hala M. M. Abd El Qader2; Michael Nasief2; Suzan shukry1 | ||||
| 1Dept. of Electrical Engineering, Higher technology Institute, of 10th Ramadan. | ||||
| 2Dept. of Electrical Engineering, faculty of Engineering-shoubra, Benha University. | ||||
| Abstract | ||||
| Public healthcare has been become appeared an increasing attention given the exponential growth human population and medical costs. Electrocardiogram (ECG) has an important role in the diagnosis of heart disease. Therefore, care for children, youths and the elderly along with a wide diversity of patients can be applied. This can be achieved through the internet of things. The Compression of digital Electrocardiogram (ECG) signals is desirable for three reasons. These reasons are economic use of storage space for databases, reduction of the data for transmission on the Internet of Things (IoT), and decrease power consumption in transmitting data from the battery 24 hours. This paper deals with the discrete wavelet-based compression method. The software results are obtained by using the MATLAB program. The improvement in the bit error rate (BER) from the original signal to the reconstructed one is about 0.4%. The achieved average compression ratio (CR) is about 12.832%. The achieved percent root mean square difference (PRD) in the mean is about 0.2987 %. The achieved signal-to-noise ratio (SNR) is about 32.2486 dB. The hardware implementation of a compressed ECG signal is performed by using the internet of things (IoT) system. This IoT system is based on the SD card sensor and interfaced with the ESP8266 Wireless Module that is connected to Cloud through the MQTT server. The power consumption of the design reduces about 13.21% from battery energy. This is achieved through hardware implementation. The ECG signals can be obtained without loss of signal shape. The best results are obtained with the Sym20 filter. | ||||
| Keywords | ||||
| Internet of things; Electrocardiograph; discrete Wavelet transform; Signal to Noise ratio; Percent Root Difference; Compression Ratio | ||||
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