A combined application of lossless and lossy compression in ECG processing and transmission via GSM-based SMS

被引：6

作者：

Department of Applied Physics, Faculty of Technology, University of Calcutta, 92 A.P.C. Road, Kolkata, India ^{[1
]}

不详 ^{[2
]}

机构：

[1] Department of Applied Physics, Faculty of Technology, University of Calcutta, 92 A.P.C. Road, Kolkata

[2] Department of Electronics, Netaji Nagar Day College (affiliated to University of Calcutta), N.S.C. Bose Road, Regent Estate, Kolkata

来源：

J. Med. Eng. Technol. | / 2卷 / 105-122期

关键词：

ASCII character; Grouping; GSM transmitter; SMS; Standard deviation;

D O I：

10.3109/03091902.2014.990159

中图分类号：

学科分类号：

摘要：

This paper presents a software-based scheme for reliable and robust Electrocardiogram (ECG) data compression and its efficient transmission using Second Generation (2G) Global System for Mobile Communication (GSM) based Short Message Service (SMS). To achieve a firm lossless compression in high standard deviating QRS complex regions and an acceptable lossy compression in the rest of the signal, two different algorithms have been used. The combined compression module is such that it outputs only American Standard Code for Information Interchange (ASCII) characters and, hence, SMS service is found to be most suitable for transmitting the compressed signal. At the receiving end, the ECG signal is reconstructed using just the reverse algorithm. The module has been tested to all the 12 leads of different types of ECG signals (healthy and abnormal) collected from the PTB Diagnostic ECG Database. The compression algorithm achieves an average compression ratio of ∼22.51, without any major alteration of clinical morphology. © 2015 Informa UK Ltd.

引用

页码：105 / 122

页数：17

共 69 条

[1]

Gupta R., Mitra M., Bera J.N., ECG Acquisition and Automated Remote Processing, (2014)

[2]

Hampton J.R., The ECG Made Easy, 6th Edn., (2013)

[3]

Gupta S., Bhattacharya S., Compression of ECG Signals using a novel discrete wavelet transform algorithm for dynamic arrythmia database, Lecture Notes in Electrical Engineering, 131, pp. 809-816, (2013)

[4]

Ranjeet K., Kumar A., Pandey R.K., ECG signal compression using different techniques, Communications in Computer and Information Science, 125, pp. 231-241, (2011)

[5]

Kumar A., Ranjeet K., Wavelet based electrocardiogram compression at different quantization levels, Communications in Computer and Information Science, 147, pp. 392-398, (2011)

[6]

Batista L.V., Melcher E.U.K., Carvalho L.C., Compression of ECG signals by optimized quantization of discrete cosine transform coefficients, Medical Engineering & Physics, 23, pp. 127-134, (2001)

[7]

Colomer A.A., Colomer A.A., Adaptive ECG data compression using discrete legendre transform, Digital Signal Processing, 7, pp. 222-228, (1997)

[8]

Nave G., Cohen A., ECG compression using long-term prediction, IEEE Transactions on Biomedical Engineering, 40, pp. 877-885, (1993)

[9]

Cox J.R., Nolle F.M., Fozzard H.A., Oliver G.C., AZTEC, a preprocessing program for real time rhythm analysis, IEEE Transactions on Biomedical Engineering, BME-15, pp. 128-129, (1968)

[10]

Chen W.S., Hsieh L., Yuan S.Y., High performance data compression method with pattern matching for biomedical ECG and arterial pulse waveforms, Computer Methods and Programs in Biomedicine, 74, pp. 11-27, (2004)

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