JSM-2 Based Joint ECG Compression Exploiting Temporal and Structural Dependency

2014 ◽  
Author(s):  
Jinguo Luo ◽  
Bin Liu ◽  
Chang Wen Chen
Keyword(s):  
Ecg Compression

Ultra-Low Power QRS Detection and ECG Compression Architecture for IoT Healthcare Devices

2019 ◽  
Vol 66 (2) ◽  
pp. 669-679 ◽  
Author(s):  
Temesghen Tekeste ◽  
Hani Saleh ◽  
Baker Mohammad ◽  
Mohammed Ismail
Keyword(s):  
Low Power ◽  
Qrs Detection ◽  
Ultra Low Power ◽  
Ecg Compression

Wavelet-based ECG compression by bit-field preserving and running length encoding

2008 ◽  
Vol 90 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Hsiao-Lung Chan ◽  
You-Chen Siao ◽  
Szi-Wen Chen ◽  
Shih-Fan Yu
Keyword(s):  
Ecg Compression

Analysis of Electrocardiogram Data Compression Techniques

2020 ◽  
pp. 930-970
Author(s):  
Anukul Pandey ◽  
Barjinder Singh Saini ◽  
Butta Singh ◽  
Neetu Sood
Keyword(s):  
Performance Evaluation ◽  
Performance Measures ◽  
Parameter Extraction ◽  
Extraction Methods ◽  
Walsh Transform ◽  
Ecg Compression ◽  
And Performance ◽  
Electrocardiogram Ecg ◽  
Ecg Database ◽  
Ecg Data

In this Chapter, a MATLAB-based approach is presented for compression of Electrocardiogram (ECG) data. The methodology employs in three different domains namely direct, transformed and parameter extraction methods. The selected techniques from direct ECG compression methods are TP, AZTEC, Fan, and Cortes. Moreover selected techniques from transformed ECG compression methods are Walsh Transform, DCT, and Wavelet transform. For each of the technique, the basic implementation of the algorithm was explored, and performance measures were calculated. All 48 records of MIT-BIH arrhythmia ECG database were employed for performance evaluation of various implemented techniques. Moreover, based on requirements, any basic techniques can be selected for further innovative processing that may include the lossless encoding.

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