An Efficient Technique for Compressing ECG Signals Using QRS Detection, Estimation, and 2D DWT Coefficients Thresholding

Abo‐Zahhad, Mohammed; Ahmed, Sabah M.; Zakaria, Ahmed

doi:10.1155/2012/742786

Cited by 35 publications

(24 citation statements)

References 21 publications

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“…The wavelet transform is also capable of capturing both frequency and location in time information. The different types of Wavelet transforms are continuous wavelet transform (CWT), a wavelet series expansion, and a DWT [6,14].…”

Section: A the Wavelet Transformmentioning

confidence: 99%

ECG Signal Compression Using the High Frequency Components of Wavelet Transform

K.S¹,

Patil²

2016

ijacsa

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Abstract-Electrocardiography (ECG) is the method of recording electrical activity of the heart by using electrodes. In ambulatory and continuous monitoring of ECG, the data that need to be handled is huge. Hence we require an efficient compression technique. The data also must retain the clinically important features after compression. For most of the signals, the low frequency component is considered as most important part of the signal.In wavelet analysis, the approximation coefficients are the low frequency components of the signal. The detail coefficients are the high frequency components of the signal. Most of the time the detail coefficients (high frequency components) are not considered. In this paper, we propose to use detail coefficients of Wavelet transform for ECG signal compression. The Compression Ratio (CR) of both the approximation and detail coefficients are compared. Threshold based technique is adopted. The Threshold value helps to remove the coefficients below the set threshold value of coefficients. Experiment is carried out using different types of Wavelet transforms.MIT BIH ECG data base is used for experimentation. MATLAB tool is used for simulation purpose. The novelty of the method is that the CR achieved by detail coefficients is better. CR of about 88% is achieved using Sym3 Wavelet. The performance measure of the reconstructed signal is carried out by PRD.

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Section: A the Wavelet Transformmentioning

confidence: 99%

ECG Signal Compression Using the High Frequency Components of Wavelet Transform

K.S¹,

Patil²

2016

ijacsa

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“…the basis of the dependencies an optimal wavelet function and decomposition depth m providing the minimum error E and the maximum value CR or minimum laboriousness are determined. The threshold Θ can be determined using different approaches: based on energetic parameters of the signal [5], using introduction and evaluation of the objective function Q(Θ) [12] of the form…”

Section: Selection Of the Applied Wavelet Function And Decomposition mentioning

confidence: 99%

Application of Discrete Wavelet Transform with Changing Presentation of Coefficients in Data Compression in Mobile ECG Monitoring Systems

Bessmeltcev

Katasonov

2016

Biomed Eng

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IntroductionThe development of wireless personal networks and miniature wireless ECG sensors that do not require elec trical contact with the human body enables construction of a wireless system for monitoring human cardiovascular activity. Continuous monitoring requires storing and transmitting large amounts of data, which is inevitably reflected in the time of continuous operation of the sys tem. Bessmeltsev et al.[1] reviewed a monitoring system developed consisting of a heart rate sensor and contactless ECG sensors that transmit data via Bluetooth LE (BT LE) to a data acquisition and processing system (microserver) based on an STM 32 microcontroller with ultra low power consumption, which performs prelimi nary data processing. The microserver is paired wirelessly with a cell phone transmitting data to a remote medical server. The data received by the microserver are transmit ted to the cell phone via BT v. 3.0, with higher consump tion compared to a BLE channel. Reducing the amount of data transmitted to the cell phone lowers the load on the communication channel and increases the battery life of the complex. Most of the existing electrocardiographic signal (ECS) compression methods are focused on the use of stationary computing devices and require performing a large amount of computing operations [2 4]. However, there are effective ways of ECS compression that do not require a large amount of computational operations, based on the use of wavelet transformation and compres sion algorithms such as LZW and RLE [5]. This approach involves lossy compression, but allows the compression ratio to reach values in the range of 10 15 and may be rep resented as a set of the following steps:− normalization and wavelet transform of the source data;− zeroing of the portion of the obtained wavelet trans form coefficients whose absolute values are close to zero;− compression of the obtained coefficients. High values of the compression ratio (CR), in our opinion, are achieved through single character represen tation of data as ASCII code for storing wavelet coeffi cients on the computer hard disk using delimiters. This gives a higher redundancy than the "direct" byte repre sentation of the data in the mobile monitoring system. This article discusses a method of compressing the ECS which eliminates the need to use algorithms for compression of the wavelet transform coefficients. This method is based on changing the size and scale of the coefficients of the wavelet transform of the original data. Obtaining the ECS and Preliminary ProcessingThe ECS is registered by wireless sensors having non contact capacitive sensors, similar to those described in [6]. The sensor has a built in analog filter, limiting the The article studies the problem of compression of data recorded by ECG sensors of mobile heart activity monitor ing systems. An ECG data compression algorithm is presented which is based on transformation of coefficients of a discrete wavelet transform implemented using a computing device with limited processing power and low...

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“…The trial and-error methodology is used for choosing the IMFs. By using Hilbert Transform, the phase of the signal gets modified and R-Peak is highlighted [7]. The length of the segment is not determined experimentally because of limited size.…”

Section: Related Workmentioning

confidence: 99%

Analysis and detection R-peak detection using Modified Pan-Tompkins algorithm

Sathyapriya

Murali

Manigandan

2014

2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies

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Cardiac diseases that gives rise to the death and possibly forms the immedicable danger in order to monitor the heart the proposed algorithm is used. An automatic detection of peaks in Electrocardiogram (ECG) signals is presented. The aim of the design work is to achieve higher performance in terms of reliability and better diagnosis of patients through Peak detection. The new method called Modified Pan Tompkins algorithm based on the slope and amplitude of ECG signal. The QRS complex detection algorithm uses optimizedBandpass-filtering to reduce false detection. The purpose of prefiltering is to reduce various noise components in order to achieve improved detection reliability. The QRS detection reliability of an algorithm was tested with an noisy stress ECG signal. The usefulness of the proposed method is shown by applying the algorithm to signal from MIT -BIH Arrhythmia database to obtain the number of heart-beats per minute which helps to diagnose the heart disease.

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An Efficient Technique for Compressing ECG Signals Using QRS Detection, Estimation, and 2D DWT Coefficients Thresholding

Cited by 35 publications

References 21 publications

ECG Signal Compression Using the High Frequency Components of Wavelet Transform

ECG Signal Compression Using the High Frequency Components of Wavelet Transform

Application of Discrete Wavelet Transform with Changing Presentation of Coefficients in Data Compression in Mobile ECG Monitoring Systems

Analysis and detection R-peak detection using Modified Pan-Tompkins algorithm

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