Advanced telemedicine requires the gathering of big data through wireless body area network or internet of things based applications. These networks perform several tasks that consume maximum energy while transmitting the big data, which in turn discharges the battery and would require a battery replacement frequently. Also, the big data to be transferred and stored would require a significant amount of storage space. The above problem is rectified by compressing the big data acquired from the sensors before transmission that would reduce the consumption of power as well as use the storage space efficiently. In this paper, a hybrid compression algorithm (HCA) based on Rice Golomb Coding is proposed. The efficiency of the proposed compression algorithm is tested on ECG data from the physionet ATM database and real-time data acquired from the ECG sensor. The proposed HCA comprises of both lossy and lossless compression. The real-time implementation of the proposed compression algorithm is carried out using NI myRIO hardware and LabVIEW graphical tool. The compressed data then stored in the Google cloud, and the analysis of storage space using the HCA shows a reduction of 70% storage space for 10 minutes of ECG data.

Design and Implementation of Hybrid Compression Algorithm for Personal Health Care Big Data Applications

Electrocardiogram (ECG) is the widely known and most common diagnosis test to analyze the electrical signal in the heart and to detect cardiac anomalies. The functionality of the human heart can be properly monitored, and cardiovascular diseases can be detected through the ECG signal. Early detection of cardiac arrhythmia is also possible by analyzing ECG heartbeats continuously. Millions of people in the world are suffering from cardiac arrhythmia which refers to irregular heartbeats. Accurate detection of irregular heartbeats in the primary stage can be also detected through the ECG signal which is very important to reduce the death caused by heart diseases.

Compression, Denoising and Classification of ECG Signals using the Discrete Wavelet Transform and Deep Convolutional Neural Networks

This paper presents an intelligent algorithm for analyzing the heart sound signal using multi-resolution analysis based on discrete wavelet transform (DWT). This signal processing technique can not only compress and encrypt the phonocardiogram (PCG) signal but also reduces the noises and the murmurs from the PCG signal. This information can assist the cardiologists for taking appropriate actions to diagnosis for the initial stage of the cardiovascular dis- order. We have presented an efficient data compression algorithm based on DWT, energy packing efficiency (EPE), and run-length encoding (RLE) that can compress the signal about 93.70% without losing any pathological information. Further, this paper explores an end-to-end encryption technique using the compression algo-rithm to maintain patient confidentiality. After the compression of the PCG signal, the noises and the murmurs are effectively separated from the signal by reusing DWT. The performance of this algorithm has been evaluated by using 5 normal and 18 abnormal PCG signals available in the University of Michigan Heart Sound and Murmur Library. This method can be used in real time for the remote monitoring of the heart sound.

Automatic Phonocardiography Analysis Using Discrete Wavelet Transform

The paper deals with the problematics of volume datasets that can be considered as the three dimensional regular grids of scalar values or regular grids of vectors of scalar values. Volume datasets represent significant amount of data located on the secondary storage. It is possible to consider related operations as time consuming in such a measure in which they cause bad user experience. That is why we proposed algorithm that creates metadata in the form of the three dimensional binary image from volume dataset. Metadata are able to significantly reduce time of important part of the dataset loading from the secondary storage into the operating memory of computer. The paper introduces designed algorithm and two different Run-Length Encoding (RLE) methods in its first part. Evaluation of proposed algorithm along with RLE compression methods, using volume datasets obtained by different medical imaging techniques including Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), takes place in the second part of the paper.

Algorithm Design for User Experience Enhancement of Volume Dataset Reading from Storage Using 3D Binary Image as the Metadata

Compression of Electrocardiogram signal is important for digital Holters recording, signal archiving, transmission over communication channels and Telemedicine. This paper introduces an effective real-time compression scheme to overcome the limitation of payload size of the transmission channel. This scheme utilizes the Discrete Wavelet Transform (DWT), Bit-Field Preserving (BFP) and Running Length Encoding (RLE) methods which showed efficient compression results. The scheme dynamically checks if the compressed packets fit into the available payload. If not, the original signal will be divided into blocks and each block will be re-compressed again. The dynamic scheme was tested on large and small number of samples. The results show that a small block of 64, 128 or 256 samples will not affect the compression performance and no distortion occurred on the reconstructed signal.

Real-time DWT-based compression for wearable Electrocardiogram monitoring system

An algorithm of the ECG signal compression, based on the combination of the run length encoding and discrete wavelet transform, intended for a simulated transmission via the IEEE 802.11b WLAN channel, is presented in this work. The algorithm consists of two basic phases that are ECG signal compression and transmission via the IEEE 802.11b WLAN channel. The algorithm is based on applying the run length coding upon the thresholded discrete wavelet transform of the real ECG signal. In terms of compression efficiency, applying the compression procedure on several ECG data, presenting diverse cardiac status, selected from the MIT-BIH arrhythmia data base, achieves compression ratio of around 10:1, normalized root mean squared error (NRMSE) of 4% and (mean± standard deviation) of the difference between the restituted ECG signal and the original one of around (3 10-6) ± 0.03. The end point of this work is to simulate transmission of the compressed ECG signal via the IEEE 802.11b WLAN channel. The unavoidable distortion introduced by the transmission channel reduces the compression ratio to about 6.7:1 in the cost of preserving the ECG signal fidelity.

M. M. Benaiad

Papers

Run length encoding and wavelet transform based ECG compression algorithm for transmission via IEEE802.11b WLAN channel