Electrocardiogram Data Compression Research Articles

ECG data compression using the formation of QRS-complex segment bank and integer DCT-based plateau region processing

Electrocardiogram (ECG) data is essential for evaluating cardiac health conditions. Long-term ECG signal monitoring needs a considerable amount of memory for storage, impacting data bandwidth allocation. The ECG data compression is challenging in preserving significant features, using low computational resources, and achieving a higher compression ratio at lower reconstruction error. This paper proposes an innovative QRS-complex and plateau (Non-QRS) region segregation for QRS-complex subsegment bank formation and integer cosine transform-based ECG data compression. An adaptive QRS-complex subsegment template bank is formed by: (i) QRS and Non-QRS region segmentation; (ii) QRS region identification; (iii) QRS region alignment; (iv) Dc equalization and sub-template formation of the QRS region. A discrete integer cosine transform, quantization, and grouping with a significance map are used on the non-QRS segment of the ECG. Both the QRS and non-QRS regions are coded by lossless arithmetic encoding. Experiments are enumerated on the MIT BIH arrhythmia database, which validates the potential of the proposed approach by achieving an average value equal to 31.51, 0.84, and 28.57 for compression ratio, percentage-root-mean-square-difference, and quality-score, respectively. The average distortion measure for the proposed ECG compressor is less than 2%, which is within the clinically acceptable limit. The proposed method is also compared to existing state-of-the-art ECG compression techniques. It performs better in terms of compression ratio and preservation of ECG features. Additionally, this method typically requires less computational resources and is easy to implement, which is an advantage over other ECG compression methods.

Low-Power Multi-Lead Wearable ECG System With Sensor Data Compression

This study proposes a low-power wearable electrocardiogram (ECG) acquisition system for monitoring human health in daily life. A flexible sensing electrode was designed to replace traditional silver–silver chloride (Ag/AgCl) electrodes for measuring ECG signals from leads I, II, and V1. The traditional multi-lead-wired ECG detector limits the movements and activities of the patient and requires medical staff to assist in collecting ECG signals. The portable ECG acquisition device proposed in this article is not only comfortable to wear but also convenient for patients to use anytime, anywhere. To increase the sampling rate of the device to obtain more ECG signal information, this study proposes an innovative Huffman lossless ECG data compression algorithm based on variable-word length coding (VLC). This algorithm can efficiently realize data compression and reduce power consumption. It makes the whole system operate continuously for a long period of time and acquire abundant ECG information, which is helpful for clinical diagnosis. Experimental results show that when the sampling rate is 400 Hz, the working current is only 3.64 mA, and the compression ratio (CR) of the device can reach 2.38.

ECG Data Compression Using Modified Run Length Encoding of Wavelet Coefficients for Holter Monitoring

ObjectiveIn cardiac patient-care, compression of long-term ECG data is essential to minimize the data storage requirement and transmission cost. Hence, this paper presents a novel electrocardiogram data compression technique which utilizes modified run-length encoding of wavelet coefficients. MethodFirst, wavelet transform is applied to the ECG data which decomposes it and packs maximum energy to less number of transform coefficients. The wavelet transform coefficients are quantized using dead-zone quantization. It discards small valued coefficients lying in the dead-zone interval while other coefficients are kept at the formulated quantized output interval. Among all the quantized coefficients, an average value is assigned to those coefficients for which energy packing efficiency is less than 99.99%. The obtained coefficients are encoded using modified run-length coding. It offers higher compression ratio than conventional run-length coding without any loss of information. ResultsCompression performance of the proposed technique is evaluated using different ECG records taken from the MIT-BIH arrhythmia database. The average compression performance in terms of compression ratio, percent root mean square difference, normalized percent mean square difference, and signal to noise ratio are 17.18, 3.92, 6.36, and 28.27 dB respectively for 48 ECG records. ConclusionThe compression results obtained by the proposed technique is better than techniques recently introduced by others. The proposed technique can be utilized for compression of ECG records of Holter monitoring.

Tunable Q-wavelet based ECG data compression with validation using cardiac arrhythmia patterns

The acquisition of longer electrocardiogram (ECG) signals is an essential step of the recent medical screening and diagnostic procedures. Considering the need of an efficient ECG data management system, this paper presents a novel ECG data compression technique based on the tunable Q-wavelet transform which provides adjustable parameters to achieve good compression performance. The tunable Q-wavelet transform compacts the maximum energy of the signal to fewer transform coefficients. Dead-zone quantization is used to discard the small valued transform coefficients. Further, transform coefficients are rounded-off to nearest integer values and encoded using run-length coding. ECG records of the MIT-BIH arrhythmia database are used to evaluate the performance of the proposed compression technique. The average compression performance obtained in terms of CR, PRD, PRD1, QS, QS1 and SNR are 20.61, 4.43, 6.37, 5.88, 3.46, and 55.93 dB respectively. The proposed technique offers better compression performance than many existing techniques. The proposed technique is also validated using cardiac arrhythmia patterns classification. In the validation phase, tunable Q-wavelet transform based features of 14,878 original cardiac patterns are used to train the support vector machine classifier while testing is performed using features of 26,219 original and reconstructed cardiac patterns separately. It examines the effect of the proposed compression technique on cardiac arrhythmia classification. In the validation part, the overall classification accuracy, sensitivity, and specificity obtained for the proposed compression technique are 98.35%, 95.77%, and 99.19% respectively. It denotes that the proposed technique compresses ECG signal efficiently with preserving diagnostic information very well.

ELECTROCARDIOGRAM DATA COMPRESSION TECHNIQUES IN 1D/2D DOMAIN

Electrocardiogram (ECG) is one of the best representatives of physiological signal that provides the state of the autonomic nervous system, primarily responsible for the cardiac activity. The ECG data compression plays a significant role in localized digital storage or efficient communication channel utilization in telemedicine applications. The lossless and lossy compression system’s compressor efficiency depends on the methodologies used for compression and the quality measure used to evaluate distortion. Based on domain ECG, data compression can be performed either one-dimensional (1D) or two-dimensional (2D) for utilization of inter and inter with intra beat correlation, respectively. In this paper, a comparative study between 1D and 2D ECG data compression methods was taken out from the existing literature to provide an update in this regard. ECG data compression techniques and algorithms in 1D and 2D domain have their own merits and limitations. Recently, numerous research and techniques in 1D ECG data compression have been developed, including direct and transformed domain. Additionally, 2D ECG data compression research is reported based on period normalization and complexity sorting in recent times. Finally, several practical issues highlight the assessment of reconstructed signal quality and performance comparisons with an average comparative of exhaustive existing 1D and 2D ECG compression methods based on the utilized digital signal processing systems.

A Comparative Analysis of Performance of Several Wavelet Based ECG Data Compression Methodologies

Compression of an electrocardiogram (ECG) signal has given much consideration to the researchers since the computer-aided analysis of ECG has come into being. In some critical cases, viz., astronauts, a person under cardiac surveillance, ambulatory patients and in Holter monitoring system, continuous ECG data recording and transmitting from one location to other location is required. However, the size of the recorded data becomes so voluminous, that its transmission of data becomes practically impossible. In this paper, ECG data compression using wavelet-based techniques are presented, such that: a) wavelet packet transform with run-length encoding (RLE), b) wavelet transform with Huffman encoding, c) wavelet transform with RLE and d) wavelet transform and Lempel ZivWelch (LZW). The results have been tested using MIT-BIH (Massachusetts Institute of Technology/Beth Israel Hospital) arrhythmia databases. The performances of these methodologies are examined in the quantitative and qualitative manner. From Tabular results, it can be observed that the methodology based on WT and LZW provides efficient results in terms of compression ratio (CR∼=20 to 30) and peak root mean square difference (PRD∼=0.01 to 1.8) both, hence the overall QS value is improved.

Quality controlled ECG data compression based on 2D discrete cosine coefficient filtering and iterative JPEG2000 encoding

This paper presents a quality controlled reconstruction of ECG signal by the formulation of 2D Discrete Cosine Transform (DCT) coefficient and iterative JPEG2000 encoding scheme for Electrocardiogram (ECG) data compression. It employs variable blockwise (L = 32 to 2048 samples) DCT to exploit inter and intra-beat correlation inherent in ECG. The Regula-Falsi method is applied to obtain the optimal threshold for the DCT coefficients, which are stacked row-wise in order to form a 2D DCT coefficient array for the correlation analysis. Afterwards, window-based filtering is applied to retain the significant DCT coefficients with varying window size (ws). Differential pulse code modulation based quantization scheme is then applied to 2D DCT coefficients array in order to make it conformal to JPEG2000 encoding. Finally, iterative JPEG2000 encoding is utilized for achieving the overall predefined reconstruction error. The experimentations were performed on all the 48 records of Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. The proposed method provides compression ratio = 39.48 & 40.18; percentage root mean square difference = 2.02 & 2.06 and the quality score = 19.45 & 19.56 at ws = 50% & 20% respectively for L = 256. The proposed technique results were also validated on all records of four standard ECG databases including i) MIT-BIH Normal sinus rhythm ii) CU ventricular tachyarrhythmia iii) CSE and iv) PTB Database. Further, a comparative analysis between the proposed method and some recent existing works was also performed. The results clearly demonstrate the superiority of proposed method.

Computationally Efficient Cosine Modulated Filter Bank Design for ECG Signal Compression

In this work, computationally efficient and reliable cosine modulated filter banks (CMFBs) are designed for Electrocardiogram (ECG) data compression. First of all, CMFBs (uniform and non-uniform) are designed using interpolated finite impulse response (IFIR) prototype filter to reduce the computational complexity. To reduce the reconstruction error, linear iteration technique is applied to optimize the prototype filter. Then after, non-uniform CMFB is used for ECG data compression by decomposing ECG signal into various frequency bands. Subsequently, thresholding is applied for truncating the insignificant coefficients. The estimation of the threshold value is done by examining the significant energy of each band. Further, Run-length encoding (RLE) is utilized for improving the compression performance. The method is applied to MIT-BIH arrhythmia database for performance analysis of the proposed work. The experimental observations demonstrate that the proposed method has accomplished high compression ratio with the admirable quality of signal reconstruction. The proposed work provides the average values of compression ratio (CR), percent root mean square difference (PRD), percent root mean square difference normalized (PRDN), quality score (QS), correlation coefficient (CC), maximum error (ME), mean square error (MSE), and signal to noise ratio (SNR) are 23.86, 1.405, 2.55, 19.08, 0.999, 0.12, 0.054 and 37.611 dB, respectively. The proposed 8-channel uniform filter bank is used to detect the R-peak locations of the ECG signal. The comparative analysis shows that beats (locations and amplitudes) of both signals (original and reconstructed signals) are same.

An Efficient Algorithm Based on Combined Encoding Techniques for Compression of ECG Data from Multiple Leads

ECG (Electrocardiogram) is a test that analyzes the electrical behaviour of the heart. ECG is used in diagnosing most of the cardiac diseases. Large amount of ECG data from multiple leads needs to be stored and transmitted, which requires compression for effective data storage and retrieval. Proposed work has been developed with Singular Value Decomposition (SVD) followed by Run Length Encoding (RLE) combined with Huffman Encoding (HE) and Arithmetic Encoding (AE) individually. The ECG signal is first preprocessed. SVD is used to factorize the signal into three smaller set of values, which preserve the significant features of the ECG. Finally, Run Length Encoding combined with Huffman encoding (RLE-HE) and Arithmetic encoding (RLE-AE) individually are employed and the compression performance metrics are compared. The proposed method is evaluated with PTB Diagnostic database. Performance measures such as Compression Ratio (CR), Percentage Root mean square Difference (PRD) and Signal to Noise Ratio (SNR) of the reconstructed signal are used to evaluate the proposed technique. It is evident that the proposed method performs well than the techniques based on SVD and Huffman Encoding. The results show that this method can be efficiently used for compression of ECG signal from multiple leads.

Robust ECG data compression method based on ε-insensitive Huber loss function

Electrocardiogram (ECG) signals are continuously monitored for early diagnosis of heart diseases. However, a long-term monitoring generates large amounts of data at a level that makes storage and transmission difficult. Moreover, these records may be subject to different types of noise distributions resulting from operating conditions. Therefore, an effective and reliable data compression technique is needed for ECG data transmission, storage and analysis without losing the clinical information content. This study proposes the ε-insensitive Huber loss based support vector regression for the compressing of ECG signals. Since the Huber loss function is a mixture of quadratic and linear loss functions, it can properly take into account the different noise types in the data set. Compression performance of the proposed method has been assessed using ECG records from the MIT-BIH arrhythmia database. Experimental results demonstrate that the proposed loss function is an attractive candidate for compressing ECG data.

Electrocardiogram data compression using DCT based discrete orthogonal Stockwell transform

This paper reports a novel electrocardiogram (ECG) data compression algorithm which employs DCT based discrete orthogonal Stockwell transform. Dead-zone quantization is utilized to apply quantization as well as a threshold condition to transform coefficients. Further, integer conversion of coefficients is performed. It improves compression at the cost of very less reconstruction error. All integer coefficients are encoded using run-length coding. It exploits the repetition of data instances. Run-length coding helps to achieve higher compression without any relevant information loss. Performance of the proposed compression algorithm is evaluated using 48 single channel ECG records which are taken from the MIT-BIH arrhythmia database. A competitive compression performance is observed in comparison with other ECG compression methods.

ECG Data Compression Using ε-insensitive Quadratic Loss Function

ElectroCardioGram (ECG) is a graphical representation of the electrical activity that occurred during the heartbeat. It plays a significant role in the diagnosis and analysis of heart diseases. ECG signals must be recorded continuously for the effective detection and diagnosis of heart diseases. However, such records as it produces large amounts of data at a level that makes it difficult storage and transmission can also be impaired due to the ambient noise. Thanks to the reasons mentioned above, an efficient ECG data compression algorithm is required even in a noisy environment. This study proposes e-insensitive quadratic loss based Support Vector Regression (e-quadratic SVR) technique for the compression of ECG signals. There is a well-known relationship between loss functions and noise distributions. The proposed e-insensitive quadratic loss function provides the optimal solution against Gaussian noise. Computer simulation results show that the proposed loss function is an attractive candidate for ECG data compression in the presence of Gaussian noise.

ECG data compression using a neural network model based on multi-objective optimization.

Electrocardiogram (ECG) data analysis is of great significance to the diagnosis of cardiovascular disease. ECG compression should be processed in real time, and the data should be based on lossless compression and have high predictability. In terms of the real time aspect, short-time Fourier transformation is applied to the processing of signal wave for reducing computational time. For the lossless compression requirement, wavelet-transformation that is a coding algorithm can be used to avoid loss of data. In practice, compression is required to avoid storing redundant recording data that are not useful in the diagnosis platform. The obtained data can be preprocessed to remove noise by using wavelet transform, and then a multi-objective optimize neural network model is used to extract feature information. Compared with the existing traditional methods such as direct data processing method and transform method, our proposed compression model has self-learning ability to achieve high data compression ratio at 1:19 without losing important ECG information and compromising quality. Upon testing, we demonstrated that the proposed ECG data compression method based on multi-objective optimization neural network is effective and efficient in clinical practice.

A real time ECG data compression scheme for enhanced bluetooth low energy ECG system power consumption

Wearable wireless medical devices have the ability to significantly improve medical care eco-system. Collecting detailed real-time bio-signals data can be straightforward and flexible by equipping patients with wearable devices. The bio-signal sending data through wireless connections which harvest most system power consumption represent one of the main challenges due to battery power limitation. Therefore, reducing the power consumption during wireless data transmitting leads to reduce size and weight of the device which make the patient comfortable especially in long-term medical monitoring. In this study, we present a system level compression scheme for enhancing real-time Bluetooth Low Energy (BLE) Electrocardiogram (ECG) monitoring and recording system. The system is designed and implemented for ECG data capturing and sending it at reduced power transmitting by employing discrete cosine transform (DCT) supported by threshold capability. It is supported by a state-of-the-art system-on-chip (SoC) BLE module as well as the ECG amplifier modules for the amplification process to achieve 6-channel ECG real-time bio-signal which is essential for more accurate and comprehensive diagnosis. A 3-volt Lithium coin cell battery is used to supply the proposed wearable system. The total current consumed by this prototype is further reduced from 2.1 mA (without applying compression algorithm) to 1.5 mA (with enhanced compression algorithm), that leads to extending battery life by 40% from 100 to 140 h. Due to its compact design and an extended period working time, this prototype provides a suitable low-cost solution for long-term monitoring approach in clinical as well as home telemedicine application. The prototype was tested to record various ECG signals for both normal persons and patients. Specifically, it has been tested to capture ten normal cases and 24 arrhythmia and ischemic heart disease in a clinical environment within a specialist heart hospital with satisfactory results.

Telemedicine using ECG data compression

Electrocardiogram (ECG) is a useful tool for monitoring the health conditions of the subjects. ECG record contains a very important information about proper functioning of the heart and cardiovascular system as well. The ECG record is representation of deflections of physiological activities of the human heart during pumping of blood. This vital information is of absolutely useful to cardiologist for disease diagnosis. In case of ambulatory monitoring or critically cardiac disease it is required to store data for off-line analysis or transmission over public telephone lines. Such data generated is huge and needs to be stored by making optimum use of storage capabilities. For this reason we need effective data compression techniques. In the last four decades many ECG compression algorithm have been proposed by using different ECG compression techniques. One of the applications is to make use of these efficient compression schemes to provide low-cost and efficient medical healthcare. In the present paper, ECG data compression for long distance telemedicine is presented which promotes the utility of ECG data compression in the field of telemedicine i.e., tele-cardiology. The proposed telemedicine scheme is tested using ECG database with analysis and results.

Analysis on ECG Data Compression Using Wavelet Transform Technique

Although digital storage media is not expensive and computational power has exponentially increased in past few years, the possibility of electrocardiogram (ECG) compression still attracts the attention, due to the huge amount of data that has to be stored and transmitted. ECG compression methods can be classified into two categories; direct method and transform method. A wide range of compression techniques were based on different transformation techniques. In this work, transform based signal compression is proposed. This method is used to exploit the redundancy in the signal. Wavelet based compression is evaluated to find an optimal compression strategy for ECG data compression. The algorithm for the one-dimensional case is modified and it is applied to compress ECG data. A wavelet ECG data code based on Run-length encoding compression algorithm is proposed in this research. Wavelet based compression algorithms for one-dimensional signals are presented along with the results of compression ECG data. Firstly, ECG signals are decomposed by discrete wavelet transform (DWT). The decomposed signals are compressed using thresholding and run-length encoding. Global and local thresholding are employed in the research. Different types of wavelets such as daubechies, haar, coiflets and symlets are applied for decomposition. Finally the compressed signal is reconstructed. Different types of wavelets are applied and their performances are evaluated in terms of compression ratio (CR), percent root mean square difference (PRD). Compression using HAAR wavelet and local thresholding are found to be optimal in terms of compression ratio.

A 2D electrocardiogram data compression method using a sample entropy-based complexity sorting approach

This paper proposes an effectual sample entropy (SampEn) based complexity sorting pre-processing technique for two dimensional electrocardiogram (ECG) data compression. The novelty of the approach lies in its ability to compress the quasi-periodic ECG signal by exploiting the intra and inter-beat correlations. The proposed method comprises the following steps: (1) QRS detection, (2) Length normalization, (3) Dc equalization, (4) SampEn based nonlinear complexity sorting and (5) Compression using JPEG2000 Codec. The performance has been evaluated over 48 records from the MIT-BIH arrhythmia database. The average quality score (QS) measurements at different residual errors were 42.25, 4.73, and 2.75 for percentage root mean square difference (PRD), PRD1024, and PRD Normalized respectively. The work also reports extensive experimentations on the compressor for various durations of the ECG records (5–30 min, with 5-min increment). The proposed algorithm demonstrates significantly better performance in comparison to the contemporary state-of-the-art works present in the literature.

A joint application of optimal threshold based discrete cosine transform and ASCII encoding for ECG data compression with its inherent encryption.

In this paper, a joint use of the discrete cosine transform (DCT), and differential pulse code modulation (DPCM) based quantization is presented for predefined quality controlled electrocardiogram (ECG) data compression. The formulated approach exploits the energy compaction property in transformed domain. The DPCM quantization has been applied to zero-sequence grouped DCT coefficients that were optimally thresholded via Regula-Falsi method. The generated sequence is encoded using Huffman coding. This encoded series is further converted to a valid ASCII code using the standard codebook for transmission purpose. Such a coded series possesses inherent encryption capability. The proposed technique is validated on all 48 records of standard MIT-BIH database using different measures for compression and encryption. The acquisition time has been taken in accordance to that existed in literature for the fair comparison with contemporary state-of-art approaches. The chosen measures are (1) compression ratio (CR), (2) percent root mean square difference (PRD), (3) percent root mean square difference without base (PRD1), (4) percent root mean square difference normalized (PRDN), (5) root mean square (RMS) error, (6) signal to noise ratio (SNR), (7) quality score (QS), (8) entropy, (9) Entropy score (ES) and (10) correlation coefficient (r x,y ). Prominently the average values of CR, PRD and QS were equal to 18.03, 1.06, and 17.57 respectively. Similarly, the mean encryption metrics i.e. entropy, ES and r x,y were 7.9692, 0.9962 and 0.0113 respectively. The novelty in combining the approaches is well justified by the values of these metrics that are significantly higher than the comparison counterparts.

A New Compression Scheme Based on Adaptive Vector Quantization and Singular Value Decomposition

This paper presents a new technique for electrocardiogram data compression. The proposed method is based on the vector quantization and the singular value decomposition. First, the singular value decomposition is performed on the two-dimensional representation of the electrocardiogram signal and then a limited number of the weighted right singular vectors is quantized using the vector quantization. In addition, a residual encoding based on the singular value decomposition of the residual error is proposed. Consequently, a low reconstruction error was obtained. A new scheme is also proposed for the generation of the vector quantization codebook, it is based on the Lindth-Buzo-Gray algorithm, but in the proposed technique, at each vector quantization stage, a new codebook is generated. This latter is only produced from the retained singular vectors. Several tests were conducted using arrhythmia database of the Massachusetts Institute of Technology-Beth Israel Hospital. The obtained results were very satisfactory. Unlike most existing methods, in the proposed scheme, the codebook storage was taken into account during the calculation of the compression ratio which was not affected, since high compression ratios were obtained. For instance, a compression ratio of 102.54 was obtained in the case of the percent root mean square difference of 1.02%.

ECG compression using non-recursive wavelet transform with quality control

ABSTRACTWhile wavelet-based electrocardiogram (ECG) data compression using scalar quantisation (SQ) yields excellent compression performance, a wavelet’s SQ scheme, however, must select a set of multilevel quantisers for each quantisation process. As a result of the properties of multiple-to-one mapping, however, this scheme is not conducive for reconstruction error control. In order to address this problem, this paper presents a single-variable control SQ scheme able to guarantee the reconstruction quality of wavelet-based ECG data compression. Based on the reversible round-off non-recursive discrete periodised wavelet transform (RRO-NRDPWT), the SQ scheme is derived with a three-stage design process that first uses genetic algorithm (GA) for high compression ratio (CR), followed by a quadratic curve fitting for linear distortion control, and the third uses a fuzzy decision-making for minimising data dependency effect and selecting the optimal SQ. The two databases, Physikalisch-Technische Bundesanstalt (PTB) and Massachusetts Institute of Technology (MIT) arrhythmia, are used to evaluate quality control performance. Experimental results show that the design method guarantees a high compression performance SQ scheme with statistically linear distortion. This property can be independent of training data and can facilitate rapid error control.