Electrocardiogram Compression Methods 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.

New ECG Compression Method for Portable ECG Monitoring System Merged with Binary Convolutional Auto-Encoder and Residual Error Compensation.

In the past few years, deep learning-based electrocardiogram (ECG) compression methods have achieved high-ratio compression by reducing hidden nodes. However, this reduction can result in severe information loss, which will lead to poor quality of the reconstructed signal. To overcome this problem, a novel quality-guaranteed ECG compression method based on a binary convolutional auto-encoder (BCAE) equipped with residual error compensation (REC) was proposed. In traditional compression methods, ECG signals are compressed into floating-point numbers. BCAE directly compresses the ECG signal into binary codes rather than floating-point numbers, whereas binary codes take up fewer bits than floating-point numbers. Compared with the traditional floating-point number compression method, the hidden nodes of the BCAE network can be artificially increased without reducing the compression ratio, and as many hidden nodes as possible can ensure the quality of the reconstructed signal. Furthermore, a novel optimization method named REC was developed. It was used to compensate for the residual between the ECG signal output by BCAE and the original signal. Complemented with the residual error, the restoration of the compression signal was improved, so the reconstructed signal was closer to the original signal. Control experiments were conducted to verify the effectiveness of this novel method. Validated by the MIT-BIH database, the compression ratio was 117.33 and the root mean square difference (PRD) was 7.76%. Furthermore, a portable compression device was designed based on the proposed algorithm using Raspberry Pi. It indicated that this method has attractive prospects in telemedicine and portable ECG monitoring systems.

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.

Energy-Efficient ECG Signal Compression for User Data Input in Cyber-Physical Systems by Leveraging Empirical Mode Decomposition

Human physiological data are naturalistic and objective user data inputs for a great number of cyber-physical systems (CPS). Electrocardiogram (ECG) as a widely used physiological golden indicator for certain human state and disease diagnosis is often used as user data input for various CPS such as medical CPS and human–machine interaction. Wireless transmission and wearable technology enable long-term continuous ECG data acquisition for human–CPS interaction; however, these emerging technologies bring challenges of storing and wireless transmitting huge amounts of ECG data, leading to energy efficiency issue of wearable sensors. ECG signal compression technique provides a promising solution for these challenges by decreasing ECG data size. In this study, we develop the first scheme of leveraging empirical mode decomposition (EMD) on ECG signals for sparse feature modeling and compression and further propose a new ECG signal compression framework based on EMD constructed feature dictionary. The proposed method features in compressing ECG signals using a very limited number of feature bases with low computation cost, which significantly improves the compression performance and energy efficiency. Our method is validated with the ECG data from MIT-BIH arrhythmia database and compared with existing methods. The results show that our method achieves the compression ratio (CR) of up to 164 with the root mean square error (RMSE) of 3.48% and the average CR of 88.08 with the RMSE of 5.66%, which is more than twice of the average CR of the state-of-the-art methods with similar recovering error rate of around 5%. For diagnostic distortion perspective, our method achieves high QRS detection performance with the sensitivity (SE) of 99.8% and the specificity (SP) of 99.6%, which shows that our ECG compression method can preserve almost all the QRS features and have no impact on the diagnosis process. In addition, the energy consumption of our method is only 30% of that of other methods when compared under the same recovering error rate.

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.

Efficient ECG Compression and QRS Detection for E-Health Applications

Current medical screening and diagnostic procedures have shifted toward recording longer electrocardiogram (ECG) signals, which have traditionally been processed on personal computers (PCs) with high-speed multi-core processors and efficient memory processing. Battery-driven devices are now more commonly used for the same purpose and thus exploring highly efficient, low-power alternatives for local ECG signal collection and processing is essential for efficient and convenient clinical use. Several ECG compression methods have been reported in the current literature with limited discussion on the performance of the compressed and the reconstructed ECG signals in terms of the QRS complex detection accuracy. This paper proposes and evaluates different compression methods based not only on the compression ratio (CR) and percentage root-mean-square difference (PRD), but also based on the accuracy of QRS detection. In this paper, we have developed a lossy method (Methods III) and compared them to the most current lossless and lossy ECG compression methods (Method I and Method II, respectively). The proposed lossy compression method (Method III) achieves CR of 4.5×, PRD of 0.53, as well as an overall sensitivity of 99.78% and positive predictivity of 99.92% are achieved (when coupled with an existing QRS detection algorithm) on the MIT-BIH Arrhythmia database and an overall sensitivity of 99.90% and positive predictivity of 99.84% on the QT database.

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.

An Efficent Lossless Compression Scheme for ECG Signal

Cardiac diseases constitute the main cause of mortality around the globe. For detection and identification of cardiac problems, it is very important to monitor the patient's heart activities for long periods during his normal daily life. The recorded signal that contains information about the condition of the heart called electrocardiogram (ECG). As a result, long recording of ECG signal amounts to huge data sizes. In this work, a robust lossless ECG data compression scheme for real-time applications is proposed. The developed algorithm has the advantages of lossy compression without introducing any distortion to the reconstructed signal. The ECG signals under test were taken from the PTB Diagnostic ECG Database. The compression procedure is simple and provides a high compression ratio compared to other lossless ECG compression methods. The compressed ECG data is generated as a text file. The decompression scheme has also been developed using the reverse logic and it is observed that there is no difference between original and reconstructed ECG signal.

A Novel ECG Data Compression Method Using Adaptive Fourier Decomposition With Security Guarantee in e-Health Applications.

This paper presents a novel electrocardiogram (ECG) compression method for e-health applications by adapting an adaptive Fourier decomposition (AFD) algorithm hybridized with a symbol substitution (SS) technique. The compression consists of two stages: first stage AFD executes efficient lossy compression with high fidelity; second stage SS performs lossless compression enhancement and built-in data encryption, which is pivotal for e-health. Validated with 48 ECG records from MIT-BIH arrhythmia benchmark database, the proposed method achieves averaged compression ratio (CR) of 17.6-44.5 and percentage root mean square difference (PRD) of 0.8-2.0% with a highly linear and robust PRD-CR relationship, pushing forward the compression performance to an unexploited region. As such, this paper provides an attractive candidate of ECG compression method for pervasive e-health applications.

Improved ECG compression method using discrete cosine transform

An improvement of a discrete cosine transform (DCT)-based method for electrocardiogram (ECG) compression is presented. The appropriate use of a block based DCT associated to a uniform scalar dead zone quantiser and arithmetic coding show very good results, confirming that the proposed strategy exhibits competitive performances compared with the most popular compressors used for ECG compression.

An Effective and Efficient Compression Algorithm for ECG Signals With Irregular Periods

This paper presents an effective and efficient preprocessing algorithm for two-dimensional (2-D) electrocardiogram (ECG) compression to better compress irregular ECG signals by exploiting their inter- and intra-beat correlations. To better reveal the correlation structure, we first convert the ECG signal into a proper 2-D representation, or image. This involves a few steps including QRS detection and alignment, period sorting, and length equalization. The resulting 2-D ECG representation is then ready to be compressed by an appropriate image compression algorithm. We choose the state-of-the-art JPEG2000 for its high efficiency and flexibility. In this way, the proposed algorithm is shown to outperform some existing arts in the literature by simultaneously achieving high compression ratio (CR), low percent root mean squared difference (PRD), low maximum error (MaxErr), and low standard derivation of errors (StdErr). In particular, because the proposed period sorting method rearranges the detected heartbeats into a smoother image that is easier to compress, this algorithm is insensitive to irregular ECG periods. Thus either the irregular ECG signals or the QRS false-detection cases can be better compressed. This is a significant improvement over existing 2-D ECG compression methods. Moreover, this algorithm is not tied exclusively to JPEG2000. It can also be combined with other 2-D preprocessing methods or appropriate codecs to enhance the compression performance in irregular ECG cases.

A 2-D ECG Compression Method Based on Wavelet Transform and Modified SPIHT

A two-dimensional (2-D) wavelet-based electrocardiogram (ECG) data compression method is presented which employs a modified set partitioning in hierarchical trees (SPIHT) algorithm. This modified SPIHT algorithm utilizes further the redundancy among medium- and high-frequency subbands of the wavelet coefficients and the proposed 2-D approach utilizes the fact that ECG signals generally show redundancy between adjacent beats and between adjacent samples. An ECG signal is cut and aligned to form a 2-D data array, and then 2-D wavelet transform and the modified SPIHT can be applied. Records selected from the MIT-BIH arrhythmia database are tested. The experimental results show that the proposed method achieves high compression ratio with relatively low distortion and is effective for various kinds of ECG morphologies.

A rate distortion optimal ECG coding algorithm.

Signal compression is an important problem encountered in many applications. Various techniques have been proposed over the years for addressing the problem. In this paper, we present a time domain algorithm based on the coding of line segments which are used to approximate the signal. These segments are fit in a way that is optimal in the rate distortion sense. Although the approach is applicable to any type of signal, we focus, in this paper, on the compression of electrocardiogram (ECG) signals. ECG signal compression has traditionally been tackled by heuristic approaches. However, it has been demonstrated [1] that exact optimization algorithms outperform these heuristic approaches by a wide margin with respect to reconstruction error. By formulating the compression problem as a graph theory problem, known optimization theory can be applied in order to yield optimal compression. In this paper, we present an algorithm that will guarantee the smallest possible distortion among all methods applying linear interpolation given an upper bound on the available number of bits. Using a varied signal test set, extensive coding experiments are presented. We compare the results from our coding method to traditional time domain ECG compression methods, as well as, to more recently developed frequency domain methods. Evaluation is based both on percentage root-mean-square difference (PRD) performance measure and visual inspection of the reconstructed signals. The results demonstrate that the exact optimization methods have superior performance compared to both traditional ECG compression methods and the frequency domain methods.

Multichannel ECG compression using multichannel adaptive vector quantization.

Adaptive vector quantization (AVQ) is a recently proposed approach for electrocardiogram (ECG) compression. The adaptability of the approach can be used to control the quality of reconstructed signals. However, like most of other ECG compression methods, AVQ only deals with the single-channel ECG, and for the multichannel (MC) ECG, coding ECG signals on a channel by channel basis is not efficient, because the correlation across channels is not exploited. To exploit this correlation, an MC version of AVQ is proposed. In the proposed approach, the AVQ index from each channel is collected to form a new input vector. The vector is then vector quantized adaptively using one additional codebook called index codebook. Both the MIT/BIH database and a clinical Holter database are tested. The experimental results show that, for exactly the same quality of reconstructed signals, the MC-AVQ performs better than single-channel AVQ in terms of bit rate. A theoretical analysis supporting this result is also demonstrated in this paper. For the same and relatively good visual quality, the average compressed data rate/channel is reduced from 293.5 b/s using the single-channel AVQ to 238.2 b/s using the MC-AVQ in the MIT/BIH case.

A wavelet transform-based ECG compression method guaranteeing desired signal quality.

This paper presents a new electrocardiogram (ECG) compression method based or orthonormal wavelet transform and an adaptive quantization strategy, by which a predetermined percent root mean square difference (PRD) can be guaranteed with high compression ratio and low implementation complexity.