Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm

Fractional calculus integration for improved ECG modeling: A McSharry model expansion

This study introduces a new method for modeling electrocardiogram (ECG)1 waveforms using Fractional Differential Equations (FDEs). By incorporating fractional calculus into the well-established McSharry model, the proposed approach achieves improved representation and high precision for a wide range of ECG waveforms. The research focuses on the impact of integrating fractional derivatives into Integer Differential Equation (IDE) models, enhancing the fidelity of ECG signal modeling. To optimize the model's unknown parameters, a combination of the Predictor-Corrector method for solving FDEs and genetic algorithms for optimization is utilized. The effectiveness of the fractional-order model is assessed through distortion metrics, providing a comprehensive evaluation of the modeling quality. Comparisons show that the fractional-order model outperforms the traditional McSharry IDE model in modeling quality and compression efficiency. It improves modeling quality by 48.40 % in MSE and compression efficiency by 23.18 % when applied on five beat types of MIT/BIH arrhythmia database. The fractional-order model demonstrates enhanced flexibility while preserving essential McSharry model characteristics, with fractional orders (α) ranging from 0.96 to 0.99 across five beat types.

A Study on Dictionary Selection in Compressive Sensing for ECG Signals Compression and Classification

The paper proposes a comparative analysis of the projection matrices and dictionaries used for compressive sensing (CS) of electrocardiographic signals (ECG), highlighting the compromises between the complexity of preprocessing and the accuracy of reconstruction. Starting from the basic notions of CS theory, this paper proposes the construction of dictionaries (constructed directly by cardiac patterns with R-waves, centered or not-centered) specific to the application and the results of their testing. Several types of projection matrices are also analyzed and discussed. The reconstructed signals are analyzed quantitatively and qualitatively by standard distortion measures and by the classification of the reconstructed signals. We used a k-nearest neighbors (KNN) classifier to evaluate the reconstructed models. The KNN module was trained with the models from the mega-dictionary used in the classification block and tested with the models reconstructed with class-specific dictionaries. In addition to the KNN classifier, a neural network was used to test the reconstructed signals. The neural network was a multilayer perceptron (MLP). Moreover, the results are compared with those obtained with other compression methods, and ours proved to be superior.

Pathologies affect the performance of ECG signals compression

The performance of ECG signals compression is influenced by many things. However, there is not a single study primarily focused on the possible effects of ECG pathologies on the performance of compression algorithms. This study evaluates whether the pathologies present in ECG signals affect the efficiency and quality of compression. Single-cycle fractal-based compression algorithm and compression algorithm based on combination of wavelet transform and set partitioning in hierarchical trees are used to compress 125 15-leads ECG signals from CSE database. Rhythm and morphology of these signals are newly annotated as physiological or pathological. The compression performance results are statistically evaluated. Using both compression algorithms, physiological signals are compressed with better quality than pathological signals according to 8 and 9 out of 12 quality metrics, respectively. Moreover, it was statistically proven that pathological signals were compressed with lower efficiency than physiological signals. Signals with physiological rhythm and physiological morphology were compressed with the best quality. The worst results reported the group of signals with pathological rhythm and pathological morphology. This study is the first one which deals with effects of ECG pathologies on the performance of compression algorithms. Signal-by-signal rhythm and morphology annotations (physiological/pathological) for the CSE database are newly published.

Complex study on compression of ECG signals using novel single-cycle fractal-based algorithm and SPIHT

Compression of ECG signal is essential especially in the area of signal transmission in telemedicine. There exist many compression algorithms which are described in various details, tested on various datasets and their performance is expressed by different ways. There is a lack of standardization in this area. This study points out these drawbacks and presents new compression algorithm which is properly described, tested and objectively compared with other authors. This study serves as an example how the standardization should look like. Single-cycle fractal-based (SCyF) compression algorithm is introduced and tested on 4 different databases-CSE database, MIT-BIH arrhythmia database, High-frequency signal and Brno University of Technology ECG quality database (BUT QDB). SCyF algorithm is always compared with well-known algorithm based on wavelet transform and set partitioning in hierarchical trees in terms of efficiency (2 methods) and quality/distortion of the signal after compression (12 methods). Detail analysis of the results is provided. The results of SCyF compression algorithm reach up to avL = 0.4460 bps and PRDN = 2.8236%.

Soft computing based compressive sensing techniques in signal processing: A comprehensive review

In this modern world, a massive amount of data is processed and broadcasted daily. This includes the use of high energy, massive use of memory space, and increased power use. In a few applications, for example, image processing, signal processing, and possession of data signals, etc., the signals included can be viewed as light in a few spaces. The compressive sensing theory could be an appropriate contender to manage these limitations. “Compressive Sensing theory” preserves extremely helpful while signals are sparse or compressible. It very well may be utilized to recoup light or compressive signals with less estimation than customary strategies. Two issues must be addressed by CS: plan of the estimation framework and advancement of a proficient sparse recovery calculation. The essential intention of this work expects to audit a few ideas and utilizations of compressive sensing and to give an overview of the most significant sparse recovery calculations from every class. The exhibition of acquisition and reconstruction strategies is examined regarding the Compression Ratio, Reconstruction Accuracy, Mean Square Error, and so on.

A compressed-sensing-based compressor for ECG

Electrocardiogram (ECG) data compression has numerous applications. The time for generating compressed samples is a vital factor when we consider ambulatory devices, with the fact that data should be sent to the physician as soon as possible. In addition, there are some wearable ECG recorders that have limited power, and may only be capable of doing simple algorithms. With the aim of increasing the speed and simplicity of the compressors, we propose a system architecture that can generate compressed ECG samples, in a linear method and with CR 75%. We used sparsity of the ECG signal and proposed a system based on compressed sensing (CS) that can compress ECG samples, almost in real-time. We applied CS in a very small size in order to accelerate the compression phase and accordingly reducing the power consumption. Also, in the recovery phase, we used the recently developed Kronecker technique to improve the quality of the recovered signal. The system designed based on full-adder/subtractor (FAS) and shift registers, without using any external processor or any training algorithm.

Adaptive Sampling of the Electrocardiogram Based on Generalized Perceptual Features

A non-uniform distribution of diagnostic information in the electrocardiogram (ECG) has been commonly accepted and is the background to several compression, denoising and watermarking methods. Gaze tracking is a widely recognized method for identification of an observer's preferences and interest areas. The statistics of experts' scanpaths were found to be a convenient quantitative estimate of medical information density for each particular component (i.e., wave) of the ECG record. In this paper we propose the application of generalized perceptual features to control the adaptive sampling of a digital ECG. Firstly, based on temporal distribution of the information density, local ECG bandwidth is estimated and projected to the actual positions of components in heartbeat representation. Next, the local sampling frequency is calculated pointwise and the ECG is adaptively low-pass filtered in all simultaneous channels. Finally, sample values are interpolated at new time positions forming a non-uniform time series. In evaluation of perceptual sampling, an inverse transform was used for the reconstruction of regularly sampled ECG with a percent root-mean-square difference (PRD) error of 3-5% (for compression ratios 3.0-4.7, respectively). Nevertheless, tests performed with the use of the CSE Database show good reproducibility of ECG diagnostic features, within the IEC 60601-2-25:2015 requirements, thanks to the occurrence of distortions in less relevant parts of the cardiac cycle.

A novel ECG signal compression method using spindle convolutional auto-encoder

BACKGROUND AND OBJECTIVES

With rapid development of telehealth system and cloud platform, traditional 12-ECG signals with high resolution generate heavy burdens in data storage and transmission. This problem is increasingly addressed with various ECG compression methods. The important objective of compression method is to achieve a high-ratio and quality guaranteed compression. Consequently, to achieve this objective, this work presents a deep-learning-based spindle convolutional auto-encoder. The spindle structure achieves the high-ratio compression by reducing the dimension and guarantees the quality by increasing the dimension and end-to-end framework.

METHODS

The spindle convolutional auto-encoder provides a high-ratio and quality-guaranteed ECG compression. It is composed of two parts as convolutional encoder and convolutional decoder with functional layers. By convolutional operation, the local information can be extracted. The spindle structure is increasing dimension in first few layers to obtain sufficient information to guarantee compression quality. And it is reducing dimension in last few layers to merge the information into a code for high-ratio compression. Meanwhile, the end-to-end framework is to obtain the optimum encoding for compression to improve the reconstruction performance.

RESULTS

Compression performance is validated with records from MIT-BIH database. The proposed method achieves high compression ratio of 106.45 and low percentage root mean square difference of 8.00%. Compared with basic convolutional auto-encoder, the spindle structure improves the compression quality with lower losses.

CONCLUSIONS

The spindle convolutional auto-encoder performs a high-ratio and quality-guaranteed compression. It can be considered as a promising compression technique used in tele-transmission and data storage.

Time-frequency localization using three-tap biorthogonal wavelet filter bank for electrocardiogram compressions

A joint time-frequency localized three-band biorthogonal wavelet filter bank to compress Electrocardiogram signals is proposed in this work. Further, the use of adaptive thresholding and modified run-length encoding resulted in maximum data volume reduction while guaranteeing reconstructing quality. Using signal-to-noise ratio, compression ratio (C_R), maximum absolute error (E_MA), quality score (Q_s), root mean square error, compression time (C_T) and percentage root mean square difference the validity of the proposed approach is studied. The experimental results deduced that the performance of the proposed approach is better when compared to the two-band wavelet filter bank. The proposed compression method enables loss-less data transmission of medical signals to remote locations for therapeutic usage.

Effective high compression of ECG signals at low level distortion

An effective method for compression of ECG signals, which falls within the transform lossy compression category, is proposed. The transformation is realized by a fast wavelet transform. The effectiveness of the approach, in relation to the simplicity and speed of its implementation, is a consequence of the efficient storage of the outputs of the algorithm which is realized in compressed Hierarchical Data Format. The compression performance is tested on the MIT-BIH Arrhythmia database producing compression results which largely improve upon recently reported benchmarks on the same database. For a distortion corresponding to a percentage root-mean-square difference (PRD) of 0.53, in mean value, the achieved average compression ratio is 23.17 with quality score of 43.93. For a mean value of PRD up to 1.71 the compression ratio increases up to 62.5. The compression of a 30 min record is realized in an average time of 0.14 s. The insignificant delay for the compression process, together with the high compression ratio achieved at low level distortion and the negligible time for the signal recovery, uphold the suitability of the technique for supporting distant clinical health care.

Algorithm and VLSI Architecture Design of Low-Power SPIHT Decoder for mHealth Applications

A real-time cost and power-efficient (CPE) set partitioning in hierarchical trees (SPIHT) decoder design with low hardware complexity and low-power dissipation is introduced in one-dimension (1-D) wavelet-based quality-assured electrocardiograph (ECG) compression systems for mobile health (mHealth) applications. However, current SPIHT coding architectures are designed for image/video processing. These architectures require a large amount of memory as well as complicated sorting algorithms, which both require time-consuming tasks and are unsuitable for mobile ECG applications. Based on our previously modified SPIHT coding work, which used flags and check bits to reduce memory requirements and coding complexity by merging three search processes into one step. Therefore, to achieve the real-time design goal for mobile ECG applications, in this paper, we first introduce a hardware-oriented SPIHT decoding algorithm that is suitable for decoding the previously presented SPIHT coding work. Accordingly, an appropriate low-power hardware architecture is developed to implement a real-time high-performance and low-cost SPIHT VLSI design for our proposed decoder algorithm, which is appropriate for mobile ECG applications. Using the distinct ECG signals in the MIT-BIH arrhythmia database (sampling rate of 360 Hz), the final simulation and VLSI implementation results reveal that the proposed CPE SPIHT decoder design outperforms the state-of-the-art designs in terms of the average decoding time, the decoding quality, the VLSI speed, and the power consumption. Most importantly, the design can be exploited to a 1-D 1024 × 1 wavelet-based quality-assured ECG data compression system.

A Comparative Analysis of Methods for Evaluation of ECG Signal Quality after Compression

The assessment of ECG signal quality after compression is an essential part of the compression process. Compression facilitates the signal archiving, speeds up signal transmission, and reduces the energy consumption. Conversely, lossy compression distorts the signals. Therefore, it is necessary to express the compression performance through both compression efficiency and signal quality. This paper provides an overview of objective algorithms for the assessment of both ECG signal quality after compression and compression efficiency. In this area, there is a lack of standardization, and there is no extensive review as such. 40 methods were tested in terms of their suitability for quality assessment. For this purpose, the whole CSE database was used. The tested signals were compressed using an algorithm based on SPIHT with varying efficiency. As a reference, compressed signals were manually assessed by two experts and classified into three quality groups. Owing to the experts' classification, we determined corresponding ranges of selected quality evaluation methods' values. The suitability of the methods for quality assessment was evaluated based on five criteria. For the assessment of ECG signal quality after compression, we recommend using a combination of these methods: PSim SDNN, QS, SNR1, MSE, PRDN1, MAX, STDERR, and WEDD SWT.

A Speed- and Power-Efficient SPIHT Design for Wearable Quality-On-Demand ECG Applications

In this paper, a speed and power-efficient set partitioning in hierarchical trees (SPIHT) design is introduced for one-dimensional (1-D) wavelet-based electrocardiography (ECG) compression systems with quality guarantee. To achieve real-time and low-power design objectives toward wearable quality-on-demand (QoD) ECG applications, we first propose a coding-time- and computation-efficient SPIHT algorithm using various types of coding status register files to overcome the disadvantages of low coding speeds and complicated hardware architectures characterizing prior SPIHT algorithms resulting from the necessity of dynamic computation and arrangement in the sorting and refinement processing phase. Second, a highly pipelined and power-efficient very large scale integration (VLSI) architecture is developed to implement a high-performance and low-power SPIHT design based on the proposed algorithm. The final simulation results demonstrate that our proposed algorithm can speed up the average coding time 1.52 to 2.74 times compared to prior work with an identical compression ratio for an 11-level $1024\times 1\,1-{\rm{D}}$ discrete wavelet transform at diverse target percentage root-mean-square differences (PRDT) on various MIT-BIH arrhythmia datasets. Applied to wearable wavelet-based QoD ECG applications, our proposed VLSI architecture attains a working frequency of 740 MHz and consumes an average of $\text{23}\ \mu {\text{W}}$ of power with Taiwan Semiconductor Manufacturing Company 90-nm CMOS technology, which shows the effectiveness of speed and power over the state-of-the-art designs.

An enhanced method for real-time modelling of cardiac related biosignals using Gaussian mixtures

Cardiac related biosignals modelling is very important for detecting, classification, compression and transmission of such health-related signals. This paper introduces a new, fast and accurate method for modelling the cardiac related biosignals (ECG and PPG) based on a mixture of Gaussian waves. For any signal, at first, the start and end of the ECG beat or PPG pulse is detected, then the baseline is detected then subtracted from the original signal, after that the signal is divided into two signals positive and negative, each modelled separately then incorporated together to form the modelled signal. The proposed method is applied in the MIMIC, and MIT-BIH Arrhythmia databases available online at PhysioNet.

Wavelet-based Encoding Scheme for Controlling Size of Compressed ECG Segments in Telecardiology Systems

One of the major issues in time-critical medical applications using wireless technology is the size of the payload packet, which is generally designed to be very small to improve the transmission process. Using small packets to transmit continuous ECG data is still costly. Thus, data compression is commonly used to reduce the huge amount of ECG data transmitted through telecardiology devices. In this paper, a new ECG compression scheme is introduced to ensure that the compressed ECG segments fit into the available limited payload packets, while maintaining a fixed CR to preserve the diagnostic information. The scheme automatically divides the ECG block into segments, while maintaining other compression parameters fixed. This scheme adopts discrete wavelet transform (DWT) method to decompose the ECG data, bit-field preserving (BFP) method to preserve the quality of the DWT coefficients, and a modified running-length encoding (RLE) scheme to encode the coefficients. The proposed dynamic compression scheme showed promising results with a percentage packet reduction (PR) of about 85.39% at low percentage root-mean square difference (PRD) values, less than 1%. ECG records from MIT-BIH Arrhythmia Database were used to test the proposed method. The simulation results showed promising performance that satisfies the needs of portable telecardiology systems, like the limited payload size and low power consumption.

An Ultra-Low Power Turning Angle Based Biomedical Signal Compression Engine with Adaptive Threshold Tuning

Intelligent sensing is drastically changing our everyday life including healthcare by biomedical signal monitoring, collection, and analytics. However, long-term healthcare monitoring generates tremendous data volume and demands significant wireless transmission power, which imposes a big challenge for wearable healthcare sensors usually powered by batteries. Efficient compression engine design to reduce wireless transmission data rate with ultra-low power consumption is essential for wearable miniaturized healthcare sensor systems. This paper presents an ultra-low power biomedical signal compression engine for healthcare data sensing and analytics in the era of big data and sensor intelligence. It extracts the feature points of the biomedical signal by window-based turning angle detection. The proposed approach has low complexity and thus low power consumption while achieving a large compression ratio (CR) and good quality of reconstructed signal. Near-threshold design technique is adopted to further reduce the power consumption on the circuit level. Besides, the angle threshold for compression can be adaptively tuned according to the error between the original signal and reconstructed signal to address the variation of signal characteristics from person to person or from channel to channel to meet the required signal quality with optimal CR. For demonstration, the proposed biomedical compression engine has been used and evaluated for ECG compression. It achieves an average (CR) of 71.08% and percentage root-mean-square difference (PRD) of 5.87% while consuming only 39 nW. Compared to several state-of-the-art ECG compression engines, the proposed design has significantly lower power consumption while achieving similar CRD and PRD, making it suitable for long-term wearable miniaturized sensor systems to sense and collect healthcare data for remote data analytics.

ECG Data Encryption Then Compression Using Singular Value Decomposition

Electrocardiogram (ECG) monitoring systems are widely used in healthcare. ECG data must be compressed for transmission and storage. Furthermore, there is a need to be able to directly process biomedical signals in encrypted domains to ensure the protection of patients' privacy. Existing encryption-then-compression (ETC) approaches for multimedia using the state-of-the-art encryption techniques inevitably sacrifice the compression efficiency or signal quality. This paper presents the first ETC approach for processing ECG data. The proposed approach not only can protect data privacy but also provide the same quality of the reconstructed signals without sacrificing the compression efficiency relative to unencrypted compressions. Specifically, the singular value decomposition technique is used to compress the data such that the proposed system can provide quality-control compressed data, even though the data has been encrypted. Experimental results prove the proposed system to be an effective technique for assuring data security as well as compression performance for ECG data.

Real time QRS complex detection using DFA and regular grammar

Background

The sequence of Q, R, and S peaks (QRS) complex detection is a crucial procedure in electrocardiogram (ECG) processing and analysis. We propose a novel approach for QRS complex detection based on the deterministic finite automata with the addition of some constraints. This paper confirms that regular grammar is useful for extracting QRS complexes and interpreting normalized ECG signals. A QRS is assimilated to a pair of adjacent peaks which meet certain criteria of standard deviation and duration.

Results

The proposed method was applied on several kinds of ECG signals issued from the standard MIT-BIH arrhythmia database. A total of 48 signals were used. For an input signal, several parameters were determined, such as QRS durations, RR distances, and the peaks’ amplitudes. σRR and σQRS parameters were added to quantify the regularity of RR distances and QRS durations, respectively. The sensitivity rate of the suggested method was 99.74% and the specificity rate was 99.86%. Moreover, the sensitivity and the specificity rates variations according to the Signal-to-Noise Ratio were performed.

Conclusions

Regular grammar with the addition of some constraints and deterministic automata proved functional for ECG signals diagnosis. Compared to statistical methods, the use of grammar provides satisfactory and competitive results and indices that are comparable to or even better than those cited 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.

Hybrid method based on singular value decomposition and embedded zero tree wavelet technique for ECG signal compression

BACKGROUND AND OBJECTIVE

In the field of biomedical, it becomes necessary to reduce data quantity due to the limitation of storage in real-time ambulatory system and telemedicine system. Research has been underway since very beginning for the development of an efficient and simple technique for longer term benefits.

METHOD

This paper, presents an algorithm based on singular value decomposition (SVD), and embedded zero tree wavelet (EZW) techniques for ECG signal compression which deals with the huge data of ambulatory system. The proposed method utilizes the low rank matrix for initial compression on two dimensional (2-D) ECG data array using SVD, and then EZW is initiated for final compression. Initially, 2-D array construction has key issue for the proposed technique in pre-processing. Here, three different beat segmentation approaches have been exploited for 2-D array construction using segmented beat alignment with exploitation of beat correlation. The proposed algorithm has been tested on MIT-BIH arrhythmia record, and it was found that it is very efficient in compression of different types of ECG signal with lower signal distortion based on different fidelity assessments.

RESULTS

The evaluation results illustrate that the proposed algorithm has achieved the compression ratio of 24.25:1 with excellent quality of signal reconstruction in terms of percentage-root-mean square difference (PRD) as 1.89% for ECG signal Rec. 100 and consumes only 162bps data instead of 3960bps uncompressed data.

CONCLUSION

The proposed method is efficient and flexible with different types of ECG signal for compression, and controls quality of reconstruction. Simulated results are clearly illustrate the proposed method can play a big role to save the memory space of health data centres as well as save the bandwidth in telemedicine based healthcare systems.

Comparison study of EMG signals compression by methods transform using vector quantization, SPIHT and arithmetic coding

In this article, we make a comparative study for a new approach compression between discrete cosine transform (DCT) and discrete wavelet transform (DWT). We seek the transform proper to vector quantization to compress the EMG signals. To do this, we initially associated vector quantization and DCT, then vector quantization and DWT. The coding phase is made by the SPIHT coding (set partitioning in hierarchical trees coding) associated with the arithmetic coding. The method is demonstrated and evaluated on actual EMG data. Objective performance evaluations metrics are presented: compression factor, percentage root mean square difference and signal to noise ratio. The results show that method based on the DWT is more efficient than the method based on the DCT.

Adaptive Dictionary Reconstruction for Compressed Sensing of ECG Signals

This paper proposes a novel adaptive dictionary (AD) reconstruction scheme to improve the performance of compressed sensing (CS) with electrocardiogram signals (ECG). The method is based on the use of multiple dictionaries, created using dictionary learning (DL) techniques for CS signal reconstruction. The modified reconstruction framework is a two-stage process that leverages information about the signal from an initial signal reconstruction stage. By identifying whether a QRS complex is present and if so, determining a location estimate of the QRS, the most appropriate dictionary is selected and a second stage more refined signal reconstruction can be obtained. The performance of the proposed algorithm is compared with state-of-the-art CS implementations in the literature, as well as the set partitioning in hierarchical trees (SPIHT) wavelet-based lossy compression algorithm. The results indicate that the proposed reconstruction scheme outperforms all existing CS implementations in terms of signal fidelity at each compression ratio tested. The performance of the proposed approach also compares favorably with SPIHT in terms of signal reconstruction quality. Furthermore, an analysis of the overall power consumption of the proposed ECG compression framework as would be used in a body area network (BAN) demonstrates positive results for the proposed CS approach when compared with existing CS techniques and SPIHT.

Energy-efficient Compressed Sensing for ambulatory ECG monitoring

Advances in Compressed Sensing (CS) are enabling promising low-energy implementation solutions for wireless Body Area Networks (BAN). While studies demonstrate the potential of CS in terms of overall energy efficiency compared to state-of-the-art lossy compression techniques, the performance of CS remains limited. The aim of this study is to improve the performance of CS-based compression for electrocardiogram (ECG) signals. This paper proposes a CS architecture that combines a novel redundancy removal scheme with quantization and Huffman entropy coding to effectively extend the Compression Ratio (CR). Reconstruction is performed using overcomplete sparse dictionaries created with Dictionary Learning (DL) techniques to exploit the highly structured nature of ECG signals. Performance of the proposed CS implementation is evaluated by analyzing energy-based distortion metrics and diagnostic metrics including QRS beat-detection accuracy across a range of CRs. The proposed CS approach offers superior performance to the most recent state-of-the-art CS implementations in terms of signal reconstruction quality across all CRs tested. Furthermore, QRS detection accuracy of the technique is compared with the well-known lossy Set Partitioning in Hierarchical Trees (SPIHT) compression technique. The proposed CS approach outperforms SPIHT in terms of achievable CR, using the area under the receiver operator characteristic (ROC) curve (AUC). For an application where a minimum AUC performance threshold of 0.9 is required, the proposed technique extends the CR from 64.6 to 90.45 compared with SPIHT, ensuring a 40% saving on wireless transmission costs. Therefore, the results highlight the potential of the proposed technique for ECG computer-aided diagnostic systems.

A constrained two-layer compression technique for ECG waves

This paper proposes a constrained two-layer compression technique for electrocardiogram (ECG) waves, of which encoded parameters can be directly used for the diagnosis of arrhythmia. In the first layer, a single ECG beat is represented by one of the registered templates in the codebook. Since the required coding parameter in this layer is only the codebook index of the selected template, its compression ratio (CR) is very high. Note that the distribution of registered templates is also related to the characteristics of ECG waves, thus it can be used as a metric to detect various types of arrhythmias. The residual error between the input and the selected template is encoded by a wavelet-based transform coding in the second layer. The number of wavelet coefficients is constrained by pre-defined maximum distortion to be allowed. The MIT-BIH arrhythmia database is used to evaluate the performance of the proposed algorithm. The proposed algorithm shows around 7.18 CR when the reference value of percentage root mean square difference (PRD) is set to ten.

Hardware design and implementation of a wavelet de-noising procedure for medical signal preprocessing

In this paper, a discrete wavelet transform (DWT) based de-noising with its applications into the noise reduction for medical signal preprocessing is introduced. This work focuses on the hardware realization of a real-time wavelet de-noising procedure. The proposed de-noising circuit mainly consists of three modules: a DWT, a thresholding, and an inverse DWT (IDWT) modular circuits. We also proposed a novel adaptive thresholding scheme and incorporated it into our wavelet de-noising procedure. Performance was then evaluated on both the architectural designs of the software and. In addition, the de-noising circuit was also implemented by downloading the Verilog codes to a field programmable gate array (FPGA) based platform so that its ability in noise reduction may be further validated in actual practice. Simulation experiment results produced by applying a set of simulated noise-contaminated electrocardiogram (ECG) signals into the de-noising circuit showed that the circuit could not only desirably meet the requirement of real-time processing, but also achieve satisfactory performance for noise reduction, while the sharp features of the ECG signals can be well preserved. The proposed de-noising circuit was further synthesized using the Synopsys Design Compiler with an Artisan Taiwan Semiconductor Manufacturing Company (TSMC, Hsinchu, Taiwan) 40 nm standard cell library. The integrated circuit (IC) synthesis simulation results showed that the proposed design can achieve a clock frequency of 200 MHz and the power consumption was only 17.4 mW, when operated at 200 MHz.

Bufferless Compression of Asynchronously Sampled ECG Signals in Cubic Hermitian Vector Space

Asynchronous level crossing sampling analog-to-digital converters (ADCs) are known to be more energy efficient and produce fewer samples than their equidistantly sampling counterparts. However, as the required threshold voltage is lowered, the number of samples and, in turn, the data rate and the energy consumed by the overall system increases. In this paper, we present a cubic Hermitian vector-based technique for online compression of asynchronously sampled electrocardiogram signals. The proposed method is computationally efficient data compression. The algorithm has complexity O(n), thus well suited for asynchronous ADCs. Our algorithm requires no data buffering, maintaining the energy advantage of asynchronous ADCs. The proposed method of compression has a compression ratio of up to 90% with achievable percentage root-mean-square difference ratios as a low as 0.97. The algorithm preserves the superior feature-to-feature timing accuracy of asynchronously sampled signals. These advantages are achieved in a computationally efficient manner since algorithm boundary parameters for the signals are extracted a priori.

A combined application of lossless and lossy compression in ECG processing and transmission via GSM-based SMS

This paper presents a software-based scheme for reliable and robust Electrocardiogram (ECG) data compression and its efficient transmission using Second Generation (2G) Global System for Mobile Communication (GSM) based Short Message Service (SMS). To achieve a firm lossless compression in high standard deviating QRS complex regions and an acceptable lossy compression in the rest of the signal, two different algorithms have been used. The combined compression module is such that it outputs only American Standard Code for Information Interchange (ASCII) characters and, hence, SMS service is found to be most suitable for transmitting the compressed signal. At the receiving end, the ECG signal is reconstructed using just the reverse algorithm. The module has been tested to all the 12 leads of different types of ECG signals (healthy and abnormal) collected from the PTB Diagnostic ECG Database. The compression algorithm achieves an average compression ratio of ∼22.51, without any major alteration of clinical morphology.

EEG compression of scalp recordings based on dipole fitting

A novel technique for electroencephalogram (EEG) compression is proposed in this paper. This technique models the intrinsic dependence inherent between the different EEG channels. It is based on methods borrowed from dipole fitting that is usually used in order to find a solution to the classic problems in EEG analysis: inverse and forward problems. To compress the EEG signals, the forward model based on approximated source dipoles is first used to provide an approximation of the recorded signals. Then, (based on a smoothness factor) appropriate coding techniques are suggested to compress the residuals of the fitting process. Results show that this technique works well for different recordings and for different patients, and is even able to provide near-lossless compression for certain types of recordings.

Exploiting prior knowledge in compressed sensing wireless ECG systems

Recent results in telecardiology show that compressed sensing (CS) is a promising tool to lower energy consumption in wireless body area networks for electrocardiogram (ECG) monitoring. However, the performance of current CS-based algorithms, in terms of compression rate and reconstruction quality of the ECG, still falls short of the performance attained by state-of-the-art wavelet-based algorithms. In this paper, we propose to exploit the structure of the wavelet representation of the ECG signal to boost the performance of CS-based methods for compression and reconstruction of ECG signals. More precisely, we incorporate prior information about the wavelet dependencies across scales into the reconstruction algorithms and exploit the high fraction of common support of the wavelet coefficients of consecutive ECG segments. Experimental results utilizing the MIT-BIH Arrhythmia Database show that significant performance gains, in terms of compression rate and reconstruction quality, can be obtained by the proposed algorithms compared to current CS-based methods.

Pre-processing of multi-channel EEG for improved compression performance using SPIHT

A novel technique for Electroencephalogram (EEG) compression is proposed in this article. This technique makes use of the inter-channel redundancy present between different EEG channels of the same recording and the intra-channel redundancy between the different samples of a specific channel. It uses Discrete Wavelet Transform (DWT) and Set partitioning in hierarchical trees (SPIHT) in 2-D to code the EEG channels. Smoothness transforms are added in order to guarantee good performance of SPIHT in 2-D. Experimental results show that this technique is able to provide low distortion values for high compression ratios (CRs). In addition, performance results of this method do not vary a lot between different patients which proves the stability of the method when used with recordings of different characteristics.