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Bannajak K, Theera-Umpon N, Auephanwiriyakul S. Signal Acquisition-Independent Lossless Electrocardiogram Compression Using Adaptive Linear Prediction. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2753. [PMID: 36768118 PMCID: PMC9915293 DOI: 10.3390/ijerph20032753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/25/2023] [Accepted: 01/30/2023] [Indexed: 06/18/2023]
Abstract
In this paper, we propose a lossless electrocardiogram (ECG) compression method using a prediction error-based adaptive linear prediction technique. This method combines the adaptive linear prediction, which minimizes the prediction error in the ECG signal prediction, and the modified Golomb-Rice coding, which encodes the prediction error to the binary code as the compressed data. We used the PTB Diagnostic ECG database, the European ST-T database, and the MIT-BIH Arrhythmia database for the evaluation and achieved the average compression ratios for single-lead ECG signals of 3.16, 3.75, and 3.52, respectively, despite different signal acquisition setup in each database. As the prediction order is very crucial for this particular problem, we also investigate the validity of the popular linear prediction coefficients that are generally used in ECG compression by determining the prediction coefficients from the three databases using the autocorrelation method. The findings are in agreement with the previous works in that the second-order linear prediction is suitable for the ECG compression application.
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Affiliation(s)
- Krittapat Bannajak
- Department of Electrical Engineering, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Nipon Theera-Umpon
- Department of Electrical Engineering, Chiang Mai University, Chiang Mai 50200, Thailand
- Biomedical Engineering Institute, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Sansanee Auephanwiriyakul
- Biomedical Engineering Institute, Chiang Mai University, Chiang Mai 50200, Thailand
- Department of Computer Engineering, Chiang Mai University, Chiang Mai 50200, Thailand
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Low-complexity lossless multichannel ECG compression based on selective linear prediction. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101705] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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3
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Tiwari A, Falk TH. Lossless electrocardiogram signal compression: A review of existing methods. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.03.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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A Real Time and Lossless Encoding Scheme for Patch Electrocardiogram Monitors. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8122379] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Cardiovascular diseases are the leading cause of death worldwide. Due to advancements facilitating the integration of electric and adhesive technologies, long-term patch electrocardiogram (ECG) monitors (PEMs) are currently used to conduct daily continuous cardiac function assessments. This paper presents an ECG encoding scheme for joint lossless data compression and heartbeat detection to minimize the circuit footprint size and power consumption of a PEM. The proposed encoding scheme supports two operation modes: fixed-block mode and dynamic-block mode. Both modes compress ECG data losslessly, but only dynamic-block mode supports the heartbeat detection feature. The whole encoding scheme was implemented on a C-platform and tested with ECG data from MIT/BIH arrhythmia databases. A compression ratio of 2.1 could be achieved with a normal heartbeat. Dynamic-block mode provides heartbeat detection accuracy at a rate higher than 98%. Fixed-block mode was also implemented on the field-programmable gate array, and could be used as a chip for using analog-to-digital convertor-ready signals as an operation clock.
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Tan C, Zhang L, Wu HT. A Novel Blaschke Unwinding Adaptive-Fourier-Decomposition-Based Signal Compression Algorithm With Application on ECG Signals. IEEE J Biomed Health Inform 2018; 23:672-682. [PMID: 29993788 DOI: 10.1109/jbhi.2018.2817192] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This paper presents a novel signal compression algorithm based on the Blaschke unwinding adaptive Fourier decomposition (AFD). The Blaschke unwinding AFD is a newly developed signal decomposition theory. It utilizes the Nevanlinna factorization and the maximal selection principle in each decomposition step, and achieves a faster convergence rate with higher fidelity. The proposed compression algorithm is applied to the electrocardiogram signal. To assess the performance of the proposed compression algorithm, in addition to the generic assessment criteria, we consider the less discussed criteria related to the clinical needs-for the heart rate variability analysis purpose, how accurate the R-peak information is preserved is evaluated. The experiments are conducted on the MIT-BIH arrhythmia benchmark database. The results show that the proposed algorithm performs better than other state-of-the-art approaches. Meanwhile, it also well preserves the R-peak information.
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Mukhopadhyay SK, Ahmad MO, Swamy M. An ECG compression algorithm with guaranteed reconstruction quality based on optimum truncation of singular values and ASCII character encoding. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.05.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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Mukhopadhyay SK, Ahmad MO, Swamy MNS. SVD and ASCII Character Encoding-Based Compression of Multiple Biosignals for Remote Healthcare Systems. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2018; 12:137-150. [PMID: 29377802 DOI: 10.1109/tbcas.2017.2760298] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Advancements in electronics and miniaturized device fabrication technologies have enabled simultaneous acquisition of multiple biosignals (MBioSigs), but the area of compression of MBioSigs remains unexplored to date. This paper presents a robust singular value decomposition (SVD) and American standard code for information interchange (ASCII) character encoding-based algorithm for compression of MBioSigs for the first time to the best of our knowledge. At the preprocessing stage, MBioSigs are denoised, down sampled and then transformed to a two-dimensional (2-D) data array. SVD of the 2-D array is carried out and the dimensionality of the singular values is reduced. The resulting matrix is then compressed by a lossless ASCII character encoding-based technique. The proposed compression algorithm can be used in a variety of modes such as lossless, with or without using the down sampling operation. The compressed file is then uploaded to a hypertext preprocessor (PHP)-based website for remote monitoring application. Evaluation results show that the proposed algorithm provides a good compression performance; in particular, the mean opinion score of the reconstructed signal falls under the category "very good" as per the gold standard subjective measure.
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Hosny KM, Khalid AM, Mohamed ER. Efficient compression of bio-signals by using Tchebichef moments and Artificial Bee Colony. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.02.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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9
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A joint application of optimal threshold based discrete cosine transform and ASCII encoding for ECG data compression with its inherent encryption. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2016; 39:833-855. [PMID: 27613706 DOI: 10.1007/s13246-016-0476-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Accepted: 08/22/2016] [Indexed: 10/21/2022]
Abstract
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.
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Capurro I, Lecumberry F, Martin A, Ramirez I, Rovira E, Seroussi G. Efficient Sequential Compression of Multichannel Biomedical Signals. IEEE J Biomed Health Inform 2016; 21:904-916. [PMID: 27337728 DOI: 10.1109/jbhi.2016.2582683] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This paper proposes lossless and near-lossless compression algorithms for multichannel biomedical signals. The algorithms are sequential and efficient, which makes them suitable for low-latency and low-power signal transmission applications. We make use of information theory and signal processing tools (such as universal coding, universal prediction, and fast online implementations of multivariate recursive least squares), combined with simple methods to exploit spatial as well as temporal redundancies typically present in biomedical signals. The algorithms are tested with publicly available electroencephalogram and electrocardiogram databases, surpassing in all cases the current state of the art in near-lossless and lossless compression ratios.
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Mukhopadhyay SK, Mitra S, Mitra M. A combined application of lossless and lossy compression in ECG processing and transmission via GSM-based SMS. J Med Eng Technol 2014; 39:105-22. [PMID: 25534118 DOI: 10.3109/03091902.2014.990159] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
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.
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Affiliation(s)
- S K Mukhopadhyay
- Department of Applied Physics, Faculty of Technology, University of Calcutta , 92 A.P.C. Road, Kolkata , India and
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12
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A lossless multichannel bio-signal compression based on low-complexity joint coding scheme for portable medical devices. SENSORS 2014; 14:17516-29. [PMID: 25237900 PMCID: PMC4208236 DOI: 10.3390/s140917516] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2014] [Revised: 09/12/2014] [Accepted: 09/12/2014] [Indexed: 11/17/2022]
Abstract
Research on real-time health systems have received great attention during recent years and the needs of high-quality personal multichannel medical signal compression for personal medical product applications are increasing. The international MPEG-4 audio lossless coding (ALS) standard supports a joint channel-coding scheme for improving compression performance of multichannel signals and it is very efficient compression method for multi-channel biosignals. However, the computational complexity of such a multichannel coding scheme is significantly greater than that of other lossless audio encoders. In this paper, we present a multichannel hardware encoder based on a low-complexity joint-coding technique and shared multiplier scheme for portable devices. A joint-coding decision method and a reference channel selection scheme are modified for a low-complexity joint coder. The proposed joint coding decision method determines the optimized joint-coding operation based on the relationship between the cross correlation of residual signals and the compression ratio. The reference channel selection is designed to select a channel for the entropy coding of the joint coding. The hardware encoder operates at a 40 MHz clock frequency and supports two-channel parallel encoding for the multichannel monitoring system. Experimental results show that the compression ratio increases by 0.06%, whereas the computational complexity decreases by 20.72% compared to the MPEG-4 ALS reference software encoder. In addition, the compression ratio increases by about 11.92%, compared to the single channel based bio-signal lossless data compressor.
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13
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Deepu CJ, Lian Y. A joint QRS detection and data compression scheme for wearable sensors. IEEE Trans Biomed Eng 2014; 62:165-75. [PMID: 25073164 DOI: 10.1109/tbme.2014.2342879] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This paper presents a novel electrocardiogram (ECG) processing technique for joint data compression and QRS detection in a wireless wearable sensor. The proposed algorithm is aimed at lowering the average complexity per task by sharing the computational load among multiple essential signal-processing tasks needed for wearable devices. The compression algorithm, which is based on an adaptive linear data prediction scheme, achieves a lossless bit compression ratio of 2.286x. The QRS detection algorithm achieves a sensitivity (Se) of 99.64% and positive prediction (+P) of 99.81% when tested with the MIT/BIH Arrhythmia database. Lower overall complexity and good performance renders the proposed technique suitable for wearable/ambulatory ECG devices.
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14
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ECG signal compression using ASCII character encoding and transmission via SMS. Biomed Signal Process Control 2013. [DOI: 10.1016/j.bspc.2013.02.007] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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15
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Zhang T, Simske S, Blakley D. Scalable ECG Compression for Long-Term Home Health Care. CONFERENCE PROCEEDINGS : ... ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL CONFERENCE 2012; 2006:390-3. [PMID: 17282196 DOI: 10.1109/iembs.2005.1616427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Most current Holter devices monitor the ECG for 24 to 72 hours. However, for the accurate diagnosis of many cardiac diseases, especially for the wide variety of asymptomatic cases, continuous ECG monitoring for weeks or even months may be required. In this paper, we focus on the issue of ECG compression during long-term monitoring of the patient. The patient may be at home, at work, or even on a trip. A scalable compression scheme is proposed which ensures optimal signal quality given the limited physical storage on the wearable device. When necessary, the signal quality is progressively and gently degraded in order to adapt to environmental and the patient's conditions. Details of the proposed scheme are described and sample results are presented.
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Affiliation(s)
- Tong Zhang
- Hewlett-Packard Company, 1501 Page Mill Road, Palo Alto, CA 94304, USA.
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16
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Nait-Ali A, Borsali R, Khaled W, Lemoine J. Time division multiplexing based method for compressing ECG signals: application for normal and abnormal cases. J Med Eng Technol 2009; 31:324-31. [PMID: 17701777 DOI: 10.1080/03091900500421271] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
The proposed ECG compression method combines three major approaches based on time division multiplexing (TDM) and multilevel wavelet decomposition followed by parametrical modelling. Before applying these techniques, a pre-processing step is required, which consists of detecting and aligning different beats. Even though this compression method is regarded as a lossy method, we will show how a high compression ratio (CR) can be achieved by preserving the major medical information within the ECG. Several normal and abnormal signals from various databases are used to evaluate the performance of the proposed technique.
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Affiliation(s)
- A Nait-Ali
- Laboratoire Images, Signaux & Systèmes Intelligents, EA 3956, Université Paris XII-Val de Marne, Créteil, France.
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17
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Arnavut Z. ECG signal compression based on Burrows-Wheeler transformation and inversion ranks of linear prediction. IEEE Trans Biomed Eng 2007; 54:410-8. [PMID: 17355052 DOI: 10.1109/tbme.2006.888820] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Many transform-based compression techniques, such as Fourier, Walsh, Karhunen-Loeve (KL), wavelet, and discrete cosine transform (DCT), have been investigated and devised for electrocardiogram (ECG) signal compression. However, the recently introduced Burrows-Wheeler Transformation has not been completely investigated. In this paper, we investigate the lossless compression of ECG signals. We show that when compressing ECG signals, utilization of linear prediction, Burrows-Wheeler Transformation, and inversion ranks yield better compression gain in terms of weighted average bit per sample than recently proposed ECG-specific coders. Not only does our proposed technique yield better compression than ECG-specific compressors, it also has a major advantage: with a small modification, the proposed technique may be used as a universal coder.
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Affiliation(s)
- Ziya Arnavut
- Department of Computer Science, SUNY Fredonia, Fredonia, NY 14063, USA.
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18
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Miaou SG, Chao SN. Wavelet-based lossy-to-lossless ECG compression in a unified vector quantization framework. IEEE Trans Biomed Eng 2005; 52:539-43. [PMID: 15759584 DOI: 10.1109/tbme.2004.842791] [Citation(s) in RCA: 67] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In a prior work, a wavelet-based vector quantization (VQ) approach was proposed to perform lossy compression of electrocardiogram (ECG) signals. In this paper, we investigate and fix its coding inefficiency problem in lossless compression and extend it to allow both lossy and lossless compression in a unified coding framework. The well-known 9/7 filters and 5/3 integer filters are used to implement the wavelet transform (WT) for lossy and lossless compression, respectively. The codebook updating mechanism, originally designed for lossy compression, is modified to allow lossless compression as well. In addition, a new and cost-effective coding strategy is proposed to enhance the coding efficiency of set partitioning in hierarchical tree (SPIHT) at the less significant bit representation of a WT coefficient. ECG records from the MIT/BIH Arrhythmia and European ST-T Databases are selected as test data. In terms of the coding efficiency for lossless compression, experimental results show that the proposed codec improves the direct SPIHT approach and the prior work by about 33% and 26%, respectively.
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Affiliation(s)
- Shaou-Gang Miaou
- Multimedia Computing and Telecommunications Laboratory, Department of Electronic Engineering, Chung Yuan Christian University, Chung-Li, 32023 Taiwan, ROC.
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Juhola M, Tossavainen T, Aalto H. Influence of lossy compression on eye movement signals. Comput Biol Med 2004; 34:221-39. [PMID: 15047434 DOI: 10.1016/s0010-4825(03)00059-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2003] [Accepted: 05/16/2003] [Indexed: 11/29/2022]
Abstract
Eye movements considered in our research are physiological signals that are measured in otoneurological balance tests. They are also investigated in other areas of medicine and in psychology. When great amounts of signals are measured in clinical and research work, signal compression is of great use in storing measurements for later investigations. In this research we assessed the influence of lossy compression on medically interesting parameter values that are computed from eye movement signals. We found that high compression ratios with bit rates lower than 1.5 bits per sample on signals with an original resolution of 13 bits per sample produced results without significant changes to the medical parameters values.
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Affiliation(s)
- Martti Juhola
- Department of Computer and Information Sciences, University of Tampere, Post Office Box 607, Tampere 33014, Finland.
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Tossavainen T, Juhola M, Grönfors T. Lossy compression of eye movement and auditory brainstem response signals. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2003; 72:43-54. [PMID: 12850296 DOI: 10.1016/s0169-2607(02)00117-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Eye movement and auditory brainstem response signals recorded for balance and hearing investigations were used as a medical test battery for several types of lossy compression techniques. These signals are associated with the function of the ears. The former signals are used to assess the balance problems (especially vertigo) of a subject and the latter his or her hearing problems. New technique is also presented based on successive approximation quantization. The effect of information loss on medical parameters computed from the signals in the course of compression was evaluated for brainstem response signals. It is important to ensure that lossy compression techniques of these biomedical signals do not impair medical parameter values computed from the signals.
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Affiliation(s)
- Timo Tossavainen
- Department of Computer and Information Sciences, University of Tampere, PO Box 607, 33014 Tampere, Finland
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Giurcăneanu CD, Tăbuş I, Mereuţă S. Using contexts and R-R interval estimation in lossless ECG compression. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2002; 67:177-186. [PMID: 11853943 DOI: 10.1016/s0169-2607(01)00126-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
The paper presents a new lossless ECG compression scheme. The short-term predictor and the coder use conditioning on a small number of contexts. The long-term prediction is based on an algorithm for R-R interval estimation. Several QRS detection algorithms are investigated to select a low complexity and reliable detection algorithm. The coding of prediction residuals uses primarily the Golomb-Rice (GR) codes, but, to improve the coding results, escape codes GR-ESC are used in some contexts for a limited number of samples. Experimental results indicate the good overall performance of the lossless ECG compression algorithms (reducing the storage needs from 12 to about 3-4 bits per sample). The scheme consistently outperforms other waveform or general purpose coding algorithms.
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Affiliation(s)
- Ciprian Doru Giurcăneanu
- Signal Processing Laboratory, Tampere University of Technology, P.O. Box 553, Tampere 33101, Finland
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Batista LV, Melcher EU, Carvalho LC. Compression of ECG signals by optimized quantization of discrete cosine transform coefficients. Med Eng Phys 2001; 23:127-34. [PMID: 11413065 DOI: 10.1016/s1350-4533(01)00030-3] [Citation(s) in RCA: 77] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
This paper presents an ECG compressor based on optimized quantization of Discrete Cosine Transform (DCT) coefficients. The ECG to be compressed is partitioned in blocks of fixed size, and each DCT block is quantized using a quantization vector and a threshold vector that are specifically defined for each signal. These vectors are defined, via Lagrange multipliers, so that the estimated entropy is minimized for a given distortion in the reconstructed signal. The optimization method presented in this paper is an adaptation for ECG of a technique previously used for image compression. In the last step of the compressor here proposed, the quantized coefficients are coded by an arithmetic coder. The Percent Root-Mean-Square Difference (PRD) was adopted as a measure of the distortion introduced by the compressor. To assess the performance of the proposed compressor, 2-minute sections of all 96 records of the MIT-BIH Arrhythmia Database were compressed at different PRD values, and the corresponding compression ratios were computed. We also present traces of test signals before and after the compression/decompression process. The results show that the proposed method achieves good compression ratios (CR) with excellent reconstruction quality. An average CR of 9.3:1 is achieved for PRD equal to 2.5%. Experiments with ECG records used in other results from the literature revealed that the proposed method compares favorably with various classical and state-of-the-art ECG compressors.
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Affiliation(s)
- L V Batista
- COPELE, Federal University of Paraiba, Av. Aprigio Veloso, 882-Bodocongo, 58.109-970, Campina Grande, PB, Brazil.
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