Compression and encryption for ECG biomedical signal in healthcare system

The ECG data needs large memory storage device due to continuous heart rate logs and vital parameter storage. Thus, efficient compression schemes are applied to it before sending it to the telemedicine center for monitoring and analysis. Proper compression mechanisms can not only improve the storage efficiency but also help in faster porting of data from one device to another due to its compact size. Also, the collected ECG signals are processed through various filtering techniques to remove unnecessary noise and then compressed. In our scheme, we propose use of buffer blocks, which is quite novel in this field. Usage of highly efficient methods for peak detection, noise removal, compression and encryption enable seamless and secure transmission of ECG signal from sensor to the monitor. This work further makes use of AES 256 CBC mode, which is barely used in embedded devices, proves to be very strong and efficient in ciphering of the information. The PRD outcome of proposed work comes as 0.41% and CR as 0.35%, which is quite better than existing schemes. Experimental results prove the efficiency of proposed schemes on five distinct signal records from MIT-BIH arrhythmia datasets

Compression of ECG signals using variable-length classified vector sets and wavelet transforms

In this article, an improved and more efficient algorithm for the compression of the electrocardiogram (ECG) signals is presented, which combines the processes of modeling ECG signal by variable-length classified signature and envelope vector sets (VL-CSEVS), and residual error coding via wavelet transform. In particular, we form the VL-CSEVS derived from the ECG signals, which exploits the relationship between energy variation and clinical information. The VL-CSEVS are unique patterns generated from many of thousands of ECG segments of two different lengths obtained by the energy based segmentation method, then they are presented to both the transmitter and the receiver used in our proposed compression system. The proposed algorithm is tested on the MIT-BIH Arrhythmia Database and MIT-BIH Compression Test Database and its performance is evaluated by using some evaluation metrics such as the percentage root-mean-square difference (PRD), modified PRD (MPRD), maximum error, and clinical evaluation. Our experimental results imply that our proposed algorithm achieves high compression ratios with low level reconstruction error while preserving the diagnostic information in the reconstructed ECG signal, which has been supported by the clinical tests that we have carried out.ISIK University [06B302]The author would like to special thank Prof. Siddik Yarman who is Board of Trustees Chairman of the ISIK University and Umit Guz, Assistant Professor at the ISIK University for their valuable contributions and continuous interest in this article. The author also would like to thank Prof. Osman Akdemir who is a cardiologist in the Department of Cardiology at the T. C. Maltepe University and Dr. Ruken Bengi Bakal who is a cardiologist in the Department of Cardiology at the Kartal Kosuyolu Yuksek Ihtisas Education and Research Hospital for their valuable clinical contributions and suggestions and the reviewers for their constructive comments which improved the technical quality and presentation of the article. The present work was supported by the Scientific Research Fund of ISIK University, Project number 06B302.Publisher's Versio

High-rate compression of ECG signals by an accuracy-driven sparsity model relying on natural basis

Long duration recordings of ECG signals require high compression ratios, in particular when storing on portable devices. Most of the ECG compression methods in literature are based on wavelet transform while only few of them rely on sparsity promotion models. In this paper we propose a novel ECG signal compression framework based on sparse representation using a set of ECG segments as natural basis. This approach exploits the signal regularity, i.e. the repetition of common patterns, in order to achieve high compression ratio (CR). We apply k-LiMapS as fine-tuned sparsity solver algorithm guaranteeing the required signal reconstruction quality PRDN (Normalized Percentage Root-mean-square Difference). Extensive experiments have been conducted on all the 48 records of MIT-BIH Arrhythmia Database and on some 24 hour records from the Long-Term ST Database. Direct comparisons of our method with several state-of-the-art ECG compression methods (namely ARLE, Rajoub's, SPIHT, TRE) prove its effectiveness. Our method achieves average performances that are two-three times higher than those obtained by the other assessed methods. In particular the compression ratio gap between our method and the others increases with growing PRDN

Wavelet-based low-delay ECG compression algorithm for continuous ECG transmission

The delay performance of compression algorithms is particularly important when time-critical data transmission is required. In this paper, we propose a wavelet-based electrocardiogram (ECG) compression algorithm with a low delay property for instantaneous, continuous ECG transmission suitable for telecardiology applications over a wireless network. The proposed algorithm reduces the frame size as much as possible to achieve a low delay, while maintaining reconstructed signal quality. To attain both low delay and high quality, it employs waveform partitioning, adaptive frame size adjustment, wavelet compression, flexible bit allocation, and header compression. The performances of the proposed algorithm in terms of reconstructed signal quality, processing delay, and error resilience were evaluated using the Massachusetts Institute of Technology University and Beth Israel Hospital (MIT-BIH) and Creighton University Ventricular Tachyarrhythmia (CU) databases and a code division multiple access-based simulation model with mobile channel noiseope

Compressão de sinais biomédicos

Compressors for electrocardiograms and electroencephalograms have been reported in the literature over the last decades, but there is a lack of works describing general solutions for all biomedical signals. Aiming to fill this gap, this work discusses compression methods that work well for all biomedical signals. The lossless compression methods are based in wavelet transforms and linear predictors associated with several entropy coders. The lossy compaction strategies use trigonometric and wavelet transforms, followed by vector quantizers based in dead-zone quantization by Lagrangian Minimization and Successive Approximation Quantizations. The entropy coders are based in Prediction by Partial Matching, Run Length Encoding and Set Partitioning in Hierarchical Trees. The methods were tested usign the signals of the MIT/BIH Polysomnographic Database. Lossless compressors achieved compression ratios of at most 4.818:1, while the lossy methods achieved compression ratios of at most 818.055:1. Smoother signals, as respiration and oxygen saturation records, presented better reconstructions with wavelets and Successive Approximation Quantization, despite the lower compression performances. Contrastively, for the same quality of visual reconstruction, trigonometric transforms and Lagrangian Minimization achieved better compression performances for rougher signals – as electroencephalograms and electromyograms.NenhumaCompressores de eletrocardiogramas e eletroencefalogramas vêm sendo descritos na literatura ao longo das últimas décadas, mas há uma falta de trabalhos que apresentem soluções gerais para todos os sinais biomédicos. Visando preencher essa lacuna, este trabalho discute métodos de compressão que funcionem satisfatoriamente para os sinais biomédicos. Os métodos de compressão sem perdas de informação estudados se baseiam em transformadas wavelet e preditores lineares associados a diversos codificadores de entropia. As estratégias de compactação com perdas de informação apresentadas usam transformadas trigonométricas e wavelets seguidas por quantizadores vetoriais baseados em quantização com zona-morta gerados por Minimização Lagrangiana e em Quantizações por Aproximações Sucessivas. Os codificadores de entropia usados se baseiam em Predição por Casamento Parcial, Codificação por Comprimento de Sequência e Particionamento de Conjuntos em Árvores Hierárquicas. Os métodos foram testados usandoossinaisdoMIT/BIHPolysomnographic Database. Oscompressoressemperdas de informação obtiveram razões de compressão até 4,818 : 1, dependendo do tipo de registro biomédico, enquanto os métodos com perdas obtiveram razões de compressão até 818,055 : 1. Os sinais mais suaves, como os de respiração e saturação de oxigênio, apresentaram melhores reconstruções com wavelets e Quantização por Aproximações Sucessivas, apesar dos menores desempenhos de compressão. Contrastivamente, para a mesma qualidade de reconstrução visual, transformadas trigonométricas e Minimização Lagrangiana obtiveram melhores eficácias de compressão para sinais mais variáveis – como eletroencefalogramas e eletromiogramas

Design and Evaluation of portable telemedicine system over wireless network

생체공학 협동과정/석사[한글]이동형 응급 의료 시스템은 최근에 발전되고 있는 이동 통신망에 맞추어 유비쿼터스 헬스 케어 서비스를 제공하기 위한 새로운 연구 분야이다. 휴대형 원격 진료 시스템의 서비스 프로세스는 생체신호의 측정 / 신호의 분석 및 신호처리 / 데이터의 전송 / 전송된 데이터의 진단과 조치로 구분된다. 본 논문에서는 현재 서비스 되고 있는 CDMA 1X 및 1X EV-DO 무선 환경에 효과적인 휴대형 원격 진료 시스템 설계하기 위해 세가지 중요한 이슈에 대한 새로운 알고리즘을 단계별로 제안하고 다양한 측면에서 성능을 분석하였다. 먼저, 산소포화도 신호의 동잡음 제거는 생체 계측기의 실제적인 정확성과 일반적인 적응성을 유지하는 중요한 요소이다. 그러므로 본 논문에서는 두개의 파장을 가진 산소포화도 신호에 대해 새롭게 적용되는 독립요소 분석 방법을 사용하여 동잡음 제거 알고리즘을 제안하였다. 보다 효과적인 독립요소 분석을 위해 자기 상관 함수를 이용한 주기 측정, 측정된 주기를 통한 인터리빙(주파수 재배열)과 시간축 저대역 필터링, 신호의 독립성 증가를 위한 이노베이션 처리라는 3가지 전처리 과정을 추가하였다. 두 번째, 무선 환경에서의 대역폭 사용을 최대화하기 위해 생체 신호 특히 심전도 신호의 효과적인 압축이 요구된다. 웨이브릿 기반 심전도 압축 방법은 많은 이동형 tele-cardiology 어플리케이션에 사용되고 있다. 하지만 높은 압축 성능을 위해 요구되는 많은 데이터 크기는 심각한 지연을 야기하기 때문에 실시간으로 지속적인 심전도 신호의 전송을 위해 낮은 지연을 가진 새로운 압축 알고리즘(WLDECG)을 제안하였다. 제안된 알고리즘은 심전도 신호의 주기적 특성을 이용하여 하나의 파형을 에너지에 따라 두 가지 블록으로 구분하여 압축률을 유기적으로 할당함으로써 낮은 지연을 가지면서 압축 성능을 기존의 알고리즘보다 향상시켰다. 이러한 낮은 지연과 압축 성능 향상을 모두 만족시키기 위해 파형 분할, 프레임 크기의 조절, 웨이브릿 압축, 유기적인 비트 할당과 헤더 압축을 통합적으로 적용하였다. 세 번째는CDMA 1X와 CDMA 1X EV-DO 무선 통신망에서 사용할 수 있는 휴대용 원격진료 시스템을 설계하였다. 동잡음이 제거되고 압축된 생체신호 데이터 패킷에 대해 순열 번호와 CRC 코드를 추가하여 에러에 대한 손실 및 손상에 대한 재전송 과정을 추가하였고, 상황에 따라 전방향 오류 정정 부호를 추가하여 전송하거나 중복 전송하는 방법을 사용하였다. 또한 각 전송 데이터에 전송되는 타임을 입력하여 수신단에 도착하는 시간을 체크함으로써 대역폭을 실시간으로 확인하고 이러한 대역폭의 변화에 따라 생체신호를 제외한 다른 데이터 전송의 크기를 제어하였다. 제안된 알고리즘의 성능은 동잡음 제거와 압축으로 인해 복원된 신호의 품질, 신호 획득 / 전송 / 데이터 처리에 관련된 모든 지연 시간 측정, 에러에 대한 강인성을 MIT-BIH 데이터 베이스와 실제 필드 테스트를 사용하여 평가하였다. [영문]The mobile tele-medicine system is the new research area that support an ubiquitous health care based on mobile telecommunication networks. The service process of the portable mobile tele-medicine system is consist of measuring many kinds of vital signal, analyzing and processing the acquired signal, considering the stable data transmission, and diagnosis of the received data. In this paper, the new algorithm of three considerable issues are proposed for effective mobile telemedicine system based on CDMA 1X and 1X EV-DO mobile network, and the performance was analyzed on many-side. First, Removing the motion artifact from measured Photoplethysmography signals is one of important issues to be tackled for accurate measurement of arterial oxygen saturation. Therefore in this paper, the motion artifact reduction method under the constraint of dual wavelengths measurement was proposed by newly adopting the independent component analysis. The pre-processing procedures, consisting of time low-pass filtering with periodicity detection and block interleaving, and innovation process, was suggested to enhance the input signal conditioning in combination with Fast ICA method. Second, efficient compression of vital signal specially ECG(Electro Cardio Gram) is required in order to maximize the available bandwidth of the installed wireless communications network. A wavelet based ECG data compression has become an attractive and efficient method in many mobile tele-cardiology applications. But large data size required for high compression performance leads a serious delay, so that we propose a wavelet-based ECG compression algorithm with a low delay property for instantaneous, continuous ECG transmission suitable for telecardiology applications over a wireless network. The proposed algorithm reduces the frame size as much as possible to achieve a low delay, while maintaining reconstructed signal quality. In order to attain both low delay and high quality, it employs waveform partitioning, adaptive frame size adjustment, wavelet compression, flexible bit allocation, and header compression.. Third, overall integrated tele-medicine system is designed over the CDMA 1X and EV-DO mobile network. Transmitting data packet, which removed the motion artifact and compressed using WLDECG(Wavelet-based Low Delay ECG Compression Algorithm for Continuous ECG Transmission) method, consist of sequence number and CRC check code for the re-transmission about error loss and corruption. According to circumstances of the mobile network, error correction or duplicated packets are generated. The performances of the proposed algorithm and system in terms of reconstructed signal quality, overall delay, and error resilience were evaluated using the MIT-BIH and CU databases and a field test over CDMA networks.ope