Real-time compression of electrocardiogram using dynamic bit allocation strategy
01 Jan 2016-pp 21-25
TL;DR: This paper describes a real-time, lossy ECG compression algorithm based on delta encoding using variable length symbols, which performs a dynamic bit allocation (DBA) algorithm for each block of ECG samples to optimize the BPS efficiency.
Abstract: Electrocardiogram (ECG) compression for patient monitoring is a pre-requisite for efficient utilization of the communication link. Few recent works using delta encoding adopted fixed length symbols were analyzed, and we found that their bits per sample efficiency (BPS) is low, specially in low slope regions of ECG wave. For real-time tele-monitoring, this may lower the link throughput. This paper describes a real-time, lossy ECG compression algorithm based on delta encoding using variable length symbols. For this, the zonal complexity and inter-sample slope was estimated in a fixed length block of 52 samples. Each block was attributed as ‘complex’, ‘semi-complex’ and ‘plain’ by comparing the local measures with global ones, which were continuously updated. The encoder performs a dynamic bit allocation (DBA) algorithm for each block of ECG samples to optimize the BPS efficiency. The algorithms were tested on simulation platform with single lead ECG record from MIT BIH compression test data (cdb) and MIT BIH arrhythmia database (mitdb) at 10 bit quantization level, yielding an average BPS of 1.92 with cdb and 1.91 with mitbdb, and low PRD (1.87 and 2.42 respectively with cdb and mitdb).
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01 Dec 2016
TL;DR: Review of the different ECG compression techniques andECG compression implementations available in literature is presented in this paper, which can be helpful for identifying suitable technique for application specific requirements.
Abstract: A duration of century past over to the invention of electrocardiogram (ECG) but even today also electrocardiogram (ECG) monitoring is playing its important role of help to take care of patients having cardiac diseases. Electrocardiogram (ECG) data compression is important for portable solutions, for reducing the storage requirements and low power consumption. Large variety of ECG compression algorithms and their implementation have been proposed in literature till date. Selection of the appropriate ECG compression technique needs important considerations such as storage requirements and communication link utilization. Further power consumption and chip area requirements depend on the computational complexity of ECG compression algorithm. Thus identifying suitable ECG compression technique for the given application requirements remains a big challenge from implementation point of view. Review of the different ECG compression techniques and ECG compression implementations available in literature is presented in this paper, which can be helpful for identifying suitable technique for application specific requirements.
13 citations
Cites background or methods from "Real-time compression of electrocar..."
...But it is found that use of magnitude encoding, run length encoding and ASCII character encoding for ECG data compression with fix length of encoded character leads to poor bits per sample (BPS) efficiency in low slope and equipotential regions of ECG [15]....
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...In direct data compression methods delta pulse code modulation (DPCM) [13][15] based ECG data compression methods provide the better bits per sample efficiency with good compromise between the CR and PRD as compared to other methods such as AZTEC [9], TP [10] and CORTES [11] etc....
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...ECG data compression algorithm based on dynamic bit allocation strategy and delta encoding has been proposed in [15]....
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TL;DR: The proposed LLDC can detect the abnormal symptoms and dynamically change the compression modes with different sample intervals and data precisions, and the symptoms are detected and the transmitted power and temperature for the wearable devices are reduced.
Abstract: In this paper, the lossless and lossy direct compression design (LLDC) is proposed for the wearable devices. The electrocardiogram (ECG), blood pressure (BP), and respiration (RESP) can be applied to the compression design for cardiovascular diseases. The proposed LLDC can detect the abnormal symptoms and dynamically change the compression modes with different sample intervals and data precisions. In our experiments, the symptoms are detected, and the transmitted power and temperature for the wearable devices are reduced. The compression ratios for ECG, BP, and RESP are up to 7.94, 6.06, and 6.51 with percentage root-mean-square difference less than 6.5%. For ECG, BP, and RESP, the energies of the wearable devices are reduced by 50%–87%, 38%–89%, and 41%–89%; the temperatures are reduced by 2.6 °C–4.2 °C, 1.3 °C–3 °C, and 1.7 °C–3.6 °C, respectively.
6 citations
Cites methods from "Real-time compression of electrocar..."
...The signals from MIT-BIH arrhythmia database (mitdb) [8] and MIT-BIH polysomnographic database (slpdb) [45] are applied....
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...Direct methods are directly carried original signals in time domain, such as AZTEC [20], TP [22], CORTES [21], FAN/SAPA [43], DPCM-based with threshold [44], and dynamic bit allocation [45]....
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TL;DR: A new data routing scheme, which is based on residual energy in a node and equalization among the neighbours to achieve enhanced average node lifetime under short range monitoring scenario is presented.
Abstract: Biomedical sensor networks find wide applications in human health monitoring. In such applications, routing strategies in the sensor nodes play a key role towards energy efficiency of the overall system. In this work, we present a new data routing scheme, which is based on residual energy in a node and equalization (named EEQ) among the neighbours to achieve enhanced average node lifetime under short range monitoring scenario. The scheme was hardware implemented using 12 number of indigenous ATmega328 based static biomedical sensor nodes (BSN) arranged in a grid matrix spread over a floor area of 564 sqm. The objective was to collect short duration electrocardiogram and photoplethysmogram signals from human subjects in a local supervisory computer placed outside the grid. Under simulation platform using 100 BSNs with first order radio model, it was found that the average node lifetime was enhanced by 17% against without EEQ evaluated over 10,000 consecutive data collection sessions. The proposed scheme can be useful for providing low cost solution in healthcare settings in developing nations like India.
5 citations
TL;DR: In this article , the authors presented wireless interconnected smart biomedical sensors for cardiac arrhythmia detection and remote monitoring of a group of in-house static patients arranged in a regular grid matrix.
Abstract: Energy consumption has been a major bottleneck in wireless sensor network applications. This article presents wireless interconnected smart biomedical sensors for cardiac arrhythmia detection and remote monitoring of a group of in-house static patients arranged in a regular grid matrix. In-node continuous detection of arrhythmias is performed using an autoencoder with a decision tree (DT) classifier from single-channel ECG data. The accumulated arrhythmic episodes over some time in each node are delivered through a residual energy-based intelligent routing to a mobile sink, which uploads them to a cloud-based server for remote access by an expert physician. The energy consumption of the node and network is minimized in three stages. First, an event-triggered data routing strategy allows intermittent transmission instead of a continuous one. Second, the arrhythmic episodes are delivered to a mobile sink (a caregiver) based on its location sensing to minimize the number of hops in a routing cone. Third, an adaptive power level adjustment at the destination node is done in accordance with the mobility of the sink. The scheme was hardware implemented with 15 sensor nodes utilizing an ARM-based standalone controller in a 52.68 sqm floor area with a latency of 1.69 ms per byte and energy consumption of 0.338 mJ per byte, with a packet reception accuracy of 97.8% over 4000 sessions of transmission. The blind test accuracy of arrhythmia detection over nearly 20 442 cardiac beats from the MIT BIH arrhythmia dataset containing NSR, L, R, and V beats was 99.48% with a beat abnormality detection latency of 16 ms.
1 citations
TL;DR: In this article , the authors presented wireless interconnected smart biomedical sensors for cardiac arrhythmia detection and remote monitoring of a group of in-house static patients arranged in a regular grid matrix.
Abstract: Energy consumption has been a major bottleneck in wireless sensor network applications. This paper presents wireless interconnected smart biomedical sensors for cardiac arrhythmia detection and remote monitoring of a group of in-house static patients arranged in a regular grid matrix. In node continuous detection of arrhythmias is performed using an autoencoder with a decision tree classifier from a single-channel ECG data. The accumulated arrhythmic episodes over some time in each node are delivered through a residual energy based intelligent routing to a mobile sink, which uploads them to a cloud-based server for remote access by an expert physician. The energy consumption of the node and network is minimized in three stages. First, an event-triggered data routing strategy allows intermittent transmission instead of continuous one. Second, the arrhythmic episodes are delivered to a mobile sink (a caregiver) based on its location sensing to minimize the number of hops in a routing cone. Third, an adaptive power level adjustment at the destination node is done in accordance with mobility of the sink. The scheme was hardware implemented with 15 sensor nodes utilizing ARM-based standalone controller in a 52.68 sqm floor area with a latency of 1.69 ms per byte and energy consumption of 0.338 mJ per byte, with a packet reception accuracy of 97.8% over 4000 sessions of transmission. The blind test accuracy of arrhythmia detection over nearly 20442 cardiac beats from MIT BIH arrhythmia dataset containing NSR, L, R and V beats was 99.48% with a beat abnormality detection latency of 16 ms.
1 citations
References
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TL;DR: The theoretical bases behind the direct ECG data compression schemes are presented and classified into three categories: tolerance-comparison compression, DPCM, and entropy coding methods and a framework for evaluation and comparison of ECG compression schemes is presented.
Abstract: Electrocardiogram (ECG) compression techniques are compared, and a unified view of these techniques is established. ECG data compression schemes are presented in two major groups: direct data compression and transformation methods. The direct data compression techniques are ECG differential pulse code modulation (DPCM) and entropy coding, AZTEC, Turning-point, CORTES, Fan and SAPA algorithms, peak-picking, and cycle-to-cycle compression methods. The transformation methods include Fourier, Walsh, and Karhunen-Loeve transforms. The theoretical bases behind the direct ECG data compression schemes are presented and classified into three categories: tolerance-comparison compression, DPCM, and entropy coding methods. A framework for evaluation and comparison of ECG compression schemes is presented. >
690 citations
TL;DR: A preprocessing program developed for real-time monitoring of the electrocardiogram by digital computer has proved useful for rhythm analysis.
Abstract: A preprocessing program developed for real-time monitoring of the electrocardiogram by digital computer has proved useful for rhythm analysis. The program suppresses low amplitude signals, reduces the data rate by a factor of about 10, and codes the result in a form convenient for analysis.
374 citations
"Real-time compression of electrocar..." refers methods in this paper
...Historically, the earlier compression techniques adopted DDC methods, some of which are amplitude zone time epoch coding (AZTEC) [2], turning point algorithm (TP) [3], and coordinate reduction time encoding system (CORTES) [4], Fan/SAPA technique and delta coding [5]....
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TL;DR: A new algorithm called CORTES is described here that is suited for real-time applications of ECG analysis and combines the best features of two other techniques called TP and AZTEC.
Abstract: Typically the ECG is sampled at a rate of 200 samples/s or more, producing a large amount of data that are difficult to store, analyze, and transmit. Data-reduction algorithms that operate in real time reduce he amount of data without losing the clinical information content. They must also leave sufficient computation time available for ECG analysis. We describe here a new algorithm called CORTES that is suited for such real-time applications. This algorithm combines the best features of two other techniques called TP and AZTEC. We present the results of a study to find optimal experimental values for the controlling variables in CORTES. We compare the computations of root-mean-square reconstruction errors for a diversity of encoded ECG signals.
277 citations
"Real-time compression of electrocar..." refers methods in this paper
...Historically, the earlier compression techniques adopted DDC methods, some of which are amplitude zone time epoch coding (AZTEC) [2], turning point algorithm (TP) [3], and coordinate reduction time encoding system (CORTES) [4], Fan/SAPA technique and delta coding [5]....
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TL;DR: A new algorithm for ECG signal compression is introduced that can be considered a generalization of the recently published average beat subtraction method, and was found superior at any bit rate.
Abstract: A new algorithm for ECG signal compression is introduced. The compression system is based on the subautoregression (SAR) model, known also as the long-term prediction (LTP) model. The periodicity of the ECG signal is employed in order to further reduce redundancy, thus yielding high compression ratios. The suggested algorithm was evaluated using an in-house database. Very low bit rates on the order of 70 b/s are achieved with a relatively low reconstruction error (percent RMS difference-PRD) of less than 10%. The algorithm was compared, using the same database, with the conventional linear prediction (short-term prediction-STP) method, and was found superior at any bit rate. The suggested algorithm can be considered a generalization of the recently published average beat subtraction method. >
262 citations
"Real-time compression of electrocar..." refers methods in this paper
...Peak picking [10], Long term prediction method [11], linear prediction methods are under parameter extraction method....
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TL;DR: The method of Fourier descriptors (FD's) is presented for ECG data compression, resistant to noisy signals and is simple, requiring implementation of forward and inverse FFT.
Abstract: The method of Fourier descriptors (FD's) is presented for ECG data compression. The two-lead ECG data are segmented into QRS complexes and S-Q intervals, expressed as a complex sequence, and are Fourier transformed to obtain the FD's. A few lower order descriptors symmetrically situated with respect to the dc coefficient represent the data in the Fourier (compressed) domain. While compression ratios of 10:1 are feasible for the S-Q interval, the clinical information requirements limit this ratio to 3:1 for the QRS complex. With an overall compression ratio greater than 7, the quality of the reconstructed signal is well suited for morphological studies. The method is resistant to noisy signals and is simple, requiring implementation of forward and inverse FFT. The results of compression of ECG data obtained from more than 50 subjects with rhythm and morphological abnormalities are presented.
183 citations