Efficient lossless compression scheme for multi-channel ECG signal processing

This paper presents a block based lossless compression approach for gray scale images. Multiple scanning methods are applied to each block, and a newly proposed adaptive linear prediction is performed. There are different prediction residuals obtained corresponding to different context based on multiple scanning. We choose the best residual for coding. That is, rather than relying on any single scanning, our approach is to select a scanning produces the best result on each block. The prediction coefficients are updated during the scanning to optimize the coding accuracy. Experiment results show that our method outperformed JPEG-LS 4-5% in compression efficiency.

This brief presents a VLSI implementation of an efficient lossless compression scheme for electrocardiogram (ECG) data encoding to save storage space and reduce transmission time. As compression algorithm is able to save storage space and reduce transmission time, this opportunity has been seized by implementing memory-less design while working at a high clock speed in VLSI. ECG compression algorithm comprises two parts: an adaptive linear prediction technique and content-adaptive Golomb Rice code. An efficient and low power VLSI implementation of compression algorithm has been presented. To improve the performance, the proposed VLSI design uses bit shifting operations as a replacement for the different arithmetic operations. VLSI implementation has been applied to the MIT-BIH arrhythmia database which is able to achieve a lossless bit compression rate of 2.77. Moreover, VLSI architecture contains 3.1 K gate count and core of the chip consumes 27.2 nW of power while working at 1 KHz frequency. The core area is 0.05 mm2 in 90 nm CMOS process.

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.

This paper deals with predictive coding techniques for efficient transmission or storage of two-level (black and white) digital images. Part I discusses algorithms for prediction. A predictor transforms the two-dimensional dependence in the original data into a form which can be handled by coding techniques for one-dimensional data. The implementation and performance of a fixed predictor, an adaptive predictor with finite memory, and an adaptive linear predictor are discussed. Results of experiments performed on various types of scanned images are also presented. Part II deals with techniques for encoding the prediction error pattern to achieve compression of data.

Development of a digital signal processor-based new 12-lead synchronization electrocardiogram automatic analysis system

Linear predictive coding is an efficient method for transmitting the amplitudes of moving-area picture elements (pels) in a conditional replenishment coder for video-telephone signals. It has been conjectured that if the linear predictor can dynamically adapt to the speed and direction of motion in the scene, then greatly improved performance should result. To test this conjecture and to get a first-order estimate of the possible saving, computer simulations were carried out using pairs of video-telephone frames stored on digital discs. Using this data, picture quality could not be studied. However, differential signal entropies could be estimated, and this was done for several nonadaptive and adaptive linear predictors. Entropies (in bits per moving-area pel) for adaptive linear predictors were significantly lower than for nonadaptive predictors, indicating that substantial bit-rate savings should be possible. However, simpler implementations will have to be devised before adaptive prediction becomes practicable.

Objective: To measure prevalence of discordance between electrical activity recorded by electrocardiography (ECG) and myocardial activity visualized by echocardiography (echo) in patients presenting after cardiac arrest and to compare survival outcomes in cohorts defined by ECG and echo. Methods: This is a secondary analysis of a previously published prospective study at twenty hospitals. Patients presenting after out-of-hospital arrest were included. The cardiac electrical activity was defined by ECG and contemporaneous myocardial activity was defined by bedside echo. Myocardial activity by echo was classified as myocardial asystole- -the absence of myocardial movement, pulseless myocardial activity (PMA)--visible myocardial movement but no pulse, and myocardial fibrillation- -visualized fibrillation. Primary outcome was the prevalence of discordance between electrical activity and myocardial activity. Secondary outcome was survival to hospital discharge. Results: 793 patients and 1943 pauses in CPR were included. 28.6% of CPR pauses demonstrated a difference in electrical activity (ECG) and myocardial activity (echo), 5.0% with asystole (ECG) and PMA (echo), and 22.1% with PEA (ECG) and myocardial asystole (echo). Survival to hospital admission for patients with PMA (echo) was 29.1% (95%CI-23.9-34.9) compared to those with PEA (ECG) (21.4%, 95%CI-17.7-25.6). Twenty-five percent of the 32 pauses in CPR with a shockable rhythm by echo demonstrated a non-shockable rhythm by ECG and were not defibrillated. One of these patients survived, a patient with asystole on ECG and vfib by echo survived because vfib was identified on ECG during a subsequent pause and was defibrillated. Conclusion: Patients in cardiac arrest commonly demonstrate different electrical (ECG) and myocardial activity (echo). Further research is needed to better define cardiac activity during cardiac arrest and to explore outcome between groups defined by electrical and myocardial activity.

For some classes of signals, particularly those dominated by low frequency components, such as seismic data, first and higher order differences between adjacent signal samples are much smaller compared with the signal samples. In this paper, evaluating the differencing approach for losslessly compressing several candidate classes of signals is given. Three different approaches employing derivatives are applied yielding different results for each signal class. The performance of these lossless techniques and the adaptive linear predictor are compared for the different signal categories. Preliminary results indicate that the derivative technique, of different orders, for lossless compression is promising. Although the residual may be slightly higher compared with the linear predictor approach, the differentiator coefficients are integers which do not have to be encoded and much fewer computations are needed to compute the residual.

Comparison of outcomes between pulseless electrical activity by electrocardiography and pulseless myocardial activity by echocardiography in out-of-hospital cardiac arrest; secondary analysis from a large, prospective study

In this paper a waveform coder configuration based on non linear adaptive prediction will be described. The coder is based on the characteristic of Volterra predictors to model non linear phenomena and to gather informations about the periodicity of the signal via high order statistical moments. The main result is that, by using this type of predictor, lower variance error signals can be obtained, as compared to the classical, linear, case.

A new method is presented for estimation of missing values for the problem of periodically missing observations. The proposed method is a modification of the conventional linear prediction method in that it uses adaptive order for prediction, it combines both forward prediction and backward prediction, and it allows estimation of missing values to be carried out for multi-passes. It is shown that the new method performs better than the conventional linear prediction method when the number of remaining samples in one period is smaller than the order of the underlying model for the observed signal. An example using real data is illustrated.

A two-dimensional (2D) fast affine projection algorithm (FAPA) is presented for 2D adaptive filtering and linear prediction. The derivation of the proposed algorithm is based on the spatial shift invariant properties that 2D discrete time signals possess. The proposed algorithm has low computational complexity, comparable to that of the 2D LMS algorithm. The convergence speed and tracking ability of the proposed scheme are comparable to those of the higher complexity 2D RLS algorithms.

Wind Energy Conversion Systems (WECS) cannot react instantaneously to wind speed variations. Therefore, the performance of the system and the energy conversion capability of the rotor – particularly for urban locations, characterized as having an erratic wind resource -- are negatively affected. This study investigates the use of a time series technique as a means to improve the performance and conversion efficiency of wind turbines. In this regard, simulation of the wind sensor maximum power point tracking (MPPT) method (ideal situation) through MATLAB-Simulink™ is considered. Analysis of the Adaptive Linear Prediction (ALP) technique as a real time control reference to improve optimal control of wind turbines suggests that predictive techniques such as ALP can offer increased energy yield opportunities. Indeed, the energy capture technique investigated in this study, suggested that an energy yield increase in excess of 5% is achievable.

We propose and experimentally demonstrate a Brillouin Optical Time Domain Analysis sensor combining adaptive linear prediction with cyclic pulse coding for fast and accurate strain measurement over 10km standard singlemode fiber with meter-scale spatial resolution.

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