In this paper, a new distortion measure for electrocardiogram (ECG) signal compression, called weighted diagnostic distortion (WDD) is introduced. The WDD measure is designed for comparing the distortion between original ECG signal and reconstructed ECG signal (after compression). The WDD is based on PQRST complex diagnostic features (such as P wave duration, QT interval, T shape, ST elevation) of the original ECG signal and the reconstructed one. Unlike other conventional distortion measures [e.g. percentage root mean square (rms) difference, or PRD], the WDD contains direct diagnostic information and thus is more meaningful and useful. Four compression algorithms were implemented (AZTEC, SAPA2, LTP, ASEC) in order to evaluate the WDD. A mean opinion score (MOS) test was applied to test the quality of the reconstructed signals and to compare the quality measure (MOS/sub error/) with the proposed WDD measure and the popular PRD measure. The evaluators in the WIGS test were three independent expert cardiologists, who studied the reconstructed ECG signals in a blind and a semiblind tests. The correlation between the proposed WDD measure and the MOS test measure (MOS/sub error/) was found superior to the correlation between the popular PRD measure and the MOS/sub error/.

The weighted diagnostic distortion (WDD) measure for ECG signal compression

Describes an approach to the design of optimum QRS detectors. The authors report on detectors including a linear or nonlinear polynomial filter, which enhances and rectifies the QRS complex, and a simple, adaptive maxima detector. The parameters of the filter and the detector, and the samples to be processed are selected by a genetic algorithm which minimizes the detection errors made on a set of reference ECG signals. Three different architectures and the experimental results achieved on the MIT-BIH Arrhythmia Database are described. >

Genetic design of optimum linear and nonlinear QRS detectors

An electrocardiogram (ECG) compression algorithm, called analysis by synthesis ECG compressor (ASEC), is introduced. The ASEC algorithm is based on analysis by synthesis coding, and consists of a beat codebook, long and short-term predictors, and an adaptive residual quantizer. The compression algorithm uses a defined distortion measure in order to efficiently encode every heartbeat, with minimum bit rate, while maintaining a predetermined distortion level. The compression algorithm was implemented and tested with both the percentage rms difference (PRD) measure and the recently introduced weighted diagnostic distortion (WDD) measure. The compression algorithm has been evaluated with the MIT-BIH Arrhythmia Database. A mean compression rate of approximately 100 bits/s (compression ratio of about 30:1) has been achieved with a good reconstructed signal quality (WDD below 4% and PRD below 8%). The ASEC was compared with several well-known ECG compression algorithms and was found to be superior at all tested bit rates. A mean opinion score (MOS) test was also applied. The testers were three independent expert cardiologists. As In the quantitative test, the proposed compression algorithm was found to be superior to the other tested compression algorithms.

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ECG signal compression using analysis by synthesis coding

Disclosed is to provide means for supporting the diagnosis by quantitatively measuring the presence of abnormal condition in the heart of a to-be-tested person and the factor thereof (cause of the disease) from the data of magnetic field strengths measured at a plurality of measuring positions. Feature parameters are automatically picked up from the measured data to calculate Mahalanobis distances thereof, and any abnormal function of the heart is detected relying upon the magnitude thereof. Further, chief factors that cause an increase in the Mahalanobis distance are analyzed to specify the cause of a disease.

Biomagnetic field measuring apparatus

Smart clothes increase the efficiency of long-term non-invasive monitoring systems by facilitating the placement of sensors and increasing the number of measurement locations. Since the sensors are either garment-integrated or embedded in an unobtrusive way in the garment, the impact on the subject's comfort is minimized. However, the main challenge of smart clothing lies in the enhancement of signal quality and the management of the huge data volume resulting from the variable contact with the skin, movement artifacts, non-accurate location of sensors and the large number of acquired signals. This paper exposes the strategies and solutions adopted in the European 1ST project MyHeart to address these problems, from the definition of the body sensor network to the description of two embedded signal processing techniques performing on-body ECG enhancement and motion activity classification.

Combination of body sensor networks and on-body signal processing algorithms: the practical case of MyHeart project

The authors have developed an algorithm for detection of QRS complexes of ambulatory electrocardiogram (ECG) signals. The threshold, which is obtained from a distribution function of the amplitude of the filtered ECG signal, changes with time, adapting to changes in the QRS morphology, levels of noise, and artifacts. The threshold extracts the period which include the QRS complex, and then the QRS complex is detected during this period. By using 20 series of single-channel ECG data selected from the AHA arrhythmia database, the usefulness of this algorithm was investigated, counting both numbers of false positive (FP) and false negative (FN) QRS complexes. Correct detection of over 99% was achieved. >

Adaptive threshold QRS detection algorithm for ambulatory ECG

PURPOSE: To compress data with high compressibility and high precision by reducing the information amount of residual data for which pulse removal is performed by using a multitemplate corresponding to normal and abnormal pulses generated by a learning function. CONSTITUTION: A QRS detection part 8 specifies a pulse position from digital electrocardiogram data Sd, inputs QRS wave data Se of the waveform to an analysis part 12, reads the template of a similar waveform from the templates registered on a template storage part 16 and sends it to a residual data generation part 10. At this part 10, residual data Sf subtracting the waveform of the template and the waveform of the digital electrocardiogram data Sd are outputted. Further, the analysis part 12 judges whether the information amount of the residual data Sf is within a previously set value or not, when the data amount of the residual data Sf exceeds the threshold value, the waveform of these digital electrocardiogram data Sd is registered as a new template, and the following residual data Sf are reduced to prescribed information quantity. COPYRIGHT: (C)1994,JPO&Japio

Electrocardiogram data processor

Proposes a new adaptive method of data compression for digital ambulatory electrocardiograms (ECGs), considering the diagnostic significance of each segment of the ECG. The R-wave is detected, followed by multi-template matching of the detected beat and judgment of the noise level; the templates are successively created during processing. The residual signal (the difference between the original ECG and the best-fit template) is approximated with the FAN data compression method SAPA2 (Scan-Along Polygonal Approximation) and then encoded. The error threshold of FAN is decreased during the P-wave segments and increased during the noise segments; the maximum error of the reconstructed signal at each time is known. This method is applied to ECGs of the AHA (American Heart Association) database and its usefulness is indicated; e.g. the bit rate is approximately 400 bps at 8% PRD (percent RMS difference) and 200 bps at 15% PRD. >

Akira Sasamori

Papers

Adaptive threshold QRS detection algorithm for ambulatory ECG

Electrocardiogram data processor

Adaptive data compression of ambulatory ECG using multi templates

Data compression of ambulatory ECG by using multi-template matching and residual coding