Electrocardiogram Compression Technique Using DWT-Based Residue Encoder with Desired Reconstruction Quality

Preserving Abnormal Beat Morphology in Long-Term ECG Recording: An Efficient Hybrid Compression Approach

Abstract: In long-term electrocardiogram (ECG) recording for arrhythmia monitoring, using a uniform compression strategy throughout the entire data to achieve high compression efficiency may result in unacceptable distortion of abnormal beats. The presented work addressed a solution to this problem, rarely discussed in published research. A support vector machine (SVM)-based binary classifier was implemented to identify the abnormal beats, achieving a classifier sensitivity (SE) and negative predictive value (NPV) of 99.89% and 0.003%, respectively with 34 records from MIT-BIH Arrhythmia database (mitdb). A hybrid lossy compression technique was implemented to ensure on-demand quality, either in terms of distortion or compression ratio (CR) of ECG data. A wavelet-based compression for the abnormal beats was implemented, while the consecutive normal beats were compressed in groups using a hybrid encoder, employing a combination of wavelet and principal component analysis. Finally, a neural network-based intelligent model was used, which was offline tuned by a particle swarm optimization (PSO) technique, to allocate optimal quantization level of transform domain coefficients generated from the hybrid encoder. The proposed technique was evaluated with four types of morphology tags, “A,” “F,” “L,” and “V,” from mitdb database, achieving less than 2% PRDN and less than 1% in two diagnostic distortion measures for abnormal beats. Overall, an average CR of 19.78 and PRDN of 3.34% was obtained. A useful outcome of the proposed technique is the low reconstruction time in rapid screening of long arrhythmia records, while only abnormal beats are presented for evaluation.

A Real-Time QRS Detection Algorithm

Abstract: We have developed a real-time algorithm for detection of the QRS complexes of ECG signals. It reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width. A special digital bandpass filter reduces false detections caused by the various types of interference present in ECG signals. This filtering permits use of low thresholds, thereby increasing detection sensitivity. The algorithm automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate. For the standard 24 h MIT/BIH arrhythmia database, this algorithm correctly detects 99.3 percent of the QRS complexes.

Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm

Abstract: A wavelet electrocardiogram (ECG) data codec based on the set partitioning in hierarchical trees (SPIHT) compression algorithm is proposed in this paper. The SPIHT algorithm (A. Said and W.A. Pearlman, IEEE Trans. Ccts. Syst. II, vol. 6, p. 243-50, 1996) has achieved notable success in still image coding. The authors modified the algorithm for the one-dimensional case and applied it to compression of ECG data. Experiments on selected records from the MIT-BIH arrhythmia database revealed that the proposed codec is significantly more efficient in compression and in computation than previously proposed ECG compression schemes. The coder also attains exact bit rate control and generates a bit stream progressive in quality or rate.

Wavelet and wavelet packet compression of electrocardiograms

Abstract: Wavelets and wavelet packets have recently emerged as powerful tools for signal compression. Wavelet and wavelet packet-based compression algorithms based on embedded zerotree wavelet (EZW) coding are developed for electrocardiogram (ECG) signals, and eight different wavelets are evaluated for their ability to compress Holter ECG data. Pilot data from a blind evaluation of compressed ECG's by cardiologists suggest that the clinically useful information present in original ECG signals is preserved by 8:1 compression, and in most cases 16:1 compressed ECG's are clinically useful.

ECG Data Compression Using Fourier Descriptors

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.

A wavelet transform-based ECG compression method guaranteeing desired signal quality

Abstract: This paper presents a new electrocardiogram (ECG) compression method based on orthonormal wavelet transform and an adaptive quantization strategy, by which a predetermined percent root mean square difference (PRD) can be guaranteed with high compression ratio and low implementation complexity.