Martin Vítek

Adaptive Wavelet Wiener Filtering of ECG Signals

In this study, we focused on the reduction of broadband myopotentials (EMG) in ECG signals using the wavelet Wiener filtering with noise-free signal estimation. We used the dyadic stationary wavelet transform (SWT) in the Wiener filter as well as in estimating the noise-free signal. Our goal was to find a suitable filter bank and to choose other parameters of the Wiener filter with respect to the signal-to-noise ratio (SNR) obtained. Testing was performed on artificially noised signals from the standard CSE database sampled at 500 Hz. When creating an artificial interference, we started from the generated white Gaussian noise, whose power spectrum was modified according to a model of the power spectrum of an EMG signal. To improve the filtering performance, we used adaptive setting parameters of filtering according to the level of interference in the input signal. We were able to increase the average SNR of the whole test database by about 10.6 dB. The proposed algorithm provides better results than the classic wavelet Wiener filter.

Classification of genomic signals using dynamic time warping

BackgroundClassification methods of DNA most commonly use comparison of the differences in DNA symbolic records, which requires the global multiple sequence alignment. This solution is often inappropriate, causing a number of imprecisions and requires additional user intervention for exact alignment of the similar segments. The similar segments in DNA represented as a signal are characterized by a similar shape of the curve. The DNA alignment in genomic signals may adjust whole sections not only individual symbols. The dynamic time warping (DTW) is suitable for this purpose and can replace the multiple alignment of symbolic sequences in applications, such as phylogenetic analysis.MethodsThe proposed method is composed of three main parts. The first part represent conversion of symbolic representation of DNA sequences in the form of a string of A,C,G,T symbols to signal representation in the form of cumulated phase of complex components defined for each symbol. Next part represents signals size adjustment realized by standard signal preprocessing methods: median filtration, detrendization and resampling. The final part necessary for genomic signals comparison is position and length alignment of genomic signals by dynamic time warping (DTW).ResultsThe application of the DTW on set of genomic signals was evaluated in dendrogram construction using cluster analysis. The resulting tree was compared with a classical phylogenetic tree reconstructed using multiple alignment. The classification of genomic signals using the DTW is evolutionary closer to phylogeny of organisms. This method is more resistant to errors in the sequences and less dependent on the number of input sequences.ConclusionsClassification of genomic signals using dynamic time warping is an adequate variant to phylogenetic analysis using the symbolic DNA sequences alignment; in addition, it is robust, quick and more precise technique.

A Wavelet-Based ECG Delineation in Multilead ECG Signals: Evaluation on the CSE Database

In this paper, we present simple and fast approach for electrocardiogram (ECG) delineation based on a continuous wavelet transform (CWT). First of all, QRS complexes are detected. Then, QRS onset and end are found for each QRS complex. In the next step, the T wave is detected between the QRS end and the next QRS onset. The T wave end is found between the position of T wave and the next QRS onset. Finally, the P wave is detected between the T wave end and the next QRS onset. In the last step, the P wave onset and end are found. These significant points are found separately in each lead of the ECG signal. Global multilead positions were determined from singlelead positions by using special selection rule. The presented algorithm was evaluated on the standard CSE database, which contains reference global positions common for all leads. We obtained two sets of results, first for evaluation on 12 standard leads and second for evaluation on Frank leads. Calculated standard deviations for 12 standard leads were much smaller than given accepted tolerances for most of the characteristic points and standard deviations for Frank leads accomplished given tolerances for most of the characteristic points. The proposed algorithm did well in comparison with other algorithms, especially standard deviations for the T wave end are very interesting: s = 12.2 ms (12 standard leads), s = 19.7 ms (Frank leads).

Progressive alignment of genomic signals by multiple dynamic time warping

This paper presents the utilization of progressive alignment principle for positional adjustment of a set of genomic signals with different lengths. The new method of multiple alignment of signals based on dynamic time warping is tested for the purpose of evaluating the similarity of different length genes in phylogenetic studies. Two sets of phylogenetic markers were used to demonstrate the effectiveness of the evaluation of intraspecies and interspecies genetic variability. The part of the proposed method is modification of pairwise alignment of two signals by dynamic time warping with using correlation in a sliding window. The correlation based dynamic time warping allows more accurate alignment dependent on local homologies in sequences without the need of scoring matrix or evolutionary models, because mutual similarities of residues are included in the numerical code of signals.

Set of rules for genomic signal downsampling

Comparison and classification of organisms based on molecular data is an important task of computational biology, since at least parts of DNA sequences for many organisms are available. Unfortunately, methods for comparison are computationally very demanding, suitable only for short sequences. In this paper, we focus on the redundancy of genetic information stored in DNA sequences. We proposed rules for downsampling of DNA signals of cumulated phase. According to the length of an original sequence, we are able to significantly reduce the amount of data with only slight loss of original information. Dyadic wavelet transform was chosen for fast downsampling with minimum influence on signal shape carrying the biological information. We proved the usability of such new short signals by measuring percentage deviation of pairs of original and downsampled signals while maintaining spectral power of signals. Minimal loss of biological information was proved by measuring the Robinson-Foulds distance between pairs of phylogenetic trees reconstructed from the original and downsampled signals. The preservation of inter-species and intra-species information makes these signals suitable for fast sequence identification as well as for more detailed phylogeny reconstruction.

ECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: A comprehensive experimental study

Accurate detection of cardiac pathological events is an important part of electrocardiogram (ECG) evaluation and subsequent correct treatment of the patient. The paper introduces the results of a complex study, where various aspects of automatic classification of various heartbeat types have been addressed. Particularly, non-ischemic, ischemic (of two different grades) and subsequent ventricular premature beats were classified in this combination for the first time. ECGs recorded in rabbit isolated hearts under non-ischemic and ischemic conditions were used for analysis. Various morphological and spectral features (both commonly used and newly proposed) as well as classification models were tested on the same data set. It was found that: a) morphological features are generally more suitable than spectral ones; b) successful results (accuracy up to 98.3% and 96.2% for morphological and spectral features, respectively) can be achieved using features calculated without time-consuming delineation of QRS-T segment; c) use of reduced number of features (3 to 14 features) for model training allows achieving similar or even better performance as compared to the whole feature sets (10 to 29 features); d) k-nearest neighbours and support vector machine seem to be the most appropriate models (accuracy up to 98.6% and 93.5%, respectively).

Prokaryotic DNA Signal Downsampling for Fast Whole Genome Comparison

Classification of prokaryotes is mainly based on molecular data, since next-generation sequencing platforms provide fast and effective way to capture prokaryotes’ characteristics. However, two different bacterial strains of the same genus can differ in the specific parts of their genomes due to copious amounts of repetitive and transposable parts. Thus, finding an ideal segment of genome for comparison is difficult. Conventional character-based methods rely on multiple sequence alignment, rendering them extremely computationally demanding. Only small parts of genomes can be compared in reasonable time. In this paper, we present a novel algorithm based on the conversion of the whole genome sequences to cumulative phase signals. Dyadic wavelet transform (DWT) is used for lossy compression of phase signals by eliminating redundant frequency bands. Signal classification is then performed as cluster analysis using Euclidean metrics where sequence alignment is replaced by dynamic time warping (DTW).

Automatic Detection of P Wave in ECG During Ventricular Extrasystoles

This work introduces a new method for P wave detection in ECG signals during ventricular extrasystoles. The authors of previous works which deal with detection of P waves tested their algorithms mainly on physiological records (sinus rhythm) and they reached good results for these records. Testing of P wave detection algorithms using pathological records is usually not provided and if it is, the results are notably worse than in the case of physiological records. The automatic and reliable detection of atrial activity in pathological situations is still an unsolved problem. In this work, phasor transform in combination with classification algorithm is used for P wave detection. Phasor transform converts each ECG sample into a phasor which enhances changes in the ECG signal. The classification is based on extraction of morphological features which are derived from each QRS complex. The results of classification are used for demarcation of areas in which P waves are searched using phasor transform. The proposed algorithm was tested on signals no. 106, 119, 214 and 223 from MIT-BIH arrhythmia database, in which the ventricular extrasystoles are present. For validation whether the algorithm is functional also for signals with physiological rhythm, it was tested on the signals no. 100, 101, 103, 117, and 122. The accuracy of the P wave detection in signals with ventricular extrasystoles is Se = 98.94% and PP = 98.30% and in signals without pathology is Se = 98.47% and PP = 99.99%.

Assessment of ECG Signal Quality After Compression

Highly efficient lossy compression algorithms for ECG signals are connected with distortion of the signals; lossy compression is a compromise between compression efficiency and signal quality. It is recommended to express this relation using rate-distortion curve. To decide whether the signal is suitable for further analysis, it is necessary to assess its quality after reconstruction. Although there exist many methods for quality assessment, neither of them is standardized or unified. The methods usually do not offer any information about their acceptable values. This paper introduces 10 new methods for signal quality assessment and their limits. Four methods are simple (entropy, mean, median, spectra similarity), two are based on delineation of ECG (SiP, SiPA), and four combine dynamic time warping, delineation, and calculation of distance (DTWdist, DTWpmfp1, DTWpmfp2, pmfp). These methods are tested on the whole standard CSE database using compression algorithm based on wavelet transform and set partitioning in hierarchical trees. The signals were compressed with various efficiency expressed by average value length (avL). Two ECG experts divided the compressed signals into three quality groups: perfect quality, good quality, not evaluable ECG. Owing to the experts’ ECG classification, we set the range of avL for each quality group. Based on this, we determined corresponding ranges of new methods’ values. Based on the trend of rate-distortion curve, its sensitivity, variability, their ratio at important boundary avL = 0.8 bps, and computational demand of the methods, we recommend four methods for further use.

Overlap Detection for a Genome Assembly Based on Genomic Signal Processing

Although the genome sequences of most studied organisms, like human, E. coli, and others are already known, de novo genome sequencing remains popular as a majority of genomes remains unknown. Unfortunately, sequencing machines are able to read only short fragments of DNA. Therefore, one of the basic steps in reconstructing novel genomes lies in putting these pieces of DNA, called reads, together into complete genome sequences using a process known as genome assembly. Reads joining, however, requires efficient detection of their overlaps. This is commonly performed by comparing the particular characters (A, C, G, T) of the reads using string processing techniques. In this paper, we present an alternative way of detecting overlaps using genomic signal processing. Unlike string comparison, numerical phase signals reflect the complementarity of double stranded DNA making the signal ideal for effective strand independent overlap detection using covariance with high accuracy.