A new database with annotations of P waves in ECGs with various types of arrhythmias.

Objective.The aim of this study is to create a database for the development, evaluation and objective comparison of algorithms for P wave detection in ECG signals.BrnoUniversity ofTechnology ECG SignalDatabase with Annotations ofP-Wave (BUT PDB) is an ECG signal database with marked peaks of P waves annotated by ECG experts. Currently, there are only a few databases of pathological ECG signals with P-wave annotations, and some are incorrect.Approach.The pathological ECG signals used in this work were selected from three existing databases of ECG signals: MIT-BIH Arrhythmia Database, MIT-BIH Supraventricular Arrhythmia Database and Long Term AF Database. The P-wave positions were manually annotated by two ECG experts in all selected signals.Main results.The final BUT PDB composed of selected signals consists of 50 two-minute, two-lead pathological ECG signal records with annotated P waves. Each record also contains a description of the diagnosis (pathology) present in the selected part of the record and information about positions and types of QRS complexes.Significance.The BUT PDB is created for developing new, more accurate and robust methods for P wave detection. These algorithms will be used in medical practice and will help cardiologists to evaluate ECG records, establish diagnoses and save time.

Reliable P wave detection in pathological ECG signals.

Accurate automated detection of P waves in ECG allows to provide fast correct diagnosis of various cardiac arrhythmias and select suitable strategy for patients' treatment. However, P waves detection is a still challenging task, especially in long-term ECGs with manifested cardiac pathologies. Software tools used in medical practice usually fail to detect P waves under pathological conditions. Most of recently published approaches have not been tested on such the signals at all. Here we introduce a novel method for accurate and reliable P wave detection, which is success in both normal and pathological cases. Our method uses phasor transform of ECG and innovative decision rules in order to improve P waves detection in pathological signals. The rules are based on a deep knowledge of heart manifestation during various arrhythmias, such as atrial fibrillation, premature ventricular contraction, etc. By involving the rules into the decision process, we are able to find the P wave in the correct location or, alternatively, not to search for it at all. In contrast to another studies, we use three, highly variable annotated ECG databases, which contain both normal and pathological records, to objectively validate our algorithm. The results for physiological records are Se = 98.56% and PP = 99.82% for MIT-BIH Arrhythmia Database (MITDP, with MITDB P-Wave Annotations) and Se = 99.23% and PP = 99.12% for QT database. These results are comparable with other published methods. For pathological signals, the proposed method reaches Se = 96.40% and PP = 91.56% for MITDB and Se = 93.07% and PP = 88.60% for Brno University of Technology ECG Signal Database with Annotations of P wave (BUT PDB). In these signals, the proposed detector greatly outperforms other methods and, thus, represents a huge step towards effective use of fully automated ECG analysis in a real medical practice.

Brno University of Technology Smartphone PPG Database (BUT PPG): Annotated Dataset for PPG Quality Assessment and Heart Rate Estimation.

To the best of our knowledge, there is no annotated database of PPG signals recorded by smartphone publicly available. This article introduces Brno University of Technology Smartphone PPG Database (BUT PPG) which is an original database created by the cardiology team at the Department of Biomedical Engineering, Brno University of Technology, for the purpose of evaluating photoplethysmographic (PPG) signal quality and estimation of heart rate (HR). The data comprises 48 10-second recordings of PPGs and associated electrocardiographic (ECG) signals used for determination of reference HR. The data were collected from 12 subjects (6 female, 6 male) aged between 21 and 61. PPG data were collected by smartphone Xiaomi Mi9 with sampling frequency of 30 Hz. Reference ECG signals were recorded using a mobile ECG recorder (Bittium Faros 360) with a sampling frequency of 1,000 Hz. Each PPG signal includes annotation of quality created manually by biomedical experts and reference HR. PPG signal quality is indicated binary: 1 indicates good quality for HR estimation, 0 indicates signals where HR cannot be detected reliably, and thus, these signals are unsuitable for further analysis. As the only available database containing PPG signals recorded by smartphone, BUT PPG is a unique tool for the development of smart, user-friendly, cheap, on-the-spot, self-home-monitoring of heart rate with the potential of widespread using.

CNproScan: Hybrid CNV detection for bacterial genomes.

Discovering copy number variation (CNV) in bacteria is not in the spotlight compared to the attention focused on CNV detection in eukaryotes. However, challenges arising from bacterial drug resistance bring further interest to the topic of CNV and its role in drug resistance. General CNV detection methods do not consider bacteria's features and there is space to improve detection accuracy. Here, we present a CNV detection method called CNproScan focused on bacterial genomes. CNproScan implements a hybrid approach and other bacteria-focused features and depends only on NGS data. We benchmarked our method and compared it to the previously published methods and we can resolve to achieve a higher detection rate together with providing other beneficial features, such as CNV classification. Compared with other methods, CNproScan can detect much shorter CNV events.

Pathologies affect the performance of ECG signals compression.

The performance of ECG signals compression is influenced by many things. However, there is not a single study primarily focused on the possible effects of ECG pathologies on the performance of compression algorithms. This study evaluates whether the pathologies present in ECG signals affect the efficiency and quality of compression. Single-cycle fractal-based compression algorithm and compression algorithm based on combination of wavelet transform and set partitioning in hierarchical trees are used to compress 125 15-leads ECG signals from CSE database. Rhythm and morphology of these signals are newly annotated as physiological or pathological. The compression performance results are statistically evaluated. Using both compression algorithms, physiological signals are compressed with better quality than pathological signals according to 8 and 9 out of 12 quality metrics, respectively. Moreover, it was statistically proven that pathological signals were compressed with lower efficiency than physiological signals. Signals with physiological rhythm and physiological morphology were compressed with the best quality. The worst results reported the group of signals with pathological rhythm and pathological morphology. This study is the first one which deals with effects of ECG pathologies on the performance of compression algorithms. Signal-by-signal rhythm and morphology annotations (physiological/pathological) for the CSE database are newly published.

Complex study on compression of ECG signals using novel single-cycle fractal-based algorithm and SPIHT.

Compression of ECG signal is essential especially in the area of signal transmission in telemedicine. There exist many compression algorithms which are described in various details, tested on various datasets and their performance is expressed by different ways. There is a lack of standardization in this area. This study points out these drawbacks and presents new compression algorithm which is properly described, tested and objectively compared with other authors. This study serves as an example how the standardization should look like. Single-cycle fractal-based (SCyF) compression algorithm is introduced and tested on 4 different databases-CSE database, MIT-BIH arrhythmia database, High-frequency signal and Brno University of Technology ECG quality database (BUT QDB). SCyF algorithm is always compared with well-known algorithm based on wavelet transform and set partitioning in hierarchical trees in terms of efficiency (2 methods) and quality/distortion of the signal after compression (12 methods). Detail analysis of the results is provided. The results of SCyF compression algorithm reach up to avL = 0.4460 bps and PRDN = 2.8236%.

Real-Time Quality Assessment of Long-Term ECG Signals Recorded by Wearables in Free-Living Conditions.

OBJECTIVE: Nowadays, methods for ECG quality assessment are mostly designed to binary distinguish between good/bad quality of the whole signal. Such classification is not suitable to long-term data collected by wearable devices. In this paper, a novel approach to estimate long-term ECG signal quality is proposed. METHODS: The real-time quality estimation is performed in a local time window by calculation of continuous signal-to-noise ratio (SNR) curve. The layout of the data quality segments is determined by analysis of SNR waveform. It is distinguished between three levels of ECG signal quality: signal suitable for full wave ECG analysis, signal suitable only for QRS detection, and signal unsuitable for further processing. RESULTS: The SNR limits for reliable QRS detection and full ECG waveform analysis are 5 and 18 dB respectively. The method was developed and tested using synthetic data and validated on real data from wearable device. CONCLUSION: The proposed solution is a robust, accurate and computationally efficient algorithm for annotation of ECG signal quality that will facilitate the subsequent tailored analysis of ECG signals recorded in free-living conditions. SIGNIFICANCE: The field of long-term ECG signals self-monitoring by wearable devices is swiftly developing. The analysis of massive amount of collected data is time consuming. It is advantageous to characterize data quality in advance and thereby limit consequent analysis to useable signals.

Advanced P Wave Detection in Ecg Signals During Pathology: Evaluation in Different Arrhythmia Contexts.

Reliable P wave detection is necessary for accurate and automatic electrocardiogram (ECG) analysis. Currently, methods for P wave detection in physiological conditions are well-described and efficient. However, methods for P wave detection during pathology are not generally found in the literature, or their performance is insufficient. This work introduces a novel method, based on a phasor transform, as well as innovative rules that improve P wave detection during pathology. These rules are based on the extraction of a heartbeats' morphological features and knowledge of heart manifestation during both physiological and pathological conditions. To properly evaluate the performance of the proposed algorithm in pathological conditions, a standard database with a sufficient number of reference P wave positions is needed. However, such a database did not exist. Thus, ECG experts annotated 12 chosen pathological records from the MIT-BIH Arrhythmia Database. These annotations are publicly available via Physionet. The algorithm performance was also validated using physiological records from the MIT-BIH Arrhythmia and QT databases. The results for physiological signals were Se = 98.42% and PP = 99.98%, which is comparable to other methods. For pathological signals, the proposed method reached Se = 96.40% and PP = 85.84%, which greatly outperforms other methods. This improvement represents a huge step towards fully automated analysis systems being respected by ECG experts. These systems are necessary, particularly in the area of long-term monitoring.

A degeneration-reducing criterion for optimal digital mapping of genetic codes.

Bioinformatics may seem to be a scientific field processing primarily large string datasets, as nucleotides and amino acids are represented with dedicated characters. On the other hand, many computational tasks that bioinformatics challenges are mathematical problems understandable as operations with digits. In fact, many computational tasks are solved this way in the background. One of the most widely used digital representations is mapping of nucleotides and amino acids with integers 0-3 and 0-20, respectively. The limitation of this mapping occurs when the digital signal of nucleotides has to be translated into a digital signal of amino acids as the genetic code is degenerated. This causes non-monotonies in a mapping function. Although map for reducing this undesirable effect has already been proposed, it is defined theoretically and for standard genetic codes only. In this study, we derived a novel optimal criterion for reducing the influence of degeneration by utilizing a large dataset of real sequences with various genetic codes. As a result, we proposed a new robust global optimal map suitable for any genetic code as well as specialized optimal maps for particular genetic codes.

Advanced DNA fingerprint genotyping based on a model developed from real chip electrophoresis data.

Large-scale comparative studies of DNA fingerprints prefer automated chip capillary electrophoresis over conventional gel planar electrophoresis due to the higher precision of the digitalization process. However, the determination of band sizes is still limited by the device resolution and sizing accuracy. Band matching, therefore, remains the key step in DNA fingerprint analysis. Most current methods evaluate only the pairwise similarity of the samples, using heuristically determined constant thresholds to evaluate the maximum allowed band size deviation; unfortunately, that approach significantly reduces the ability to distinguish between closely related samples. This study presents a new approach based on global multiple alignments of bands of all samples, with an adaptive threshold derived from the detailed migration analysis of a large number of real samples. The proposed approach allows the accurate automated analysis of DNA fingerprint similarities for extensive epidemiological studies of bacterial strains, thereby helping to prevent the spread of dangerous microbial infections.

Rapid Bacterial Species Delineation Based on Parameters Derived From Genome Numerical Representations.

Species delineation based on bacterial genomes is an essential part of the research of prokaryotes. In silico genome-to-genome comparison methods are computationally demanding, but much less tedious and error prone than the wet-lab methods. In this paper, we present a novel method for the delineation of bacterial genomes based on genomic signal processing. The proposed method uses numerical representations of whole bacterial genomes, phase signal and cumulated phase signal, from which four parameters are derived for each genome. The parameters characterize a genome and their calculation is independent of the other genomes comprising a delineation dataset. The delineation itself is processed as a calculation of the parameters' average similarity. The method was statistically verified on 1826 bacterial genomes. A similarity threshold of 96% was set based on the receiver operating characteristic curve that featured sensitivity of 99.78% and specificity of 97.25%. Additionally, comparative analysis on another 33 bacterial genomes was conducted using standard delineation tools as these tools were not able to process the dataset of 1826 genomes using desktop computer. The proposed method achieved comparable or better delineation results in comparison with the standard tools. Besides the excellent delineation results, another great advantage of the method is its small computational demands, which enables the delineation of thousands of genomes on a desktop computer. The calculation of the parameters takes tens of minutes for thousands of genomes. Moreover, they can be calculated in advance by creating a database, meaning the delineation itself is then completed in a matter of seconds.

A Comparative Analysis of Methods for Evaluation of ECG Signal Quality after Compression.

The assessment of ECG signal quality after compression is an essential part of the compression process. Compression facilitates the signal archiving, speeds up signal transmission, and reduces the energy consumption. Conversely, lossy compression distorts the signals. Therefore, it is necessary to express the compression performance through both compression efficiency and signal quality. This paper provides an overview of objective algorithms for the assessment of both ECG signal quality after compression and compression efficiency. In this area, there is a lack of standardization, and there is no extensive review as such. 40 methods were tested in terms of their suitability for quality assessment. For this purpose, the whole CSE database was used. The tested signals were compressed using an algorithm based on SPIHT with varying efficiency. As a reference, compressed signals were manually assessed by two experts and classified into three quality groups. Owing to the experts' classification, we determined corresponding ranges of selected quality evaluation methods' values. The suitability of the methods for quality assessment was evaluated based on five criteria. For the assessment of ECG signal quality after compression, we recommend using a combination of these methods: PSim SDNN, QS, SNR1, MSE, PRDN1, MAX, STDERR, and WEDD SWT.

Multi-stage SVM approach for cardiac arrhythmias detection in short single-lead ECG recorded by a wearable device.

OBJECTIVE: Use of wearable ECG devices for arrhythmia screening is limited due to poor signal quality, small number of leads and short records, leading to incorrect recognition of pathological events. This paper introduces a novel approach to classification (normal/'N', atrial fibrillation/'A', other/'O', and noisy/'P') of short single-lead ECGs recorded by wearable devices. APPROACH: Various rhythm and morphology features are derived from the separate beats ('local' features) as well as the entire ECGs ('global' features) to represent short-term events and general trends respectively. Various types of atrial and ventricular activity, heart beats and, finally, ECG records are then recognised by a multi-level approach combining a support vector machine (SVM), decision tree and threshold-based rules. MAIN RESULTS: The proposed features are suitable for the recognition of 'A'. The method is robust due to the noise estimation involved. A combination of radial and linear SVMs ensures both high predictive performance and effective generalisation. Cost-sensitive learning, genetic algorithm feature selection and thresholding improve overall performance. The generalisation ability and reliability of this approach are high, as verified by cross-validation on a training set and by blind testing, with only a slight decrease of overall F1-measure, from 0.84 on training to 0.81 on the tested dataset. 'O' recognition seems to be the most difficult (test F1-measures: 0.90/'N', 0.81/'A' and 0.72/'O') due to high inter-patient variability and similarity with 'N'. SIGNIFICANCE: These study results contribute to multidisciplinary areas, focusing on creation of robust and reliable cardiac monitoring systems in order to improve diagnosis, reduce unnecessary time-consuming expert ECG scoring and, consequently, ensure timely and effective treatment.

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).

Testing of features for fatigue detection in EOG.

The article deals with the testing of features for fatigue detection in electrooculography (EOG) records. An optimal methodology for EOG signal acquisition is described; the Biopac data acquisition system was used. EOG signals were being recorded while 10 volunteers were watching prepared scenes. Three scenes were created for this purpose - a rotating ball, a video of driving a car, and a cross. Recorded EOG signals were processed and 20 features were extracted. The features involved blinks, slow eye movement (SEM), rapid eye movement (REM), eye instability, magnitude, and periodicity. These features were statistically tested and discussed in terms of fatigue detection ability. Some of the features were compared with published results. Finally, the best features - fatigue indicators - were selected.

CSE database: extended annotations and new recommendations for ECG software testing.

Nowadays, cardiovascular diseases represent the most common cause of death in western countries. Among various examination techniques, electrocardiography (ECG) is still a highly valuable tool used for the diagnosis of many cardiovascular disorders. In order to diagnose a person based on ECG, cardiologists can use automatic diagnostic algorithms. Research in this area is still necessary. In order to compare various algorithms correctly, it is necessary to test them on standard annotated databases, such as the Common Standards for Quantitative Electrocardiography (CSE) database. According to Scopus, the CSE database is the second most cited standard database. There were two main objectives in this work. First, new diagnoses were added to the CSE database, which extended its original annotations. Second, new recommendations for diagnostic software quality estimation were established. The ECG recordings were diagnosed by five new cardiologists independently, and in total, 59 different diagnoses were found. Such a large number of diagnoses is unique, even in terms of standard databases. Based on the cardiologists' diagnoses, a four-round consensus (4R consensus) was established. Such a 4R consensus means a correct final diagnosis, which should ideally be the output of any tested classification software. The accuracy of the cardiologists' diagnoses compared with the 4R consensus was the basis for the establishment of accuracy recommendations. The accuracy was determined in terms of sensitivity = 79.20-86.81%, positive predictive value = 79.10-87.11%, and the Jaccard coefficient = 72.21-81.14%, respectively. Within these ranges, the accuracy of the software is comparable with the accuracy of cardiologists. The accuracy quantification of the correct classification is unique. Diagnostic software developers can objectively evaluate the success of their algorithm and promote its further development. The annotations and recommendations proposed in this work will allow for faster development and testing of classification software. As a result, this might facilitate cardiologists' work and lead to faster diagnoses and earlier treatment.

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.

A Wavelet-Based ECG Delineation Method: Adaptation to an Experimental Electrograms with Manifested Global Ischemia.

We present a novel wavelet-based ECG delineation method with robust classification of P wave and T wave. The work is aimed on an adaptation of the method to long-term experimental electrograms (EGs) measured on isolated rabbit heart and to evaluate the effect of global ischemia in experimental EGs on delineation performance. The algorithm was tested on a set of 263 rabbit EGs with established reference points and on human signals using standard Common Standards for Quantitative Electrocardiography Standard Database (CSEDB). On CSEDB, standard deviation (SD) of measured errors satisfies given criterions in each point and the results are comparable to other published works. In rabbit signals, our QRS detector reached sensitivity of 99.87% and positive predictivity of 99.89% despite an overlay of spectral components of QRS complex, P wave and power line noise. The algorithm shows great performance in suppressing J-point elevation and reached low overall error in both, QRS onset (SD = 2.8 ms) and QRS offset (SD = 4.3 ms) delineation. T wave offset is detected with acceptable error (SD = 12.9 ms) and sensitivity nearly 99%. Variance of the errors during global ischemia remains relatively stable, however more failures in detection of T wave and P wave occur. Due to differences in spectral and timing characteristics parameters of rabbit based algorithm have to be highly adaptable and set more precisely than in human ECG signals to reach acceptable performance.

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.

Classification of genomic signals using dynamic time warping.

BACKGROUND: Classification 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. METHODS: The 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). RESULTS: The 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. CONCLUSIONS: Classification 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.