Nejvíce citovaný článek - PubMed ID 28040865
CSE database: extended annotations and new recommendations for ECG software testing
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.
- MeSH
- algoritmy MeSH
- artefakty MeSH
- chytrý telefon MeSH
- databáze faktografické * MeSH
- dospělí MeSH
- elektrokardiografie MeSH
- fotopletysmografie statistika a číselné údaje MeSH
- lidé středního věku MeSH
- lidé MeSH
- počítačové zpracování signálu přístrojové vybavení MeSH
- referenční hodnoty MeSH
- referenční standardy MeSH
- srdeční frekvence fyziologie MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- dataset MeSH
- Geografické názvy
- Česká republika MeSH
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.
- MeSH
- algoritmy MeSH
- databáze faktografické MeSH
- elektrokardiografie metody MeSH
- fraktály MeSH
- komprese dat metody MeSH
- lidé MeSH
- vlnková analýza MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
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%.
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.
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.
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).
- MeSH
- analýza dat MeSH
- automatizace metody MeSH
- elektrokardiografie metody MeSH
- králíci MeSH
- nemoci srdce diagnóza MeSH
- zvířata MeSH
- Check Tag
- králíci MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
