QRS detection
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- MeSH
- akutní lymfatická leukemie farmakoterapie MeSH
- antracykliny aplikace a dávkování škodlivé účinky terapeutické užití MeSH
- dítě MeSH
- doxorubicin aplikace a dávkování škodlivé účinky terapeutické užití MeSH
- elektrokardiografie metody přístrojové vybavení MeSH
- finanční podpora výzkumu jako téma MeSH
- lidé MeSH
- longitudinální studie MeSH
- myokard MeSH
- nežádoucí účinky léčiv MeSH
- srdce růst a vývoj účinky léků MeSH
- vrozené srdeční vady MeSH
- Check Tag
- dítě MeSH
- lidé MeSH
- mužské pohlaví MeSH
- Publikační typ
- kazuistiky MeSH
- přehledy MeSH
- Geografické názvy
- Slovenská republika MeSH
While various QRS detection and classification methods were developed in the past, the Holter ECG data acquired during daily activities by wearable devices represent new challenges such as increased noise and artefacts due to patient movements. Here, we present a deep-learning model to detect and classify QRS complexes in single-lead Holter ECG. We introduce a novel approach, delivering QRS detection and classification in one inference step. We used a private dataset (12,111 Holter ECG recordings, length of 30 s) for training, validation, and testing the method. Twelve public databases were used to further test method performance. We built a software tool to rapidly annotate QRS complexes in a private dataset, and we annotated 619,681 QRS complexes. The standardised and down-sampled ECG signal forms a 30-s long input for the deep-learning model. The model consists of five ResNet blocks and a gated recurrent unit layer. The model's output is a 30-s long 4-channel probability vector (no-QRS, normal QRS, premature ventricular contraction, premature atrial contraction). Output probabilities are post-processed to receive predicted QRS annotation marks. For the QRS detection task, the proposed method achieved the F1 score of 0.99 on the private test set. An overall mean F1 cross-database score through twelve external public databases was 0.96 ± 0.06. In terms of QRS classification, the presented method showed micro and macro F1 scores of 0.96 and 0.74 on the private test set, respectively. Cross-database results using four external public datasets showed micro and macro F1 scores of 0.95 ± 0.03 and 0.73 ± 0.06, respectively. Presented results showed that QRS detection and classification could be reliably computed in one inference step. The cross-database tests showed higher overall QRS detection performance than any of compared methods.
- MeSH
- algoritmy MeSH
- artefakty MeSH
- elektrokardiografie ambulantní metody MeSH
- elektrokardiografie metody MeSH
- komorové extrasystoly * MeSH
- lidé MeSH
- nositelná elektronika * MeSH
- počítačové zpracování signálu MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- MeSH
- algoritmy MeSH
- databáze faktografické využití MeSH
- elektrokardiografie ambulantní metody přístrojové vybavení využití MeSH
- elektrokardiografie metody přístrojové vybavení využití MeSH
- financování organizované MeSH
- lidé MeSH
- počítačové zpracování signálu MeSH
- senzitivita a specificita MeSH
- srdeční arytmie diagnóza MeSH
- zátěžový test metody využití MeSH
- Check Tag
- lidé MeSH
Introduction: This study proposes an algorithm for preprocessing VCG records to obtain a representative QRS loop. Methods: The proposed algorithm uses the following methods: Digital filtering to remove noise from the signal, wavelet-based detection of ECG fiducial points and isoelectric PQ intervals, spatial alignment of QRS loops, QRS time synchronization using root mean square error minimization and ectopic QRS elimination. The representative QRS loop is calculated as the average of all QRS loops in the VCG record. The algorithm is evaluated on 161 VCG records from a database of 58 healthy control subjects, 69 patients with myocardial infarction, and 34 patients with bundle branch block. The morphologic intra-individual beat-to-beat variability rate is calculated for each VCG record. Results and Discussion: The maximum relative deviation is 12.2% for healthy control subjects, 19.3% for patients with myocardial infarction, and 17.2% for patients with bundle branch block. The performance of the algorithm is assessed by measuring the morphologic variability before and after QRS time synchronization and ectopic QRS elimination. The variability is reduced by a factor of 0.36 for healthy control subjects, 0.38 for patients with myocardial infarction, and 0.41 for patients with bundle branch block. The proposed algorithm can be used to generate a representative QRS loop for each VCG record. This representative QRS loop can be used to visualize, compare, and further process VCG records for automatic VCG record classification.
- Publikační typ
- časopisecké články MeSH
The objective of this study was to find out the implication of QRS duration in dogs with rapid pacing-induced heart failure. Sixteen Beagle dogs were implanted with transvenous cardiac pacemakers and underwent rapid right ventricular pacing for 3 weeks at 260 bpm to induce heart failure. Dogs were divided into two groups according to the QRS duration: 9 with normal QRS duration (<100 ms) and 7 with prolonged QRS duration (≥100 ms). Cardiac systolic function and size was analyzed by real time 3-dimensional echocardiography and left ventricular dyssynchrony was assessed by speckle tracking strain imaging. Congestive heart failure developed 3 weeks after rapid right ventricular pacing. Dogs with prolonged QRS duration showed more extensive radial strain and circumferential strain dyssynchrony than dogs with normal QRS duration. At the end of 4-week recovery, greater improvement of left ventricular ejection fraction and left ventricular end-systolic volume was detected in dogs with normal QRS duration. The findings suggested that left ventricular dyssynchrony, indicated by a prolonged QRS duration, predicted an unsatisfying recovery in dogs with rapid pacinginduced heart failure. QRS duration had the potential to be a prognostic indicator for dogs with heart failure.
- MeSH
- časové faktory MeSH
- dysfunkce levé srdeční komory parazitologie ultrasonografie MeSH
- elektrokardiografie MeSH
- kardiostimulace umělá metody MeSH
- modely nemocí na zvířatech MeSH
- psi MeSH
- srdeční komory patofyziologie ultrasonografie MeSH
- srdeční selhání patofyziologie ultrasonografie MeSH
- zvířata MeSH
- Check Tag
- mužské pohlaví MeSH
- psi MeSH
- ženské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- práce podpořená grantem MeSH
Ambulatory monitoring represents an effective tool for the assessment of silent and transient myocardial ischemia during routine daily activities. Incidence of silent ischemia can provide important prognostic information about patients with coronary artery disease or acute coronary syndrome, as well as about post-myocardial infarction patients. The current technological progress enables development of powerful and miniaturized wearable devices for Holter monitoring. Higher sampling rates, dynamic range, and extended computational and storage capacity allow for considering of more complex methodological solutions such as high-frequency QRS analysis for diagnosing myocardial ischemia. Implementation of suitable methodologies for advanced detection of myocardial ischemia into modern ambulatory monitoring devices creates the potential of making the ambulatory myocardial ischemia monitoring a valuable diagnostic tool in clinical practice.
- MeSH
- algoritmy * MeSH
- diagnóza počítačová metody MeSH
- elektrokardiografie ambulantní metody MeSH
- ischemická choroba srdeční diagnóza MeSH
- lidé MeSH
- počítačové zpracování signálu MeSH
- reprodukovatelnost výsledků MeSH
- senzitivita a specificita MeSH
- určení tepové frekvence metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
