Atrial fibrillation (AF) is the most common arrhythmia in adults and is associated with a higher risk of heart failure or death. Here, we introduce simple and efficient method for automatic AF ...detection based on symbolic dynamics and Shannon entropy. This method comprises of three parts. Firstly, QRS complex detection is provided, than the raw RR sequence is transformed into a sequence of specific symbols and subsequently into a word sequence and finally, Shannon entropy of the word sequence is calculated. According to the value of Shannon entropy, it is decided, whether AF is present in the current cardiac beat. We achieved sensitivity Se=96.32% and specificity Sp=98.61% on MIT-BIH Atrial Fibrillation database, Se=91.30% and Sp=90.8% on MIT-BIH Arrhythmia database, Se=95.6% and Sp=80.27% for Long Term Atrial Fibrillation database and Se=93.04% and Sp=87.30% for CinC Challenge database 2020. The achieved results of our one-feature method are comparable with other authors of more complicated and computationally expensive methods. Our ECG experts found that public databases contain errors in annotations (in sense of AF). It means that results are affected by errors in annotations. Many errors were found in Long-Term AF database, several also in MIT-BIH AF database and MIT-BIH Arrhythmia database. Testing algorithms on poorly annotated databases cannot bring reliable results and algorithms useful in real medical practice. The examples of such annotations are reported in this study.
Respiratory rate (RR) is one of the most important physiological parameters. In recent years, the RR estimation from PPGs widely used in smart devices has been promoted. The effect of respiration on ...PPGs manifests in three ways: BW (intensity variation), AM (amplitude variation), FM (frequency variation). In addition to sophisticated RR estimation methods, reliable results can be achieved with simple and efficient methods implementable in wearables. The BW signal (respiratory signal estimation, RS) can be obtained by linear filtering of the PPG. The RR estimation is based on BW extremes (sBW), BW autocorrelation extremes (aBW) and their spectra (SBW, ABW). Estimation of the AM RS requires PPG extremes detection and interpolation. The RR estimation is based on extremes of the AM signal (sAM), its autocorrelation (aAM) and their spectra (SAM, AAM). The fusion of RR estimates leads to more robust results. To test the algorithms, the annotated BIDMC and CapnoBase Datasets were used. RR estimates were made for 60 s sections. The simplest and the most accurate method for both datasets is the RR estimation based on sBW (RsBW). The median absolute error was 0.40 (0.16-1.09 interquartile range 25-75 th ) bpm for the 60s window, mean absolute error was 1.42 bpm.
Background: Aerobic fitness level (AFL) is a parameter closely related to a person's overall health. The gold standard of measurement is currently using expensive laboratory equipment. Aims: This ...study aimed to estimate AFL automatically using data measured with wearables. Methods: AFL was estimated in 2D space. The first dimension is the exertion level, and the second is the body's response to the exertion. Exertion level was determined based on metabolic equivalent calculated for each classified activity using the data of speed and elevation. The activity classification is based on deep neural networks. The body's response estimation is based on heart rate calculated from ECG or PPG. The test set contained 27 subjects. The reference was measured under laboratory conditions using the gold standard method. AFL classification by ACSM guidelines was used. Results: AFL determined by our algorithm were 0.44\pm 0.09,0.50\pm 0.10,0.53\pm 0.09 , 0.58\pm 0.15 , and 0.70\pm 0.07 for the reference classes very poor, poor, fair, good, and excellent, respectively. The correlation between the reference and determined values is 0.76. Conclusion: Our method showed promising results and will be further developed.
Background: Automatic detection and classification of cardiac abnormalities in ECG is one of the basic and often solved problems. The aim of this paper is to present a proposed algorithm for ECG ...classification into 19 classes. This algorithm was created within PhysioNet/CinC Challenge 2020, name of our team was HITTING. Methods: Our algorithm detects each pathology separately according to the extracted features and created rules. Signals from the 6 databases were used. Detector of QRS complexes, T-waves and P-waves including detection of their boundaries was designed. Then, the most common morphology of the QRS was found in each record. All these QRS were averaged. Features were extracted from the averaged QRS and from intervals between detected points. Appropriate features and rules were set using classification trees. Results: Our approach achieved a challenge validation score of 0.435, and full test score of 0.354, placing us 11 out of 41 in the official ranking. Conclusion: The advantage of our algorithm is easy interpretation. It is obvious according to which features algorithm decided and what thresholds were set.
Background: Smartphone-based ECG devices comprise great potential in screening for arrhythmias. However, its feasibility is limited by poor signal quality leading to incorrect rhythm classification. ...In this study, advanced method for automatic classification of normal rhythm (N), atrial fibrillation (A), other rhythm (O), and noisy records (P) is introduced. Methods: Two-step SVM approach followed by simple threshold based rules was used for data classification. In the first step, various features were derived from separate beats to represent particular events (normal as well as pathological and artefacts) in more detail. Output of the first classifier was used to calculate global features describing entire ECG. These features were then used to train the second classification model. Both classifiers were evaluated on training set via cross-validation technique, and additionally on hidden testing set. Results: In the Phase II of challenge, total F1 score of the method is 0.81 and 0.84 within hidden challenge dataset and training set, respectively. Particular F1 scores within hidden challenge dataset are 0.90 (N), 0.81 (A), 0.72 (O), and 0.55 (P). Particular F1 scores within training set are 0.91 (N), 0.85 (A), 0.76 (O), and 0.73 (P).
The standard method for assessment of effect of revascularization in patients with diabetic foot (DF) and critical limb ischemia (CLI) is transcutaneous oxygen pressure (TcPO2). Phosphorus magnetic ...resonance spectroscopy (31P MRS) enables to evaluate oxidative muscle metabolism that could be impaired in patients with diabetes and its complications. The aim of our study was to compare MRS of calf muscle between patients with DF and CLI and healthy controls and to evaluate the contribution of MRS in the assessment of the effect of revascularization.
Thirty-four diabetic patients with DF and CLI treated either by autologous cell therapy (ACT; 15 patients) or percutaneous transluminal angioplasty (PTA; 12 patients) in our foot clinic during 2013-2016 and 19 healthy controls were included into the study. TcPO2 measurement was used as a standard method of non-invasive evaluation of limb ischemia. MRS examinations were performed using the whole-body 3T MR system 1 day before and 3 months after the procedure. Subjects were examined in a supine position with the coil fixed under the m. gastrocnemius. MRS parameters were obtained at rest and during the exercise period. Rest MRS parameters of oxidative muscle metabolism such as phosphocreatine (PCr), inorganic phosphate (Pi), phosphodiesters (PDE), adenosine triphosphate (ATP), dynamic MRS parameters such as recovery constant PCr (τPCr) and mitochondrial capacity (Qmax), and pH were compared between patients and healthy controls, and also before and 3 months after revascularization.
Patients with CLI had significantly lower PCr/Pi (p < 0.001), significantly higher Pi and pH (both p < 0.01), significantly lower Qmax and prolonged τPCr (both p < 0.001) in comparison with healthy controls. We observed a significant improvement in TcPO2 at 3 months after revascularization (from 26.4 ± 11.7 to 39.7 ± 17.7 mm Hg, p < 0.005). However, the rest MRS parameters did not change significantly after revascularization. In individual cases we observed improvement of dynamic MRS parameters. There was no correlation between MRS parameters and TcPO2 values.
Results of our study show impaired oxidative metabolism of calf muscles in patients with CLI in comparison with healthy controls. We observed an improvement in dynamic MRS parameters in individual cases; this finding should be verified in a large number of patients during longer follow-up.Key words: autologous cell therapy - critical limb ischemia - diabetic foot - MR spectroscopy.
One of the most serious complications of the diabetic foot (DF) is a major amputation, which is associated with poor patient prognosis. The occurrence of major amputations may be influenced by a ...variety of factors including deep infection caused by resistant pathogens.The aims of our study were to compare the incidence of major amputations in podiatric center, characteristics of amputated patients with the DF and other factors contributing to major amputations in last decade.
We included into our study all patients hospitalized for the DF in our center whose underwent major amputations from 9/2004 to 9/2006 (group 1) and from 9/2013 to 9/2015 (group 2). Risk factors such as severity of DF ulcers based on Texas classification, duration of previous anti-biotic therapy, the presence and severity of peripheral arterial disease (PAD) according to Graziani classification, the number of revascularizations, renal failure/hemodialysis, osteomyelitis, infectious agents found before amputations and their resistance were compared between the study groups.
During the 1st study period (9/2004-9/2006) 373 patients were hospitalized for the DF, of whom 3.2 % underwent major amputation (12/373 - group 1), during the 2nd study period (9/2013-9/2015) 376 patients, of whom 5.1 % absolved major amputation (19/376 - group 2). As the numbers of major amputations as their indications were similar in both study groups. The study groups did not differ significantly in the age, BMI, duration and type of diabetes, duration of DF and severity of DF ulcers, the presence of renal failure/hemodialysis, osteomyelitis and PAD. Group 2 had milder forms of PAD by Graziani classification (4.4 ±1.4 vs 5.7 ± 0.9; p = 0.012) and a higher number of revascularizations before major amputations (2.5 ± 1.5 vs 1 ± 1; p = 0.003) compared to the group 1. These patients were significantly longer treated by antibiotics (5.4 ± 2.4 vs 2.5 ± 2 months; p = 0.002) and underwent more resections and minor amputations (3.1 ± 2.1 vs 0.9 ± 0.5; p = 0.0004) before major amputations in contrast to the group 1. There was a trend to higher incidence of Gram-negatives (65.1 % vs 61.5 %; NS) with a predominance of Enterobacteriacae species (60.7 % vs 56 %; NS) and a trend to the increase of Pseudomonas (25 % vs 18.8 %; NS) and Enterococci sp. (46.7 % vs 20 %; NS) in the group 2 compared to the group 1. The incidences as of MRSA, multidrug resistant Pseudomonas sp. of other resistant microbes were similar in both study groups.
The incidence of major amputations in patients hospitalized for the DF remains unchanged during the last decade. The therapy of factors leading to amputations has evidently intensified. This is in accordance with the latest international recommendations for the therapy of DF. In the future, it is appropriate to focus on the improvement of detection and treatment of infection and ischemia in such risk group of patients.Key words: diabetic foot - major amputation.
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Ztrátová komprese signálů EKG je užitečná a v současnosti stále se rozvíjející oblast. Stále se vyvíjí nové a nové kompresní algoritmy. V této oblasti ale chybí standardy pro hodnocení kvality ...signálu po kompresi. Existuje tedy sice mnoho různých kompresních algoritmů, které ale buď nelze objektivně porovnat vůbec, nebo jen zhruba. V oblasti komprese navíc nikde není popsáno, zda mají na výkon kompresních algoritmů vliv patologie, popřípadě jaký. Tato dizertační práce poskytuje přehled všech nalezených metod pro hodnocení kvality signálů EKG po kompresi. Navíc bylo vytvořeno 10 nových metod. V rámci práce byla provedena analýza všech těchto metod a na základě jejích výsledků bylo doporučeno 12 metod vhodných pro hodnocení kvality signálu EKG po kompresi. Také je zde představen nový kompresní algoritmus „Single-Cycle Fractal-Based (SCyF)“. Algoritmus SCyF je inspirován metodou založenou na fraktálech a využívá jednoho cyklu signálu EKG jako domény. Algoritmus SCyF byl testován na čtyřech různých databázích, přičemž kvalita signálů po kompresi byla vyhodnocena 12 doporučenými metodami. Výsledky byly porovnány s velmi populárním kompresním algoritmem založeným na vlnkové transformaci, který využívá metodu „Set Partitioning in Hierarchical Trees (SPIHT)“. Postup testování zároveň slouží jako příklad, jak by měl vypadat standard hodnocení výkonu kompresních algoritmů. Dále bylo statisticky prokázáno, že existuje rozdíl mezi kompresí fyziologických a patologických signálů. Patologické signály byly komprimovány s nižší efektivitou a kvalitou než signály fyziologické.
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