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Implementation of an artificial intelligence-based ECG acquisition system for the detection of cardiac abnormalities

Benba Achraf, El Attaoui Fatima Zahra, Sandabad Sara

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

2023

T. 13, nr 1

22--25

The electrocardiogram (ECG) is a common test that measures the electrical activity of the heart. On the ECG, several cardiac abnormalities can be seen, including arrhythmias, which are one of the major causes of cardiac mortality worldwide. The objective for the research community is accurate and automated cardiovascular analysis, especially given the maturity of artificial intelligence technology and its contribution to the health area. The goal of this effort is to create an acquisition system and use artificial intelligence to classify ECG readings. This system is designed in two parts: the first is the signal acquisition using the ECG Module AD8232; the obtained signal is a single derivation that has been amplified and filtered. The second section is the classification for heart illness identification; the suggested model is a deep convolutional neural network with 12 layers that was able to categorize five types of heartbeats from the MIT-BIH arrhythmia database. The results were encouraging, and the embedded system was built.

Elektrokardiogram (EKG) to powszechny test, który mierzy aktywność elektryczną serca. W zapisie EKG można zauważyć kilka nieprawidłowości serca, w tym arytmie, które są jedną z głównych przyczyn śmiertelności sercowej na całym świecie. Celem społeczności naukowej jest dokładna i zautomatyzowana analiza układu sercowo-naczyniowego, zwłaszcza biorąc pod uwagę dojrzałość technologii sztucznej inteligencji i jej wkład w obszar zdrowia. Celem tych wysiłków jest stworzenie systemu akwizycji i wykorzystanie sztucznej inteligencji do klasyfikacji odczytów EKG. System ten składa się z dwóch części: pierwsza to akwizycja sygnału za pomocą modułu EKG AD8232; uzyskany sygnał jest pojedynczą pochodną, która została wzmocniona i przefiltrowana. Druga sekcja to klasyfikacja identyfikacji chorób serca; sugerowany model to głęboka konwolucyjna sieć neuronowa z 12 warstwami, która była w stanie sklasyfikować pięć typów uderzeń serca z bazy danych arytmii MIT-BIH. Wyniki były zachęcające i zbudowano system wbudowany.

The occurrence of arrhythmias and heart rate variability during diving in recreational divers using continuous electrocardiographic holter monitoring

Stelmaszczyk Łukasz, Starek-Stelmaszczyk Magdalena, Starek Maria, Kurek Łukasz, Żuchlińska-Kwiatkowska Joanna, Dębiński Michał

Polish Hyperbaric Research

2019

nr 4(69)

71--80

The aim of the research was to evaluate the occurrence of arrhythmias and heart rate variability during diving in recreational divers. Continuous electrocardiographic (ECG) Holter monitoring was conducted in a group of 50 divers (age 36,8 } 8,7). The recorded data included the duration of the dive, including a period of 60 minutes before the dive and 60 minutes after the dive. Moreover, divers filled in a questionnaire that had been prepared for the purpose of the study and the psychological tests State-Trait Anxiety Inventory (STAI). The ECG recordings were synchronised with dive computers to correlate the ECG changes with diving events and analysed for the heart rate, arrhythmias and conduction disorders. The average heart rate was the highest (M=107.34 beats/minute) before diving, and the lowest after diving (M = 102.00 beats/minute). Supraventricular arrhythmias were recorded in nineteen (38%) of the participants of the study. The number of arrhythmias during diving (M = 14,45) is significantly higher than before (M = 9,93, p < 0,01) and after dive (M = 6,02, p < 0,05). All results were obtained from the continuous ECG Holter monitoring. It seems that using continuous ECG monitoring in conditions similar to diving (physical and psychological stress), brings more benefits than traditional, resting electrocardiogram.

Entropia w badaniach zaburzeń rytmu serca

Graff B., Graff G., Kolesiak A.

Matematyka Stosowana : matematyka dla społeczeństwa

2008

Nr 9

46-56

Artykuł prezentuje zastosowanie Approximate Entropy, będącej miarą stopnia złożoności szeregów czasowych, do analizy zmienności rytmu serca.

Healthy human heart rate is characterized by oscillations observed in intervals between consecutive heartbeats (RR intervals). Conventional methods of heart rate variability analysis measure the overall magnitude of RR interval fluctuations around its mean value or the magnitude of fluctuations in predetermined frequencies. The new methods of chaos theory and nonlinear dynamics provide powerful tools, which allow to predict clinical outcome in patients with cardiovascular diseases. The main aim of our article is to present Approximate Entropy (ApEn), a measure of system regularity and complexity, introduced by Pincus in 1991. ApEn estimation used for clinical purposes is applied for finite number of records, divided in vectors, and depends on two fixed parameters m and r. Then Approximate Entropy may be interpreted as the average of negative natural logarithms of conditional probability, that two vectors of length m + 1 are similar (we define here r-similarity), if two vectors of the length m are similar. The article provides a formal mathematical description of ApEn and presents a simple algorithm for its assessment. The choice of input parameters m and r is also discussed. In vast majority of publications r depends on standard deviation (SD) of average of all records, when individual features of heart rhythm are taken into account. The fraction of r, equal to 0, 2SD, and m = 2 are usually chosen on the basis of previous findings of good statistical validity. With the above set of parameters we can avoid the influence of outliers and do not loose too much information. ApEn has also some disadvantages - the main is counting self similarities. To reduce this kind of bias some improvements of the methods based on Pincus’ algorithm were developed. For example Sample Entropy (SampEn), which has similar algorithm but does not count self-matches, was proposed and easily applied to clinical time-series. In the article we present also an application of ApEn in predicting atrial fibrillation (AF), a type of arrhythmia which is the most common sustained heart rhythm disturbance. Both ApEn and SampEn decrease before the spontaneous onset of AF. What is more, ApEn is not sensitive to ectopy beats and therefore can be assessed fully automatically. The potential application of ApEn is the possibility to detect an increased vulnerability to AF before the onset of arrhythmia during continuous heart rate recording, for example for patients with implantable pacemakers. The recognition of the higher risk of AF would be followed by immediate pacemaker reprogramming to prevent an episode of arrhythmia. It would result not only in better quality of life of the patient but also in decreased number of hospitalization and cost of treatment.