1,240 research outputs found

    Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators

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    Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survivalof out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrilla-tors (AED). AED algorithms for VF-detection are customarily assessed using Holter record-ings from public electrocardiogram (ECG) databases, which may be different from the ECGseen during OHCA events. This study evaluates VF-detection using data from both OHCApatients and public Holter recordings. ECG-segments of 4-s and 8-s duration were ana-lyzed. For each segment 30 features were computed and fed to state of the art machinelearning (ML) algorithms. ML-algorithms with built-in feature selection capabilities wereused to determine the optimal feature subsets for both databases. Patient-wise bootstraptechniques were used to evaluate algorithm performance in terms of sensitivity (Se), speci-ficity (Sp) and balanced error rate (BER). Performance was significantly better for publicdata with a mean Se of 96.6%, Sp of 98.8% and BER 2.2% compared to a mean Se of94.7%, Sp of 96.5% and BER 4.4% for OHCA data. OHCA data required two times morefeatures than the data from public databases for an accurate detection (6 vs 3). No signifi-cant differences in performance were found for different segment lengths, the BER differ-ences were below 0.5-points in all cases. Our results show that VF-detection is morechallenging for OHCA data than for data from public databases, and that accurate VF-detection is possible with segments as short as 4-s

    ΠžΠ±Π·ΠΎΡ€ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² автоматичСской диагностики сСрдСчной Π°Ρ€ΠΈΡ‚ΠΌΠΈΠΈ для принятия Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΉ ΠΎ нСобходимости провСдСния дСфибрилляции

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    Ventricular fibrillation is considered the most common cause of sudden cardiac arrest. The fibrillation, and ventricular tachycardia often preceding it, are cardiac rhythms that may respond to emergency electroshock therapy and return to normal sinus rhythm when diagnosed early after cardiac arrest with the restoration of adequate cardiac pumping function. However, manually checking ECG signals on the existence of a pattern of such arrhythmias is a risky and time-consuming task in stressful situations and practically impossible in the absence of a qualified medical specialist. Therefore, systems of the computer classification of arrhythmias with the function of making a decision on the necessity of electric cardioversion with the parameters of a high-voltage pulse calculated adaptively for each patient are widely used for the automatic diagnosis of such conditions. This paper discusses methods of analyzing the electrocardiographic signal taken from the electrodes of an external automatic or semi-automatic defibrillator in order to make a decision on the necessity for defibrillation, which are applicable in the embedded software of automatic and semiautomatic external defibrillators. The paper includes an overview of applicable filtering techniques as well as subsequent algorithms for extracting, classifying and compressing features for the ECG signal.Β Β Lipchak D. A., Chupov A. A. Methods of Signal Analysis for Automatic Diagnosis of Shockable Cardiac Arrhythmias: A Review. Ural Radio Engineering Journal. 2021;5(4):380–409. (In Russ.) DOI: 10.15826/ urej.2021.5.4.004. Ѐибрилляция ΠΆΠ΅Π»ΡƒΠ΄ΠΎΡ‡ΠΊΠΎΠ² сСрдца считаСтся Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ часто Π²ΡΡ‚Ρ€Π΅Ρ‡Π°ΡŽΡ‰Π΅ΠΉΡΡ ΠΏΡ€ΠΈΡ‡ΠΈΠ½ΠΎΠΉ Π²Π½Π΅Π·Π°ΠΏΠ½ΠΎΠΉ остановки сСрдца. Вакая фибрилляция ΠΈ часто ΠΏΡ€Π΅Π΄ΡˆΠ΅ΡΡ‚Π²ΡƒΡŽΡ‰Π°Ρ Π΅ΠΉ ТСлудочковая тахикардия – это Ρ€ΠΈΡ‚ΠΌΡ‹ сСрдца, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ ΠΌΠΎΠ³ΡƒΡ‚ Ρ€Π΅Π°Π³ΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ Π½Π° ΡΠΊΡΡ‚Ρ€Π΅Π½Π½ΡƒΡŽ ΡΠ»Π΅ΠΊΡ‚Ρ€ΠΎΡˆΠΎΠΊΠΎΠ²ΡƒΡŽ Ρ‚Π΅Ρ€Π°ΠΏΠΈΡŽ ΠΈ Π²Π΅Ρ€Π½ΡƒΡ‚ΡŒΡΡ ΠΊ Π½ΠΎΡ€ΠΌΠ°Π»ΡŒΠ½ΠΎΠΌΡƒ синусовому Ρ€ΠΈΡ‚ΠΌΡƒ ΠΏΡ€ΠΈ Ρ€Π°Π½Π½Π΅ΠΉ диагностикС послС остановки сСрдца с восстановлСниСм Π°Π΄Π΅ΠΊΠ²Π°Ρ‚Π½ΠΎΠΉ насосной Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΈ сСрдца. Однако ручная ΠΏΡ€ΠΎΠ²Π΅Ρ€ΠΊΠ° сигналов Π­ΠšΠ“ Π½Π° Π½Π°Π»ΠΈΡ‡ΠΈΠ΅ ΠΏΠ°Ρ‚Ρ‚Π΅Ρ€Π½Π° Ρ‚Π°ΠΊΠΎΠΉ Π°Ρ€ΠΈΡ‚ΠΌΠΈΠΈ являСтся слоТной аналитичСской Π·Π°Π΄Π°Ρ‡Π΅ΠΉ, Ρ‚Ρ€Π΅Π±ΡƒΡŽΡ‰Π΅ΠΉ Π½Π΅ΠΌΠ΅Π΄Π»Π΅Π½Π½ΠΎΠ³ΠΎ принятия Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ Π² стрСссовой ситуации, практичСски Π½Π΅Π²Ρ‹ΠΏΠΎΠ»Π½ΠΈΠΌΠΎΠΉ Π² отсутствиС ΠΊΠ²Π°Π»ΠΈΡ„ΠΈΡ†ΠΈΡ€ΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ мСдицинского спСциалиста. ΠŸΠΎΡΡ‚ΠΎΠΌΡƒ для автоматичСской диагностики острых состояний ΡˆΠΈΡ€ΠΎΠΊΠΎΠ΅ ΠΏΡ€ΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΏΠΎΠ»ΡƒΡ‡ΠΈΠ»ΠΈ систСмы ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½ΠΎΠΉ классификации Π°Ρ€ΠΈΡ‚ΠΌΠΈΠΉ с Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠ΅ΠΉ принятия Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ ΠΎ нСобходимости провСдСния элСктрокардиотСрапии с ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Π°ΠΌΠΈ Π²Ρ‹ΡΠΎΠΊΠΎΠ²ΠΎΠ»ΡŒΡ‚Π½ΠΎΠ³ΠΎ ΠΈΠΌΠΏΡƒΠ»ΡŒΡΠ°, вычислСнного Π°Π΄Π°ΠΏΡ‚ΠΈΠ²Π½ΠΎ для ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚Π°. Π’ Π΄Π°Π½Π½ΠΎΠΉ Ρ€Π°Π±ΠΎΡ‚Π΅ рассмотрСны ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ Π°Π½Π°Π»ΠΈΠ·Π° элСктрокардиографичСского сигнала, снимаСмого с элСктродов Π½Π°Ρ€ΡƒΠΆΠ½ΠΎΠ³ΠΎ автоматичСского ΠΈΠ»ΠΈ полуавтоматичСского дСфибриллятора, с Ρ†Π΅Π»ΡŒΡŽ принятия Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ ΠΎ нСобходимости оказания дСфибрилляции, ΠΏΡ€ΠΈΠΌΠ΅Π½ΠΈΠΌΡ‹Π΅ Π²ΠΎ встроСнном ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΠ½ΠΎΠΌ обСспСчСнии автоматичСских ΠΈ полуавтоматичСских Π²Π½Π΅ΡˆΠ½ΠΈΡ… дСфибрилляторов. Π Π°Π±ΠΎΡ‚Π° Π²ΠΊΠ»ΡŽΡ‡Π°Π΅Ρ‚ ΠΎΠ±Π·ΠΎΡ€ ΠΏΡ€ΠΈΠΌΠ΅Π½ΠΈΠΌΡ‹Ρ… ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² Ρ„ΠΈΠ»ΡŒΡ‚Ρ€Π°Ρ†ΠΈΠΈ, Π° Ρ‚Π°ΠΊΠΆΠ΅ ΠΏΠΎΡΠ»Π΅Π΄ΡƒΡŽΡ‰ΠΈΡ… Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΠΎΠ² извлСчСния, классификации ΠΈ сТатия Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€Π½Ρ‹Ρ… ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠΎΠ² для сигнала Π­ΠšΠ“.Β Β Π›ΠΈΠΏΡ‡Π°ΠΊ Π”. А., Π§ΡƒΠΏΠΎΠ² А. А. ΠžΠ±Π·ΠΎΡ€ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² автоматичСской диагностики сСрдСчной Π°Ρ€ΠΈΡ‚ΠΌΠΈΠΈ для принятия Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΉ ΠΎ нСобходимости провСдСния дСфибрилляции. Ural Radio Engineering Journal. 2021;5(4):380–409. DOI: 10.15826/urej.2021.5.4.004.

    Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia

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    Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (VF) in AED shock decision algorithms. Recently, deep learning architectures based on 1D Convolutional Neural Networks (CNN) have been proposed for this task. This study introduces a deep learning architecture based on 1D-CNN layers and a Long Short-Term Memory (LSTM) network for the detection of VF. Two datasets were used, one from public repositories of Holter recordings captured at the onset of the arrhythmia, and a second from OHCA patients obtained minutes after the onset of the arrest. Data was partitioned patient-wise into training (80%) to design the classifiers, and test (20%) to report the results. The proposed architecture was compared to 1D-CNN only deep learners, and to a classical approach based on VF-detection features and a support vector machine (SVM) classifier. The algorithms were evaluated in terms of balanced accuracy (BAC), the unweighted mean of the sensitivity (Se) and specificity (Sp). The BAC, Se, and Sp of the architecture for 4-s ECG segments was 99.3%, 99.7%, and 98.9% for the public data, and 98.0%, 99.2%, and 96.7% for OHCA data. The proposed architecture outperformed all other classifiers by at least 0.3-points in BAC in the public data, and by 2.2-points in the OHCA data. The architecture met the 95% Sp and 90% Se requirements of the American Heart Association in both datasets for segment lengths as short as 3-s. This is, to the best of our knowledge, the most accurate VF detection algorithm to date, especially on OHCA data, and it would enable an accurate shock no shock diagnosis in a very short time.This study was supported by the Ministerio de EconomΓ­a, Industria y Competitividad, Gobierno de EspaΓ±a (ES) (TEC-2015-64678-R) to UI and EA and by Euskal Herriko Unibertsitatea (ES) (GIU17/031) to UI and EA. The funders, Tecnalia Research and Innovation and Banco Bilbao Vizcaya Argentaria (BBVA), provided support in the form of salaries for authors AP, AA, FAA, CF, EG, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the author contributions section

    Ventricular Fibrillation and Tachycardia Detection Using Features Derived from Topological Data Analysis

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    A rapid and accurate detection of ventricular arrhythmias is essential to take appropriate therapeutic actions when cardiac arrhythmias occur. Furthermore, the accurate discrimination between arrhythmias is also important, provided that the required shocking therapy would not be the same. In this work, the main novelty is the use of the mathematical method known as Topological Data Analysis (TDA) to generate new types of features which can contribute to the improvement of the detection and classification performance of cardiac arrhythmias such as Ventricular Fibrillation (VF) and Ventricular Tachycardia (VT). The electrocardiographic (ECG) signals used for this evaluation were obtained from the standard MIT-BIH and AHA databases. Two input data to the classify are evaluated: TDA features, and Persistence Diagram Image (PDI). Using the reduced TDA-obtained features, a high average accuracy near 99% was observed when discriminating four types of rhythms (98.68% to VF; 99.05% to VT; 98.76% to normal sinus; and 99.09% to Other rhythms) with specificity values higher than 97.16% in all cases. In addition, a higher accuracy of 99.51% was obtained when discriminating between shockable (VT/VF) and non-shockable rhythms (99.03% sensitivity and 99.67% specificity). These results show that the use of TDA-derived geometric features, combined in this case this the k-Nearest Neighbor (kNN) classifier, raises the classification performance above results in previous works. Considering that these results have been achieved without preselection of ECG episodes, it can be concluded that these features may be successfully introduced in Automated External Defibrillation (AED) and Implantable Cardioverter Defibrillation (ICD) therapie

    Sequential algorithm for life threatening cardiac pathologies detection based on mean signal strength and EMD functions

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    Abstract Background Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the most serious cardiac arrhythmias that require quick and accurate detection to save lives. Automated external defibrillators (AEDs) have been developed to recognize these severe cardiac arrhythmias using complex algorithms inside it and determine if an electric shock should in fact be delivered to reset the cardiac rhythm and restore spontaneous circulation. Improving AED safety and efficacy by devising new algorithms which can more accurately distinguish shockable from non-shockable rhythms is a requirement of the present-day because of their uses in public places. Method In this paper, we propose a sequential detection algorithm to separate these severe cardiac pathologies from other arrhythmias based on the mean absolute value of the signal, certain low-order intrinsic mode functions (IMFs) of the Empirical Mode Decomposition (EMD) analysis of the signal and a heart rate determination technique. First, we propose a direct waveform quantification based approach to separate VT plus VF from other arrhythmias. The quantification of the electrocardiographic waveforms is made by calculating the mean absolute value of the signal, called the mean signal strength. Then we use the IMFs, which have higher degree of similarity with the VF in comparison to VT, to separate VF from VTVF signals. At the last stage, a simple rate determination technique is used to calculate the heart rate of VT signals and the amplitude of the VF signals is measured to separate the coarse VF from VF. After these three stages of sequential detection procedure, we recognize the two components of shockable rhythms separately. Results The efficacy of the proposed algorithm has been verified and compared with other existing algorithms, e.g., HILB 1, PSR 2, SPEC 3, TCI 4, Count 5, using the MIT-BIH Arrhythmia Database, Creighton University Ventricular Tachyarrhythmia Database and MIT-BIH Malignant Ventricular Arrhythmia Database. Four quality parameters (e.g., sensitivity, specificity, positive predictivity, and accuracy) were calculated to ascertain the quality of the proposed and other comparing algorithms. Comparative results have been presented on the identification of VTVF, VF and shockable rhythms (VF + VT above 180 bpm). Conclusions The results show significantly improved performance of the proposed EMD-based novel method as compared to other reported techniques in detecting the life threatening cardiac arrhythmias from a set of large databases.</p

    Methods of Signal Analysis for Automatic Diagnosis of Shockable Cardiac Arrhythmias: A Review

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    Ventricular fibrillation is considered the most common cause of sudden cardiac arrest. Ventricular fibrillation, and ventricular tachycardia often preceding it, are cardiac rhythms that can respond to emergency electroshock therapy and return to normal sinus rhythm when diagnosed early after cardiac arrest with the restoration of adequate cardiac pumping function. However, manually checking ECG signals for the presence of a pattern of such arrhythmias is a risky and time- consuming task in stressful situations and practically impossible in the absence of a qualified medical specialist. Therefore, for the automatic diagnosis of such conditions, systems for the computer classification of arrhythmias to decide on the need for electric cardioversion with the parameters of a high-voltage pulse, calculated adaptively for each patient, are widely used. This paper discusses methods for analyzing the electrocardiographic signal taken from external automatic or semi-automatic defibrillator electrodes to decide the need for defibrillation, which is applicable in the embedded software of automatic, semi-automatic external defibrillators. The paper includes an overview of applicable filtering techniques and subsequent algorithms for extracting, classifying, and compressing features for the ECG signal. Both advantages and disadvantages are discussed for the studied algorithms. © 2022 IEEE.Russian Foundation for Basic Research, РЀЀИ, (20-37-90037)The reported study is funded by RFBR according to research project No. 20-37-90037

    Methods of Signal Analysis for Automatic Diagnosis of Shockable Cardiac Arrhythmias: A Review

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    ΠŸΠΎΡΡ‚ΡƒΠΏΠΈΠ»Π°: 19.12.2021. ΠŸΡ€ΠΈΠ½ΡΡ‚Π° Π² ΠΏΠ΅Ρ‡Π°Ρ‚ΡŒ: 18.01.2022.Received: 19.12.2021. Accepted: 18.01.2022.Ѐибрилляция ΠΆΠ΅Π»ΡƒΠ΄ΠΎΡ‡ΠΊΠΎΠ² сСрдца считаСтся Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ часто Π²ΡΡ‚Ρ€Π΅Ρ‡Π°ΡŽΡ‰Π΅ΠΉΡΡ ΠΏΡ€ΠΈΡ‡ΠΈΠ½ΠΎΠΉ Π²Π½Π΅Π·Π°ΠΏΠ½ΠΎΠΉ остановки сСрдца. Вакая фибрилляция ΠΈ часто ΠΏΡ€Π΅Π΄ΡˆΠ΅ΡΡ‚Π²ΡƒΡŽΡ‰Π°Ρ Π΅ΠΉ ТСлудочковая тахикардия – это Ρ€ΠΈΡ‚ΠΌΡ‹ сСрдца, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ ΠΌΠΎΠ³ΡƒΡ‚ Ρ€Π΅Π°Π³ΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ Π½Π° ΡΠΊΡΡ‚Ρ€Π΅Π½Π½ΡƒΡŽ ΡΠ»Π΅ΠΊΡ‚Ρ€ΠΎΡˆΠΎΠΊΠΎΠ²ΡƒΡŽ Ρ‚Π΅Ρ€Π°ΠΏΠΈΡŽ ΠΈ Π²Π΅Ρ€Π½ΡƒΡ‚ΡŒΡΡ ΠΊ Π½ΠΎΡ€ΠΌΠ°Π»ΡŒΠ½ΠΎΠΌΡƒ синусовому Ρ€ΠΈΡ‚ΠΌΡƒ ΠΏΡ€ΠΈ Ρ€Π°Π½Π½Π΅ΠΉ диагностикС послС остановки сСрдца с восстановлСниСм Π°Π΄Π΅ΠΊΠ²Π°Ρ‚Π½ΠΎΠΉ насосной Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΈ сСрдца. Однако ручная ΠΏΡ€ΠΎΠ²Π΅Ρ€ΠΊΠ° сигналов Π­ΠšΠ“ Π½Π° Π½Π°Π»ΠΈΡ‡ΠΈΠ΅ ΠΏΠ°Ρ‚Ρ‚Π΅Ρ€Π½Π° Ρ‚Π°ΠΊΠΎΠΉ Π°Ρ€ΠΈΡ‚ΠΌΠΈΠΈ являСтся слоТной аналитичСской Π·Π°Π΄Π°Ρ‡Π΅ΠΉ, Ρ‚Ρ€Π΅Π±ΡƒΡŽΡ‰Π΅ΠΉ Π½Π΅ΠΌΠ΅Π΄Π»Π΅Π½Π½ΠΎΠ³ΠΎ принятия Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ Π² стрСссовой ситуации, практичСски Π½Π΅Π²Ρ‹ΠΏΠΎΠ»Π½ΠΈΠΌΠΎΠΉ Π² отсутствиС ΠΊΠ²Π°Π»ΠΈΡ„ΠΈΡ†ΠΈΡ€ΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ мСдицинского спСциалиста. ΠŸΠΎΡΡ‚ΠΎΠΌΡƒ для автоматичСской диагностики острых состояний ΡˆΠΈΡ€ΠΎΠΊΠΎΠ΅ ΠΏΡ€ΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΏΠΎΠ»ΡƒΡ‡ΠΈΠ»ΠΈ систСмы ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½ΠΎΠΉ классификации Π°Ρ€ΠΈΡ‚ΠΌΠΈΠΉ с Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠ΅ΠΉ принятия Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ ΠΎ нСобходимости провСдСния элСктрокардиотСрапии с ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Π°ΠΌΠΈ Π²Ρ‹ΡΠΎΠΊΠΎΠ²ΠΎΠ»ΡŒΡ‚Π½ΠΎΠ³ΠΎ ΠΈΠΌΠΏΡƒΠ»ΡŒΡΠ°, вычислСнного Π°Π΄Π°ΠΏΡ‚ΠΈΠ²Π½ΠΎ для ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚Π°. Π’ Π΄Π°Π½Π½ΠΎΠΉ Ρ€Π°Π±ΠΎΡ‚Π΅ рассмотрСны ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ Π°Π½Π°Π»ΠΈΠ·Π° элСктрокардиографичСского сигнала, снимаСмого с элСктродов Π½Π°Ρ€ΡƒΠΆΠ½ΠΎΠ³ΠΎ автоматичСского ΠΈΠ»ΠΈ полуавтоматичСского дСфибриллятора, с Ρ†Π΅Π»ΡŒΡŽ принятия Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ ΠΎ нСобходимости оказания дСфибрилляции, ΠΏΡ€ΠΈΠΌΠ΅Π½ΠΈΠΌΡ‹Π΅ Π²ΠΎ встроСнном ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΠ½ΠΎΠΌ обСспСчСнии автоматичСских ΠΈ полуавтоматичСских Π²Π½Π΅ΡˆΠ½ΠΈΡ… дСфибрилляторов. Π Π°Π±ΠΎΡ‚Π° Π²ΠΊΠ»ΡŽΡ‡Π°Π΅Ρ‚ ΠΎΠ±Π·ΠΎΡ€ ΠΏΡ€ΠΈΠΌΠ΅Π½ΠΈΠΌΡ‹Ρ… ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² Ρ„ΠΈΠ»ΡŒΡ‚Ρ€Π°Ρ†ΠΈΠΈ, Π° Ρ‚Π°ΠΊΠΆΠ΅ ΠΏΠΎΡΠ»Π΅Π΄ΡƒΡŽΡ‰ΠΈΡ… Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΠΎΠ² извлСчСния, классификации ΠΈ сТатия Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€Π½Ρ‹Ρ… ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠΎΠ² для сигнала Π­ΠšΠ“.Ventricular fibrillation is considered the most common cause of sudden cardiac arrest. The fibrillation, and ventricular tachycardia often preceding it, are cardiac rhythms that may respond to emergency electroshock therapy and return to normal sinus rhythm when diagnosed early after cardiac arrest with the restoration of adequate cardiac pumping function. However, manually checking ECG signals on the existence of a pattern of such arrhythmias is a risky and time-consuming task in stressful situations and practically impossible in the absence of a qualified medical specialist. Therefore, systems of the computer classification of arrhythmias with the function of making a decision on the necessity of electric cardioversion with the parameters of a high-voltage pulse calculated adaptively for each patient are widely used for the automatic diagnosis of such conditions. This paper discusses methods of analyzing the electrocardiographic signal taken from the electrodes of an external automatic or semi-automatic defibrillator in order to make a decision on the necessity for defibrillation, which are applicable in the embedded software of automatic and semiautomatic external defibrillators. The paper includes an overview of applicable filtering techniques as well as subsequent algorithms for extracting, classifying and compressing features for the ECG signal

    Detection of shockable heart rhythms with convolutional neural networks : Based on ECG spectrograms

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    Purpose Automated feature extraction combined with deep learning has had and continues to have a strong impact on the improvement and implementation of pattern recognition driven by machine learning. Systems without prior expertise about a problem but with the ability to iteratively learn strategies to solve problems, tend to outperform concepts of manual feature engineering in vari-ous fields. In ECG data analysis as well as in other medical domains, models based on manual feature extraction are tedious to develop, require scientific expertise, and are oftentimes not easily adaptive to variations of the problem to be solved. This work aims to examine automated feature extraction and classification of ECG data, specifically of shockable heart rhythms, with convolu-tional neural networks and residual neural networks. The precise and rapid determination of shockable cardiac conditions is a decisive step to improve the chances of survival for patients having a sudden cardiac arrest. Conventional, commercially available automated external defib-rillators (AEDs) deploy algorithms based on manual feature extraction. Approximately 1 out of 10 shockable conditions is not recognized by the AED. Consequently, strategies for improvement need to be explored. Methods 125 ECG recordings from four annotated cardiac arrhythmia databases (American Heart Association Database, Creighton University Tachyarrhythmia Database, MIT-BIH Arrhythmia Da-tabase, MIT-BIH Malignant Ventricular Arrhythmia Database) with a duration of 30 mins or 8 mins (Creighton University Tachyarrhythmia Database) per recording were processed. Shockable con-ditions are identified as ventricular tachycardia, ventricular fibrillation, and ventricular flutter. The 1 channel ECG recordings (modified limb lead II) were normalized to 250 Hz sampling frequency, high-pass filtered (1 Hz cutoff and 0.85 filter steepness), second order Butterworth low-pass fil-tered (30 Hz cutoff), and notch filtered at 50 Hz. Consistent wavelet transformation with 5 octaves, 20 voices per octave, and a time bandwidth product parameter of 50 was applied to generate greyscale spectrogram representations of the ECG data (pixel value range from 0 to 255). The recordings were segmented into 3 s segments. Data augmentation around the borders of shock-able episodes and along shockable episodes was carried out to create balanced datasets con-sisting of 60340 samples. 45% of samples in the balanced dataset contain shockable rhythms with more than 60% temporal prevalence within each sample. Conventional convolutional neural networks and residual neural networks with varying architectures and hyperparameter settings were trained and evaluated on balanced datasets (train/val/test: 70/15/15). The approach focused on examining a broader range of parameter settings and model architectures rather than optimiz-ing a specific configuration. The best performing model was evaluated in a 5-fold cross-validation. Exemplarily, a leave-one-subject-out cross-validation was deployed with 3 randomly chosen re-cordings, with the constraints that each subject must come from a different database and contain a different shockable condition. Results and Conclusion The best performing model was a residual neural network with 96 residual blocks. The 5-fold cross-validation results on average in an accuracy of 0.987, a sensitivity of 0.992 on shock-able rhythms, and a specificity of 0.984 for non-shockable rhythms on the test sets. The ROC AUC score is 0.998 on average. The 3-fold leave-one-subject-out cross-validation reaches on average an accuracy of 0.984, a sensitivity of 0.984, and a specificity of 0.980. The ROC AUC score reaches 0.997 on average. The analysis of misclassified segments reveals that the classi-fier performs less accurately on border segments containing a shockable and at least one non-shockable rhythm. While the test set contains 4.73% border segments, the set of misclassified samples includes 11.29% border segments. The label distributions of the test set and the set of misclassified samples show that segments annotated as β€œnot defined” (ND) and β€œventricular fibril-lation or flutter” (VF-VFL) are significantly more prevalent in the set of misclassified samples. Histogram analysis, referring to the mean pixel intensity of the spectrograms, indicates that the classifier works less accurately on spectrograms with mean pixel values below 2 (practically flat-line signals or signals with very small amplitude). The results indicate that it is possible to improve the analysis of ECG data by deploying automated feature detection combined with artificial neural networks. The methods presented in this work are not restricted to the detection of shockable cardiac arrhythmias, they likewise em-phasize the potential of machine learning in the domain of biosignal analysis and correlated med-ical data. In the next step, the approach needs to be verified on a broader database. The tech-nology can even help create more comprehensive databases of clinical ECG data by supporting automated annotation

    Seinale prozesaketan eta ikasketa automatikoan oinarritutako ekarpenak bihotz-erritmoen analisirako bihotz-biriketako berpiztean

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    Tesis inglΓ©s 218 p. -- Tesis euskera 220 p.Out-of-hospital cardiac arrest (OHCA ) is characterized by the sudden loss of the cardiac function, andcauses around 10% of the total mortality in developed countries. Survival from OHCA depends largelyon two factors: early defibrillation and early cardiopulmonary resuscitation (CPR). The electrical shock isdelivered using a shock advice algorithm (SAA) implemented in defibrillators. Unfortunately, CPR mustbe stopped for a reliable SAA analysis because chest compressions introduce artefacts in the ECG. Theseinterruptions in CPR have an adverse effect on OHCA survival. Since the early 1990s, many efforts havebeen made to reliably analyze the rhythm during CPR. Strategies have mainly focused on adaptive filtersto suppress the CPR artefact followed by SAAs of commercial defibrillators. However, these solutionsdid not meet the American Heart AssociationΒΏs (AHA) accuracy requirements for shock/no-shockdecisions. A recent approach, which replaces the commercial SAA by machine learning classifiers, hasdemonstrated that a reliable rhythm analysis during CPR is possible. However, defibrillation is not theonly treatment needed during OHCA, and depending on the clinical context a finer rhythm classificationis needed. Indeed, an optimal OHCA scenario would allow the classification of the five cardiac arrestrhythm types that may be present during resuscitation. Unfortunately, multiclass classifiers that allow areliable rhythm analysis during CPR have not yet been demonstrated. On all of these studies artefactsoriginate from manual compressions delivered by rescuers. Mechanical compression devices, such as theLUCAS or the AutoPulse, are increasingly used in resuscitation. Thus, a reliable rhythm analysis duringmechanical CPR is becoming critical. Unfortunately, no AHA compliant algorithms have yet beendemonstrated during mechanical CPR. The focus of this thesis work is to provide new or improvedsolutions for rhythm analysis during CPR, including shock/no-shock decision during manual andmechanical CPR and multiclass classification during manual CPR
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