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1

A novel multi-class approach for early-stage prediction of sudden cardiac death

Devi Reeta, Tyagi Hitender Kumar, Kumar Dinesh

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 3

586--598

EN

Sudden cardiac death (SCD) is a complex issue that may occur in population groups with either known or unknown cardiovascular disease (CVD). Given the complex nature of SCD, the discovery of a suitable biomarker will prove essential in identifying individuals at risk of SCD, while discriminating it from patients with other cardiac pathologies as well as healthy individuals. Thus, this study aimed to develop an efficient approach to support a better comprehension of heart rate variability (HRV) as a predictive biomarker to identify SCD patients at an early stage. The present study proposed a novel multi-class classification approach using signal processing methods of HRV to predict SCD 10 min before its occurrence. The developed algorithm was qualitatively and quantitatively analyzed in terms of discriminating SCD patients from patients of heart failure and normal people. A total of 51 HRV signals of all three classes obtained from PhysioBank were processed to extract 32 features in each subject. The optimal feature selection was performed by a hybrid approach of sequential feature selection-random under sampling boosting algorithms. Multi-class classifiers, namely decision tree, support vector machine, and k-nearest neighbors were used for classification. An average classification accuracy of SCD prediction 10 min before occurrence was obtained as 83.33%. Therefore, this study suggests a new efficient approach for the early-stage prediction of SCD that is considerably different from that reported in the literature to date. However, to generalize the findings, the algorithm needs to be tested for a larger population group.

2

Early detection of sudden cardiac death using nonlinear analysis of heart rate variability

Khazaei M., Raeisi K., Goshvarpour A., Ahmadzadeh M.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 4

931--940

EN

Background and objective: Sudden cardiac death (SCD) is one of the most widespread reasons for death around the world. A precise and early prediction of SCD can improve the chance of survival by administering cardiopulmonary resuscitation (CPR). Hence, there is a vital need for an SCD prediction system. Methods: In this work, a novel and efficient algorithm for automated detection of SCD six minutes before its onset is proposed. This algorithm uses features based on the nonlinear modeling of heart rate variability (HRV). In fact, after the extraction of the HRV signals, increment entropy and recurrence quantification analysis-based features are extracted. The one-way ANOVA is applied for the dimension reduction of feature space—this results in lower computational cost. Finally, the distinguishing features are fed to classifiers such as the decision tree, K-nearest neighbor, naive Bayes, and the support vector machine. Results: By using the decision tree classifier we have achieved SCD detection six minutes before its onset with an accuracy, specificity, and sensitivity of 95%. These results demonstrate the superiority of the presented algorithm compared to the existing ones in performance. Conclusions: This study shows that a combination of features based on the nonlinear modeling of HRV, such as laminarity (based on recurrence quantification analysis), and increment entropy leads to early detection of SCD. Choosing the decision tree improves the performance of the algorithm. The results could help in the development of a tool that would allow the detection of cardiac arrest six minutes before its onset.

3

Project of open-source software platform for sudden cardiac death (SCD) risk stratification and advanced ECG analysis

Kotas R., Kamiński M., Mazur P., Chłapiński Jakub, Sakowicz B., Napieralski A., Kurpesa M.

Przegląd Elektrotechniczny

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2013

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R. 89, nr 2a

64--67

PL

Artykuł prezentuje główny cel projektu, którym jest opracowanie otwartej platformy do stratyfikacji ryzyka nagłego zgonu sercowego w oparciu o przebiegi EKG z zapisów holterowskich na postawie znanych i zweryfikowanych klinicznie wskaźników: mikrowoltowej naprzemienności załamka T, turbulencji rytmu serca, zmienności rytmu zatokowego i współczynnika akceleracji/deceleracji rytmu serca.

EN

This paper presents the main goal of the “Sudden Cardiac Death risk stratification based on functional assessment of autonomic nervous system with the use of Holter methods” project, which is development of unified, generally available, extendable, open-source software platform for ECG signal analysis from numerous types of Holter recorders, allowing for high-risk markers assessment. Realization of this project would greatly simplify selection of high-risk patients.

4

Models for Risk Stratification of Sudden Cardiac Death Based on Logical Conjunction and Decision Tree

Kamiński M., Kotas R., Mazur P., Sakowicz B., Napieralski A.

International Journal of Microelectronics and Computer Science

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2013

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Vol. 4, nr 2

65--71

EN

This article presents methodology of simple model building for risk stratification of sudden cardiac death based on an original ECG signal analysis platform. The method includes the analysis of the ECG signal extraction of selected parameters and model optimization for predicting sudden cardiac death.

5

ECG signal processing for deceleration capacity assessment

Kamiński M., Chłapiński J., Sakowicz B., Kotas R., Napieralski A.

Elektronika : konstrukcje, technologie, zastosowania

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2011

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Vol. 52, nr 12

73-77

EN

In this paper is presented further development of ECG signal analysis software system "Cardio" with capability of Deceleration Capacity (DC) assessment. In the first part of the paper the algorithms used for DC assessment are described. In the second part of the paper are presented results of our implementation verification on ECG signals from group of patients. In the third part is presented proposed modification of the original algorithm allowing for DC assessment during sleep and wakefulness separately.

PL

Celem artykułu jest przedstawienie prac poświęconych rozwojowi autorskiego oprogramowania do analizy sygnału EKG - ''Cardio" o możliwość wyznaczania współczynnika deceleracji (DC). W pierwszej części artykułu omówiono wykorzystywane do tego algorytmy obliczeniowe. W drugiej części przedstawiono wyniki badań na grupie pacjentów z wykorzystaniem nowego oprogramowania celem weryfikacji jego działania i oceny możliwości wykorzystania parametru DC w diagnostyce. W części trzeciej zaproponowano oryginalną koncepcję obliczania parametru DC niezależnie dla okresu snu i czuwania a także przedstawiono algorytm umożliwiający odróżnienie snu od czuwania wyłącznie na podstawie samego sygnału EKG.

6

Comparison of T-wave alternans detection methods

Janusek D., Pawłowski Z., Karczmarewicz S., Przybylski A.

Biocybernetics and Biomedical Engineering

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2004

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Vol. 24, no. 4

31-41

EN

T-wave alternans (TWA), which has recently been detected at the micro-volt level, is a non-invasive marker of the vulnerability to ventricular arrhythmia. The aim of this study was to compare the presently used TWA detection methods and to determine the ECG signal acquisition and processing conditions ensuring the TWA detection at its lowest possible level. The results of the evaluation and comparison of seven methods are presented. The ECG signal analysis was performed in the time and frequency domain. The differential and correlation methods were applied as the time domain methods. The complex demodulation method and methods based on the FFT and Karhunen-Loeve transform were tested as the frequency domain methods. The T-wave alternans markers were measured in 28 patients, and the results were compared. The usefulness of TWA detection methods has been demonstrated to dependant on the signal properties, such as the nonstationarity of TWA and the level of noise. Strong correlation between the magnitude of the T-wave alternans and ventricular repolarization time is described. A new, spectral methods is proposed.