Ventricular Fibrillation Detection Algorithms Research Articles

Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set.

Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system.

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

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.

Real-Time Ventricular Fibrillation Detection Using an Embedded Microcontroller in a Pervasive Environment

Many healthcare problems are life threatening and need real-time detection to improve patient safety. Heart attack or ventricular fibrillation (VF) is a common problem worldwide. Most previous research on VF detection has used ECG devices to capture data and sent to other higher performance units for processing and has relied on domain experts and/or sophisticated algorithms for detection. In this case, it delayed the response time and consumed much more energy of the ECG module. In this study, we propose a prototype that an embedded microcontroller where an ECG sensor is used to capture, filter and process data, run VF detection algorithms, and only transmit the detected event to the smartphone for alert and call for services. We discuss how to adapt a common filtering and scale process and five light-weighted algorithms from open literature to realize the idea. We also develop an integrated prototype, which emulates the VF process from existing data sets, to evaluate the detection capability of the framework and algorithms. Our results show that (1) TD outperforms the other four algorithms considered with sensitivity reaching 96.56% and specificity reaching 81.53% in the MIT-BIH dataset. Our evaluations confirm that with some adaptation the conventional filtering process and detection algorithms can be efficiently deployed in a microcontroller with good detection accuracy while saving battery power, shortening response time, and conserving the network bandwidth.

Arrhythmia Evaluation in Wearable ECG Devices.

This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the evaluation of the sensitivity, positive predictivity and false positive rate. Sample entropy, fast Fourier transform (FFT), and multilayer perceptron neural network with backpropagation training algorithm are selected for the integrated detection algorithms. For this study, the result for SVEB has some improvements compared to a previous study that also utilized ANSI/AAMI EC57. In further, VEB sensitivity and positive predictivity gross evaluations have greater than 80%, except for the positive predictivity of the NSTDB database. For AF gross evaluation of MITDB database, the results show very good classification, excluding the episode sensitivity. In advanced, for VF gross evaluation, the episode sensitivity and positive predictivity for the AHADB, MITDB, and CUDB, have greater than 80%, except for MITDB episode positive predictivity, which is 75%. The achieved results show that the proposed integrated SVEB, VEB, AF, and VF detection algorithm has an accurate classification according to ANSI/AAMI EC57:2012. In conclusion, the proposed integrated detection algorithm can achieve good accuracy in comparison with other previous studies. Furthermore, more advanced algorithms and hardware devices should be performed in future for arrhythmia detection and evaluation.

A new method without reference channels used for ventricular fibrillation detection during cardiopulmonary resuscitation.

Ventricular fibrillation (VF) is observed as the initial rhythm in the majority of patients suffering from sudden cardiac arrest. It is vitally important to accurately recognize the initial VF rhythm and then implement electrical defibrillation. However, artifacts produced by chest compression during cardiopulmonary resuscitation (CPR) make the VF detection algorithms utilized by current automated external defibrillators (AEDs) unreliable. CPR must be traditionally interrupted for a reliable diagnosis. However, interruptions in chest compression have a deleterious effect on the success of defibrillation. The elimination of the CPR artifacts would enable compressions to continue during AED VF detection and thereby increase the likelihood of resuscitation success. We have estimated a model of this artifact by adaptively incorporating noise-assisted multivariate empirical mode decomposition (NA-MEMD) and least mean squares (LMS) and then removing the artifact from the corrupted ECGs. The simulation experiment indicated that the CPR artifact could be accurately modeled without any reference channels. We constructed a BP neural network to evaluate the results. A total of 372 VF and 645 normal sinus rhythm (SR) ECG samples were included in the analysis, and 24 CPR artifact signals were used to construct corrupted ECGs. The results indicated that at different SNR levels ranging from 0 to -12dB, the sensitivity and specificity were always above 95 and 80%, respectively.

ETD: an extended time delay algorithm for ventricular fibrillation detection.

Ventricular fibrillation (VF) is the most serious type of heart attack which requires quick detection and first aid to improve patients' survival rates. To be most effective in using wearable devices for VF detection, it is vital that the detection algorithms be accurate, robust, reliable and computationally efficient. Previous studies and our experiments both indicate that the time-delay (TD) algorithm has a high reliability for separating sinus rhythm (SR) from VF and is resistant to variable factors, such as window size and filtering method. However, it fails to detect some VF cases. In this paper, we propose an extended time-delay (ETD) algorithm for VF detection and conduct experiments comparing the performance of ETD against five good VF detection algorithms, including TD, using the popular Creighton University (CU) database. Our study shows that (1) TD and ETD outperform the other four algorithms considered and (2) with the same sensitivity setting, ETD improves upon TD in three other quality measures for up to 7.64% and in terms of aggregate accuracy, the ETD algorithm shows an improvement of 2.6% of the area under curve (AUC) compared to TD.

Universal Fluctuations in Very Short ECG Episodes

We propose a new algorithm for the detection of ventricular fibrillation (VF) in very short surface electrocardiogram (ECG) episodes. Ventricular fibrillation is the most commonly identified arrhythmia in cardiac arrest patients and can lead to syncope, within seconds. The fast detection of ventricular fibrillation is necessary for prompt defibrillation either with an implantable cardioverter/defibrillator or an automated external defibrillator. Ventricular fibrillation generates stochastic waveforms and recently it has been shown that it exhibits characteristics similar to a non-chaotic signal and contains determinism Probability Density Function (PDF), for the different physical fluctuations was described previously. Accordingly, we describe scaling properties of very short shockable, VF and non-shockable ECG episodes and show that a universal PDF exists for the fluctuations of shockable ECG episodes. We compared the proposed algorithm with nine standard VF detection algorithms. The comparison indicated that our algorithm consistently produced more accurate detection results, then with standard algorithm. We conclude that the proposed method, based on fluctuation analysis, provides new information on the dynamics underlying VF, and allows a better detection compared to other algorithms.

Support Vector Machine Algorithm for Real-Time Detection of VF Signals

An algorithm for detecting ventricular fibrillation (VF) by the method of support vector machine is presented. The algorithm first extracts the feature of electrocardiogram in every 4s sliding window by the improved time delay method and the parameter d is obtained as feature; the support vector machine method is used to realize the discrimination of VF and non-VF signals. For evaluating the new algorithm, the complete BIH-MIT arrhythmia database and the CU database were used to simulate without any pre-selection. The sensitivity, specificity, positive predictability and accuracy were calculated and compared these values with results from an earlier investigation of several different ventricular fibrillation detection algorithms. It shows that the new algorithm has good performance and has greater advantages in real-time execution.

A simple time domain algorithm for the detection of ventricular fibrillation in electrocardiogram

Ventricular fibrillation (VF) is the most serious variety of arrhythmia which requires quick and accurate detection to save lives. In this paper, we propose a new time domain algorithm, called threshold crossing sample count (TCSC), which is an improved version of the threshold crossing interval (TCI) algorithm for VF detection. The algorithm is based on an important feature of the VF signal which relies on the random behavior of the electrical heart vector. By two simple operations: comparison and count, the technique calculates an effective measure which is used to separate life-threatening VF from other heart rhythms. For assessment of the performance of the algorithm, the method is applied on the complete MIT-BIH arrhythmia and CU databases, and a promising good performance is observed. Seven other classical and new VF detection algorithms, including TCI, have been simulated and comparative performance results in terms of different quality parameters are presented. The TCSC algorithm yields the highest value of the area under the receiver operating characteristic curve (AUC). The new algorithm shows strong potential to be applied in clinical applications for faster and accurate detection of VF.

Automated Detection of Ventricular Fibrillation to Guide Cardiopulmonary Resuscitation

Sudden death due to ventricular fibrillation (VF) is a catastrophic event, especially in out-of-hospital settings. Prompt detection of VF and preparedness to intervene with cardiopulmonary resuscitation (CPR) and especially the delivery of an electrical shock is potentially lifesaving. The reliability and accuracy of automated VF detection by current versions of automated external defibrillators (AEDs) require interruption of CPR because the ECG signal, which is the source of rhythm detection, is corrupted by chest compressions. Significantly better outcomes have been reported if effective chest compression precedes electrical defibrillation and especially if interruptions are minimized. We therefore sought a method by which VF detection could proceed without interrupting chest compressions. A VF detection algorithm was therefore derived based on a method by which continuous wavelet transform is used, together with measurement of morphologic consistency. This method was intended to distinguish between disorganized and organized rhythms. The Fourier-transform-based amplitude spectrum analysis was then used to detect the likelihood that VF was the rhythm prompting the delivery of an electrical shock. The algorithm was validated on 33,095 electrocardiographic segments, including 8840 segments corrupted by compression artifacts from 232 patients after out-of-hospital cardiac arrest. Nine thousand one hundred eighty-seven of 10,042 VF segments and 20,884 of 23,053 non-VF segments were correctly classified, with a sensitivity of 91.5% and a specificity of 90.6%. Although the proposed algorithm has a lesser predictive value for VF detection than the uncorrupted ECGs in clinical settings, it has the major potential for automated rhythm identification to guide defibrillation without repetitive interruptions of CPR.

Detecting Ventricular Fibrillation by Time-Delay Methods

A pivotal component in automated external defibrillators (AEDs) is the detection of ventricular fibrillation (VF) by means of appropriate detection algorithms. In scientific literature there exists a wide variety of methods and ideas for handling this task. These algorithms should have a high detection quality, be easily implementable, and work in realtime in an AED. Testing of these algorithms should be done by using a large amount of annotated data under equal conditions. For our investigation we simulated a continuous analysis by selecting the data in steps of 1 s without any preselection. We used the complete BIH-MIT arrhythmia database, the CU database, and files 7001-8210 of the AHA database. For a new VF detection algorithm we calculated the sensitivity, specificity, and the area under its receiver operating characteristic curve and compared these values with the results from an earlier investigation of several VF detection algorithms. This new algorithm is based on time-delay methods and outperforms all other investigated algorithms.

Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators

BackgroundA pivotal component in automated external defibrillators (AEDs) is the detection of ventricular fibrillation by means of appropriate detection algorithms. In scientific literature there exists a wide variety of methods and ideas for handling this task. These algorithms should have a high detection quality, be easily implementable, and work in real time in an AED. Testing of these algorithms should be done by using a large amount of annotated data under equal conditions.MethodsFor our investigation we simulated a continuous analysis by selecting the data in steps of one second without any preselection. We used the complete BIH-MIT arrhythmia database, the CU database, and the files 7001 – 8210 of the AHA database. All algorithms were tested under equal conditions.ResultsFor 5 well-known standard and 5 new ventricular fibrillation detection algorithms we calculated the sensitivity, specificity, and the area under their receiver operating characteristic. In addition, two QRS detection algorithms were included. These results are based on approximately 330 000 decisions (per algorithm).ConclusionOur values for sensitivity and specificity differ from earlier investigations since we used no preselection. The best algorithm is a new one, presented here for the first time.

Reproducibility of ventricular fibrillation characteristics in patients undergoing implantable cardioverter defibrillator implantation.

The purpose of this study was to evaluate the immediate reproducibility of local electrogram characteristics recorded during repeated episodes of induced ventricular fibrillation (VF) in patients undergoing implantable cardioverter defibrillator (ICD) implantation. Power spectral analysis (using a fast Fourier transform algorithm) of electrograms recorded during 3 seconds of VF were analyzed in 24 patients undergoing ICD implantation using a Medtronic Transvene lead. Patients had 2 to 7 episodes of VF that were induced during defibrillation threshold testing. VF was induced by burst pacing (n = 20) or T wave shock (n = 4). Simultaneous electrograms during VF were recorded from a Medtronic Transvene lead with the following configurations: (1) a narrow spaced (12 mm) dedicated bipole used clinically for sensing; (2) a unipolar electrogram from the right ventricular coil; and (3) a widely spaced (18.3 mm) integrated bipole using the distal tip and the coil. Intraclass correlation coefficients (ICCs) were determined to examine the reproducibility of these VF characteristics among VF episodes in each patient. Recordings from both bipolar configurations had ICCs from 0.40 to 0.55, whereas unipolar recordings ICCs were below 0.40. Reproducibility was similar for dedicated and integrated recordings. Frequency characteristics of repeated episodes of VF induced in the same subjects show fair-to-good but not excellent reproducibility. Bipolar recordings were far more reproducible than unipolar recordings, but both bipolar configurations had similar reproducibility. These findings have implications for both the pathophysiology of induced VF and the design of VF detection algorithms.

American Heart Association Report on the Public Access Defibrillation Conference December 8-10, 1994

During the past 20 years, morbidity and mortality rates for nearly all types of cardiovascular disease have declined. Progress in these areas is in stark contrast to that for sudden cardiac death, which continues unabated at a rate of approximately 1000 times per day in the United States, with little decline in incidence or improved outcome. Clearly, the problem of sudden cardiac death is best approached through prevention, but horizons in that area seem no more promising and in some respects less promising and substantially more costly than 2 decades ago. The means necessary for successful resuscitation of a patient in cardiac arrest were known by the early 1960s. Externally performed cardiopulmonary resuscitation (CPR) could maintain an “oxygen plateau” and delay permanent brain damage long enough to allow external defibrillation using direct current (DC). The possibility of long-term survival was increasingly recognized, as early anecdotal experiences accumulated into published series.1 2 3 Given the hindsight of 3 decades, the obstacles to be overcome before significant progress could be made in out-of-hospital resuscitation were formidable. First, cardiac arrest was perceived as an event that typically occurred in the hospital. In-hospital cardiac arrests are now recognized to represent only a small proportion of sudden deaths based in the community. Second, the CPR technique was known to only a limited number of hospital-based physicians. CPR is no longer restricted to hospitals or physicians; it is routinely taught to the lay public. Third, only line-powered, bulky, and awkward defibrillators were available. The first out-of-hospital defibrillation device weighed 110 lb. Contemporary external defibrillators are available that weigh less than 10 lb. The present report details progress made in achieving the goal of facilitating out-of-hospital resuscitation and specifies those areas in which further headway is needed. This effort began in 1990 with an American Heart …

Assessment of the ventricular fibrillation detection algorithm in the semi-automatic Cardio-Aid defibrillator

The sensitivity and specificity of ventricular fibrillation (VF) detection in the semi-automatic Cardio-Aid defibrillator was assessed with 25 ECG recordings, each of length 40 s. Of the 25 ECG recordings, 12 contained VF requiring defibrillation, 3 contained a tachyarrhythmia with a waveform similar to VF but which self-terminated, and 10 were selected from abnormal rhythms and artefacts which contained some features similar to VF. Sensitivity was assessed from the VF data. Specificity was assessed from both the rhythm preceding VF or the tachyarrhythmias, and from the VF-like data. The response to a changing rhythm was assessed from the self-terminating tachyarrhythmias. Each recording was replayed to the defibrillators at 3 signal amplitudes (normal, half and double). Requests to analyse the ECG because of possible VF and advice to shock were noted separately. The sensitivity for recommending a shock when a shock was required was 92%. The sensitivity for drawing attention to VF, through requesting analysis was 97%. There were no false detections in the rhythms preceding VF or the tachyarrhythmias (specificity with good quality signals 100%). The specificity with the VF-like data was 90%. There was no significant difference between this defibrillator and other semi-automated defibrillators previously assessed.

Comparative assessment of the ventricular fibrillation detection algorithms in five semi-automatic or advisory defibrillators

The sensitivity and specificity of ventricular fibrillation (VF) detection in three semi-automatic defibrillators (Laerdal Heartstart 3000, Spacelabs First Medic 610, Physio-Control Lifepak 300) and two advisory defibrillators (S&W DMS940, Marquette Responder 1500) were assessed with 25 ECG recordings, each of length 40 s. Of the 25 ECG recordings, 12 contained VF requiring defibrillation, three contained a tachyarrhythmia with a waveform similar to VF but which self-terminated, and 10 were selected from abnormal rhythms and artefacts which contained some features similar to VF. Sensitivity was assessed from the VF data. Specificity was assessed from both the rhythm preceding VF or the tachyarrhythmias, and from the VF-like data. The response to a changing rhythm was assessed from the self-terminating tachyarrhythmias. Each recording was replayed to the defibrillators at three signal amplitudes (normal, half and double). For each defibrillator, requests to check the patient and advice to shock were noted separately. The sensitivity for recommending a shock when a shock was required varied from 81 to 97%. The sensitivity for drawing attention to VF, either through requesting the patient to be checked or advising a shock, varied from 92% to 100%. There were no false detections in the rhythms preceding VF or the tachyarrhythmias (specificity with good quality signals 100%). The specificity with the VF-like data ranged from 63 to 90% for recommending a shock, and from 63% to 70% for requesting the patient be checked or shocked. There was no difference between the defibrillators for VF detection, but there was a significant difference between the semi-automatic and advisory defibrillators (P < 0.05) for the specificity of the final recommendation.