Methods for Improved Discrimination Between Ventricular Fibrillation and Tachycardia

Alwan, Yaqub; Cvetković, Zoran; Curtis, Michael J.

doi:10.1109/tbme.2017.2785442

Cited by 13 publications

(2 citation statements)

References 21 publications

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“…The results show that the proposed approach has achieved an AC of 81.25% for the classi cation of VT and VF classes using 8second ECG frames with a 10-fold cross-veri cation. Alwan et al (2017) classi ed VT and VF using the leakage characteristics of the VF lter and spectral parameters. A set of high-dimensional functions preserves more up-to-date information about the ECG dataset and provides an AC of detection of 73.5% using a support vector classi er.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Bidirectional LSTM Model for identifying Ventricular Tachyarrhythmia using ECG Signal Variability

Ojha¹,

Wadhwani²,

Wadhwani³

et al. 2021

Preprint

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An electrocardiogram (ECG) signal is used widely to detect ventricular tachyarrhythmia (VTA) and to diagnose heart disease. Deep learning models and large ECG data have made the diagnosis of VTA an attractive task to demonstrate the power of artificial intelligence in clinical applications. One of the life-threatening complications of VTA is cardiac arrest (CA). The VTA is divided into two categories: ventricular fibrillation (VF) and ventricular tachycardia (VT). Abnormal electrical activity in the ventricle causes VT, which leads to CA, whereas the chaotic electrical activity in the ventricle leads to VF. To improve the clinical diagnostic system and to help cardiologists, it is essential to identify the risk of VTA at an early stage. The goal of this paper is to develop an end-to-end (E2E) deep learning model that uses a convolution neural network (CNN) and a bidirectional long-short term memory network (BiLSTM) to classify VT and VF arrhythmias from multiple ECG databases. The CNN extracts features from ECG signals, and BiLSTM learns information. The ECG signals are acquired from the MIT-BIH malignant ventricular arrhythmia database (VFDB) and the Creighton University VTA database (CUDB). These ECG signals indicate that heart rate variability is a fast and dynamic event. Before the method's implementation, ECG signals are windowed at a fixed size according to annotation information and then normalized within each window. In terms of accuracy and sensitivity, the proposed CNN-BiLSTM deep learning model outperforms existing state-of-the-art methods. These results made it possible to obtain a relatively higher average accuracy (AC) of 99.37%, precision (PE) of 97.12%, a sensitivity (SE) of 98.15%, and F-score (FS) of 98.43%, and an overall accuracy of 99.07%, respectively.

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Section: Introductionmentioning

confidence: 99%

Deep Convolutional Bidirectional LSTM Model for identifying Ventricular Tachyarrhythmia using ECG Signal Variability

Ojha¹,

Wadhwani²,

Wadhwani³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…EKG feature extraction has been approached in the time [16], [17], frequency [18], [19], time-frequency [15], [20], [21], and complexity domains [22], [23]. The machine learning approaches explored in the classification stage include K-nearest neighbors [15], [24], support vector machines [10], [25], [26], artificial neural networks [13], [19], [27], and ensembles of decision trees [11], [14].…”

mentioning

confidence: 99%

Automatic Cardiac Rhythm Classification With Concurrent Manual Chest Compressions

et al. 2019

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Electrocardiogram (EKG) based classification of out-of-hospital cardiac arrest (OHCA) rhythms is important to guide treatment and to retrospectively elucidate the effects of therapy on patient response. OHCA rhythms are grouped into five categories: ventricular fibrillation (VF) and tachycardia (VT), asystole (AS), pulseless electrical activity (PEA), and pulse-generating rhythms (PR). Clinically these rhythms are grouped into broader categories like shockable (VF/VT), non-shockable (AS/PEA/PR), or organized (ORG, PEA/PR). OHCA rhythm classification is further complicated because EKGs are corrupted by cardiopulmonary resuscitation (CPR) artifacts. The objective of this study was to demonstrate a framework for automatic multiclass OHCA rhythm classification in the presence of CPR artifacts. In total, 2133 EKG segments from 272 OHCA patients were used: 580 AS, 94 PR, 953 PEA, 479 VF, and 27 VT. CPR artifacts were adaptively filtered, 93 features were computed from the stationary wavelet transform analysis, and random forests were used for classification. A repeated stratified nested cross-validation procedure was used for feature selection, parameter tuning, and model assessment. Data were partitioned patient-wise. The classifiers were evaluated using per class sensitivity, and the unweighted mean of sensitivities (UMS) as a global performance metric. Four levels of clinical detail were studied: shock/no-shock, shock/AS/ORG, VF/VT/AS/ORG, and VF/VT/AS/PEA/PR. The median UMS (interdecile range) for the 2, 3, 4, and 5-class classifiers were: 95.4% (95.1-95.6), 87.6% (87.3-88.1), 80.6% (79.3-81.8), and 71.9% (69.5-74.6), respectively. For shock/no-shock decisions sensitivities were 93.5% (93.0-93.9) and 97.2% (97.0-97.4), meeting clinical standards for artifact-free EKG. The UMS for five classes with CPR artifacts was 5.8-points below that of the best algorithms without CPR artifacts, but improved the UMS of latter by over 19-points for EKG with CPR artifacts. A robust and accurate approach for multiclass OHCA rhythm classification during CPR has been demonstrated, improving the accuracy of the current state-of-the-art methods.INDEX TERMS Out-of-hospital cardiac arrest (OHCA), electrocardiogram (EKG), cardiopulmonary resuscitation (CPR), adaptive filter, stationary wavelet transform (SWT), random forest (RF) classifier. I. INTRODUCTIONOut-of-hospital cardiac arrest (OHCA) is a leading cause of death in the industrialized world. In Europe the estimated annual average incidence of ambulance treated casesThe associate editor coordinating the review of this manuscript and approving it for publication was Nuno Garcia. is 41 (range 19-104) per 100 000 persons [1]. Patients in cardiac arrest lose their cardiac and respiratory function, and die within minutes if not treated. Treatment consists of highly time-sensitive interventions such as: recognition, call for help, cardiopulmonary resuscitation (CPR), defibrillation, and post-resuscitation care. Bystanders and lay rescuers can provide CPR to maintain an artificial perfus...

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Clinical Application of AI-ECG

Xue,

Chen,

et al. 2024

AI Augmented ECG Technology

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Methods for Improved Discrimination Between Ventricular Fibrillation and Tachycardia

Cited by 13 publications

References 21 publications

Deep Convolutional Bidirectional LSTM Model for identifying Ventricular Tachyarrhythmia using ECG Signal Variability

Deep Convolutional Bidirectional LSTM Model for identifying Ventricular Tachyarrhythmia using ECG Signal Variability

Automatic Cardiac Rhythm Classification With Concurrent Manual Chest Compressions

Clinical Application of AI-ECG

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