Deep Feature Learning for Sudden Cardiac Arrest Detection in Automated External Defibrillators

Abstract

Ventricular fibrillation and ventricular tachycardia (VF/VT), known as shockable (SH) rhythms, are the mainly cause of sudden cardiac arrests (SCA), which is cured efficiently by the automated external defibrillator (AED). The performance of the shock advice algorithm (SAA) applied in the AED has been improved by using machine learning technique and variously conventional features, recently. In this paper, we propose a novel algorithm with relatively high performance for the SCA detection on electrocardiogram (ECG) signal. The algorithm consists of a convolutional neural network as a feature extractor (CNNE) and a Boosting (BS) classifier. A grid search with nested 5-folds cross validation (CV) is used to select the CNNE trained with preprocessed ECG, SH, and NSH signals using the modified variational mode decomposition technique. The deep feature vector learned by this CNNE is extracted at the first fully connected layer and then fed into BS classifier to validate its performance using 5-folds CV procedure. The secondary learning of the BS classifier and the use of three input channels for the CNNE improve certainly the detection performance of the proposed SAA with the validated accuracy of 99.26%, sensitivity of 97.07%, and specificity of 99.44%.

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

“…Finally, we redesigned the classical SVM learner but fed it with the LSTM features instead, in line with a recent proposal that uses learned features from a 1-D CNN to detect VF [48]. The objective was to show if, for a classical machine learning approach based on SVM, the accuracy improved using the LSTM features instead of the classical VF-features.…”

“…It includes the complete record set from the physionet MIT-BIH Malignant Ventricular Arrhythmia Database (VFDB) and the Creighton University Ventricular Tachyarrhythmia Database (CUDB), and ten episodes (8201-8210) from the series 1 American Heart Association ECG Database (AHADB) [26]. When more than one ECG channel were available (VFDB and AHADB) only the first channel was extracted to avoid redundancies, as done in recent studies on this topic [27, 39, 48]. The records included arrhythmia annotations in the Physionet’s scheme, and all databases included recordings with lethal ventricular arrhythmia from the onset of the arrhythmia.…”

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

“…65 ). Machine learning technology ('deep learning') 60 has also been shown to improve the performance of shock advice algorithms in an automated external defibrillator 66 to predict the onset of ventricular arrhythmias with the use of an artificial neural network 67 and to predict the onset of sudden cardiac arrest within 72 h by incorporating heart rate variability parameters with vital sign data 68 . A system that can warn patients of an impending life-threatening cardiac event, even if only by several minutes, will greatly increase the availability and efficacy of a bystander or emergency medical response 67 .…”

Deep Learning in Physiological Signal Data: A Survey

“…Hand motion recognition [9][10][11][12][13][14][15][16][17], Muscle activity recognition [18][19][20][21][22][23] ECG Heartbeat signal classification , Heart disease classification [49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], Sleep-stage classification [64][65][66][67][68], Emotion classification [69], age and gender prediction [70] EEG Brain functionality classification , Brain disease classification , Emotion classification [122][123][124][125][126][127][128][129], Sleep-stage classification [130][131][132][133]…”

“…Nguyen et al [63] proposed an algorithm for detecting sudden cardiac arrest in automated external defibrillators, in which CNN is used as feature extractor (CNNE) and a boosting (BS) classifier.…”