ECG-based pulse detection during cardiac arrest using random forest classifier

Abstract

Sudden cardiac arrest is one of the leading causes of death in the industrialized world. Pulse detection is essential for the recognition of the arrest and the recognition of return of spontaneous circulation during therapy, and it is therefore crucial for the survival of the patient. This paper introduces the first method based exclusively on the ECG for the automatic detection of pulse during cardiopulmonary resuscitation. Random forest classifier is used to efficiently combine up to nine features from the time, frequency, slope, and regularity analysis of the ECG. Data from 191 cardiac arrest patients was used, and 1177 ECG segments were processed, 796 with pulse and 381 without pulse. A leave-one-patient out cross validation approach was used to train and test the algorithm. The statistical distributions of sensitivity (SE) and specificity (SP) for pulse detection were estimated using 500 patient-wise bootstrap partitions. The mean (std) SE/SP for nine-feature classifier was 88.4 (1.8) %/89.7 (1.4) %, respectively. The designed algorithm only requires 4-s-long ECG segments and could be integrated in any commercial automated external defibrillator. The method permits to detect the presence of pulse accurately, minimizing interruptions in cardiopulmonary resuscitation therapy, and could contribute to improve survival from cardiac arrest.

A Random Forest-Based Accuracy Prediction Model for Augmented Biofeedback in a Precision Shooting Training System

“…In this paper, we investigate the use of kinematic sensors to accelerate motor learning in precision shooting training. We have developed a shot accuracy prediction model of precision shooting based on the Random Forest (RF) [18] classification algorithm. Its hyperparameters are optimized by a Bayesian hyperparameter optimization method [19].…”

Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest

“…Comparative analyses were performed between the 9 hand-crafted features of the baseline models ( ) and the features learnt by DNN solutions and ( and respectively). The area under the curve (AUC) for ranged between 0.88 and 0.94, showing that they had been wisely selected in different domains as described in [ 34 ]; but the features ( ) that extracted reported high discriminative values from 0.61 to 0.97, showing that the deep architecture found some very selective features. Next, feature sets from the deep learners and were fed into the baseline classifiers to compare their performance with that of the original .…”

“…The two DNN models proposed in this study outperformed the best PR/PEA discriminators based exclusively on the ECG published to date. A RF classifier based on hand-crafted features was proposed in [ 34 ] and reported Se/Sp of 88.4%/89.7% for a smaller dataset. A DNN model using a single convolutional layer followed by a recurrent layer was introduced in a conference paper [ 65 ], but the Se/Sp/BAC were 91.7%/92.5%/92.1% on the dataset used for this study, that is the BAC was 1.5 percentage points below the current solution.…”

“…Machine learning solutions based on well-known ECG features were implemented and compared with and . A total of nine hand-crafted features proposed in [ 34 ], , were computed. They quantify the PR/PEA differences in terms of QRS complex rate and narrowness, slope steepness, spectral energy distribution, and regularity of the signal (fuzzy entropy).…”

“…The main objective of this study was to develop a pulse detection algorithm based exclusively on the ECG acquired by defibrillation pads. Previously a machine learning technique was proposed using a random forest (RF) classifier based on hand-crafted features [ 34 ]. Alternatively, deep neural networks (DNN) have shown superior performance in classification problems with large datasets in many fields [ 35 , 36 , 37 , 38 ].…”