An Automated Strategy for Early Risk Identification of Sudden Cardiac Death by Using Machine Learning Approach on Measurable Arrhythmic Risk Markers

Abstract

Early risk identification of an unexpected sudden cardiac death (SCD) in a person who is suffering malignant ventricular arrhythmias is highly significant for timely intervention and increasing the survival rate. For this purpose, we have presented an automated strategy for prediction of SCD with a high-level accuracy by using measurable arrhythmic markers in this paper. The set of arrhythmic parameters includes three repolarization interval ratios, such as T p T e /QT, JT p /JT e , and T p T e /JT p and two conduction-repolarization markers, such as T p T e /QRS and T p T e /(QT×QRS). Each of them is calculated directly from the detected QRS complex waves and T-wave of electrocradiogram (ECG) signals. Then, all calculated markers are used for the automatical classification of normal and SCD risk groups by employing machine learning classifiers, such as k-nearest neighbor (KNN), decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and random forest (RF). The effectiveness and usefulness of the proposed method is evaluated using a database of measured ECG data acquired from 28 SCD and 18 normal patients. For the automated strategy, the set of five arrhythmic risk markers can predict SCD in less than one second with an average accuracy of 98.91% (KNN), 98.70% (SVM), 98.99% (DT), 97.46% (NB), and 99.49% (RF) for 30 minutes before the occurrence of SCD. Moreover, a practical and straightforward SCD index (SCDI) through a judicious integration of these markers is also proposed by using the Student's t-test. The obtained SCDIs are 1.2058 ± 0.0795 and 1.7619 ± 0.1902 for normal and SCD patients, respectively, which provide a sufficient discrimination degree with a p-value of 6.5061e-35. The present results show that both the automated classifier and the integrated SCDI can predict the SCD up to 30 minutes earlier, and that these predictions could be more practical and efficient if applied in portable smart devices with real-time requirements in hospital settings or at home.

Predicting Patent Transactions Using Patent-Based Machine Learning Techniques

“…As shown in Table 8, except some variables such as NFP and ANFP, most variables show low correlations. Considering that Bayes Theorem assumes independence among predictor variables [52], two models were developed for NB: a model containing all variables (NB_all) and a model containing mutually independent variables only (NB_partial). For LR which is also affected by correlations, we conducted VIF test to remove variables with multicollinearity.…”

Using deep learning algorithms to simultaneously identify right and left ventricular dysfunction from the electrocardiogram

“…Deep learning (DL), a class of machine learning that uses hierarchical networks to extract lower-dimensional features from a higher dimensional data input, has demonstrated significant potential for enabling ECG-based predictions and diagnoses 33 . For example, DL has been used to identify patients with atrial fibrillation while in normal sinus rhythm 34 , predict incident atrial fibrillation 35 , identify patients amenable to cardiac resynchronization therapy 36 , evaluate LV diastolic function 37 , evaluation of patients with echocardiographically concealed long QT syndrome 38 , predict risk of sudden cardiac death 39 , and to predict low LVEF. 40,41 .…”

An Efficient Algorithm for Cardiac Arrhythmia Classification Using Ensemble of Depthwise Separable Convolutional Neural Networks

“…After QRS detection, amplitude and duration-based ECG features can also be measured using weighted diagnostic distortion (WDD) [16]. The feature extraction stage is usually followed by a classification stage with various methods such as vector quantization [17], random forest [18][19][20], k-nearest neighbor (kNN) [10,20,21], support vector machine (SVM) [10,13,18,20], multi-layer perceptron (MLP) [22][23][24] and convolutional neural network (CNN) [25].…”