Detection of sudden cardiac death by a comparative study of heart rate variability in normal and abnormal heart conditions

Rohila, Ashish; Sharma, Ambalika

doi:10.1016/j.bbe.2020.06.003

Cited by 27 publications

(15 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 5 studies [63][64][65][66][67], AI technologies were used to distinguish patients who were at high risk of cardiac arrest from patients who were not at risk. Three studies highlighted HRV [63][64][65] as an important feature to distinguish high-risk patients.…”

Section: Stratification Of High-risk Patientsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review

Alamgir¹,

Mousa²,

Shah³

2021

JMIR Med Inform

View full text Add to dashboard Cite

Background Cardiac arrest is a life-threatening cessation of activity in the heart. Early prediction of cardiac arrest is important, as it allows for the necessary measures to be taken to prevent or intervene during the onset. Artificial intelligence (AI) technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. Objective This study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. Methods A scoping review was conducted in line with the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. Scopus, ScienceDirect, Embase, the Institute of Electrical and Electronics Engineers, and Google Scholar were searched to identify relevant studies. Backward reference list checks of the included studies were also conducted. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. Results Out of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. Of the 47 studies, we were able to classify the approaches taken by the studies into 3 different categories: 26 (55%) studies predicted cardiac arrest by analyzing specific parameters or variables of the patients, whereas 16 (34%) studies developed an AI-based warning system. The remaining 11% (5/47) of studies focused on distinguishing patients at high risk of cardiac arrest from patients who were not at risk. Two studies focused on the pediatric population, and the rest focused on adults (45/47, 96%). Most of the studies used data sets with a size of <10,000 samples (32/47, 68%). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (38/47, 81%), and the most used algorithm was the neural network (23/47, 49%). K-fold cross-validation was the most used algorithm evaluation tool reported in the studies (24/47, 51%). Conclusions AI is extensively used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in improving cardiac medicine. There is a need for more reviews to learn the obstacles to the implementation of AI technologies in clinical settings. Moreover, research focusing on how to best provide clinicians with support to understand, adapt, and implement this technology in their practice is also necessary.

show abstract

Section: Stratification Of High-risk Patientsmentioning

confidence: 99%

“…ML was used in the majority of the studies [63,64,67], and only 1 study used a DL algorithm [66]. One study used both ML and DL models to stratify patients [65].…”

Section: Stratification Of High-risk Patientsmentioning

confidence: 99%

Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review

Alamgir¹,

Mousa²,

Shah³

2021

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

“…HRV analysis has been accepted as a biomarker and widely used by researchers to assess the health of control subjects and patients suffering from hypertension, diabetes, renal failure, stress, and various cardiovascular diseases (CVDs) such as coronary artery disease (CAD), myocardial infarction (MI), atrial fibrillation (AF), ventricular fibrillation (VF), congestive heart failure (CHF), ventricular tachyarrhythmia (VTA), and cardiac arrhythmia [5,7,[10][11][12][13][14][15][16][17][18][19]. Conventionally, HRV analysis has been categorized into long-term analysis, performing analysis of 24 h recordings, and short-term analysis, processing 5 min ECG.…”

Section: Introductionmentioning

confidence: 99%

“…Five classifiers were employed on different combinations of these features and among which multilayer perceptron (MLP) classified CHF patients with an accuracy of 98.8%. Rohila and Sharma [16] extracted nonlinear features including five entropy, four PCP, and one DFA exponent feature whereas five timefrequency domain features using S-transform, from 5 min ECG for HRV analysis to detect CAD. SVM and RF were applied to classify sudden cardiac death (SCD) accurately up to 91.67% from CAD and CHF patients.…”

Section: Introductionmentioning

confidence: 99%

An Effective Deep Learning Model for Automated Detection of Myocardial Infarction Based on Ultrashort-Term Heart Rate Variability Analysis

Shahnawaz

Dawood

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Myocardial infarction (MI), usually termed as heart attack, is one of the main cardiovascular diseases that occur due to the blockage of coronary arteries. This blockage reduces the blood supply to heart muscles, and a prolonged deficiency of blood supply causes the death of heart muscles leading to a heart attack that may cause death. An electrocardiogram (ECG) is used to diagnose MI as it causes variations like ST-T changes in the recorded ECG. Manual inspection of these variations is a tedious task and also requires expertise as the variations produced by MI are often very short in duration with a low amplitude. Hence, these changes may be misinterpreted, leading to delayed diagnosis and appropriate treatment. Therefore, computer-aided analysis of ECG may help to detect MI automatically. In this study, a robust deep learning model is proposed to detect MI based on heart rate variability (HRV) analysis of ECG signals from a single lead. Ultrashort-term HRV analysis is performed to compute HRV analysis features from time-domain and frequency-domain parameters through power spectral density estimations. Nonlinear HRV parameters are also computed using Poincare Plot, Recurrence Analysis, and Detrended Fluctuation Analysis. A finely tuned customized artificial neural network (ANN) algorithm is applied on 23 HRV features for MI detection and classification. The K-fold validation method is used to avoid any biases in results and reported 99.1% accuracy, 100% sensitivity, 98.1% specificity, and 99.0% F1 for MI detection, whereas 98.85% accuracy, 97.40% sensitivity, 99.05% specificity, and 97.70% F1 score is achieved for classification. Furthermore, the ANN algorithm completed its execution in just 59 seconds that indicates the efficiency of the proposed ANN model. The overall performance in terms of computed evaluation matrices and execution time indicates the robustness and cost-effectiveness of the proposed methodology. Thus, the proposed model can be used for high-performance MI detection, even in wearable devices.

show abstract

“…Despite the use of the PEn for time-series analysis representing a still investigated issue [32], it has been successfully applied on EEG data for detecting epileptic seizures [31,33,34]. Further, PEn was also used in ECG data analysis in order to investigate behavioral states [35] and for the analysis of heart rate variability [36,37]. However, the validity of using a complexity measure as the PEn cannot be blindly extended to other biological signals, different to those listed above, such as the sEMG ones, due to their different characteristics [1]; bear in mind also that it relies on ordinal mapping of data samples, thereby avoiding amplitude information which otherwise appears worth considering for some applications [31].…”

Section: Introductionmentioning

confidence: 99%

On the Use of Fuzzy and Permutation Entropy in Hand Gesture Characterization from EMG Signals: Parameters Selection and Comparison

et al. 2020

View full text Add to dashboard Cite

The surface electromyography signal (sEMG) is widely used for gesture characterization; its reliability is strongly connected to the features extracted from sEMG recordings. This study aimed to investigate the use of two complexity measures, i.e., fuzzy entropy (FEn) and permutation entropy (PEn) for hand gesture characterization. Fourteen upper limb movements, sorted into three sets, were collected on ten subjects and the performances of FEn and PEn for gesture descriptions were analyzed for different computational parameters. FEn and PEn were able to properly cluster the expected numbers of gestures, but computational parameters were crucial for ensuring clusters’ separability and proper gesture characterization. FEn and PEn were also compared with other eighteen classical time and frequency domain features through the minimum redundancy maximum relevance algorithm and showed the best predictive importance scores in two gesture sets; they also had scores within the subset of the best five features in the remaining one. Further, the classification accuracies of four different feature sets presented remarkable increases when FEn and PEn are included as additional features. Outcomes support the use of FEn and PEn for hand gesture description when computational parameters are properly selected, and they could be useful in supporting the development of robotic arms and prostheses myoelectric control.

show abstract

Detection of sudden cardiac death by a comparative study of heart rate variability in normal and abnormal heart conditions

Cited by 27 publications

References 57 publications

Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review

Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review

An Effective Deep Learning Model for Automated Detection of Myocardial Infarction Based on Ultrashort-Term Heart Rate Variability Analysis

On the Use of Fuzzy and Permutation Entropy in Hand Gesture Characterization from EMG Signals: Parameters Selection and Comparison

Contact Info

Product

Resources

About