Hybrid CNN-LSTM deep learning model and ensemble technique for automatic detection of myocardial infarction using big ECG data

2021 ◽  
Author(s):  
Hari Mohan Rai ◽  
Kalyan Chatterjee
Keyword(s):  
Myocardial Infarction ◽  
Deep Learning ◽  
Automatic Detection ◽  
Learning Model ◽  
Ensemble Technique ◽  
Deep Learning Model ◽  
Ecg Data

scholarly journals Accessory pathway analysis using a multimodal deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makoto Nishimori ◽  
Kunihiko Kiuchi ◽  
Kunihiro Nishimura ◽  
Kengo Kusano ◽  
Akihiro Yoshida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electrophysiological Study ◽  
Accessory Pathway ◽  
Learning Model ◽  
X Rays ◽  
X Ray ◽  
Wpw Syndrome ◽  
Chest X Ray ◽  
Deep Learning Model ◽  
Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

EEG-based deep learning model for the automatic detection of clinical depression

2020 ◽  
Vol 43 (4) ◽  
pp. 1349-1360
Author(s):  
Pristy Paul Thoduparambil ◽  
Anna Dominic ◽  
Surekha Mariam Varghese
Keyword(s):  
Deep Learning ◽  
Automatic Detection ◽  
Learning Model ◽  
Clinical Depression ◽  
Deep Learning Model

P5014The NPAC score for predicting survival after incident acute myocardial infarction

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
K H Li ◽  
J Ho ◽  
Z Xu ◽  
I Lakhani ◽  
G Bazoukis ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Deep Learning ◽  
Learning Model ◽  
Peripheral Vascular ◽  
Short Term ◽  
C Statistic ◽  
Short Term Mortality ◽  
Deep Learning Model

Abstract Background Risk stratification in acute myocardial infarction (AMI) is important for guiding clinical management. Current risk scores are mostly derived from clinical trials with stringent patient selection. We aimed to establish and evaluate a composite scoring system to predict short-term mortality after index episodes of AMI, independent of electrocardiography (ECG) pattern, in a large real-world cohort. Methods Using electronic health records, patients admitted to our regional teaching hospital (derivation cohort, n=2127) and an independent tertiary care center (validation cohort, n=1276) with index acute myocardial infarction between January 2013 and December 2017 as confirmed by principal diagnosis and laboratory findings, were identified retrospectively. Results Univariate logistic regression was used as the primary model to identify potential contributors to mortality. Stepwise forward likelihood ratio logistic regression revealed that neutrophil-to-lymphocyte ratio, peripheral vascular disease, age, and serum creatinine (NPAC) were significant predictors for 90-day mortality (Hosmer-Lemeshow test, P=0.21). Each component of the NPAC score was weighted by beta-coefficients in multivariate analysis. The C-statistic of the NPAC score was 0.75, which was higher than the conventional Charlson's score (C-statistic=0.63). Application of a deep learning model to our dataset improved the accuracy of classification with a C-statistic of 0.81. Multivariate binary logistic regression Variable β Adjusted Odds ratio (95% CI) P-value Points Age ≥65 years 1.304 3.68 (2.63–5.17) <0.001 2 Peripheral vascular disease 1.109 3.03 (1.52–6.04) 0.002 2 NLRt ≥9.51 1.100 2.73 (2.12–3.51) <0.001 1 Creatinine≥109 μmol/L 1.003 3.00 (2.35–3.85) <0.001 2 NPAC deep learning model Conclusions The NPAC score comprised of four items from routine laboratory parameters and basic clinical information and can facilitate early identification of cases at risk of short-term mortality following index myocardial infarction. Deep learning model can serve as a gate-keeper to provide more accurate prediction to facilitate clinical decision making.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Muhammad Bilal Shahnawaz ◽  
Hassan Dawood
Keyword(s):  
Myocardial Infarction ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Deep Learning ◽  
Blood Supply ◽  
Heart Attack ◽  
Learning Model ◽  
Fluctuation Analysis ◽  
Hrv Analysis ◽  
Deep Learning Model

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.

scholarly journals Automatic detection of acute ischemic stroke using non-contrast computed tomography and two-stage deep learning model

2020 ◽  
Vol 196 ◽  
pp. 105711
Author(s):  
Mizuho Nishio ◽  
Sho Koyasu ◽  
Shunjiro Noguchi ◽  
Takao Kiguchi ◽  
Kanako Nakatsu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Ischemic Stroke ◽  
Deep Learning ◽  
Acute Ischemic Stroke ◽  
Automatic Detection ◽  
Learning Model ◽  
Two Stage ◽  
Deep Learning Model
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