Heart rate variability (HRV) analysis using DSP for the detection of myocardial infarction

2012 ◽  
Author(s):  
Firas Zakaria ◽  
Mohamad Khalil
Keyword(s):  
Myocardial Infarction ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Hrv Analysis

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.

Heart Rate Variability Assessment Early After Acute Myocardial Infarction

Circulation ◽  
1996 ◽  
Vol 93 (7) ◽  
pp. 1388-1395 ◽  
Author(s):  
Narendra Singh ◽  
Dmitry Mironov ◽  
Paul W. Armstrong ◽  
Allan M. Ross ◽  
Anatoly Langer
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Heart Rate ◽  
Heart Rate Variability

scholarly journals Field Monitoring the Effects of Overnight Shift Work on Specialist Tactical Police Training with Heart Rate Variability Analysis

2021 ◽  
Vol 13 (14) ◽  
pp. 7895
Author(s):  
Colin Tomes ◽  
Ben Schram ◽  
Robin Orr
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Shift Work ◽  
Emergency Response ◽  
Police Training ◽  
Heart Rate Variability Analysis ◽  
Response Team ◽  
Hrv Analysis ◽  
And Performance ◽  
Personnel Allocation

Police work exposes officers to high levels of stress. Special emergency response team (SERT) service exposes personnel to additional demands. Specifically, the circadian cycles of SERT operators are subject to disruption, resulting in decreased capacity to compensate in response to changing demands. Adaptive regulation loss can be measured through heart rate variability (HRV) analysis. While HRV Trends with health and performance indicators, few studies have assessed the effect of overnight shift work on HRV in specialist police. Therefore, this study aimed to determine the effects overnight shift work on HRV in specialist police. HRV was analysed in 11 SERT officers and a significant (p = 0.037) difference was found in pRR50 levels across the training day (percentage of R-R intervals varying by >50 ms) between those who were off-duty and those who were on duty the night prior. HRV may be a valuable metric for quantifying load holistically and can be incorporated into health and fitness monitoring and personnel allocation decision making.

Determinants of frequency domain measures of heart rate variability in the acute and convalescent phases of myocardial infarction

1994 ◽  
Vol 28 (8) ◽  
pp. 1273-1276 ◽  
Author(s):  
J. O Valkama ◽  
H. V Huikuri ◽  
K E J. Airaksinen ◽  
M. L Linnaluoto ◽  
J. T Takkunen
Keyword(s):  
Myocardial Infarction ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Frequency Domain
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