Electrocardiogram (ECG): A New Burgeoning Utility for Biometric Recognition

2014 ◽  
pp. 349-382 ◽  
Author(s):  
Manal Tantawi ◽  
Kenneth Revett ◽  
Abdel-Badeeh Salem ◽  
M. Fahmy Tolba
Keyword(s):  
Biometric Recognition ◽  
Electrocardiogram Ecg

scholarly journals BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets

2021 ◽  
Vol 11 (13) ◽  
pp. 5880
Author(s):  
Paloma Tirado-Martin ◽  
Raul Sanchez-Reillo
Keyword(s):  
Deep Learning ◽  
Temporal Variations ◽  
Physical Activities ◽  
Learning Tools ◽  
Biometric Recognition ◽  
Learning Network ◽  
Deep Learning Network ◽  
Electrocardiogram Ecg

Nowadays, Deep Learning tools have been widely applied in biometrics. Electrocardiogram (ECG) biometrics is not the exception. However, the algorithm performances rely heavily on a representative dataset for training. ECGs suffer constant temporal variations, and it is even more relevant to collect databases that can represent these conditions. Nonetheless, the restriction in database publications obstructs further research on this topic. This work was developed with the help of a database that represents potential scenarios in biometric recognition as data was acquired in different days, physical activities and positions. The classification was implemented with a Deep Learning network, BioECG, avoiding complex and time-consuming signal transformations. An exhaustive tuning was completed including variations in enrollment length, improving ECG verification for more complex and realistic biometric conditions. Finally, this work studied one-day and two-days enrollments and their effects. Two-days enrollments resulted in huge general improvements even when verification was accomplished with more unstable signals. EER was improved in 63% when including a change of position, up to almost 99% when visits were in a different day and up to 91% if the user experienced a heartbeat increase after exercise.

A wavelet feature extraction method for electrocardiogram (ECG)-based biometric recognition

2013 ◽  
Vol 9 (6) ◽  
pp. 1271-1280 ◽  
Author(s):  
Manal M. Tantawi ◽  
Kenneth Revett ◽  
Abdel-Badeeh Salem ◽  
Mohamed F. Tolba
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Biometric Recognition ◽  
Feature Extraction Method ◽  
Electrocardiogram Ecg

Electrocardiogram Recognition and Ablation of Atrial Tachycardia

2011 ◽  
Vol 3 (1) ◽  
pp. 80
Author(s):  
Alexander Feldman ◽  
Jonathan M Kalman ◽  
◽  
Keyword(s):  
Catheter Ablation ◽  
Atrial Tachycardia ◽  
Primary Treatment ◽  
P Wave ◽  
Wave Morphology ◽  
Focal Atrial Tachycardia ◽  
Mapping Techniques ◽  
Electrocardiogram Ecg ◽  
Surface Electrocardiogram

Focal atrial tachycardia (AT) is a relatively uncommon cause of supraventricular tachycardia, but when present is frequently difficult to treat medically. Atrial tachycardias tend to originate from anatomically determined atrial sites. The P-wave morphology on surface electrocardiogram (ECG) together with more sophisticated contemporary mapping techniques facilitates precise localisation and ablation of these ectopic foci. Catheter ablation of focal AT is associated with high long-term success and may be viewed as a primary treatment strategy in symptomatic patients.

Prevalence of abnormalities in electrocardiogram conduction in dialysis patients: a comparative study

2020 ◽  
Author(s):  
Firas Ajam ◽  
Arda Akoluk ◽  
Anas Alrefaee ◽  
Natasha Campbell ◽  
Avais Masud ◽  
...  
Keyword(s):  
Peritoneal Dialysis ◽  
Cardiac Conduction ◽  
Future Research ◽  
Dialysis Patients ◽  
Bundle Branch Block ◽  
Conduction Abnormality ◽  
Ckd Stage ◽  
Stage 1 ◽  
Electrocardiogram Ecg ◽  
Ecg Abnormalities

ABSTRACT Background: The electrocardiogram (ECG) can aid in identification of chronic kidney disease (CKD) patients at high risk for cardiovascular diseases. Cohort studies describe ECG abnormalities in patients on hemodialysis (HD), but we did not find data comparing ECG abnormalities among patients with normal kidney function or peritoneal dialysis (PD) to those on hemodialysis. We hypothesized that ECG conduction abnormalities would be more common, and cardiac conduction interval times longer, among patients on hemodialysis vs. those on peritoneal dialysis and CKD 1 or 2. Methods: Retrospective review of adult inpatients’ charts, comparing those with billing codes for “Hemodialysis” vs. inpatients without those charges, and an outpatient peritoneal dialysis cohort. Patients with CKD 3 or 4 were excluded. Results: One hundred and sixty-seven charts were reviewed. ECG conduction intervals were consistently and statistically longer among hemodialysis patients (n=88) vs. peritoneal dialysis (n=22) and CKD stage 1 and 2 (n=57): PR (175±35 vs 160±44 vs 157±22 msec) (p=0.009), QRS (115±32 vs. 111±31 vs 91±18 msec) (p=0.001), QT (411±71 vs. 403±46 vs 374±55 msec) (p=0.006), QTc (487±49 vs. 464±38 vs 452±52 msec) (p=0.0001). The only significantly different conduction abnormality was prevalence of left bundle branch block: 13.6% among HD patients, 5% in PD, and 2% in CKD 1 and 2 (p=0.03). Conclusion: To our knowledge, this is the first study to report that ECG conduction intervals are significantly longer as one progresses from CKD Stage 1 and 2, to PD, to HD. These and other data support the need for future research to utilize ECG conduction times to identify dialysis patients who could potentially benefit from proactive cardiac evaluations and risk reduction.

Facets and Promises of Gait Biometric Recognition

2018 ◽  
pp. 233-253 ◽  
Author(s):  
James Eric Mason ◽  
Issa Traore ◽  
Isaac Woungang
Keyword(s):  
Biometric Recognition

scholarly journals Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Elisa Mejía-Mejía ◽  
James M. May ◽  
Mohamed Elgendi ◽  
Panayiotis A. Kyriacou
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulse Rate ◽  
Pulse Transit Time ◽  
Pulse Rate Variability ◽  
Physiological Factors ◽  
Ecg Signals ◽  
Non Invasive ◽  
Electrocardiogram Ecg

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

scholarly journals Time Course of ECG Depolarization and Repolarization Changes during Ischemia in PTCA Recordings

2004 ◽  
Vol 43 (01) ◽  
pp. 43-46 ◽  
Author(s):  
J. García ◽  
G. Wagner ◽  
R. Bailón ◽  
L. Sörnmo ◽  
P. Laguna ◽  
...  
Keyword(s):  
Time Course ◽  
Wave Amplitude ◽  
Temporal Evolution ◽  
Delayed Response ◽  
S Wave ◽  
Ecg Changes ◽  
T Complex ◽  
Karhunen Loeve Transform ◽  
Low Amplitude ◽  
Electrocardiogram Ecg

Summary Objectives: In this work we studied the temporal evolution of changes in the electrocardiogram (ECG) as a consequence of the induced ischemia during prolonged coronary angioplasty, comparing the time course of indexes reflecting depolarization and those reflecting repolarization. Methods: We considered both local (measured at specific points of the ECG) and global (obtained from the Karhunen-Loève transform) indexes. In particular, the evolution of Q, R and S wave amplitudes during ischemia was analyzed with respect to classical indexes such as ST level. As a measurement of sensitivity we used an Ischemic Changes Sensor (ICS), which reflects the capacity of an index to detect changes in the ECG. Results: The results showed that, in leads with low-amplitude ST-T complexes, the S wave amplitude was more sensitive in detecting ischemia than was the commonly used index ST60. It was found that in such leads the S wave amplitude initially exhibited a delayed response to ischemia when compared to ST60, but its performance was better from the second minute of occlusion. The global indexes describing the ST-T complex were, in terms of the ICS, superior to the S wave amplitude for ischemia detection. Conclusions: Ischemic ECG changes occur both at repolarization and depolarization, with alterations in the depolarization period appearing later in time. Local indexes are less sensitive to ischemia than global ones.

Sign in / Sign up

Export Citation Format

Share Document