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2022 ◽  
Vol 2022 ◽  
pp. 1-4
Author(s):  
Joshua H. Arnold ◽  
Neil Brandon

We present the case of a 61-year-old male who developed persistent hiccups concurrently with the onset of atrial fibrillation (AF). The hiccups were refractory to traditional treatment but resolved immediately upon electrical cardioversion (ECV) to normal sinus rhythm (NSR). The patient has remained in NSR and free of hiccups. The potential etiologies for hiccups are numerous and varied, and the management of persistent hiccups can be difficult. Cardiac associations including myocardial infarction and pericarditis have been described, while few cases of first-time onset of atrial fibrillation leading to hiccups have been documented. This case discusses a unique instance demonstrating a connection between hiccups and cardiac pathology and an overview of its management.


2022 ◽  
Vol 54 (4) ◽  
pp. 370-372
Author(s):  
Intisar Ahmed ◽  
Hunaina Shahab ◽  
Aamir Hameed Khan

A 77 -year-old lady with history of hypertension and Parkinson`s disease was admitted with cough and fever and diagnosed as pneumonia. On second day of admission, she started having chest pain, initial ECG was interpreted as atrial flutter. When her ECG was reviewed by a cardiologist, ECG features were found to be consistent with artifacts due to tremors. A repeat 12 leads ECG clearly demonstrated normal sinus rhythm and the patient remained completely asymptomatic throughout the hospital stay. Tremor induced artifacts can be mistaken for arrhythmias. Correct diagnosis is important, in order to avoid inappropriate treatment and unnecessary interventions.


2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Yu Sun ◽  
Yin-Yin Yang ◽  
Bing-Jhang Wu ◽  
Po-Wei Huang ◽  
Shao-En Cheng ◽  
...  

AbstractAtrial fibrillation (AF) is often asymptomatic and paroxysmal. Screening and monitoring are needed especially for people at high risk. This study sought to use camera-based remote photoplethysmography (rPPG) with a deep convolutional neural network (DCNN) learning model for AF detection. All participants were classified into groups of AF, normal sinus rhythm (NSR) and other abnormality based on 12-lead ECG. They then underwent facial video recording for 10 min with rPPG signals extracted and segmented into 30-s clips as inputs of the training of DCNN models. Using voting algorithm, the participant would be predicted as AF if > 50% of their rPPG segments were determined as AF rhythm by the model. Of the 453 participants (mean age, 69.3 ± 13.0 years, women, 46%), a total of 7320 segments (1969 AF, 1604 NSR & 3747others) were analyzed by DCNN models. The accuracy rate of rPPG with deep learning model for discriminating AF from NSR and other abnormalities was 90.0% and 97.1% in 30-s and 10-min recording, respectively. This contactless, camera-based rPPG technique with a deep-learning model achieved significantly high accuracy to discriminate AF from non-AF and may enable a feasible way for a large-scale screening or monitoring in the future.


2022 ◽  
Vol 12 ◽  
Author(s):  
Cláudia de Faria Cardoso ◽  
Natalia Tiemi Ohe ◽  
Yazan Bader ◽  
Nariman Afify ◽  
Zahrah Al-Homedi ◽  
...  

Background: Psychological distress, such as posttraumatic stress disorder (PTSD), is commonly evaluated using subjective questionnaires, a method prone to self-report bias. The study's working hypothesis was that levels of autonomic dysfunction determined by heart rate variability (HRV) measures are associated with the severity of PTSD in women following pregnancy loss.Methods: This was an observational prospective cohort study with 53 patients enrolled. The DSM-5 (Diagnostic and Statistical Manual of Mental Disorders) PTSD scale (PCL-5) was used to assess the severity of PTSD in women after pregnancy loss. The cardiac autonomic function was assessed using HRV measurements during a deep breathing test using an HRV scanner system with wireless ECG enabling real-time data analysis and visualization. HRV measures were: standard deviation (SD) of normal R-R wave intervals [SDNN, ms], square root of the mean of the sum of the squares of differences between adjacent normal R wave intervals [RMSSD, ms], and the number of all R-R intervals in which the change in consecutive normal sinus intervals exceeds 50 milliseconds divided by the total number of R-R intervals measured [pNN50 = (NN50/n-1)*100%] [pNN50%].Results: The PCL-5 scores had a statistically significant association with HRV indices (SDNN; RMSSD, and pNN50%). Patients with PTSD had similar mean heart rate values as compared to patients without PTSD (PCL-5), but significantly higher SDNN [median[IQR, interquartile range]: 90.1 (69.1–112.1) vs. 52.5 (36.8–65.6)], RMSSD [59.4 (37.5–74.9) vs. 31.9 (19.3 – 44.0)], and PNN50% values [25.7 (16.4–37.7) vs. 10.6 (1.5–21.9)]. The SDNN of the deep breathing test HRV was effective at distinguishing between patients with PTSD and those without, with an AUC = 0.83 +/− 0.06 (95 % CI 0.94, p = 0.0001) of the ROC model.Conclusions: In this study, HRV indices as biomarkers of cardiac dysautonomia were found to be significantly related to the severity of PTSD symptoms in women after pregnancy loss.


2021 ◽  
Vol 10 (2) ◽  
pp. 63-66
Author(s):  
Navaraj Paudel ◽  
Namrata Thapa ◽  
Ramchandra Kafle ◽  
Subash Sapkota ◽  
Abhishek Maskey

Background: Stroke/ cerebrovascular accidents are common and among the major causes of mortality and morbidity. Thromboembolism are also among the causes of ischemic strokes. Diagnosis of atrial fibrillation makes the difference in the management of ischemic strokes for long term as anticoagulation are given in these cases for prevention of further embolic events. Methods: A prospective observational study was done from july 2019 to june 2021 for patients admitted for ischemic strokes who were otherwise found to have normal sinus rhythm. A 24 hour holter monitor was connected and analyzed for possible paroxysmal atrial fibrillation. Baseline investigations including trans-thoracic echocardiography was done. Data were analyzed and results were sought. Results: Out of 212 patients admitted for stroke, only 116 were eligible for the study. Male female ratio was 2:1. Ninety-four percent of patients had at least one or more risk factors: Smokers (74%) followed by Hypertensives (70%), Dyslipidemics (54%) and Diabetics (20%). Twenty-two percent of patients were found to have paroxysmal atrial fibrillation. There was no gender difference between the occurrences of paroxysmal atrial fibrillation. Among the risk factors, smoking and hypertension were significantly associated with the occurrence of paroxysmal atrial fibrillation (P: 0.001 and 0.002 respectively) while other risk factors like diabetes and dyslipidemia had no significant association. There was significant association of paroxysmal atrial fibrillation with mortality (P: 0.0013). Conclusion: Patients who are in otherwise normal sinus rhythm in electrocardiography with ischemic cerebrovascular accidents may have paroxysmal atrial fibrillation as cause of event. Smoking and hypertensive patients are significantly associated with occurrence of paroxysmal atrial fibrillation and stroke and these patients are more likely to die than the patients having normal heart rhythm. Management of these patients definitely defer in terms of possible use of anticoagulants. 


Author(s):  
Arya Bhardwaj ◽  
J. Sivaraman ◽  
S. Venkatesan

Objective: This study aims to characterize P and Ta wave of Modified Limb Lead (MLL) Electrocardiogram (ECG) in Normal Sinus Rhythm (NSR) and Atrioventricular Block (AVB). Methods: ECGs were recorded using MLL configuration from 100 NSR volunteers (mean age 31 years, 35 women) and 20 male AVB patients (mean age 72 years). Amplitudes and durations of P, Ta wave, and PTa Interval (PTaI) were measured, plotted, and analyzed for both the groups. Results: P-wave amplitudes were larger in AVB, and also P, Ta waves correlated significantly in both groups with higher correlation in AVB (NSR: [Formula: see text]; AVB: [Formula: see text]). Ta-wave duration ([Formula: see text] ms) was longer than P-wave duration ([Formula: see text] ms) in AVB patients and was opposite to P-wave polarity in all the leads. PP Interval (PPI) correlated significantly with P wave (NSR: [Formula: see text]; AVB: [Formula: see text]), Ta wave ([Formula: see text]; [Formula: see text]), PTaI ([Formula: see text]; [Formula: see text]), and corrected PTaI ([Formula: see text]; [Formula: see text]). Conclusion: P-wave right axis shift leads to the higher P-wave amplitude in AVB which may be due to the advancing age and atrial chamber enlargement. In NSR, the duration of observable Ta wave was longer than P wave, whereas in AVB, the Ta wave duration was 3–3.5 times longer than P wave.


Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8210
Author(s):  
Shirin Hajeb-Mohammadalipour ◽  
Alicia Cascella ◽  
Matt Valentine ◽  
Ki H. Chon

Cardiopulmonary resuscitation (CPR) corrupts the morphology of the electrocardiogram (ECG) signal, resulting in an inaccurate automated external defibrillator (AED) rhythm analysis. Consequently, most current AEDs prohibit CPR during the rhythm analysis period, thereby decreasing the survival rate. To overcome this limitation, we designed a condition-based filtering algorithm that consists of three stop-band filters which are turned either ‘on’ or ‘off’ depending on the ECG’s spectral characteristics. Typically, removing the artifact’s higher frequency peaks in addition to the highest frequency peak eliminates most of the ECG’s morphological disturbance on the non-shockable rhythms. However, the shockable rhythms usually have dynamics in the frequency range of (3–6) Hz, which in certain cases coincide with CPR compression’s harmonic frequencies, hence, removing them may lead to destruction of the shockable signal’s dynamics. The proposed algorithm achieves CPR artifact removal without compromising the integrity of the shockable rhythm by considering three different spectral factors. The dataset from the PhysioNet archive was used to develop this condition-based approach. To quantify the performance of the approach on a separate dataset, three performance metrics were computed: the correlation coefficient, signal-to-noise ratio (SNR), and accuracy of Defibtech’s shock decision algorithm. This dataset, containing 14 s ECG segments of different types of rhythms from 458 subjects, belongs to Defibtech commercial AED’s validation set. The CPR artifact data from 52 different resuscitators were added to artifact-free ECG data to create 23,816 CPR-contaminated data segments. From this, 82% of the filtered shockable and 70% of the filtered non-shockable ECG data were highly correlated (>0.7) with the artifact-free ECG; this value was only 13 and 12% for CPR-contaminated shockable and non-shockable, respectively, without our filtering approach. The SNR improvement was 4.5 ± 2.5 dB, averaging over the entire dataset. Defibtech’s rhythm analysis algorithm was applied to the filtered data. We found a sensitivity improvement from 67.7 to 91.3% and 62.7 to 78% for VF and rapid VT, respectively, and specificity improved from 96.2 to 96.5% and 91.5 to 92.7% for normal sinus rhythm (NSR) and other non-shockables, respectively.


2021 ◽  
Vol 68 (4) ◽  
pp. 230-234
Author(s):  
Kenichi Sato ◽  
Yoshihisa Miyamae ◽  
Miwako Kan ◽  
Shu Sato ◽  
Motoi Yaegashi ◽  
...  

Some anesthetic agents or adjunct medications administered during general anesthesia can cause an accelerated idioventricular rhythm (AIVR), which is associated with higher vagal tone and lower sympathetic activity. We encountered AIVR induced by vagal response to injection-related pain following local anesthetic infiltration into the oral mucosa during general anesthesia. A 48-year-old woman underwent extraction of a residual tooth root from the left maxillary sinus under general anesthesia. Routine preoperative electrocardiogram (ECG) was otherwise normal. Eight milliliters of 1% lidocaine (80 mg) with 1:100,000 epinephrine (80 μg) was infiltrated around the left maxillary molars over 20 seconds using a 23-gauge needle and firm pressure. Widened QRS complexes consistent with AIVR were observed for ∼60 seconds, followed by an atrioventricular junctional rhythm and the return of normal sinus rhythm. A cardiology consultation and 12-lead ECG in the operating room produced no additional concerns, so the operation continued with no complications. AIVR was presumably caused by activation of the trigeminocardiac reflex triggered by intense pain following rapid local anesthetic infiltration with a large gauge needle and firm pressure. Administration of local anesthetic should be performed cautiously when using a large gauge needle and avoid excessive pressure.


Author(s):  
Hugo A. Gonzalez-Jassi ◽  
Nicole LeBlanc ◽  
Benjamin E. Alcantar ◽  
Rodrigo S. Garces Torres

Abstract OBJECTIVE To describe qualitative and quantitative cardiothoracic values in geriatric Sika deer (Cervus nippon) using digital radiography, 6-lead ECG (sECG), and smartphone-based ECG (aECG). ANIMALS 10 healthy geriatric Sika deer (9 females and 1 male). PROCEDURES Deer were chemically immobilized, thoracic radiographs were obtained, and inhalant anesthesia was initiated. An sECG and aECG were simultaneously recorded for each animal using the same ECG specifications. Results were compared between devices. RESULTS Radiographically, no deer had any cardiopulmonary abnormalities. Median (range) values for the most important cardiac measurements were 170 (153–193) mm for cardiac height, 135 (122–146) mm for cardiac width, 9 (8–9) for vertebral heart score, and 99 (69–124) mm for cardiosternal contact. All deer had a normal sinus rhythm with no pathological arrhythmias noted. A significant difference between sECG and aECG was identified for minimum heart rate (49 vs 51 beats/min, respectively), P wave duration (0.05 vs 0.03 seconds), P wave amplitude (0.28 vs 0.10 mV), PR interval (0.15 vs 0.12 seconds), and QT interval (0.39 vs 0.30 seconds). CLINICAL RELEVANCE Thoracic radiographs were suitable to evaluate basic cardiothoracic morphology in Sika deer. The aECG was useful for assessing heart rate and rhythm but, compared with sECG, proved no substitute for evaluating duration and amplitude of ECG waveforms.


2021 ◽  
Author(s):  
Lotfi Mostefai ◽  
Benouis Mohamed ◽  
Denai Mouloud ◽  
Bouhamdi Merzoug

Abstract Electrocardiogram (ECG) signals have distinct features of the electrical activity of the heart which are unique among individuals and have recently emerged as a potential biometric tool for human identification. The paper attempts to address the problem of ECG identification based on non-fiducial approach using unsupervised classifier and a Deep Learning approaches. This work investigates the ability of local binary pattern to extract the significant pattern/feature that describes the heartbeat activity for each person’s ECG and the use of staked autoencoders and deep belief network to further enhance the extracted features and classify them based on their heartbeat activity. The proposed approach is validated using experimental datasets from two publicly available databases MIT-BIH Normal Sinus Rhythm and ECG-ID and the results demonstrate the effectiveness of this approach for ECG-based human authentication.


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