Atrial Fibrillation Caused by Intractable Hiccups: A Unique Cause and Cure

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.

TREMORS MIMICKING ATRIAL FLUTTER IN AN ELDERLY LADY

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.

Contactless facial video recording with deep learning models for the detection of atrial fibrillation

Atrial 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.

Holter monitoring for assessment of possible paroxysmal atrial fibrillation as a cause in otherwise normal sinus rhythm patients with acute ischemic stroke: a hospital based study

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.

INSIGHTS ON ATRIAL ELECTROCARDIOGRAM IN SINUS RHYTHM AND ATRIOVENTRICULAR BLOCK FOR IMPROVED CLINICAL DIAGNOSIS

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.

Case Report on Guillain-Barre Syndrome (GBS) in Adult

Introduction: Guillain-Barre-syndrome is when the immune system attacks the peripheral nervous system were disease progresses to trembling and muscle weakness in both hands and legs, which progress to upper body and arms. Clinical Findings: High grade intermittent fever, low back pain, B/L LL weak, urinary incontinence which is intermittent in nature. Diagnostic Evaluation: Neurological examination- revealed B/L UL and LL weakness, acute onset of quadriparesis. X-Ray – revealed normal sinus rhythm. CSF examination – revealed No RBC; No pus cell; No Organism seen. Lab investigation – Hb% 10%, total RBC count 4.45, total WBC 10400, total platelet 2.33, SGOT 226 SGPT 83, Peripheral Smear RBCs - Normocytic Normochromic platelets adequate smear no Haemoparasite seen. Blood Culture: revealed Growth of Acinetobacter species. Therapeutic Intervention: Inj. Optineurone 1gm, Inj.Pantop 40mg, Tab.PCM,Inj. Tramadol 500mg, Immunoglobin (Ig) 100ml , physiotherapy and supportive therapy. Outcome: The symptoms and clinical state of the patient improved over time. The patient's weakness began to improve after 5 days of IV-Ig therapy. Conclusion: The patient was hospitalised to the neurosurgery ICU AVBRH on 05/06/20 with the known case of guillain-barre syndrome-(GBS). After receiving therapy, she showed significant progress.

Automated Condition-Based Suppression of the CPR Artifact in ECG Data to Make a Reliable Shock Decision for AEDs during CPR

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.

Automatic detection of Atrial Fibrillation

<p>Atrial Fibrillation is an abnormal arrhythmia of the heart and is a growingconcern in the health sector affecting 1% of the population. The incidenceof atrial fibrillation increases with age and has been found to be more detri-mental to long term cardiac health than previously thought. Sufferers arefive times more likely to experience a stroke than others. Often, atrial fib-rillation is asymptomatic and is frequently discovered only when a patient visits a hospital for other reasons. The detection of paroxysmal atrial fib-rillation can be difficult. Holter monitors are used to record the ECG overlong periods of time, but the resulting recording still needs to be analysed.This can be a time consuming task and one prone to errors. If a miniature,low-power, wearable device could be designed to detect and record whena heart experiences atrial fibrillation, then health professionals would havemore timely information to carry out better, more cost effective courses of treatment. This thesis presents progress towards development of such a device. Atrial fibrillation is characterised by random RR interval, missing Pwave and presence of atrial activity. The detection of the P wave and atrialactivity can be unreliable due to low signal levels and differences in wave-form morphology between subjects. The random RR interval appears tobe a more reliable method of detection. By analysing the ECG signal inboth the frequency and time domains, feature sets can be extracted for thedetection process. In this research, the Discrete Wavelet Transform is used to generate several sub-bands for analysing wave form morphology, and anumber of RR interval metrics are created for analysing the rhythm. All features are further processed and presented to a support vector machine classification stage for the ultimate detection of atrial fibrillation. Forty eight files from the MITDB database of the PhysioNet online ECG reposi-tory were downloaded and processed to form separate training and test-ing data sets. Overall classification accuracy for normal sinus rhythm was93% sensitivity and 95% specificity, and for atrial fibrillation, 95% sensitiv-ity and 93% specificity. These results were found to be sensitive to the ECG morphology of the individual subjects. This means that the system either needs to be trained on a greater number of ECG morphologies or perhaps trained on the morphology of the individual under investigation. Putting this issue aside, the research to date shows that it is reasonable to expect a small, low powered, wearable device, to be capable of automatically detecting whena heart experiences atrial fibrillation.</p>

Automatic detection of Atrial Fibrillation

<p>Atrial Fibrillation is an abnormal arrhythmia of the heart and is a growingconcern in the health sector affecting 1% of the population. The incidenceof atrial fibrillation increases with age and has been found to be more detri-mental to long term cardiac health than previously thought. Sufferers arefive times more likely to experience a stroke than others. Often, atrial fib-rillation is asymptomatic and is frequently discovered only when a patient visits a hospital for other reasons. The detection of paroxysmal atrial fib-rillation can be difficult. Holter monitors are used to record the ECG overlong periods of time, but the resulting recording still needs to be analysed.This can be a time consuming task and one prone to errors. If a miniature,low-power, wearable device could be designed to detect and record whena heart experiences atrial fibrillation, then health professionals would havemore timely information to carry out better, more cost effective courses of treatment. This thesis presents progress towards development of such a device. Atrial fibrillation is characterised by random RR interval, missing Pwave and presence of atrial activity. The detection of the P wave and atrialactivity can be unreliable due to low signal levels and differences in wave-form morphology between subjects. The random RR interval appears tobe a more reliable method of detection. By analysing the ECG signal inboth the frequency and time domains, feature sets can be extracted for thedetection process. In this research, the Discrete Wavelet Transform is used to generate several sub-bands for analysing wave form morphology, and anumber of RR interval metrics are created for analysing the rhythm. All features are further processed and presented to a support vector machine classification stage for the ultimate detection of atrial fibrillation. Forty eight files from the MITDB database of the PhysioNet online ECG reposi-tory were downloaded and processed to form separate training and test-ing data sets. Overall classification accuracy for normal sinus rhythm was93% sensitivity and 95% specificity, and for atrial fibrillation, 95% sensitiv-ity and 93% specificity. These results were found to be sensitive to the ECG morphology of the individual subjects. This means that the system either needs to be trained on a greater number of ECG morphologies or perhaps trained on the morphology of the individual under investigation. Putting this issue aside, the research to date shows that it is reasonable to expect a small, low powered, wearable device, to be capable of automatically detecting whena heart experiences atrial fibrillation.</p>

Accelerated Idioventricular Rhythm Following Intraoral Local Anesthetic Injection During General Anesthesia

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.