Identification Of Arrhythmias Research Articles

Application research on ICU admission time management

Objective To explore the effects of ICU admission time management on the prognosis of critically ill patients. Methods A total of 449 patients in ICU were selected and divided into experimental group (229 patients and 80 nurses) and control group (220 patients and 81 nurses). The experimental group was given ICU admission time management, and the control group was given routine nursing and grading nursing. The prognostic data including the cure rate, mortality and the incidence of complication were analyzed retrospectively. Results The scores of ICU early warning, critical patient care assessment, arrhythmia identification, intracavitary pressure monitoring and application, critically ill patients′ communication factors and assessment of anxiety and depression were 50.00% (40/80), 51.25% (41/80), 48.75%(39/80), 47.50%(38/80), 48.75%(39/80), 46.25%(37/80) in experimental group, and those in control group were 7.41%(6/81), 6.17%(5/81), 9.88%(8/81), 8.64%(7/81), 7.41%(6/81), 6.17%(5/81), there were significant differences(χ2=29.42-40.07, P 0.05). The cure rate, mortality and the incidence of complication in experimental group were 91.70%(210/229), 1.31%(3/229), 6.99%(16/229), and those in control group were 70.00%(154/220), 6.82%(15/220), 25.00% (55/220), there were significant differences (χ2=34.44, 8.85, 27.35,P<0.01). ICU stay time in experimental group was (6.18±3.16) d, and that in control group was (11.52±3.39) d, there was significant difference(t=12.273,P<0.01). Conclusions Applying continuous special management in ICU admission time can increase the cure rate and decrease the hospitalization time and the incidence of complication. Key words: Prognosis; Management; Critically ill patients; ICU admission time

99.9% Accurate R Wave Extraction from ECG Signal using Wavelet Transform

ElectroCardiogram(ECG) is used to measure and diagnose electrical activity of heart. R peak detection from ECG signal is our main concern. It is the basic mark for the identification of different arrhythmias. In this paper, R wave extraction is performed by using Wavelet Transform. The wavelet transform has risen over late years as an effective time– frequency analysis and it is efficiently analyze complex non stationary signals. In this research, R wave is extracted accurately then heart beat is analyzed by the detection of RR intervals. R wave extraction is performed and implemented in the most familiar multipurpose tool, MATLAB .In this research,99.9% accurate R peak is detected by this type of approach. By accurate detection of R peak, cardiac diseases can easily be identified such as Sinus tachycardia, Sinus bradycardia, Supraventricular tachycardia (SVT), Atrial fibrillation (AF), Ventricular tachycardia and Heart block.

Análisis no supervisado aplicado a la detección de arritmias cardiacas

Una arritmia es una patología que consiste en la alteración de los latidos del corazón. A pesar de que el electrocardiograma de 12 derivaciones permite evaluar el comportamiento eléctrico del corazón para determinar ciertas patologías, existen algunas arritmias que son de difícil detección con este tipo de electrocardiografía. Por tanto, es necesario recurrir al uso del monitor Holter, debido a que facilita el registro de la actividad eléctrica del corazón durante largos periodos de tiempo, por lo general de 24 a 48 horas. Debido a la extensión de los registros proporcionados por el monitor, es común acudir al uso de sistemas computacionales para evaluar características diagnósticas y morfológicas de los latidos con el fin de determinar si existe algún tipo de anormalidad. Estos sistemas computacionales pueden basarse en técnicas supervisadas o no supervisadas de reconocimiento de patrones, pero teniendo en cuenta que en la primera opción el realizar una inspección visual de la gran cantidad de latidos presentes en un registro Holter, resulta ser una ardua tarea, además de implicar costos monetarios, en este trabajo se presenta el diseño de un sistema completo para la identificación de arritmias en registros Holter usando técnicas no supervisadas de reconocimiento de patrones. El sistema propuesto involucra etapas de pre-procesamiento de la señal, segmentación y caracterización de latidos, además de selección de características y agrupamiento. En este caso, la técnica utilizada es k-medias. Dichas etapas se aplican dentro del marco de una metodología basada en segmentos que mejora la detección de clases minoritarias. Asimismo, se considera criterios de inicialización que permiten mejorar las medidas de desempeño, en especial, la sensibilidad. Como resultado, se determina que usar k-medias con el criterio de inicialización máx-mín y un número de grupos igual a 12, permite obtener los mejores resultados, siendo: 99,36 %, 91,31 % y 99,16 % para exactitud, sensibilidad y especificidad, respectivamente.

Genetic algorithm for the optimization of features and neural networks in ECG signals classification

Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

Study of cardiac arrhythmias in acute myocardial infarction within 48 hours

Background: The profile of coronary artery disease is different in India in terms of incidence and risk factors. Indians show higher incidence of hospitalization, morbidity and mortality than other ethnic groups. Majority of deaths in acute myocardial infarction are due to arrhythmias. These would suggest that more aggressive identification and modulation of cardiac arrhythmias in acute myocardial infarction is necessary among Asian Indian. This study is undertaken to study the profile of arrhythmias in acute myocardial infarction within in 48 hours of hospitalization in our hospital.Methods: This study was conducted in KMCH, Katihar, Bihar, India. A total of 50 (38 males, 12 females) patients admitted to the coronary care unit with diagnosis of acute myocardial infarction were included to the study. Patients were monitored for 48 hours and pattern of arrhythmias were noted.Results: 38 males and 12 females were studied. 65% of the patients were thrombolysed. Arrhythmia was seen in 78% of the patients. 70% of the arrhythmias occurred during the first hour. 58% of the arrhythmias underwent spontaneous resolution. 23% of the patients had sinus bradycardia.Conclusions: The present study suggests that majority of the patients with acute myocardial infarction had arrhythmias. Sinus bradycardia was the commonest arrhythmia, followed by ventricular premature contractions. However ventricular premature contractions also occurred along with other arrhythmias.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System.

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

Hybrid Bijective soft set - Neural network for ECG arrhythmia classification

Reliable identification of arrhythmias built by digital signal processing of Electrocardiogram (ECG) is significant in providing appropriate and suitable treatment to a cardiac arrhythmia patient. Due to exploitation of ECG signals with numerous frequency noises and the occurrence of various arrhythmic events in a cardiac rhythm, computerized interpretation of abnormal ECG rhythms is a thought-provoking task. The objective of this paper is to construct novel automatic classification system for analysis of ECG signal and decision making purposes. The proposed classification method is a hybridization of Bijective soft set and back propagation neural network. The Hybrid Bijective soft set neural network based classification algorithm (BISONN) is applied to classify the ECG signals into normal and four abnormal heart beats. Initially, discrete wavelet transform is applied be- fore classification for signal De-noising and feature extraction. The experimental results are obtained by evaluating the proposed method on ECG data from the MIT-BIH arrhythmia database. The experimental analysis of the proposed BISONN algorithm is compared with the Multi-layered Perceptron (MLP), Decision table (DT), Naive Bayes (NB) and J48 classification algorithms. The performance of the classifier is measured in terms of sensitivity, specificity, Positive predictive value, negative predictive value, false predictive value, Matthews's correlation coefficients, F-Measure, Folke-mallows Index and Kulcznski Index. The acquired results clearly confirm the superiority of the BISONN algorithm as compared with other classifiers.

Abstract 16305: Incidence and Timing of High-Risk Arrhythmias With Long-Term Continuous Ambulatory Electrocardiographic Monitoring

Background: Ambulatory electrocardiographic (ECG) monitoring is the standard of care to screen patients for arrhythmias. However, there is marked variation in the technological features and patient adherence among different ECG monitoring systems. We evaluated a novel adhesive, wireless, single-lead, full-disclosure, up to 14-day ECG monitoring system to measure the burden and timing of high-risk arrhythmias. Methods: We examined data from 122,815 long term continuous ambulatory ECG Monitors. (ZIO® Patch Service, iRhythm Technologies, San Francisco, CA) from 2011-2013 and categorized high-risk arrhythmias into two types: (1) ventricular arrhythmias including non-sustained and sustained ventricular tachycardia and (2) high risk bradyarrhythmias including sinus pauses &gt;3 seconds, atrial fibrillation pauses &gt;5 seconds, and high grade heart block including Mobitz Type II or third-degree heart block. We calculated the proportion of patients with each arrhythmia and the elapsed wear time until each arrhythmia. Results: Of 122,815 Ziopatch recordings, median wear time was 9.9 (IQR 6.8-13.8) days and median analyzable time was 9.1 (IQR 6.4-13.1) days. There were 22,443 (18.3%) with at least one episode of non-sustained ventricular tachycardia (NSVT), 238 (0.2%) with sustained VT, 1,766 (1.4%) with a sinus pause &gt;3 seconds (SP), 520 (0.4%) with a pause during atrial fibrillation &gt;5 seconds (AFP), and 1,486 (1.2%) with high-grade heart block. Median time to first arrhythmia was 74 hours (IQR 26 -149 hours) for NSVT, 22 hours (IQR 5-73 hours) for sustained VT, 22 hours (IQR 7-64 hours) for SP, 31 hours (IQR 11-82 hours) for AFP, and 40 hours (SD 10-118 hours) for high-grade heart block. The yield over time is shown in the Figure 1. Conclusions: A significant percentage of high-risk arrhythmias can be identified by long term continuous monitoring for longer than 24 to 48 hours. The clinical impact from better identification of high-risk arrhythmias warrants further study.

Novel wireless devices for cardiac monitoring.

The digital revolution and the rapid development of smartphones, mobile connectivity, and social networking have changed the way we live. The average American is constantly connected via high bandwidth to a vast network of data and sophisticated digital platforms, with >90% of American adults owning a cell phone and 55% having a smartphone.1 The digital revolution has transformed virtually every industry and every facet of our personal lives but has been conspicuously absent from the world of medicine. Physicians and healthcare networks have been slow to adopt electronic medical records and to integrate medical data with the ubiquitous mobile device. Recently, however, novel devices for wireless monitoring have emerged and begun to be integrated with the care of the cardiac patient. We believe that the evolution of these wireless cardiac monitoring devices will mark a new era in medicine and a transition from population-level health care to individualized medicine in which suitable patients are equipped with advanced biosensors that, in turn, have their data processed through sophisticated algorithms to predict events before they occur. This review is meant to be a comprehensive overview of the novel wireless cardiac monitoring devices that are available, as well as the technologies that are currently under development and poised to revolutionize the way we practice cardiology. A comprehensive list of a number of devices that are currently available or under development is given in the Table for reference. View this table: Table. Comprehensive Overview of Existing Wireless Cardiac Monitoring Devices Cardiac arrhythmias such as atrial fibrillation are common and can be associated with adverse outcomes such as embolic stroke. Less common but more malignant rhythm disorders such as ventricular tachycardia can herald sudden cardiac death.2 The identification and management of arrhythmias in the patient with palpitations, syncope, a history of arrhythmia, or high risk for sudden …

In-Hospital Arrhythmia Development and Outcomes in Pediatric Patients With Acute Myocarditis

Cardiac arrhythmias are a complication of myocarditis. There are no large studies of in-hospital arrhythmia development and outcomes in pediatric patients with acute myocarditis. This was a retrospective 2-center review of patients ≤21 years hospitalized with acute myocarditis from 1996 to 2012. Fulminant myocarditis was defined as the need for inotropic support within 24 hours of presentation. Acute arrhythmias occurred at presentation and subacute after admission. Eighty-five patients (59% men) presented at a median age of 10 years (1 day to 18 years). Arrhythmias occurred in 38 patients (45%): 16 acute, 12 subacute, and 9 acute and subacute (1 onset unknown). Arrhythmias were associated with low voltages on the electrocardiogram (14 of 34, 41% vs 6 of 47, 13%; odds ratio [OR] 4.78, 95% confidence interval [CI] 1.60 to 14.31) and worse outcome (mechanical support, orthotopic heart transplant, or death; OR 7.59, 95% CI 2.61 to 22.07) but were not statistically significantly associated with a fulminant course, ST changes, initial myocardial function, lactate, creatinine level, C-reactive protein and/or erythrocyte sedimentation rate, or troponin I level, after adjusting for multiple comparisons. Subacute arrhythmias were associated with preceding ST changes (10 of 15, 67% vs 15 of 59, 25%, OR 5.87, 95% CI 1.73 to 19.93). All patients surviving to discharge had arrhythmia resolution or control before discharge (10 on antiarrhythmic), with 1 exception (patient with complete heart block requiring a pacemaker). At 1-year follow-up, there were 3 recurrences of ventricular arrhythmias, but no arrhythmia-related mortality. In conclusion, arrhythmias are common in pediatric patients with myocarditis, occurring in nearly 1/2 of all hospitalized children and are associated with a worse outcome. Early identification of subacute arrhythmias using electrocardiographic changes may help management. A majority of patients do not require continued postdischarge arrhythmia treatment.

Remote Monitoring of Cardiovascular Implantable Devices in the Pediatric Population Improves Detection of Adverse Events

With the exponential growth of cardiovascular implantable electronic devices (CIEDs) in pediatric patients, a new method of long-term surveillance, remote monitoring (RM), has become the standard of care. The purpose of this study was to determine the usefulness of RM as a monitoring tool in the pediatric population. A retrospective review was performed of 198 patients at the University of Iowa Children’s Hospital who had CIEDs. Data transmitted by RM were analyzed. The following data were examined: patient demographics; median interval between transmissions; detection of adverse events requiring corrective measures, including detection of lead failure; detection of arrhythmias and device malfunctions independent of symptoms; time gained in the detection of events using RM versus standard practice; the validity of RM; and the impact of RM on data management. Of 198 patients, 162 submitted 615 RM transmissions. The median time between remote transmissions was 91 days. Of 615 total transmissions, 16 % had true adverse events with 11 % prompting clinical intervention. Of those events requiring clinical response, 61 % of patients reported symptoms. The median interval between last follow-up and occurrence of events detected by RM was 46 days, representing a gain of 134 days for patients followed-up at 6-month intervals and 44 days for patients followed-up at 3 month-intervals. The sensitivity and specificity of RM were found to be 99 and 72 %, respectively. The positive and negative predictive values were found to be 41 and 99 %, respectively. RM allows for early identification of arrhythmias and device malfunctions, thus prompting earlier corrective measures and improving care and safety in pediatric patients.

Arrhythmia Identification with Two-Lead Electrocardiograms Using Artificial Neural Networks and Support Vector Machines for a Portable ECG Monitor System

An automatic configuration that can detect the position of R-waves, classify the normal sinus rhythm (NSR) and other four arrhythmic types from the continuous ECG signals obtained from the MIT-BIH arrhythmia database is proposed. In this configuration, a support vector machine (SVM) was used to detect and mark the ECG heartbeats with raw signals and differential signals of a lead ECG. An algorithm based on the extracted markers segments waveforms of Lead II and V1 of the ECG as the pattern classification features. A self-constructing neural fuzzy inference network (SoNFIN) was used to classify NSR and four arrhythmia types, including premature ventricular contraction (PVC), premature atrium contraction (PAC), left bundle branch block (LBBB), and right bundle branch block (RBBB). In a real scenario, the classification results show the accuracy achieved is 96.4%. This performance is suitable for a portable ECG monitor system for home care purposes.

Interpreting the equine ECG

Electrocardiography has many applications in equine practice. The identification of arrhythmias and interpretation of their significance is frequently challenging. When interpreting the ECG trace the clinician is faced with several important questions: What is the diagnosis, the potential clinical significance and, most importantly, is the horse safe to ride? This article will try to present a logical approach to interpreting electrocardiogram traces in the horse. Please note: Wiley-Blackwell are not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

The role of a new arrhythmia specialist nurse in providing support to patients and caregivers

In 2005, the English Department of Health developed a National Service Framework for the identification and treatment of Arrhythmia. A new specialist role was recommended, the Arrhythmia Care Co-ordinator (ACC), to guide patients through their illness and coordinate their care. In 2006, to implement this policy, the British Heart Foundation (BHF) sponsored 32 new nursing ACC staff posts across England and Wales. The BHF also sponsored an evaluation to investigate the impact of ACC support on patients and caregivers, with 30 patients (18 men, 22 women) and 10 caregivers being drawn from a purposive sample across seven arrhythmia treatment centres. Two main key themes emerged to describe the patients' experience of the new service: personalized coordinated care and learning to live with their condition.

Intraoperative MRI electrical noise and monitor ECG filters affect arrhythmia detection and identification

Most electrical equipment in the modern operating room (OR) radiates electrical noise (EN) that can interfere with patient monitors. We have described the EN that an intraoperative magnetic resonance imaging (iMRI) system emits and have shown that this high-energy EN diminishes the quality of the ECG waveform during iMRI scans in our neurosurgical OR. We have also shown that the ECG signal filters in our iMRI-compatible patient monitor reduce this interference but, in the process, disturb the true morphology of the displayed waveform. This simulation study evaluates how iMRI-generated EN affects the ability of the anesthetist to detect and identify ECG arrhythmias and whether the patient monitor's ECG signal filters can improve arrhythmia recognition. Using an ECG simulator, we generated Lead II and V5 ECG signal segments that contained either no arrhythmia or one of four common cardiac arrhythmias. We filtered the ECG segments with four filters available on our iMRI-compatible monitor (Veris MR, MEDRAD Inc., Indianola, PA USA). We then digitized the segments and mixed simulated iMRI EN into the resultant tracings. With institutional approval and written informed consent, board-certified anesthesiologists reviewed the tracings, determined if an arrhythmia was present and identified the arrhythmia. We conducted the study anonymously. We reported the data as percent correct arrhythmia detection and correct arrhythmia identification. Thirty-one anesthesiologists completed the study. Overall, the participants correctly detected 79.5% (95% CI: 77.2, 81.7%) of the arrhythmias and correctly identified 62.5% (95% CI: 59.8, 65.3%) of the arrhythmias, regardless of EN presence. Although the proportions among monitor noise filters studied were not significant, the manufacturer-designated MR5 Veris MR filter optimized arrhythmia detection and arrhythmia identification for our participants, regardless if EN was present in the ECG tracings. In the neurosurgical OR, the anesthetist must be able to effectively monitor a patient in the presence of iMRI-generated EN. Depending on the OR design, the patient may be out of the anesthetist's direct view during a scan procedure. The anesthetist must rely on monitored physiologic parameters to assess patient status during this time. He/she should be familiar with his/her monitor's filtering capabilities and routinely adjust the ECG filters to achieve the best compromise between minimized EN effects and maximized displayed ECG signal quality.

A systematic review of validated methods for identifying ventricular arrhythmias using administrative and claims data

Drug-induced pro-arrhythmia is a serious and unexpected event. Large administrative and claims databases can potentially identify drugs or interactions leading to cardiac arrhythmias. The purpose of this study is to evaluate the evidence supporting the validity of algorithms or codes to identify ventricular arrhythmias using administrative and claims data. A search of MEDLINE database is supplemented by manual searches of bibliographies of key relevant articles. We selected all studies in which an administrative and claims data algorithm or code was validated against a medical record. We report the positive predictive value (PPV) for ICD-9 codes compared to medical records. Our search strategy yielded 664 studies, of which only seven met our eligibility criteria. Two additional studies were identified by peer reviewers. The most commonly included databases were Medicare and Medicaid, and the most commonly evaluated ICD-9 codes were 426.x and 427.x. The individual use of ICD-9 codes 427.x yielded a high PPV (78%-100%). The highest PPV was seen when both ICD-9 codes 427.x and 798.x were used (92%). The same codes yielded the highest PPV when found in the principal diagnosis position (100%). The use of ICD-9 codes 427.x, alone or in combination with code 798.x, in the principal position is appropriate for the identification of ventricular arrhythmias in administrative and claims databases.

Abstract P252: Rates of Anticoagulation in Patients with Atrial Fibrillation and Flutter in the Kentucky Women's Health Registry

Introduction: Therapy with warfarin has been shown to decrease the risk of stroke associated with atrial fibrillation and flutter. Current guidelines recommend warfarin therapy for patients with atrial fibrillation who are at intermediate to high risk of thromboembolism. Multiple studies have shown that increasing age and female gender have been associated with underutilization of warfarin. We evaluated women in the Kentucky Women's Health Registry (KWHR) with self-reported atrial fibrillation to determine the rates of warfarin utilization in this population. Methods: Between November 2009 and June 2010, we administered electronic or mailed surveys to KWHR participants who reported an arrhythmia on the Registry questionnaire. Survey questions targeted arrhythmia identification and treatment, medical and social history, pregnancy history and demographics. Herein we analyze the data to determine baseline characteristics of the atrial fibrillation (and flutter) group, including CHADS 2 score and use of anticoagulation. Results: Of the 572 women initially surveyed, 365 responded to the questionnaire, yielding 107 cases of atrial fibrillation and flutter. Baseline characteristics of this group were notable for high education levels and greater than average annual income (see table). Of this population, 36 of 106, (34%) had a CHADS 2 score of &gt;1. Of the women with CHADS 2 scores of &gt;1 who reported their warfarin use, only 10 of 33 [30% (95% confidence intervals, 17-47%)] were receiving warfarin. As expected, the warfarin treated group tended to be older with mean age 73.4 years vs 61.8 years (p&lt;0.001) with a higher CHADS 2 score (p&lt;0.003). Conclusion: Warfarin anticoagulation is underutilized in this group of Kentucky women. This is not attributable to disparities in socioeconomic status given the relatively affluent, highly educated status of the survey respondents. Baseline Characterisitics of Paticipants with Atrial Fibrillation and Flutter Variable Proportion of responders n 95% Confidence Intervals Age (mean years) 63.2 102 61.1-65.3 Hypertension 65% (68) 104 0.56 -0.74 Diabetes 13% (13) 102 0.08-0.21 Congestive heart failure 11% (11) 104 0.06-0 .18 Stroke/TIA 8% (8) 102 0.04-0.15 BMI in kg/m 2 (mean) 28.8 104 27.5-30.1 Aspirin use 49% (51) 104 0.40-0.59 Clopidogrel use 5 % (5) 104 0.02 -0.11 Warfarin use 15% (16) 104 0.10-0.24 CHADS 2 score &gt;1 34% (36) 106 0.25-0 .43 White Race 93% (98) 105 0.87-0 .97 Level of education: Some college or higher 85% (89) 105 0.77-0.90 Median annual household income (Dollars) 60-79,999 101 Inter-quartile range: 40-89,999 (Kentucky median 41,489)

A novel method for removal of in-band noise and accurate extraction of information from ECGs

High pass filter Many publications have documented the issues associated with noise in ECGs collected in safety pharmacology studies. The presence of noise can result in excess variability in interval measurements, make arrhythmias difficult to detect, and confound ECG analysis software to the point where a large percentage of cardiac cycles may be uninterpretable. The affects of noise on ECG analysis can also increase the sample size required to detect QT prolongation. To address these issues, techniques have been developed that target reducing noise levels in acquired ECGs. Most problematic is noise that has energy in the same part of the frequency spectrum occupied by the ECG signal. This is generally caused by EMG and by changes in the tissueelectrode interface. The most common technique used to remove this noise is band-pass filtering (BPF). However, BPF is problematic in that using it to reduce in-band noise will result in distortion of the ECG. Multi-Domain Signal Processing (MDSP) is a novel approach that removes in-band noise without distorting the ECG signal. It works by independently isolating noise and other aspects of the signal in multiple mathematical domains. Once noise is isolated, it can be removed without distorting the ECG. MDSP also leverages the ability to isolate features of the ECG to provide for accurate interval measurement, identification of arrhythmias, and shows promise for measurement of respiratory rate by isolating aspects of the ECG that change during the respiratory cycle.

Adaptive noise cancellation to suppress electrocardiography artifacts during real‐time interventional MRI

To develop a system for artifact suppression in electrocardiogram (ECG) recordings obtained during interventional real-time magnetic resonance imaging (MRI). We characterized ECG artifacts due to radiofrequency pulses and gradient switching during MRI in terms of frequency content. A combination of analog filters and digital least mean squares adaptive filters were used to filter the ECG during in vivo experiments and the results were compared with those obtained with simple low-pass filtering. The system performance was evaluated in terms of artifact suppression and ability to identify arrhythmias during real-time MRI. Analog filters were able to suppress artifacts from high-frequency radiofrequency pulses and gradient switching. The remaining pulse artifacts caused by intermittent preparation sequences or spoiler gradients required adaptive filtering because their bandwidth overlapped with that of the ECG. Using analog and adaptive filtering, a mean improvement of 38 dB (n = 11, peak QRS signal to pulse artifact noise) was achieved. This filtering system was successful in removing pulse artifacts that obscured arrhythmias such as premature ventricular complexes and complete atrioventricular block. We have developed an online ECG monitoring system employing digital adaptive filters that enables the identification of cardiac arrhythmias during real-time MRI-guided interventions.

Communication strategies and timeliness of response to life critical telemetry alarms.

A centralized electrocardiogram telemetry monitoring system (TMS) facilitates early identification of critical arrhythmias and acute medical decompensation. Timely intervention can only be performed if abnormalities are communicated rapidly to the direct caregiver. The study objectives were to measure effectiveness of bi-directional voice communication badges versus one-way alphanumeric pagers for telemetry alarm response and communication loop closure. A sequential observational pilot study of nursing response to TMS alarms compared communication technologies on four nursing units in a 1,061 bed tertiary care hospital with 264 TMS channels of telemetry over a 2-year period. Subsequently, the communication technologies were compared in a randomized fashion on a 68-bed progressive cardiac care unit. Caregivers were blinded to the protocol. All alarm responses were recorded during two periods using either pagers or voice communication devices. Alarm response time and closure of the communication loop were analyzed in a blinded fashion. The direct communication functionality of the badge significantly shortened the time to first contact, time to completion, and rate of closure of the communication loop in both the pilot and study phases. Median time to first contact with the communication badge was 0.5 min, compared to 1.6 min with pager communication (p < 0.0003). Communication loop closure was achieved in 100% of clinical alarms using the badge versus 19% with the pager (p < 0.0001). Communication badge technology reduced alarm time to first contact and completion as well as facilitated communication loop closures. Immediate two-way communication significantly impacted practice, alarm management, and resulted in faster bedside care.