Journal of electrocardiology
-
Analyzing cardiac rhythm in the presence of chest compression artifact for automated shock advisory.
Defibrillation is often required to terminate a ventricular fibrillation or fast ventricular tachycardia rhythm and resume a perfusing rhythm in sudden cardiac arrest patients. Automated external defibrillators rely on automatic ECG analysis algorithms to detect the presence of shockable rhythms before advising the rescuer to deliver a shock. For a reliable rhythm analysis, chest compression must be interrupted to prevent corruption of the ECG waveform due to the artifact induced by the mechanical activity of compressions. ⋯ Using this method only a small percentage of cases need compressions interruption, hence a significant reduction in hands-off time is achieved. Our algorithm comprises a novel filtering technique for the ECG and thoracic impedance waveforms, and an innovative method to combine analysis from both filtered and unfiltered data. Requiring compression interruption for only 14% of cases, our algorithm achieved a sensitivity of 92% and specificity of 99%.
-
An 83-year-old woman with chronic left bundle branch block and remote history of pacemaker implantation for intermittent AV block was hospitalized for fatigue and leg swelling. She had no cardiac complaints. Routine 12-lead electrocardiogram showed sinus rhythm with left bundle branch block. ⋯ The electrocardiographic changes were consistent with cardiac memory. This case is unique because cardiac memory in patients with intermittent left bundle branch block typically occurs when the QRS complexes normalize and not during left bundle branch block itself. Our findings indicate that memory Ts can develop not only after normalization of wide complex rhythms but also with alternating wide complex rhythms as in the presented case where a ventricular paced rhythm was replaced by left bundle branch block.
-
Review Comparative Study
Development of three methods for extracting respiration from the surface ECG: a review.
Respiration rate (RR) is a critical vital sign that can be monitored to detect acute changes in patient condition (e.g., apnea) and potentially provide an early warning of impending life-threatening deterioration. Monitoring respiration signals is also critical for detecting sleep disordered breathing such as sleep apnea. Additionally, analyzing a respiration signal can enhance the quality of medical images by gating image acquisition based on the same phase of the patient's respiratory cycle. Although many methods exist for measuring respiration, in this review we focus on three ECG-derived respiration techniques we developed to obtain respiration from an ECG signal. ⋯ Over the last five years, we have performed six studies using the above methods: 1) In 1907 sleep lab patients with >1.5M 30-second epochs, EDR achieved an apnea detection accuracy of 79%. 2) In 24 adult polysomnograms, use of EDR and chest belts for RR computation was compared to airflow RR; mean RR error was EDR: 1.8±2.7 and belts: 0.8±2.1. 3) During cardiac MRI, a comparison of EMGDR breath locations to the reference abdominal belt signal yielded sensitivity/PPV of 94/95%. 4) Another comparison study for breath detection during MRI yielded sensitivity/PPV pairs of EDR: 99/97, RSA: 79/78, and EMGDR: 89/86%. 5) We tested EMGDR performance in the presence of simulated respiratory disease using CPAP to produce PEEP. For 10 patients, no false breath waveforms were generated with mild PEEP, but they appeared in 2 subjects at high PEEP. 6) A patient monitoring study compared RR computation from EDR to impedance-derived RR, and showed that EDR provides a near equivalent RR measurement with reduced hardware circuitry requirements.
-
Over the past few years, reducing the number of false positive cardiac monitor alarms (FA) in the intensive care unit (ICU) has become an issue of the utmost importance. In our work, we developed a robust methodology that, without the need for additional non-ECG waveforms, suppresses false positive ventricular tachycardia (VT) alarms without resulting in false negative alarms. Our approach is based on features extracted from the ECG signal 20 seconds prior to a triggered alarm. ⋯ These representations are presented to a L1-regularized logistic regression classifier. Results are shown in two datasets of physiological waveforms with manually assessed cardiac monitor alarms: the MIMIC II dataset, where we achieved a false alarm (FA) suppression of 21% with zero true alarm (TA) suppression; and a dataset compiled by UCSF and General Electric, where a 36% FA suppression was achieved with a zero TA suppression. The methodology described in this work could be implemented to reduce the number of false monitor alarms in other arrhythmias.
-
Pre-hospital 12-lead ECG interpretation is important because pre-hospital activation of the coronary catheterization laboratory reduces ST-segment elevation myocardial infarction (STEMI) discovery-to-treatment time. In addition, some ECG features indicate higher risk in STEMI such as proximal left anterior descending (LAD) culprit lesion location. The challenging nature of the pre-hospital environment can lead to noisier ECGs which make automated STEMI detection difficult. We describe an automated system to classify lesion location as proximal LAD, LAD, right coronary artery (RCA) and left circumflex (LCx) and test the performance on pre-hospital 12-lead ECG. ⋯ Although our test database is not large, algorithm performance suggests culprit lesion location can be reliably determined from pre-hospital ECG. Further research is needed however to evaluate the impact of automated culprit lesion location on patient treatment and outcomes.