Journal of electrocardiology
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With the advent of thrombolytics, guidelines for ST-elevated myocardial infarction (STEMI) recognition were presented in terms of an ST segment exceeding a particular level (1 or 2 mm) in 2 contiguous leads. However, more than half of prehospital electrocardiograms that exceed these ST criteria are from patients not having an acute myocardial infarction. In contrast, expert physicians (EXMD) maintain a high specificity (>95%) for the recognition of STEMI. ⋯ Thus, the EXMD uses additional electrocardiogram features to identify patients for appropriate intervention. Given that STEMI can be defined in terms of a pattern that is recognized by the EXMD as well as a clinical classification that can be evaluated in terms of clinical outcomes, the development and validation of a computer algorithm for STEMI need to include both the art of understanding how the human is detecting STEMI as well as the science required to develop quantified criteria based on clinical outcomes. Evidence is presented that demonstrates that reciprocal depression is a strong indicator of STEMI versus other causes of ST elevation.
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The Poincaré map is a visual technique to recognize the hidden correlation patterns of a time series signal. The standard descriptors of the Poincaré map are used to quantify the plot that measures the gross variability of the time series data. However, the problem lies in capturing temporal information of the plot quantitatively. ⋯ To justify the importance of the temporal measure, SD1^(w), SD2^(w) are calculated for the 2 case studies (normal sinus rhythm [NSR] vs congestive heart failure and NSR vs arrhythmia) and are compared with the performance using the overall measures (SD1, SD2). Using overall SD1, receiver operating characteristic areas of 0.72 and 0.86 were obtained for NSR vs congestive heart failure and NSR vs arrhythmia, and using the proposed method resulted in 0.82 and 0.89. Because we have shown that the overall SD1 and SD2 are functions of the respective localized measures SD1^(w) and SD2^(w), we can conclude that use of localized measure provides equal or higher performance in pathology detection compared with the overall SD1 or SD2.
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Detection of sleep apnea using electrocardiographic (ECG) parameters is noninvasive and inexpensive. Our approach is based on the hypothesis that the patient's sleep-wake cycle during episodes of sleep apnea modulates heart rate (HR) oscillations. These HR oscillations appear as low-frequency fluctuations of instantaneous HR (IHR) and can be detected using HR variability analysis in the frequency domain. ⋯ As a measure of quantification, the algorithm correctly classified 84.7% of all the 1-minute epochs in the evaluation database; and as a measure of the accuracy of apnea classification, the algorithm correctly classified all 30 test recordings in the evaluation database either as apneic or normal. Our sleep apnea detection algorithm based on analysis of a single-lead ECG provides accurate apnea detection and quantification. Because of its noninvasive and low-cost nature, this algorithm has the potential for numerous applications in sleep medicine.
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We describe the case of a 63-year-old man whose electrocardiogram showed transition of the ST segment from a J wave to a coved-type elevation in precordial leads before ventricular fibrillation induced by right coronary artery vasospasm. Simultaneously, the ST segment in inferior leads was gradually depressed with a J wave. Considering the sudden death of his son, induced ventricular fibrillation by programmed electrical stimulation, and modulations of the ST segment in the precordial and inferior leads by pilsicainide, some abnormalities in repolarization associated with Brugada syndrome or early repolarization syndrome might have caused these atypical ST-segment manifestations.