PubMed · 11059975
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S C Hammill, P A Friedman. 2000. Shocking sinus--how inappropriate!. https://doi.org/10.1111/j.1540-8167.2000.tb01757.x
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BACKGROUND: Computer-assisted arrhythmia recognition is critical for the management of cardiac disorders. Various techniques have been utilized to classify arrhythmias. Generally, these techniques classify two or three arrhythmias or have significantly large processing times. A simpler autoregressive modeling (AR) technique is proposed to classify normal sinus rhythm (NSR) and various cardiac arrhythmias including atrial premature contraction (APC), premature ventricular contraction (PVC), superventricular tachycardia (SVT), ventricular tachycardia (VT) and ventricular fibrillation (VF). METHODS: AR Modeling was performed on ECG data from normal sinus rhythm as well as various arrhythmias. The AR coefficients were computed using Burg's algorithm. The AR coefficients were classified using a generalized linear model (GLM) based algorithm in various stages. RESULTS: AR modeling results showed that an order of four was sufficient for modeling the ECG signals. The accuracy of detecting NSR, APC, PVC, SVT, VT and VF were 93.2% to 100% using the GLM based classification algorithm. CONCLUSION: The results show that AR modeling is useful for the classification of cardiac arrhythmias, with reasonably high accuracies. Further validation of the proposed technique will yield acceptable results for clinical implementation.
Obstructive sleep apnea (OSA) affects almost one fifth of the male and 10% of the female middle-aged population. Only one fifth of subjects with more or less severe disorder of breathing report simultaneous daytime sleepiness. There is growing research evidence for an independent association between OSA and cardiovascular disease (CVD). The suggestion that this link is not only correlative but also causative is strongly supported by a series of recent clinical and epidemiological studies. The association between OSA and traditionally recognized cardiovascular risk factors suggests that OSA may provide an additive and synergistic risk in cases with co-existing obesity, insulin resistance, diabetes and/or dyslipidaemia. These recent insights advocate better awareness of OSA and potentially also a wider use of screening-tools for early identification and treatment of sleep related breathing disorders. Moreover, current research within the fields of obesity and cardiovascular prevention needs to identify OSA as a study confounder. Continuous intense research into pathophysiological mechanisms and therapeutic possibilities of CVD related to OSA appears to be an important and potentially rewarding area of disease prevention.
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