Background: For patients with suspected acute coronary syndrome, international guidelines indicate that an Electrocardiogram (ECG) should be performed within 10 min of first medical contact, however success at achieving these guidelines is limited. Aims: The purpose of this study was to develop and perform initial testing of a clinical prediction rule embedded in a tablet application, and to expedite the identification of patients who require an electrocardiogram within 10 min. Methods: This derivation of the Acute Coronary Syndrome Application (AcSAP) comprised of three local studies, an unpublished audit and literature critique. The AcSAP was prospectively tested over four months in patients presenting to the Emergency Department (ED) of a Dublin teaching hospital. An audit form retrieved data pertaining to times of: registration to the emergency department, triage, first electrocardiogram and diagnosis. The AcSAP was subsequently evaluated by experienced triage nurses (n=18) who had utilised it. Results: The AcSAP was activated 379 times. Patients with ST Elevation Myocardial Infarction (STEMI) and non-ST Elevation Myocardial Infarction (NSTEMI) were significantly more likely to return a categorisation of 'immediate ECG' or 'ECG within 10 min' (p<0.001). There was a significant difference in 'triage to ECG' times across categories, the 'immediate ECG' categorisation resulting in the shortest time (p=0.002). Evaluations suggest that staff found the tool quick and easy to use and results seemed accurate. Conclusion: Testing of the AcSAP suggests that it accurately identifies patients who require an ECG within 10 min. As such, it has the potential to support the meeting of clinical guidelines for ECG acquisition.
A technique for voice source analysis is described which is currently being used to study voice quality variation, in dysphonic and normal speakers. The analysis technique involves a two-step procedure. The first, inverse filtering of the speech pressure waveform, yields the differential glottal flow. The settings for the inverse filter are initially estimated using a pitch-synchronous semi-automatic procedure based on the covariance method of LPC. The filter is then interactively edited on a pulse-by-pulse basis to correct for errors to which current automatic procedures are prone. The second step in the analysis involves matching a voice source model (the Liljencrants-Fant model) to the output of the inverse filter, again on a pulse-by-pulse basis. This allows quantification and comparison of the salient voice source characteristics. An illustration of some voice quality variation in normal and dysphonic speech is presented.
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