No abstract

The efficacy and safety of recombinant tissue plasminogen activator (rt-PA) administered on a dosing per weight basis was evaluated in a randomized, placebo-controlled, double-blind trial in 115 patients with acute myocardial infarction. The principal outcomes were global and regional left ventricular function in the distribution of the qualifying myocardial infarction, determined 9 days after the onset of symptoms. Global and regional ejection fraction values were significantly better for patients treated with rt-PA than for placebo-treated patients (the differences were 5.8 +/- 2.7% units [p = 0.017] and 7.1 +/- 3.1% units [p = 0.012], respectively). This benefit was also evident from visual assessment of left ventricular segmental wall motion. After adjustment for differences in important prognostic variables at baseline, the estimates of treatment effect were 4.0 +/- 2.4% units (p = 0.048) for global and 4.3 +/- 2.6% units (p = 0.047) for regional ejection fraction. Early patency of the infarct-related vessel was demonstrable in 7 (29%) of 24 placebo-treated patients and 18 (78%) of 23 rt-PA-treated patients, whereas 15 (56%) of 27 patients in the placebo group and 23 (72%) of 32 in the rt-PA group had a patent infarct-related vessel at hospital day 9. There was no significant difference in irreversible or reversible defect size as assessed by thallium scintigraphy on day 7.(ABSTRACT TRUNCATED AT 250 WORDS)

Signal classifications have benefited from the successes of ML and DNN architectures. Cough classification techniques mainly extract features such as the Mel Frequency CepstralCoefficients for training. Most of these works also focus on obtaining information from single data modalities. However, multimodal analysis has been shown to aggregate useful information from different modalities thereby improving the internal capacity of ML models at data analysis. In this research, we propose a multimodal cough data classification approach with scalograms images obtained by decomposing cough signals using continuous wavelet transform and clinical information of subjects obtained from the COUGHVID dataset. Our result shows improved precision as compared to expert analysis.