Background: As a relatively high number of ST-segment elevation myocardial infarction (STEMI) patients develop major adverse cardiovascular events (MACE) following percutaneous coronary intervention (PCI), our aim was to determine the significance, and possible predictive value of QRS complex width and ST-segment elevation. Methods: Our patient sample included 200 PCI-treated STEMI patients, which were divided into two groups, based on the duration of symptoms: (I) less than 6 hours, and (II) 6-12 hours. For every patient, ECG was performed at 6 different time points. Long-term follow-up lasted 6 years from the STEMI onset. Statistical analysis was performed. Results: The mean age was 60.6±11.39 years, and 142 were male. Wider QRS complex, more pronounced ST-segment elevation, and more frequently absent ST-segment resolution were more common in the second group. Presentation of MACE was more frequent in this group. An increase of pre-PCI QRS complex width by 1 msec increases the risk of 6-year MACE by 3%, and an increase in pre-PCI ST-segment elevation by 1mm increases the risk of 6-year MACE by 3.26 times. Conclusion: To the best of our understanding, this study represents a pioneer research that utilized these ECG elements in the context of predicting a 6-year MACE. A wider QRS complex and a more pronounced ST-segment elevation were associated with longer total ischemic time. More research is needed to confirm these findings.

We propose an electrocardiogram (ECG) signal-based algorithm to estimate the respiratory rate is a significant informative indicator of physiological state of a patient. The consecutive ECG signals reflect the information about the respiration because inhalation and exhalation make transthoracic impedance vary. The proposed algorithm extracts the respiration-related signal by finding out the commonality between the frequency and amplitude features in the ECG pulse train. The respiration rate can be calculated from the principle components after the procedure of the singular spectrum analysis. We achieved 1.7569 breaths per min of root-mean-squared error and 1.7517 of standard deviation with a 32-seconds signal window of the Capnobase dataset, which gives notable improvement compared with the conventional Autoregressive model based estimation methods.