QT-interval prolongation is an important parameter for heart arrhythmia diagnosis. It is the time interval from QRS-onset to the T-end of electrocardiogram (ECG). Manual measurement of QT-interval, especially for 12-leads ECG, is time-consuming. Hence, an Automatic QT-interval measurement is necessary. A new method for Automatic QTinterval measurement is presented in this paper, which mainly consists of three parts, including QRS-complex detection, determination of QRS-onset, and T-end determination. The QRS-complex detection is based on the modified Pan-Tompkins algorithm. The T-end is defined based on Region of Interest (ROI) maximum limit. We compare and test our proposed QT-interval measurement method with reference measurement in term of correlation coefficient and range of 95% LoA. The correlation coefficient and the range of 95% LoA are 0. 575 and 0. 290, respectively. The proposed method is successfully implemented in ECG monitoring system using smartphone with high performance. The accuracy, positive predictive, and sensitivity of the QRS-complex detection in the system are 99. 70%, 99. 78%, and 99. 92%, respectively. The range of 95% LoA for the comparison between manual and the system’, s QT-interval measurement is 0. 216. The results show that the proposed method is dependable on the measure of the QT-interval and outperforms the other methods in term of correlation coefficient and range of 95% LoA.

Public infrastructure is often monitored to assess quality. Alarm thresholds indicate when actions are necessary to improve passenger convenience and operational safety. Based on safety assessments, urgent rail replacements are proposed with a new approach for physical damage threshold extraction. Railway tracks are crucial in transportation, and physical damage can greatly affect their efficiency. During regular visual inspections, inspectors can identify physical damages such as head checks or fatigue cracks. However, there are no definite thresholds to determine the severity of the damages except for rail profile deformation known as wear, which is based on references and standards. Therefore, during the survey and inspection, it is essential to establish alarm thresholds based on limit states for each case. Defects detected before the ultimate limit state can lead to a reduction in quality, which affects the convenience of travel but is not harmful to serviceability. Structure quality control methods use both qualitative and quantitative approaches to assess these cases. However, quantitative assessment reduces the probability of human errors and subjective judgments by inspectors when compared to qualitative assessment. To begin with, image processing is a quantitative tool used to detect defects on rail surfaces. However, the determination of the threshold has been neglected, leading to a lack of anticipation of rail physical damages' lifetime and cost estimation. Therefore, machine learning tools evaluate the output of image processing based on the proposed threshold, which has been expressed in this research. In order to ensure safety, contingency actions will be taken when needed based on the predetermined alarm thresholds.