Quantification of radial arterial pulse characteristics change during exercise and recovery

It is physiologically important to understand the arterial pulse waveform characteristics change during exercise and recovery. However, there is a lack of a comprehensive investigation. This study aimed to provide scientific evidence on the arterial pulse characteristics change during exercise and recovery. Sixty-five healthy subjects were studied. The exercise loads were gradually increased from 0 to 125 W for female subjects and to 150 W for male subjects. Radial pulses were digitally recorded during exercise and 4-min recovery. Four parameters were extracted from the raw arterial pulse waveform, including the pulse amplitude, width, pulse peak and dicrotic notch time. Five parameters were extracted from the normalized radial pulse waveform, including the pulse peak and dicrotic notch position, pulse Area, Area1 and Area2 separated by notch point. With increasing loads during exercise, the raw pulse amplitude increased significantly with decreased pulse period, reduced peak and notch time. From the normalized pulses, the pulse Area, pulse Area1 and Area2 decreased, respectively, from 38 ± 4, 61 ± 5 and 23 ± 5 at rest to 34 ± 4, 52 ± 6 and 13 ± 5 at 150-W exercise load. During recovery, an opposite trend was observed. This study quantitatively demonstrated significant changes of radial pulse characteristics during different exercise loads and recovery phases

Outlier Detection Under Interval and Fuzzy Uncertainty: Algorithmic Solvability and Computational Complexity

In many application areas, it is important to detect outliers. Traditional engineering approach to outlier detection is that we start with some "normal" values x 1 ; : : : ; xn , compute the sample average E, the sample standard variation oe, and then mark a value x as an outlier if x is outside the k 0 -sigma interval [E \Gamma k 0 \Delta oe; E + k 0 \Delta oe] (for some pre-selected parameter k 0 ). In real life, we often have only interval ranges [x i ; x i ] for the normal values x 1 ; : : : ; xn . In this case, we only have intervals of possible values for the bounds E \Gamma k 0 \Delta oe and E + k 0 \Delta oe. We can therefore identify outliers as values that are outside all k 0 -sigma intervals

Outlier Detection Under Interval and Fuzzy Uncertainty: Algorithmic Solvability and

In many application areas, it is important to detect outliers. Traditional engineering approach to outlier detection is that we start with some “normal ” values  ¢¡¤£¦¥§¥¦¥§£ ¨  � ©, compute the sample average �, the sample standard varia-� tion, and then mark a value   as an outlier if   is ��� outside the-sigma � ������ � £ ��� �������� � interval (for �� � some pre-selected parameter �� �). In real life, we often have only interval �   �� £   � � ranges for the normal values  �¡�£§¥§¥¦¥§£ �  � ©. In this case, we only have intervals of possible values for the bounds ��� � �� � and � ��� � �¨ �. We can therefore identify outliers as values that are outside � � all-sigma intervals. In this paper, we analyze the computational complexity of these outlier detection problems, and provide efficient algorithms that solve some of these problems (under reasonable conditions). We also provide algorithms that estimate the degree of “outlier-ness ” of a given value   – measured as the largest �� � value for which   is outside the �� � corresponding-sigma interval. 1