A high-speed C++/MEX solution for long-duration arterial blood pressure characteristic locations detection
The major concentration of this study is to describe the structure of a C++/MEX solution for robust detection and delineation of arterial blood pressure (ABP) signal events. Toward this objective, the original ABP signal was pre-processed by application of  trous discrete wavelet transform (DWT) to extract several dyadic scales. Then, a sliding window with fixed length was moved on the appropriately selected scale. In each slid, mean, variance, Skewness and Kurtosis values of the excerpted segment were superimposed to generate a newly defined multiple higher order moments (MHOM) metric to be used as the detection decision statistic (DS). Then, after application of an adaptive-nonlinear transformation for making the DS baseline static, the histogram parameters of the enhanced DS were used for regulation of the ?-level NeymanPearson classifier aimed for false alarm probability (FAP)-bounded delineation of the ABP events. The proposed method was applied to all 18 subjects of the MIT-BIH Polysomnographic Database (359,000 beats). The end-systolic and end-diastolic locations of the ABP signal as well as the dicrotic notch pressure were extracted and values of sensitivity Se=99.86% and positive predictivity P+=99.95% were obtained for the detection of all ABP events. This paper proves the proposed MHOM-based ABP events detectiondelineation algorithm as an improvement because of its merits such as: high robustness against measurement noises, acceptable detectiondelineation accuracy of the ABP events in the presence of severe heart valvular, arrhythmic dysfunctions within a tolerable computational burden (processing time) and having no parameters dependency on the acquisition sampling frequency.
mhom multiple higher order moment edes end-diastolic end-systolic map mean arterial pressure sbp systolic blood pressure dbp diastolic blood pressure sf sampling frequency ssn smoothing stage number asf adaptive smoothing filter ds decision statistic fap false alarm probability pdf probability density function abp arterial blood pressure dwt discrete wavelet transform fp false positive fn false negative psdb mit-bih polysomnographic database smf smoothing function fir finite-duration impulse response iir infinite-duration impulse response le location error check#0 procedure of evaluating obtained results with the qrs annotations of the mit-bih polysomnographic database check#1 procedure of evaluating obtained results consulting with a control cardiologist check#2 procedure of evaluating obtained results consulting with a control cardiologist and also at least with 3 cardiology residents