Effect of Respiration on the Characteristic Ratios of Oscillometric Pulse Amplitude Envelope in Blood Pressure Measurement

Systolic and diastolic blood pressures (BPs) are important physiological parameters for disease diagnosis. Systolic and diastolic characteristic ratios derived from oscillometric pulse waveform have been widely used to estimate automated non-invasive BPs in oscillometric BP measurement devices. The oscillometric pulse waveform is easily influenced by respiration, which may cause variability to the characteristic ratios and subsequently BP measurement. This study quantitatively investigated how respiration patterns (i.e., normal breathing and deep breathing) affect the systolic and diastolic characteristic ratios. The study was performed with clinical data collected from 39 healthy subjects, and each subject conducted BP measurements during normal and deep breathings. Analytical results showed that the systolic characteristic ratio increased significantly from 0.52 ± 0.13 under normal breathing to 0.58 ± 0.14under deep breathing (p < 0.05), and the diastolic characteristic ratio was not significantly affected from 0.75 ± 0.12 under normal breathing to 0.76 ± 0.13 under deep breathing (p = 0.48). In conclusion, deep breathing significantly affected the systolic characteristic ratio, suggesting that automated oscillometric BP device which is validated under resting condition should be strictly used for measurements under resting condition

Extracting fetal heart beats from maternal abdominal recordings: Selection of the optimal principal components

This study presents a systematic comparison of different approaches to the automated selection of the principal components (PC) which optimise the detection of maternal and fetal heart beats from non-invasive maternal abdominal recordings. A public database of 75 4-channel non-invasive maternal abdominal recordings was used for training the algorithm. Four methods were developed and assessed to determine the optimal PC: (1) power spectral distribution, (2) root mean square, (3) sample entropy, and (4) QRS template. The sensitivity of the performance of the algorithm to large-amplitude noise removal (by wavelet de-noising) and maternal beat cancellation methods were also assessed. The accuracy of maternal and fetal beat detection was assessed against reference annotations and quantified using the detection accuracy score F1 [2*PPV*Se / (PPV + Se)], sensitivity (Se), and positive predictive value (PPV). The best performing implementation was assessed on a test dataset of 100 recordings and the agreement between the computed and the reference fetal heart rate (fHR) and fetal RR (fRR) time series quantified. The best performance for detecting maternal beats (F1 99.3%, Se 99.0%, PPV 99.7%) was obtained when using the QRS template method to select the optimal maternal PC and applying wavelet de-noising. The best performance for detecting fetal beats (F1 89.8%, Se 89.3%, PPV 90.5%) was obtained when the optimal fetal PC was selected using the sample entropy method and utilising a fixed-length time window for the cancellation of the maternal beats. The performance on the test dataset was 142.7 beats2/min2 for fHR and 19.9 ms for fRR, ranking respectively 14 and 17 (out of 29) when compared to the other algorithms presented at the Physionet Challenge 2013

Application of Neural Network in the Prediction of NOx Emissions from Degrading Gas Turbine

This paper is aiming to apply neural network algorithm for predicting the process response (NOx emissions) from degrading natural gas turbines. Nine different process variables, or predictors, are considered in the predictive modelling. It is found out that the model trained by neural network algorithm should use part of recent data in the training and validation sets accounting for the impact of the system degradation. R-Square values of the training and validation sets demonstrate the validity of the model. The residue plot, without any clear pattern, shows the model is appropriate. The ranking of the importance of the process variables are demonstrated and the prediction profile confirms the significance of the process variables. The model trained by using neural network algorithm manifests the optimal settings of the process variables to reach the minimum value of NOx emissions from the degrading gas turbine system

Spectral Feature Selection for Data Mining

This timely introduction to spectral feature selection illustrates the potential of this powerful dimensionality reduction technique in high-dimensional data processing. It presents the theoretical foundations of spectral feature selection, its connections to other algorithms, and its use in handling both large-scale data sets and small sample problems. Readers learn how to use spectral feature selection to solve challenging problems in real-life applications and discover how general feature selection and extraction are connected to spectral feature selection. Source code for the algorithms is available online

A method for extracting respiratory frequency during blood pressure measurement, from oscillometric cuff pressure pulses and Korotkoff sounds recorded during the measurement

Respiratory frequency is an important physiological feature commonly used to assess health. However, the current measurements involve dedicated devices which not only increase the medical cost but also make health monitoring inconvenient. Earlier studies have shown that respiratory frequency could be extracted from electrocardiography (ECG) signal, but little was done to assess the possibility of extracting respiratory frequency from oscillometric cuff pressure pulses (OscP) or Korotkoff sounds (KorS), which are normally used for measuring blood pressure and more easily accessible than the ECG signal. This study presented a method to extract respiratory frequency from OscP and KorS during clinical blood pressure measurement. The method was evaluated with clinical data collected from 15 healthy participants, and its measurement accuracy was compared with a reference respiratory rate obtained with a magnetometer. Experimental results showed small non-significant mean absolute bias (0.019 Hz for OscP and 0.024 Hz for KorS) and high correlation (0.7 for both OscP and KorS) between the reference respiratory frequency and respiratory frequency extracted from OscP or KorS, indicating the high reliability of extracting respiratory frequency from OscP and KorS during normal blood pressure measurement

Comparison of stethoscope bell and diaphragm, and of stethoscope tube length, for clinical blood pressure measurement

OBJECTIVE: This study investigated the effect of stethoscope side and tube length on auscultatory blood pressure (BP) measurement. METHODS: Thirty-two healthy participants were studied. For each participant, four measurements with different combinations of stethoscope characteristics (bell or diaphragm side, standard or short tube length) were each recorded at two repeat sessions, and eight Korotkoff sound recordings were played twice on separate days to one experienced listener to determine the systolic and diastolic BPs (SBP and DBP). Analysis of variance was carried out to study the measurement repeatability between the two repeat sessions and between the two BP determinations on separate days, as well as the effects of stethoscope side and tube length. RESULTS: There was no significant paired difference between the repeat sessions and between the repeat determinations for both SBP and DBP (all P-values>0.10, except the repeat session for SBP using short tube and diaphragm). The key result was that there was a small but significantly higher DBP on using the bell in comparison with the diaphragm (0.66 mmHg, P=0.007), and a significantly higher SBP on using the short tube in comparison with the standard length (0.77 mmHg, P=0.008). CONCLUSION: This study shows that stethoscope characteristics have only a small, although statistically significant, influence on clinical BP measurement. Although this helps understand the measurement technique and resolves questions in the published literature, the influence is not clinically significant