Differential Threshold Method Research Articles

DC Series Arc Fault Detector With Differential DWT and Variable Threshold Method for Photovoltaic Systems

DC Series Arc Fault Detector With Differential DWT and Variable Threshold Method for Photovoltaic Systems

Dual adaptive differential threshold method for automated detection of faint and strong echo features in radar observations of winter storms

Abstract. Radar observations of winter storms often exhibit locally enhanced linear features in reflectivity, sometimes labeled as snow bands. We have developed a new, objective method for detecting locally enhanced echo features in radar data from winter storms. In comparison to convective cells in warm season precipitation, these features are usually less distinct from the background echo and often have more fuzzy or feathered edges. This technique identifies both prominent, strong features and more subtle, faint features. A key difference from previous radar reflectivity feature detection algorithms is the combined use of two adaptive differential thresholds, one that decreases with increasing background values and one that increases with increasing background values. The algorithm detects features within a snow rate field rather than reflectivity and incorporates an underestimate and overestimate of feature areas to account for uncertainties in the detection. We demonstrate the technique on several examples from the US National Weather Service operational radar network. The feature detection algorithm is highly customizable and can be tuned for a variety of data sets and applications.

Non-Contact Monitoring of Fetal Movement Using Abdominal Video Recording

Fetal movement (FM) is an important indicator of fetal health. However, the current methods of FM detection are unsuitable for ambulatory or long-term observation. This paper proposes a non-contact method for monitoring FM. We recorded abdominal videos from pregnant women and then detected the maternal abdominal region within each frame. FM signals were acquired by optical flow color-coding, ensemble empirical mode decomposition, energy ratio, and correlation analysis. FM spikes, indicating the occurrence of FMs, were recognized using the differential threshold method. FM parameters including number, interval, duration, and percentage were calculated, and good agreement was found with the manual labeling performed by the professionals, achieving true detection rate, positive predictive value, sensitivity, accuracy, and F1_score of 95.75%, 95.26%, 95.75%, 91.40%, and 95.50%, respectively. The changes in FM parameters with gestational week were consistent with pregnancy progress. In general, this study provides a novel contactless FM monitoring technology for use at home.

Pulse Signal Analysis Based on Deep Learning Network.

Pulse signal is one of the most important physiological features of human body, which is caused by the cyclical contraction and diastole. It has great research value and broad application prospect in the detection of physiological parameters, the development of medical equipment, and the study of cardiovascular diseases and pulse diagnosis objective. In recent years, with the development of the sensor, measuring and saving of pulse signal has become very convenient. Now the pulse signal feature analysis is a hotspot and difficulty in the signal processing field. Therefore, to realize pulse signal automatic analysis and recognition is vital significance in the aspects of the noninvasive diagnosis and remote monitoring, etc. In this article, we combined the pulse signal feature extraction in time and frequency domain and convolution neural network to analyze the pulse signal. Firstly, a theory of wavelet transform and the ensemble empirical mode decomposition (EEMD) which is gradually developed in recent years have been used to remove the noises in the pulse signal. Moreover, a method of feature point detection based on differential threshold method is proposed which realized the accurate positioning and extraction time-domain values. Finally, a deep learning method based on one-dimensional CNN has been utilized to make the classification of multiple pulse signals in the article. In conclusion, a deep learning method is proposed for the pulse signal classification combined with the feature extraction in time and frequency domain in this article.

Quantitative analysis of heart rate variability parameter and mental stress index.

BackgroundCardiovascular disease not only occurs in the elderly but also tends to become a common social health problem. Considering the fast pace of modern life, quantified heart rate variability (HRV) indicators combined with the convenience of wearable devices are of great significance for intelligent telemedicine. To quantify the changes in human mental state, this article proposes an improved differential threshold algorithm for R-wave detection and recognition of electrocardiogram (ECG) signals.MethodsHRV is a specific quantitative indicator of autonomic nerve regulation of the heart. The recognition rate is increased by improving the starting position of R wave and the time-window function of the traditional differential threshold method. The experimental platform is a wearable sign monitoring system constructed based on body area networks (BAN) technology. Analytic hierarchy process (AHP) is used to construct the mental stress assessment model, the weight judgment matrix is constructed according to the influence degree of HRV analysis parameters on mental stress, and the consistency check is carried out to obtain the weight value of the corresponding HRV analysis parameters.ResultsExperimental results show that the recognition rate of R wave of real-time 5 min ECG data collected by this algorithm is >99%. The comprehensive index of HRV based on weight matrix can greatly reduce the deviation caused by the measurement error of each parameter. Compared with traditional characteristic wave recognition algorithms, the proposed algorithm simplifies the process, has high real-time performance, and is suitable for wearable analysis devices with low-configuration requirements.ConclusionOur algorithm can describe the mental stress of the body quantitatively and meet the requirements of application demonstration.

A New Assessment Method of the Pilot Stress Using ECG Signals During Complex Special Flight Operation

With the continuous improvement in the man-machine-designed fighter aircraft, the pilot training intensity has increased. Complex special operation of the fighter aircraft is greatly affected by flight conditions and human control reliability, which imposes a great burden on the pilot psychophysiologically. To understand the intensity of the stress on the pilot, which is a crucial parameter, accurate evaluation of the changes in the stress intensity of the pilot during the flight is very important. This study aimed to analyze the stress intensity of pilots by collecting electrocardiogram (ECG) signals during the long flight involving 11 pilot trainees. First, an improved soft-threshold denoising method for the ECG data and an improved differential threshold method were used to process the first-order difference and the threshold value to the filtered signal to determine the R wave. Then, the multiscale analysis method was used to fuse the area of the heart rate curve of the pilots. The method of flight training and the division of the stress stage were derived. Further, the functional relationship of the stress intensity with the frequency of training was constructed. All these effectively improved the performance of the flight operation training and helped make a reasonable training plan. This was of great practical significance to the measurement and evaluation of the stress state of the individuals in dangerous jobs, such as special flight operations, high-altitude skydiving, bungee jumping, etc.

Fetal Electrocardiogram Extraction Based on SWT-MM Method

Fetal electrocardiogram (FECG) is of great importance due to the potentially precise information that FECG carries could assist clinicians in making more appropriate and timely decisions during pregnancy and labor. In this paper, a method based on combined Stationary Wavelet Transform and Modulus-Maxima (SWT-MM) method is proposed for extracting the complete morphology of the FECG from maternal abdominal ECG (AECG). It particularly provides a different way of constructing the maternal ECG (MECG) template. The Efficacy of the method was validated using real data in Non-Invasive Fetal Electrocardiogram Database. The morphology of the extracted FECG was clearly seen that the fetal R-peak detection by simple differential-threshold method acquired the average accuracy of 96.8%. The method provides additional important benefits of fast speed and automated control for applying into the fetal monitors. Therefore, the method is potentially a strong tool for FECG extraction, especially in real-time use.

A ECG Waveform Detection Algorithm Based on Differential Threshold and Wavelet Transform

With the portable ECG device develops rapidly, the ECG waveform detection is particularly important. As far as we know, the goal of most researchers is designing a new, real-time, high efficiency, perfect effect ECG detection algorithm. In order to obtain better detection accuracy, a new type of algorithm which combines differential threshold and the wavelet transform is designed, the method uses improved version of the differential threshold method to detect the QRS wave group, and then the P, T-wave is identified by biorthogonal wavelet transform. Dozens of MIT-BIT test data show that the algorithm fully meets the testing requirements, achieve the desired goal and obtain good results.

结合近红外光谱法的肝血流参数测量

A pulse spectroscopic scheme based on dye dilution and an excretion test was developed to overcome the shortcomings of conventional blood dye concentration measuring method in complex operation,invasive treatment and non-continuance.Firstly,Indocyanine Green(ICG) was intravenously injected and a finger clip sensor was used to extract spectral signals of fingertips for a sufferer.Then,by recording the dye dilution curve simultaneously,the algorithm to isolate ICG absorption spectrum from the disturbance of peripheral arterial blood was implemented.Finally,the ICG plasma disappearance rate K,15 min retention rate R15and other hepatic hemodynamic parameters were deduced by analyzing the semi-logarithmic dye concentration graph.Combining a lab-VIEW software platform with an adaptive differential threshold method,proposed scheme eliminates the interference come from the hemoglobin absorption spectrum and baseline drift,improves the measuring accuracy greatly,and the average measurement error of K is less than 0.008.This method is great value to not only the assessing hepatic functional reserve at pre-operation,intra-operation,post-operation,but also providing a way to monitor some middle variables for clinical research purposes.

Automated versus Manual Measurement of the QT Interval and Corrected QT Interval

The International Conference on Harmonization E14 Guideline specifies detailed assessment of QT interval or corrected QT interval prolongation when developing new drugs. We recently devised new software to precisely measure the QT interval. The QT intervals of all leads for a selected single heart beat were compared between automated measurement with the new software from Fukuda Denshi and manual measurement. With both automated and manual measurement, QT intervals obtained by the tangent method were shorter than those obtained by the differential threshold method, but the extent of correction was smaller. QT interval data obtained by the differential threshold method were more similar to values obtained by visual measurement than were data obtained by the tangent method, but the extent of correction was larger. Variability was related to the T-wave amplitude and to setting the baseline and tangent in the tangent method, while skeletal muscle potential noise affected the differential threshold method. Drift, low-amplitude recordings, and T-wave morphology were problems for both methods. Among the 12 leads, corrections were less frequent for leads II and V(3) -V(6) . We conclude that, for a thorough assessment of the QT/QTc interval, the tangent method or the differential threshold method appears to be suitable because of smaller interreader differences and better reproducibility. Correction of data should be done by readers who are experienced in measuring the QT interval. It is also important for electrocardiograms to have little noise and for a suitable heart rate and appropriate leads to be selected.

Accuracy of popular automatic QT Interval algorithms assessed by a 'Gold Standard' and comparison with a Novel method: computer simulation study

BackgroundAccurate measurement of the QT interval is very important from a clinical and pharmaceutical drug safety screening perspective. Expert manual measurement is both imprecise and imperfectly reproducible, yet it is used as the reference standard to assess the accuracy of current automatic computer algorithms, which thus produce reproducible but incorrect measurements of the QT interval. There is a scientific imperative to evaluate the most commonly used algorithms with an accurate and objective 'gold standard' and investigate novel automatic algorithms if the commonly used algorithms are found to be deficient.MethodsThis study uses a validated computer simulation of 8 different noise contaminated ECG waveforms (with known QT intervals of 461 and 495 ms), generated from a cell array using Luo-Rudy membrane kinetics and the Crank-Nicholson method, as a reference standard to assess the accuracy of commonly used QT measurement algorithms. Each ECG contaminated with 39 mixtures of noise at 3 levels of intensity was first filtered then subjected to three threshold methods (T1, T2, T3), two T wave slope methods (S1, S2) and a Novel method. The reproducibility and accuracy of each algorithm was compared for each ECG.ResultsThe coefficient of variation for methods T1, T2, T3, S1, S2 and Novel were 0.36, 0.23, 1.9, 0.93, 0.92 and 0.62 respectively. For ECGs of real QT interval 461 ms the methods T1, T2, T3, S1, S2 and Novel calculated the mean QT intervals(standard deviations) to be 379.4(1.29), 368.5(0.8), 401.3(8.4), 358.9(4.8), 381.5(4.6) and 464(4.9) ms respectively. For ECGs of real QT interval 495 ms the methods T1, T2, T3, S1, S2 and Novel calculated the mean QT intervals(standard deviations) to be 396.9(1.7), 387.2(0.97), 424.9(8.7), 386.7(2.2), 396.8(2.8) and 493(0.97) ms respectively. These results showed significant differences between means at >95% confidence level. Shifting ECG baselines caused large errors of QT interval with T1 and T2 but no error with Novel.ConclusionThe algorithms T2, T1 and Novel gave low coefficients of variation for QT measurement. The Novel technique gave the most accurate measurement of QT interval, T3 (a differential threshold method) was the next most accurate by a large margin. The objective and accurate 'gold standard' presented in this paper may be useful to assess new QT measurement algorithms. The Novel algorithm may prove to be more accurate and reliable method to measure the QT interval.