Wavelet-based analysis of heart-rate-dependent ECG features

Fetal Heart Rate Extraction Based on Wavelet Transform to Prevent Fetal Distress In Utero

In order to improve the effective extraction of fetal heart rate and prevent fetal distress in utero, a study of fetal heart rate feature extraction based on wavelet transform to prevent fetal distress in utero was proposed. This paper adopts a fetal heart rate detection method based on the maximum value of the binary wavelet transform modulus. The method is simulated by the Doppler fetal heart signal obtained from the clinic. Compared with the original curve, the transformed curve can roughly see the change rule of the original signal and identify the peak point of the signal, but due to the large disturbance of the peak point, the influence on the computer processing is also great. The periodicity of the transformed signal is greatly enhanced, making it easier to deal with the computation. A total of 300 pregnant women with full-term fetal heart monitoring from January 2018 to January 2020 were selected as the research subjects and divided into the observation group and the control group. The observation group consisted of 100 patients with abnormal fetal heart monitoring, and the control group consisted of 200 patients with normal fetal heart monitoring. The uterine contractions and fetal heart rate were recorded, and the incidence of fetal distress, cesarean section, neonatal asphyxia, and amniotic fluid and fecal contamination were observed. The incidence of fetal distress, cesarean section, neonatal asphyxia, and amniotic fluid fecal stain in the observation group were significantly higher than those in the control group. Fetal heart monitoring can accurately judge the situation of the fetus in pregnant women and timely diagnose the abnormal fetal heart rate, which has a better effect on the prognosis of perinatal infants and can reduce their mortality. It can effectively solve the problems existing in the autocorrelation algorithm and extract the fetal heart rate more accurately. It is an effective improved scheme of fetal heart rate extraction. It is very helpful in preventing fetal distress in utero.

Electrocardiography (ECG) analysis and a new feature extraction method using wavelet transform with scalogram analysis

Electrocardiography (ECG) signals and the information obtained through the analysis of these signals constitute the main source of diagnosis for many cardiovascular system diseases. Therefore, accurate analyses of ECG signals are very important for correct diagnosis. In this study, an ECG analysis toolbox together with a user-friendly graphical user interface, which contains the all ECG analysis steps between the recording unit and the statistical investigation, is developed. Furthermore, a new feature calculation methodology is proposed for ECG analysis, which carries distinct information than amplitudes and durations of ECG main waves and can be used in artificial intelligence studies. Developed toolbox is tested using both Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia ECG Database and an experimentally collected dataset for performance evaluation. The results show that ECG analysis toolbox presented in this study increases the accuracy and reliability of the ECG main wave detection analysis, highly fasten the process duration compared to manual ones and the new feature set can be used as a new parameter for decision support systems about ECG based on artificial intelligence.

Heart rate variability analysis during central hypovolemia using wavelet transformation

Detection of hypovolemia prior to overt hemodynamic decompensation remains an elusive goal in the treatment of critically injured patients in both civilian and combat settings. Monitoring of heart rate variability has been advocated as a potential means to monitor the rapid changes in the physiological state of hemorrhaging patients, with the most popular methods involving calculation of the R-R interval signal's power spectral density (PSD) or use of fractal dimensions (FD). However, the latter method poses technical challenges, while the former is best suited to stationary signals rather than the non-stationary R-R interval. Both approaches are also limited by high inter- and intra-individual variability, a serious issue when applying these indices to the clinical setting. We propose an approach which applies the discrete wavelet transform (DWT) to the R-R interval signal to extract information at both 500 and 125 Hz sampling rates. The utility of machine learning models based on these features were tested in assessing electrocardiogram signals from volunteers subjected to lower body negative pressure induced central hypovolemia as a surrogate of hemorrhage. These machine learning models based on DWT features were compared against those based on the traditional PSD and FD, at both sampling rates and their performance was evaluated based on leave-one-subject-out fold cross-validation. Results demonstrate that the proposed DWT-based model outperforms individual PSD and FD methods as well as the combination of these two traditional methods at both sample rates of 500 Hz (p value <0.0001) and 125 Hz (p value <0.0001) in detecting the degree of hypovolemia. These findings indicate the potential of the proposed DWT approach in monitoring the physiological changes caused by hemorrhage. The speed and relatively low computational costs in deriving these features may make it particularly suited for implementation in portable devices for remote monitoring.

Wavelet transforms and the ECG: a review

The wavelet transform has emerged over recent years as a powerful time-frequency analysis and signal coding tool favoured for the interrogation of complex nonstationary signals. Its application to biosignal processing has been at the forefront of these developments where it has been found particularly useful in the study of these, often problematic, signals: none more so than the ECG. In this review, the emerging role of the wavelet transform in the interrogation of the ECG is discussed in detail, where both the continuous and the discrete transform are considered in turn.