Fetal QRS Detection in Noninvasive Abdominal Electrocardiograms Using Principal Component Analysis and Discrete Wavelet Transforms with Signal Quality Estimation.

BACKGROUND: Fetal heart rate (FHR) extracted from abdominal electrocardiogram (ECG) is a powerful non-invasive method in appropriately assessing the fetus well-being during pregnancy. Despite significant advances in the field of electrocardiography, the analysis of fetal ECG (FECG) signal is considered a challenging issue which is mainly due to low signal to noise ratio (SNR) of FECG. OBJECTIVE: In this study, we present an approach for accurately locating the fetal QRS complexes in non-invasive FECG. MATERIALS AND METHODS: In this experimental study, the proposed method included 4 steps. In step 1, comb notching filter was employed to pre-process the abdominal ECG (AECG). Furthermore, low frequency noises were omitted using wavelet decomposition. In next step, principal component analysis (PCA) and signal quality assessment (SQA) were used to obtain an optimal AECG reference channel for maternal R-peaks detection. In step 3, maternal ECG (MECG) was removed from mixture signal and FECG was extracted. In final step, the extracted FECG was first decomposed by discrete wavelet transforms at level 10. Then, by employing details of levels 2, 3, 4, the new FECG signal was reconstructed in which various noises and artifacts were removed and FECG components whose frequency were close to the fetal QRS complexes remained which increased the performance of the method. RESULTS: For evaluation, 15 recordings of PhysioNet Noninvasive FECG database were used and the average F1 measure of 98.77% was obtained. CONCLUSION: The results indicate that use of both an efficient analysis of major component of AECG along with a signal quality assessment technique has a promising performance in FECG analysis.

Eigen Decomposition of Cardiac Synchronous EEGs for Investigation of Neural Effects of Tempo and Cognition of Songs.

There is growing evidence of palliative effects of listening to songs on neural and cardiovascular function. It is also known that listening to songs can entrain cardiac variability. These results suggest that the neural changes in response to listening to songs in turn affect cardiac rhythm. How these effects come about is less clearly known. Therefore, investigation of the changes in neural rhythms that are synchronous with cardiac rhythm is likely to shed further light on the mechanisms via which songs produce these effects. Towards this aim, we conducted eigen decomposition of cardiac-synchronized EEGs to investigate the effects of tempo and cognition by auditory stimuli (listening to songs). For evaluating the effects of tempo, songs of slow and fast tempo were used, and for cognition, each subjects' favorite song was used. ECG and six EEGs (F3, F4, T3, T4, P3, P4) were recorded as subjects listened to songs. For cardiac synchronization, R waves from the ECG were localized and the EEGs during 300-millisecond segments ending at each R wave were extracted. Eigen decomposition of the covariance matrix of these EEG segments was performed. Results from 14 subject showed that, compared with other locations, P3 appears to have the ability to discriminate between songs. All songs lowered the second and the third largest eigenvalues compared to control, among these, the slow tempo song induced more significant decreases in T3, T4 and P3. During the slow song, 80% of the variance in all six EEGs could be represented with less eigenvalue/vectors while during the favorite song this number was larger. These results show that eigen decomposition of cardiac synchronized EEGs has the potential to investigate effects of music on neural and cardiovascular systems.

Baroreflex Sensitivity During Listening to Music Computed from Time Domain Sequences and Frequency Domain Transfer Function.

Listening to music has been known to affect autonomic function of cardiovascular regulation. Baroreflex is a feedback control loop that uses rate changes of the heart in order to regulate beat by beat changes in blood pressure (BP). In this study, we used two approaches to compute measures of sensitivity of the baroreflex (BRS), a time domain sequence approach and frequency domain transfer functions. Subjects listened to slow and fast tempo songs during the study. Electrocardiogram (ECG) and non-invasive continuous BP were recorded in 14 subjects (7 males and females). From these signals, either beat by beat or equi-sampled in time RR intervals and systolic BP (SBP) were computed. BRS was then estimated using RR and SBP. Our results show that the sequence method consistently provided higher values of BRS than the transfer function method (up to two fold). The two measures were reasonably well correlated $( \mathrm {R}>0.84)$ during control and the slow song, but not during the fast song. The BRS was lower $( \sim 20$%) than control when listening to fast songs $( \mathrm {p}<0.005)$. These results show the effects of listening to songs on BRS changes, but also show that the two methods to estimate BRS, although reasonably correlated, do not always provide similar estimates of BRS.

Robust fetal QRS detection from noninvasive abdominal electrocardiogram based on channel selection and simultaneous multichannel processing.

The purpose of this study is to provide a new method for detecting fetal QRS complexes from non-invasive fetal electrocardiogram (fECG) signal. Despite most of the current fECG processing methods which are based on separation of fECG from maternal ECG (mECG), in this study, fetal heart rate (FHR) can be extracted with high accuracy without separation of fECG from mECG. Furthermore, in this new approach thoracic channels are not necessary. These two aspects have reduced the required computational operations. Consequently, the proposed approach can be efficiently applied to different real-time healthcare and medical devices. In this work, a new method is presented for selecting the best channel which carries strongest fECG. Each channel is scored based on two criteria of noise distribution and good fetal heartbeat visibility. Another important aspect of this study is the simultaneous and combinatorial use of available fECG channels via the priority given by their scores. A combination of geometric features and wavelet-based techniques was adopted to extract FHR. Based on fetal geometric features, fECG signals were divided into three categories, and different strategies were employed to analyze each category. The method was validated using three datasets including Noninvasive fetal ECG database, DaISy and PhysioNet/Computing in Cardiology Challenge 2013. Finally, the obtained results were compared with other studies. The adopted strategies such as multi-resolution analysis, not separating fECG and mECG, intelligent channels scoring and using them simultaneously are the factors that caused the promising performance of the method.