Selection of Reference Channels Based on Mutual Information for Frequency-Dependent Subtraction Method Applied to Fetal Biomagnetic Signals

Evaluation of Pelvic Floor Muscles in Pregnancy and Postpartum With Non-Invasive Magnetomyography

Objective: To record and characterize features of levator ani muscles (LAM) activity in pregnancy and postpartum using non-invasive and novel Magnetomyography (MMG) technique with amplitude and spectral parameters. Methods: Nulliparous women with uncomplicated pregnancies participated in the MMG data collection during rest and voluntary LAM contractions (Kegels) with modulated intensity in third trimester and approximately 2 months postpartum (PP). Simultaneous surface electromyography was recorded to document the recruitment of accessory muscles. Moderate strength Kegel (MK) MMG trials were analyzed. Amplitude and spectral parameters including root-mean square (RMS) amplitude, power spectrum density (PSD) and normalized PSD (rPSD) in three frequency bands (low, middle, high) were computed on MK epochs. Statistical comparisons between pregnancy and postpartum were calculated. Results: MMG recordings were measured from 10 pregnant women. Results showed decreased RMS and power between third trimester and postpartum, trending towards significance. rPSD values in the low frequency band decreased significantly (p = 0.028) from third trimester to postpartum, while significant increase was observed in the middle frequency band (p = 0.018). Conclusions: This study shows that MMG as non-invasive tool has the ability to detect and characterize changes of LAM activity with amplitude and spectral parameters during pregnancy and postpartum.

Verification of fetal evoked response by magnetic dipole fitting

BACKGROUND

Investigation of fetal evoked response to auditory or visual stimuli is an important means of understanding the developmental stages and potential problems in prenatal life. It is, however, not without certain imperfections. The biggest challenge with fetal evoked response is its low signal to noise ratio. Under noisy conditions, the detected fetal evoked response should, therefore, be further investigated to confirm that the source of the signal is from fetal brain and is not related to random noise. Existing methods for verification are: (1) visual inspection of magnetic field maps, which requires user intervention and expert knowledge which can be highly subjective; (2) simultaneous ultrasound measurement, which is expensive and technically difficult to manage; and (3) equivalent current dipole fitting, which requires knowledge of the orientation of fetal head and its dimensions that may not be available at all times.

OBJECTIVE

To verify that the detected fetal evoked response signal is originating from the fetal head by using an objective and feasible method that employs magnetic dipole fitting to fetal evoked response.

STUDY DESIGN

From raw fetal magnetoencephalography data, the cardiac interference was removed by frequency dependent subtraction. After averaging over stimulus triggers, the resulting signal was taken as the candidate fetal evoked response. The fetal evoked response was investigated for the highest peak in between 0.2-0.5 s, which is the expected latency of the response to the stimulus. The magnetic field at this highest peak was used for magnetic dipole fitting. The validation of peak was based on the closeness of the magnetic dipole fit to vicinity of fetal head location determined by ultrasound and the anatomically reasonable distance from the fetal heart. The methodology was first tested on a sample neonatal data before application to fetal data.

RESULTS

The results of neonatal application confirmed that the source localization by magnetic dipole fitting for the brain produced meaningful results. When applied to fetal data, auditory and visual evoked response was detected in 27 of the 38 recordings. This implied that with our verification method, fetal evoked responses were detected in 71% of fetuses.

CONCLUSION

Detection rate of the evoked responses were similar to earlier reports where subjective visual inspection or simultaneous ultrasound measurement were used. Our method using magnetic dipole fitting for verification is more feasible and objective compared to the earlier methods.

Feature Selections Using Minimal Redundancy Maximal Relevance Algorithm for Human Activity Recognition in Smart Home Environments

In this paper, maximal relevance measure and minimal redundancy maximal relevance (mRMR) algorithm (under D-R and D/R criteria) have been applied to select features and to compose different features subsets based on observed motion sensor events for human activity recognition in smart home environments. And then, the selected features subsets have been evaluated and the activity recognition accuracy rates have been compared with two probabilistic algorithms: naïve Bayes (NB) classifier and hidden Markov model (HMM). The experimental results show that not all features are beneficial to human activity recognition and different features subsets yield different human activity recognition accuracy rates. Furthermore, even the same features subset has different effect on human activity recognition accuracy rate for different activity classifiers. It is significant for researchers performing human activity recognition to consider both relevance between features and activities and redundancy among features. Generally, both maximal relevance measure and mRMR algorithm are feasible for feature selection and positive to activity recognition.

Bio-Inspired Algorithms for Optimal Feature Selection in Motor Imagery-Based Brain-Computer Interface. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020;2020:519-522. [PMID: 33018041 DOI: 10.1109/embc44109.2020.9176244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Recently, there is an increasing recognition that sensory feedback is critical for proper motor control. With the help of BCI, people with motor disabilities can communicate with their environments or control things around them by using signals extracted directly from the brain. The widely used non-invasive EEG based BCI system require that the brain signals are first preprocessed, and then translated into significant features that could be converted into commands for external control. To determine the appropriate information from the acquired brain signals is a major challenge for a reliable classification accuracy due to high data dimensions. The feature selection approach is a feasible technique to solving this problem, however, an effective selection method for determining the best set of features that would yield a significant classification performance has not yet been established for motor imagery (MI) based BCI. This paper explored the effectiveness of bio-inspired algorithms (BIA) such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), Cuckoo Search Algorithm (CSA), and Modified Particle Swarm Optimization (M-PSO) on EEG and ECoG data. The performance of SVM classifier showed that M-PSO is highly efficacious with the least selected feature (SF), and converges at an acceptable speed in low iterations.

An enhanced frequency dependent subtraction algorithm for removal of cardiac interference from fMEG by using minimum norm projection operator

BACKGROUND

A frequency dependent subtraction method, SUBTR, is developed to remove maternal and fetal magnetocardiography (mMCG and fMCG) interference from fetal magnetoencephalography (fMEG). But channels close to fetal head cannot be used as references for SUBTR in order to protect fMEG from subtraction and this results in cardiac residual when these channels have important fMCG frequency components. Cardiac residual creates noise in evoked response (ER) which results in poor ER detection.

NEW METHOD

We developed an enhanced SUBTR algorithm, which we call SUBTR with minimum norm projection operator (SUBTRwMNPO), by employing covariance based minimum norm projection operators (MNPO). mMCG and fMCG signals are extracted from the raw data using MNPO and they are subtracted in the frequency domain from raw data to extract fetal Evoked Response (fER).

RESULTS

When tested on 87 datasets, SUBTRwMNPO is shown to attenuate cardiac interference almost totally resulting in a clean fER signal.

COMPARISON WITH EXISTING METHODS

Cardiac attenuation with SUBTRwMNPO is either as good as or better than SUBTR. SUBTRwMNPO has higher attenuation rate for the datasets where SUBTR leaves cardiac residual.

CONCLUSIONS

SUBTRwMNPO is successful in removing cardiac interference regardless of the orientation of fMCG and fMEG signal spaces. It can also be used to remove cardiac interference when there is no prior knowledge of fetal head location.

Magnetomyographic Recordings of Pelvic Floor Activity During Pregnancy and Postpartum: A Novel Non-invasive Approach

The levator ani muscles (LAM) are integral to pelvic floor support and injury to this muscle complex has been associated with pelvic floor disorders, but our ability to evaluate their neuromuscular integrity is limited. During pregnancy, gravidas undergo systemic functional and anatomic modifications, including pelvic floor muscular adaptations. Magnetomyography (MMG) is a novel and non-invasive tool to passively measure the magnetic fields generated by depolarization activity of muscles and offers a unique method to evaluate the LAM. We collected serial MMG data in a pregnant woman with singleton gestation. Pregnant woman performed LAM contractions (Kegels) with intervening rest periods. Kegel signals were isolated by using the frequency dependent subtraction (SUBTR) and independent component analysis (ICA) methods. Concurrent body-surface electromyography (EMG) was used to evaluate for accessory-muscle recruitment by placing bipolar electrodes on the perineum, abdomen, and thigh. Amplitude and spectral-related indicators were computed across moderate intensity MMG Kegel epochs: root-mean square (RMS) amplitude, power spectrum density (PSD) and relative PSD (rPSD) in three frequency bands. Indicators were extracted from two pregnancy recordings and one postpartum. Parameters were represented in terms of gestation and postpartum weeks. We observed that postpartum RMS Kegel amplitudes had lower values than seen in pregnancy. Changes in spectral indicators were observed between pregnancy and postpartum.

A Comparison of Five Algorithms for Fetal Magnetocardiography Signal Extraction

Fetal magnetocardiography (fMCG) provides accurate and reliable measurements of electrophysiological events in the fetal heart and is capable of studying fetuses with congenital heart diseases. A variety of techniques exist to extract the fMCG signal with the demand for non-invasively obtained fetal cardiac information. To the best of our knowledge, there is no comparative study published in the field as to how the various extraction algorithms perform. We perform a comparative study of the ability of five methods to extract the fMCG using real biomagnetic signals, two of those methods are applied to real fMCG data for the first time. Biomagnetic signals were recorded and processed with each of the five methods to obtain fMCG. The R peaks of the fMCG traces were obtained via a peak-detection algorithm. From whole recording for each method, the fetal heart rate (FHR) was calculated and used to perform FHR variability (FHRV) analysis. Additionally, we calculated durations from the PQRST complex from time-averaged data during sinus rhythm. The five methods recovered the fMCG signals, but two of them were able to extract cleaner fMCG and the morphology was observed from the continuous data. The time-averaged data showed very similar morphologies between methods, but two of them displayed a signal amplitude reduction on the R-waves and T-waves. Values of PQRST durations, FHR and FHRV were in the range of previous fetal cardiac studies. We have compared five methods for fMCG extraction and showed their ability to perform fMCG analysis.