Cardiac Interference Research Articles

Design of optimized adaptive filter for artifact removal from EEG signal

Removal of artifacts from the acquired biomedical signal is a major issue. Various signal processing techniques are developed by the researchers for some decades to extract the clinical information from EEG signals after the removal of unwanted artifacts during the time of its recording. In this work, an optimized weighted RLS filter is designed to suppress cardiac interference from the EEG signal. The cardiac artifact are estimated with the help of an optimized weighted RLS algorithm and eliminated from the EEG signal. Based on validation measures, Mean Square Error(MSE), Gain in Signal to Artifact Ratio(GSAR), Signal to Noise Ratio(SNR), Information Quantity(IQ), and INPS are used. The performance of the proposed method compared with the earlier methods like the Least square notch filter, ICA method are given in the result section. For the proposed method the MSE,GSAR,SNR,IQ, and INPS are found to be 0.1898 μV2 , 10.4807dB, 68.1117dB, 0.330, 10.91dB respectively. In every case, the performance of the proposed method is better than the existing method which proves its robustness.

Toward Online Removal of Cardiac Interference From Trunk Electromyography by Morphological Modeling of the Electrocardiography

Trunk electromyography (EMG) has been widely used in many biomedical applications, which is usually contaminated by electrocardiography (ECG) interference. Several methods have been proposed for ECG removal from trunk EMG. However, most of them are either inaccurate or unsuitable for online applications, e.g., prosthesis control. The aim of the present study is therefore to develop an accurate ECG removal algorithm suitable for online applications. Each ECG wave was modeled by Gaussian kernel functions and subtracted from the trunk measurement to obtain a clean EMG. Two synthetic datasets were generated by mixing a real EMG with a healthy ECG and a dysrhythmia ECG, respectively. Average rectified value (ARV) and mean frequency (MF) were calculated from the reconstructed EMG and the clean EMG for performance evaluation. Moreover, real trunk EMG was recorded under isometric contractions with different forces. Correlation coefficient (CC) between the amplitude of the reconstruct EMG and the contraction force was calculated as performance metric. Small root mean square errors were observed in ARV and MF between the clean EMG and reconstructed EMG, i.e., 2.5±0.7 μv and 2.0±0.4 Hz for the synthetic dataset containing healthy ECG and 3.1±1.7 μv and 3.0±1.2 Hz for that containing dysrhythmia ECG. High CC (0.91±0.12) between EMG amplitude and contraction force was observed for real trunk EMG. Our algorithm outperforms many of the state-of-the-art algorithms and is implemented in each cardiac cycle, enabling possible online applications such as prosthesis control.

Concentric ring electrodes for non-invasive recording of gastric myoelectric activity

Electrogastrography has emerged as a non-invasive technique for diagnosing an extensive variety of gastrointestinal disorders. The non-invasive electrogastrogram (EGG) remains a challenge due to the poor spatial resolution of conventional disk electrodes. In this work we attempted to determine the possibility of detecting gastric myoelectric activity using concentric ring electrodes (CRE) proposed to improve the spatial resolution of bioelectrical recordings. We simultaneously recorded 8 bipolar and bipolar concentric (BC) EGGs acquired by disk electrodes and CREs, respectively. The BC EGG showed lower signal amplitude than the bipolar recordings but were less influenced by cardiac interference and had a slow wave (SW) detectability above 80% when positioned over the stomach. We found a similar gastric SW frequency in both bipolar and BC EGG records in both fasting and postprandial states and a similar postprandial/fasting power ratio, suggesting the feasibility of using CRE to identify gastric myoelectric activity.

Verification of fetal evoked response by magnetic dipole fitting

BackgroundInvestigation 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. ObjectiveTo 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 designFrom 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. ResultsThe 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. ConclusionDetection 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.

Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram.

A new algorithm based on singular value decomposition (SVD) to remove cardiac contamination from trunk electromyography (EMG) is proposed. Its performance is compared to currently available algorithms at different signal-to-noise ratios (SNRs). The algorithm is applied on individual channels. An experimental calibration curve to adjust the number of SVD components to the SNR (0–20 dB) is proposed. A synthetic dataset is generated by the combination of electrocardiography (ECG) and EMG to establish a ground truth reference for validation. The performance is compared with state-of-the-art algorithms: gating, high-pass filtering, template subtraction (TS), and independent component analysis (ICA). Its applicability on real data is investigated in an illustrative diaphragm EMG of a patient with sleep apnea. The SVD-based algorithm outperforms existing methods in reconstructing trunk EMG. It is superior to the others in the time (relative mean squared error < 15%) and frequency (shift in mean frequency < 1 Hz) domains. Its feasibility is proven on diaphragm EMG, which shows a better agreement with the respiratory cycle (correlation coefficient = 0.81, p-value < 0.01) compared with TS and ICA. Its application on real data is promising to non-obtrusively estimate respiratory effort for sleep-related breathing disorders. The algorithm is not limited to the need for additional reference ECG, increasing its applicability in clinical practice.

Is Brain Dynamics Preserved in the EEG After Automated Artifact Removal? A Validation of the Fingerprint Method and the Automatic Removal of Cardiac Interference Approach Based on Microstate Analysis.

The assessment of a method for removing artifacts from electroencephalography (EEG) datasets often disregard verifying that global brain dynamics is preserved. In this study, we verified that the recently introduced optimized fingerprint method and the automatic removal of cardiac interference (ARCI) approach not only remove physiological artifacts from EEG recordings but also preserve global brain dynamics, as assessed with a new approach based on microstate analysis. We recorded EEG activity with a high-resolution EEG system during two resting-state conditions (eyes open, 25 volunteers, and eyes closed, 26 volunteers) known to exhibit different brain dynamics. After signal decomposition by independent component analysis (ICA), the independent components (ICs) related to eyeblinks, eye movements, myogenic interference, and cardiac electromechanical activity were identified with the optimized fingerprint method and ARCI approach and statistically compared with the outcome of the expert classification of the ICs by visual inspection. Brain dynamics in two different groups of denoised EEG signals, reconstructed after having removed the artifactual ICs identified by either visual inspection or the automated methods, was assessed by calculating microstate topographies, microstate metrics (duration, occurrence, and coverage), and directional predominance (based on transition probabilities). No statistically significant differences between the expert and the automated classification of the artifactual ICs were found (p > 0.05). Cronbach’s α values assessed the high test–retest reliability of microstate parameters for EEG datasets denoised by the automated procedure. The total EEG signal variance explained by the sets of global microstate templates was about 80% for all denoised EEG datasets, with no significant differences between groups. For the differently denoised EEG datasets in the two recording conditions, we found that the global microstate templates and the sequences of global microstates were very similar (p < 0.01). Descriptive statistics and Cronbach’s α of microstate metrics highlighted no significant differences and excellent consistency between groups (p > 0.5). These results confirm the ability of the optimized fingerprint method and the ARCI approach to effectively remove physiological artifacts from EEG recordings while preserving global brain dynamics. They also suggest that microstate analysis could represent a novel approach for assessing the ability of an EEG denoising method to remove artifacts without altering brain dynamics.

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

BackgroundA 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 methodWe 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). ResultsWhen tested on 87 datasets, SUBTRwMNPO is shown to attenuate cardiac interference almost totally resulting in a clean fER signal. Comparison with existing methodsCardiac 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. ConclusionsSUBTRwMNPO 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.

High-Frequency Irreversible Electroporation for Treatment of Primary Liver Cancer: A Proof-of-Principle Study in Canine Hepatocellular Carcinoma.

To determine the safety and feasibility of percutaneous high-frequency irreversible electroporation (HFIRE) for primary liver cancer and evaluate the HFIRE-induced local immune response. HFIRE therapy was delivered percutaneously in 3 canine patients with resectable hepatocellular carcinoma (HCC) in the absence of intraoperative paralytic agents or cardiac synchronization. Pre- and post-HFIRE biopsy samples were processed with histopathology and immunohistochemistry for CD3, CD4, CD8, and CD79a. Blood was collected on days 0, 2, and 4 for complete blood count and chemistry. Numeric models were developed to determine the treatment-specific lethal thresholds for malignant canine liver tissue and healthy porcine liver tissue. HFIRE resulted in predictable ablation volumes as assessed by posttreatment CT. No detectable cardiac interference and minimal muscle contraction occurred during HFIRE. No clinically significant adverse events occurred secondary to HFIRE. Microscopically, a well-defined ablation zone surrounded by a reactive zone was evident in the majority of samples. This zone was composed primarily of maturing collagen interspersed with CD3+/CD4-/CD8- lymphocytes in a proinflammatory microenvironment. The average ablation volumes for the canine HCC patients and the healthy porcine tissue were 3.89 cm3 ± 0.74 and 1.56 cm3 ± 0.16, respectively (P= .03), and the respective average lethal thresholds were 710 V/cm ± 28.2 and 957 V/cm ± 24.4 V/cm (P= .0004). HFIRE can safely and effectively be delivered percutaneously, results in a predictable ablation volume, and is associated with lymphocytic tumor infiltration. This is the first step toward the use of HFIRE for treatment of unresectable liver tumors.

Removing Cardiac Artifacts From Single-Channel Respiratory Electromyograms

Electromyographic (EMG) measurements of the respiratory muscles provide a convenient and noninvasive way to assess respiratory muscle function and detect patient activity during assisted mechanical ventilation. However, surface EMG measurements of the diaphragm and intercostal muscles are substantially contaminated by cardiac activity due to the vicinity of the cardiac muscles. Many algorithmic solutions to this problem have been proposed, yet a conclusive performance comparison of the most promising candidates currently is missing. The objective of this work is to provide a quantitative performance comparison of six previously proposed cardiac artifact removal algorithms operating on single-channel EMG measurements, and two newly proposed, improved versions of these algorithms. Algorithmic performance is evaluated quantitatively based on four different measures of separation success, using both synthetic validation signals and electromyographic measurements of the respiratory muscles in eight subjects. The compared algorithms are two versions of the empirical template subtraction algorithm, two model-based Bayesian filtering procedures, a wavelet denoising approach, an empirical mode decomposition (EMD) based approach, and classical high-pass filtering. Different algorithms perform well with respect to different performance measures. Template subtraction algorithms yield the best results if the characteristics of the raw signal are of interest, while filtering algorithms like simple high-pass filtering, wavelet denoising, and EMD-based denoising show superior performance for calculating a cleaned envelope signal. No algorithm completely removes the cardiac interference, but the characteristic errors introduced by the considered algorithms differ. Hence, the choice of the algorithm to use should be made depending on the target application. Finally, we also demonstrate that our empirical SNR measure, which can be calculated without knowledge of the true, undisturbed signals, correlates strongly with the exact reconstruction error. Thus, it represents a reliable indicator for algorithm performance on real measurement data.

Automatic Removal of Cardiac Interference (ARCI): A New Approach for EEG Data.

EEG recordings are generally affected by interference from physiological and non-physiological sources which may obscure underlying brain activity and hinder effective EEG analysis. In particular, cardiac interference can be caused by the electrical activity of the heart and/or cardiovascular activity related to blood flow. Successful EEG application in sports science settings requires a method for artifact removal that is automatic and flexible enough to be applied in a variety of acquisition conditions without requiring simultaneous ECG recordings that could restrict movement. We developed an automatic method for classifying and removing both electrical cardiac and cardiovascular artifacts (ARCI) that does not require additional ECG recording. Our method employs independent component analysis (ICA) to isolate data independent components (ICs) and identifies the artifactual ICs by evaluating specific IC features in the time and frequency domains. We applied ARCI to EEG datasets with cued artifacts and acquired during an eyes-closed condition. Data were recorded using a standard EEG wet cap with either 128 or 64 electrodes and using a novel dry electrode cap with either 97 or 64 dry electrodes. All data were decomposed into different numbers of components to evaluate the effect of ICA decomposition level on effective cardiac artifact detection. ARCI performance was evaluated by comparing automatic ICs classifications with classifications performed by experienced investigators. Automatic and investigator classifications were highly consistent resulting in an overall accuracy greater than 99% in all datasets and decomposition levels, and an average sensitivity greater than 90%. Best results were attained when data were decomposed into a fewer number of components where the method achieved perfect sensitivity (100%). Performance was also evaluated by comparing automatic component classification with externally recorded ECG. Results showed that ICs automatically classified as artifactual were significantly correlated with ECG activity whereas the other ICs were not. We also assessed that the interference affecting EEG signals was reduced by more than 82% after automatic artifact removal. Overall, ARCI represents a significant step in the detection and removal of cardiac-related EEG artifacts and can be applied in a variety of acquisition settings making it ideal for sports science applications.

Assessment of Respiratory Muscle Activity with Surface Electromyographic Signals Acquired by Concentric Ring Electrodes.

The assessment of respiratory muscle activity by surface electromyography (sEMG) is a promising noninvasive technique for the diagnosis and monitoring of chronic obstructive pulmonary disease. The diaphragm is the most important muscle in breathing, although in forced inspiration other muscles, such as sternocleidomastoid, are activated and contribute to the respiratory process. The measurement of the sEMG in these muscles (sEMGdi and sEMGscm, respectively) by means of two electrodes in conventional bipolar configuration (BEs) is a common practice to evaluate the respiratory muscle activity and allows to indirectly quantify the level of muscular activation. However, the resulting signals are usually contaminated by electrocardiographic (ECG) activity, hindering the assessment of the activity of these muscles. sEMG signals can also be recorded using concentric ring electrodes (CREs). CREs have greater spatial resolution and attenuate distant bioelectrical interferences. In this scenario, the objective of this work has been to evaluate the applicability of CREs for the acquisition of sEMGdi and sEMGscm. For this purpose, both sEMG signals were recorded simultaneously with BEs and CREs in healthy subjects while performing an inspiratory load protocol. To evaluate the effect of the cardiac interference, the ratio between the mean power in inspiratory segments without ECG and the mean power in expiratory segments with ECG (Rcardio) was calculated. Additionally, the ratio between the mean power in inspiratory segments without ECG and the mean power in expiratory segments without ECG (Rinex) was also calculated. The results revealed that the Rcardio and bandwidth is greater in sEMG signals acquired with the CREs, while the Rinex is higher in the signals acquired with BEs. These results suggest that the use of CREs is a recommended alternative for the acquisition of sEMG in muscles with high cardiac interference, such as the diaphragm muscle.

A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings.

BackgroundEEG may be affected by artefacts hindering the analysis of brain signals. Data-driven methods like independent component analysis (ICA) are successful approaches to remove artefacts from the EEG. However, the ICA-based methods developed so far are often affected by limitations, such as: the need for visual inspection of the separated independent components (subjectivity problem) and, in some cases, for the independent and simultaneous recording of the inspected artefacts to identify the artefactual independent components; a potentially heavy manipulation of the EEG signals; the use of linear classification methods; the use of simulated artefacts to validate the methods; no testing in dry electrode or high-density EEG datasets; applications limited to specific conditions and electrode layouts.MethodsOur fingerprint method automatically identifies EEG ICs containing eyeblinks, eye movements, myogenic artefacts and cardiac interference by evaluating 14 temporal, spatial, spectral, and statistical features composing the IC fingerprint. Sixty-two real EEG datasets containing cued artefacts are recorded with wet and dry electrodes (128 wet and 97 dry channels). For each artefact, 10 nonlinear SVM classifiers are trained on fingerprints of expert-classified ICs. Training groups include randomly chosen wet and dry datasets decomposed in 80 ICs. The classifiers are tested on the IC-fingerprints of different datasets decomposed into 20, 50, or 80 ICs. The SVM performance is assessed in terms of accuracy, False Omission Rate (FOR), Hit Rate (HR), False Alarm Rate (FAR), and sensitivity (p). For each artefact, the quality of the artefact-free EEG reconstructed using the classification of the best SVM is assessed by visual inspection and SNR.ResultsThe best SVM classifier for each artefact type achieved average accuracy of 1 (eyeblink), 0.98 (cardiac interference), and 0.97 (eye movement and myogenic artefact). Average classification sensitivity (p) was 1 (eyeblink), 0.997 (myogenic artefact), 0.98 (eye movement), and 0.48 (cardiac interference). Average artefact reduction ranged from a maximum of 82% for eyeblinks to a minimum of 33% for cardiac interference, depending on the effectiveness of the proposed method and the amplitude of the removed artefact. The performance of the SVM classifiers did not depend on the electrode type, whereas it was better for lower decomposition levels (50 and 20 ICs).DiscussionApart from cardiac interference, SVM performance and average artefact reduction indicate that the fingerprint method has an excellent overall performance in the automatic detection of eyeblinks, eye movements and myogenic artefacts, which is comparable to that of existing methods. Being also independent from simultaneous artefact recording, electrode number, type and layout, and decomposition level, the proposed fingerprint method can have useful applications in clinical and experimental EEG settings.

Characterizing sleep spindles in 11,630 individuals from the National Sleep Research Resource

Sleep spindles are characteristic electroencephalogram (EEG) signatures of stage 2 non-rapid eye movement sleep. Implicated in sleep regulation and cognitive functioning, spindles may represent heritable biomarkers of neuropsychiatric disease. Here we characterize spindles in 11,630 individuals aged 4 to 97 years, as a prelude to future genetic studies. Spindle properties are highly reliable but exhibit distinct developmental trajectories. Across the night, we observe complex patterns of age- and frequency-dependent dynamics, including signatures of circadian modulation. We identify previously unappreciated correlates of spindle activity, including confounding by body mass index mediated by cardiac interference in the EEG. After taking account of these confounds, genetic factors significantly contribute to spindle and spectral sleep traits. Finally, we consider topographical differences and critical measurement issues. Taken together, our findings will lead to an increased understanding of the genetic architecture of sleep spindles and their relation to behavioural and health outcomes, including neuropsychiatric disorders.

0001 GENOME-WIDE ANALYSES OF SLEEP SPINDLES IN THE NATIONAL SLEEP RESEARCH RESOURCE

Sleep spindles are associated with various aspects of learning and memory and are potential biomarkers of neuropsychiatric disease. Although twin studies indicate that spindle activity is partially heritable, specific genes are yet to be identified. Here we detect and characterize spindle phenotypes in 11,630 individuals (aged 5 to 95), confirm their heritable basis and initiate genome-wide association analyses to map individual genes. We compiled whole-night polysomnography, demographic and medical data from the US National Sleep Research Resource (NSRR), applying automated artifact rejection and wavelet analyses to detect spindles from two central electrodes. Univariate heritabilities and genetic correlations were estimated using within-family intraclass correlations and variance components models for the genome-wide single nucleotide polymorphism (SNP) data. Spindle and spectral phenotypes demonstrated high test-retest reliabilities (r>0.8), based on over 4,000 individuals with repeated polysomnograms. We identified and corrected potential confounders that might impact genetic studies, including body mass index (mediated by cardiac interference in the EEG) and age. In 730 individuals from the Cleveland Family Study, spindle density was highly heritable in both white (h2 = 0.45, p = 8x10-6) and black individuals (h2 = 0.43, p = 3x10-6), adjusting for age and sex. Spindle density in stage 3 sleep (N3) had a high genetic overlap (rG = 0.89, p = 2x10-4 in whites, rG = 0.88, p = 1x10-5 in blacks) with N2 spindles. In contrast, fast (15Hz) and slow (11Hz) spindles showed significant heritabilities but no genetic overlap (rG = -0.15 and 0.16, p = 0.31 and 0.18 for whites and blacks respectively), suggesting distinct genetic architectures. These results will help in the optimal selection of independent phenotypes for ongoing genome-wide association analyses, the results of which are expected early 2017. We observed evidence for robust genetic influences on spindle phenotypes, controlling for a range of demographic and clinical covariates. This work can inform future genetic studies that aim to understand better the genetic architecture of spindles and their relation to health and disease. NIH grants MH108908 (Purcell), MH107579 (Manoach), HL114473 (Redline & Mariani), HL45369 (Redline), HL113338 (Redline) and MH48832 (Stickgold).

Time-frequency representations of the sternocleidomastoid muscle electromyographic signal recorded with concentric ring electrodes.

The use of non-invasive methods for the study of respiratory muscle signals can provide clinical information for the evaluation of the respiratory muscle function. The aim of this study was to evaluate time-frequency characteristics of the electrical activity of the sternocleidomastoid muscle recorded superficially by means of concentric ring electrodes (CREs) in a bipolar configuration. The CREs enhance the spatial resolution, attenuate interferences, as the cardiac activity, and also simplify the orientation problem associated to the electrode location. Five healthy subjects underwent a respiratory load test in which an inspiratory load was imposed during the inspiratory phase. During the test, the electromyographic signal of the sternocleidomastoid muscle (EMGsc) and the inspiratory mouth pressure (Pmouth) were acquired. Time-frequency characteristics of the EMGsc signal were analyzed by means of eight time-frequency representations (TFRs): the spectrogram (SPEC), the Morlet scalogram (SCAL), the Wigner-Ville distribution (WVD), the Choi-Williams distribution (CHWD), two generalized exponential distributions (GED1 and GED2), the Born-Jordan distribution (BJD) and the Cone-Kernel distribution (CKD). The instantaneous central frequency of the EMGsc showed an increasing behavior during the inspiratory cycle and with the increase of the inspiratory load. The bilinear TFRs (WVD, CHWD, GEDs and BJD) were less sensitive to cardiac activity interference than classical TFRs (SPEC and SCAL). The GED2 was the TFR that shown the best results for the characterization of the instantaneous central frequency of the EMGsc.

Design and validation of a periodic leg movement detector.

Periodic Limb Movements (PLMs) are episodic, involuntary movements caused by fairly specific muscle contractions that occur during sleep and can be scored during nocturnal polysomnography (NPSG). Because leg movements (LM) may be accompanied by an arousal or sleep fragmentation, a high PLM index (i.e. average number of PLMs per hour) may have an effect on an individual’s overall health and wellbeing. This study presents the design and validation of the Stanford PLM automatic detector (S-PLMAD), a robust, automated leg movement detector to score PLM. NPSG studies from adult participants of the Wisconsin Sleep Cohort (WSC, n = 1,073, 2000–2004) and successive Stanford Sleep Cohort (SSC) patients (n = 760, 1999–2007) undergoing baseline NPSG were used in the design and validation of this study. The scoring algorithm of the S-PLMAD was initially based on the 2007 American Association of Sleep Medicine clinical scoring rules. It was first tested against other published algorithms using manually scored LM in the WSC. Rules were then modified to accommodate baseline noise and electrocardiography interference and to better exclude LM adjacent to respiratory events. The S-PLMAD incorporates adaptive noise cancelling of cardiac interference and noise-floor adjustable detection thresholds, removes LM secondary to sleep disordered breathing within 5 sec of respiratory events, and is robust to transient artifacts. Furthermore, it provides PLM indices for sleep (PLMS) and wake plus periodicity index and other metrics. To validate the final S-PLMAD, experts visually scored 78 studies in normal sleepers and patients with restless legs syndrome, sleep disordered breathing, rapid eye movement sleep behavior disorder, narcolepsy-cataplexy, insomnia, and delayed sleep phase syndrome. PLM indices were highly correlated between expert, visually scored PLMS and automatic scorings (r2 = 0.94 in WSC and r2 = 0.94 in SSC). In conclusion, The S-PLMAD is a robust and high throughput PLM detector that functions well in controls and sleep disorder patients.

Brain Function Diagnosis Enhanced Using Denoised fNIRS Raw Signals

Nowadays, brain function evaluation using Functional Near Infrared Spectroscopy (fNIRS) is one of the most potential non-invasive monitoring techniques. This paper concerns usefulness of the NIRS signals denoising using the Hemodynamic Evoked Response (HomER) as graphical user interface displays the NIRS data, fast independent component analysis (FASTICA) method to reduce data dimension and the combined Wavelet & PCA method for enhancing NIRS signals. NIRS signals include many types of noise, spread across a broad spectrum of frequencies, such as: low frequency noise from respiratory interference, 0.1 - 0.3 Hz, Mayer wave, about 0.1 Hz, cardiac interference, 0.8 - 2.0 Hz, and other artifacts from head and facial motions. Meanwhile, electronic components generate high frequency noise. Multi-resolution wavelet and PCA was applied successfully to enhance the NIRS signals. It consists of adaptively modifying the wavelet coefficients based on the degree of noise contamination of the processed NIRS signal. This is done subsequently to the signal pre-processing by reducing data dimension using the FASTICA method. We demonstrate, using signal-to-noise ratio and correlation indicators, that the technique used is superior to the wavelet and moving average filter and outperforms the proposed denoising NIRS signal.

Comparison of non-invasive electrohysterographic recording techniques for monitoring uterine dynamics

Non-invasive recording of uterine myoelectric activity (electrohysterogram, EHG) could provide an alternative to monitoring uterine dynamics by systems based on tocodynamometers (TOCO). Laplacian recording of bioelectric signals has been shown to give better spatial resolution and less interference than mono- and bipolar surface recordings. The aim of this work was to study the signal quality obtained from monopolar, bipolar and Laplacian techniques in EHG recordings, as well as to assess their ability to detect uterine contractions. Twenty-two recording sessions were carried out on singleton pregnant women during the active phase of labour. In each session the following simultaneous recordings were obtained: internal uterine pressure (IUP), external tension of abdominal wall (TOCO) and EHG signals (5 monopolar and 4 bipolar recordings, 1 discrete approximation to the Laplacian of the potential and 2 estimates of the Laplacian from two active annular electrodes). The results obtained show that EHG is able to detect a higher number of uterine contractions than TOCO. Laplacian recordings give improved signal quality over monopolar and bipolar techniques, reduce maternal cardiac interference and improve the signal-to-noise ratio. The optimal position for recording EHG was found to be the uterine median axis and the lower centre-right umbilical zone.

Wild type and K897T polymorphisms of the hERG gene: modeling the APD in Caucasians.

The presented study aims to assess the possibility of simulating changes in cardiac cell electrophysiology due to K897T polymorphism in the Caucasian population. In the first part of the experiment, the parameters of the equations describing channel gating were fitted to the experimental data. Then, the action potentials of midmyocardial cells of 100 individuals were simulated in the in vitro - in vivo extrapolation system - ToxComp. Mean APD90 for the entire simulated population is 352.05 ms (SD = 21.69 ms). Mean APD90 for the 80 individuals with the WT version of the hERG gene and for the 20 K897T homo- and heterozygotes is respectively 349.08 ms (SD = 21.09 ms) and 363.95ms (SD = 20.41 ms). The ToxComp system can be useful in predicting the impact of genetic variability on drug triggered cardiac cell electrophysiology interference.

Maternal MCG Interference Cancellation Using Splined Independent Component Subtraction

Signal distortion is commonly observed when using independent component analysis (ICA) to remove maternal cardiac interference from the fetal magnetocardiogram. This can be seen even in the most conservative case where only the independent components dominated by maternal interference are subtracted from the raw signal, a procedure we refer to as independent component subtraction (ICS). Distortion occurs when the subspaces of the fetal and maternal signals have appreciable overlap. To overcome this problem, we employed splining to remove the fetal signal from the maternal source component. The maternal source components were downsampled and then interpolated to their original sampling rate using a cubic spline. A key aspect of the splining procedure is that the maternal QRS complexes are downsampled much less than the rest of the maternal signal so that they are not distorted, despite their higher bandwidth. The splined maternal source components were projected back onto the magnetic field measurement space and then subtracted from the raw signal. The method was evaluated using data from 24 subjects. We compared the results of conventional, i.e., unsplined, ICS with our method, splined ICS, using matched filtering as a reference. Correlation and subjective assessment of the P-wave and QRS complex were used to assess the performance. Using ICS, we found that the P-wave was adversely affected in 7 of 24 (29%) subjects, all having correlations less than 0.8. Splined ICS showed negligible distortion and improved the signal fidelity to some extent in all subjects. We also demonstrated that maternal T-wave interference could be problematic when the fetal and maternal heartbeats were synchronous. In these instances, splined ICS was more effective than matched filtering.