T. S. Radhakrishnan

Denoising of multichannel MCG data by the combination of EEMD and ICA and its effect on the pseudo current density maps

Abstract The signal preprocessing is prerequisite for reduction of noise and for better estimation of sources from the measured field distribution of multichannel data, since different measurement channels may be contaminated by different types of artifacts and noise. Toward this, we use a combination of independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD) to denoise the multichannel magnetocardiography (MCG) data. In this technique, MCG time series data is first subjected to ICA to obtain the statistically independent components (ICs) and subsequently the EEMD-interval threshold based denoising is applied to the ICs prior to the reconstruction of the signal. We compare the results obtained from EEMD-ICA with those obtained using the conventional ICA alone and also using the wavelet enhanced ICA (wICA). We illustrate the effect of these denoising techniques on the pseudo current density (PCD) maps, which aid in visualizing the source location. The results obtained from the EEMD-ICA are seen to be decidedly superior compared to those obtained by ICA alone and wICA methods.

Ocular artifact suppression from EEG using ensemble empirical mode decomposition with principal component analysis

A novel methodology for ocular artifact suppression in EEG data using EEMD with PCA.The proposed method eliminates the ocular artifacts from the measured EEG without using reference electrooculogram channel.The proposed method exhibits effective suppression of ocular artifact with low distortion compared to wavelet approach. Display Omitted Signals associated with eye blinks (230-350źmicro-volts) are orders of magnitude larger than electric potentials (7-20źmicro-volts) generated on the scalp because of cortical activity. These and other such non-cortical biological artifacts spread across the scalp and contaminate the Electroencephalogram (EEG). We present here a novel approach for efficient detection and effective suppression of these artifacts using single channel EEG data by combining Ensemble Empirical Mode Decomposition (EEMD) along with Principal Component Analysis (PCA). We present a methodology for ocular artifact suppression, by performing EEMD on the contaminated EEG data segment to get the intrinsic mode functions (IMFs) and subsequent elimination of artifacts by automatic selection of particular principal components, which capture ocular artifact features after using PCA on IMFs.

Baseline drift removal and denoising of MCG data using EEMD: Role of noise amplitude and the thresholding effect

We adopt the Ensemble Empirical Mode Decomposition (EEMD) method, with an appropriate thresholding on the Intrinsic Mode Functions (IMFs), to denoise the magnetocardiography (MCG) signal. To this end, we discuss the two associated problems that relate to: (i) the amplitude of noise added to the observed signal in the EEMD method with a view to prevent mode mixing and (ii) the effect of direct thresholding that causes discontinuities in the reconstructed denoised signal. We then denoise the MCG signals, having various signal-to-noise ratios, by using this method and compare the results with those obtained by the standard wavelet based denoising method. We also address the problem of eliminating the high frequency baseline drift such as the sudden and discontinuous changes in the baseline of the experimentally measured MCG signal using the EEMD based method. We show that the EEMD method used for denoising and the elimination of baseline drift is superior in performance to other standard methods such as wavelet based techniques and Independent Component Analysis (ICA).

Suppression of Eye-Blink Associated Artifact Using Single Channel EEG Data by Combining Cross-Correlation With Empirical Mode Decomposition

Eye-blink signals are the major sources of artifacts in the electroencephalogram (EEG). Conventionally, the wavelet-based approach is used for analysis and suppression of these artifacts from single channel EEG data by applying the threshold in the decomposition of the signal in terms of a set of predefined basis functions. Here, we report a novel approach for effective suppression of these artifacts by combining the data-driven technique called empirical mode decomposition (EMD) with cross-correlation. The contaminated EEG signal is decomposed into a series of intrinsic mode functions (IMFs) using EMD; in this decomposition, some of the IMFs capture features corresponding to the eye-blink signals and are termed as noisy IMFs. The artifact suppression proposed in this paper relies on the elimination of noisy IMFs based on cross-correlation with a suitable template extracted from the contaminated segment of EEG. We illustrate the method by applying it for suppressing artifacts corresponding to eye blinks during the measurement of visual evoked EEG response and compare it with conventionally used single channel wavelet technique.

Effective Extraction of Visual Event-Related Pattern by Combining Template Matching With Ensemble Empirical Mode Decomposition

In the field of signal processing, it is always a major challenge to extract event-based weak or low signal in the presence of high background noise. Conventionally, this is achieved by trigger-based averaging, which suppresses uncorrelated background noise and unmasks the event related pattern. In some of the previouspapers, extraction of weak event related pattern is also achieved by decomposing the signal into a set of predefined basis functions, such as wavelets. We present here, a novel approach by combining template matching with the ensemble empirical mode decomposition (EEMD). The EEMD technique is applied to decompose the noisy data corresponding to single-trial event related potentials into the so-called intrinsic mode functions (IMFs). These functions are of the same length and in the same time domain as the original signal. Therefore, the EEMD technique preserves varying frequency content along the time axis. The effective extraction of the event-related pattern proposed in this paper relies on the elimination of IMFs, which capture the features corresponding to artifacts and brain signals, based on cross-correlation with a suitable template extracted from the evoked potential obtained by the conventional unrestricted averaging across a large number of trials. We illustrate the method and compare it with conventionally used single channel wavelet-based approach for denoising visual evoked potentials during the measurement of visual evoked electroencephalogram response.

Establishment of 37 channel SQUID system for magnetocardiography

We report the development of a thirty seven channel SQUID based Magnetocardiography (MCG) system for the measurement of biomagnetic fields originating from the human heart. These fields are extremely weak and can be non-invasively measured only by using SQUID sensors. The system can simultaneously record biomagnetic signals at thirty seven spatial locations on the chest with a total coverage area of 300 cm2. The typical noise level of the system is measured to be about 2.5 fTrms/cm/√Hz for most gradiometer channels and around 7.3 fTrms/√Hz for magnetometer channels. The measurement of Magnetocardiogram (MCG) from human heart carried out using this system is shown.

Designing a Low-Cost, Single-Supply ECG System for Suppression of Movement Artifact from Contaminated Magnetocardiogram:

Measurement of the late potentials and His-bundle activity is crucial for many clinical studies using the noncontact and noninvasive magnetocardiography (MCG) technique; these weak signals are extracted by averaging many cardiac cycles aligned using the R-peak of the cardiac cycle identified using an electrocardiography (ECG) lead. ECG is measured simultaneously with MCG using a conventional dual-supply ECG amplifier, which requires either two separate batteries or a single battery with a switching voltage inverter circuit for its proper operation. The ECG circuitry based on two separate batteries requires a relatively large voltage supply (–18 to +18 V). The single-supply (low voltage: 0–9 V) ECG circuitry may be implemented using a switching voltage inverter; however, this mode of operation introduces switching noise in the system. The objective of the present work is to overcome these problems by carefully designing a low-voltage, single-supply ECG system, which can be used simultaneously with the MCG setup without introducing a significant level of additional noise in the MCG measurement system.

Noninvasive Determination of HV Interval Using Magnetocardiography

The His‐ventricular (HV) interval is an important index of atrioventricular conduction, but at present can be reliably measured only during an invasive electrophysiology (EP) study. Magnetocardiography (MCG) is a noninvasive measurement of weak magnetic fields generated by the heart. We compared HV interval noninvasively assessed using MCG with the corresponding values measured directly in an EP study.

Extraction of breathing pattern using temperature sensor based on Arduino board

Most of the basic functions of human body are assessed by measuring the different parameters from the body such as temperature, pulse activity and blood pressure etc. Respiration rate is the number of inhalations a person takes per minute and needs to be quantitatively assessed as it modulates other measurements such as SQUID based magnetocardiography (MCG) by bringing the chest closer to or away from the sensor array located inside a stationary liquid helium cryostat. The respiration rate is usually measured when a person is at rest and simply involves counting the number of inhalations for one minute. This paper aims at the development of a suitable methodology for the measurement of respiration rate with the help of a temperature sensor which monitors the very slight change in temperature near the nostril during inhalation & exhalation. The design and development of the proposed system is presented, along with typical experiment results.

An investigation on the influence of yogic methods on heart rate variability

The role of underlying mechanisms of yogic strategies which exert beneficiary effects on cardiac autonomic control is poorly understood. We have performed heart rate variability (HRV) analysis on subjects performing yogic methods and control subjects who mimic them through paced breathing and focused attention tasks using external cues.