A novel application of theS-transform in removing powerline interference from biomedical signals

Comparison of different shielding methods in acquisition of physiological signals

Power line interference in the surrounding environment could usually introduce many difficulties when collecting and analyzing physiological signals. Since power line interference is usually several orders of amplitude larger than the physiological electrical signals, methods of suppressing power line interference should be considered during the signal acquisition. Many studies used a hardware or software band-stop filter to suppress power line interference but it could easily cause attenuations and distortions to the signal of interest. In this study, two kinds of methods that used different signals to drive the shields of the electrodes were proposed to reduce the impacts of power line interference. Three channels of two physiological signals (ECG and EMG) were simultaneously collected when the electrodes were not shielded (No-Shield), shielded by ground signals (GND-Shield) and shielded by buffered signals of the corresponding electrodes (Active-Shield), respectively, on a custom hardware platform based on TI ADS1299. The results showed that power line interference would be significantly suppressed when using shielding approaches, and the Active-Shield method could achieve the best performance with a power line interference reduction up to 36dB. The study suggested that the Active-Shield method at the analog front-end was a great candidate to reduce power line interference in routine acquisitions of physiological signals.

Efficient implementation of Stockwell Transform for real-time embedded processing of physiologic signals

Physiologic monitoring enables scientists and physicians to study both normal and pathologic signals of the body. While wearable technologies are available today, many of these technologies are limited to data collection only. Embedded processors have minimal computational capabilities. We propose an efficient implementation of the Stockwell Transform which can enable real-time time-frequency analysis of biological signals in a microcontroller. The method is built upon the fact that the Stockwell Transform can be implemented as a compact filter bank with pre-computed filter taps. Additionally, due to the long tails of the gaussian windowing function, low amplitude filter taps can be removed. The method was implemented on a TI MSP430 processor. Simulated ECG data was fed into the processor to demonstrate performance and evaluate computational efficiency.

A stacked contractive denoising auto-encoder for ECG signal denoising

As a primary diagnostic tool for cardiac diseases, electrocardiogram (ECG) signals are often contaminated by various kinds of noise, such as baseline wander, electrode contact noise and motion artifacts. In this paper, we propose a contractive denoising technique to improve the performance of current denoising auto-encoders (DAEs) for ECG signal denoising. Based on the Frobenius norm of the Jacobean matrix for the learned features with respect to the input, we develop a stacked contractive denoising auto-encoder (CDAE) to build a deep neural network (DNN) for noise reduction, which can significantly improve the expression of ECG signals through multi-level feature extraction. The proposed method is evaluated on ECG signals from the bench-marker MIT-BIH Arrhythmia Database, and the noises come from the MIT-BIH noise stress test database. The experimental results show that the new CDAE algorithm performs better than the conventional ECG denoising method, specifically with more than 2.40 dB improvement in the signal-to-noise ratio (SNR) and nearly 0.075 to 0.350 improvements in the root mean square error (RMSE).

The relationship between sympathetic nervous activity and cerebral hemodynamics and oxygenation: a study using skin conductance measurement and functional near-infrared spectroscopy

Simultaneous measurement of cortical and peripheral affective processing is relevant in many neuroscientific research fields. The aim was to investigate the influence of different affective task components on the coherence between cortical hemodynamic signals and peripheral autonomic skin potential signals. Seventeen healthy subjects performed four tasks, i.e. a finger-tapping task, a hyperventilation task, a working memory task and a risk-taking task. Cortical hemodynamic responses were measured using functional near-infrared spectroscopy (fNIRS). Peripheral skin conductance responses (SCRs) were assessed using electrodermal activity (EDA). Coherence between the fNIRS and the EDA time series was calculated using the S transform coherence (STC), a method that tests the temporal dynamics between two time series for consistent phase relationships and thus for a functional relationship. The following characteristics of fNIRS-EDA coherence were observed: (1) Simple motor performance was not a contributor to enhanced coherence, as revealed by the finger-tapping task. (2) Changes in respiration rate and/or heart rate acted as relevant contributors to enhanced coherence, as revealed by the hyperventilation task. (3) Working memory performance did not induce changes in coherence, (4) whereas risk-taking behavior was a significant contributor to enhanced coherence. (5) Based on all four tasks, we also observed that coherence may be subject to habituation or sensitization effects over the trial-to-trial course of a task. Increased fNIRS-EDA coherence may be an indicator of a psychophysiological link between the underlying cortical and peripheral affective systems. Our findings are relevant for several neuroscientific research areas seeking to evaluate the interplay between cortical and peripheral affective performance.