Cutaneous measurements of electrogastrogram (EGG) signals are heavily contaminated by artifacts due to cardiac activity, breathing, motion artifacts, and electrode drifts whose effective elimination remains an open problem. A common methodology is proposed by combining independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD) to denoise gastric slow-wave signals in multichannel EGG data. Sixteen electrodes are fixed over the upper abdomen to measure the EGG signals under three gastric conditions, namely, preprandial, postprandial immediately, and postprandial 2 h after food for three healthy subjects and a subject with a gastric disorder. Instantaneous frequencies of intrinsic mode functions that are obtained by applying the EEMD technique are analyzed to individually identify and remove each of the artifacts. A critical investigation on the proposed ICA-EEMD method reveals its ability to provide a higher attenuation of artifacts and lower distortion than those obtained by the ICA-EMD method and conventional techniques, like bandpass and adaptive filtering. Characteristic changes in the slow-wave frequencies across the three gastric conditions could be determined from the denoised signals for all the cases. The results therefore encourage the use of the EEMD-based technique for denoising gastric signals to be used in clinical practice.

Contact heat evoked potentials (CHEPs) are recorded from the brain by giving thermal stimulations through heating pads kept on the surface of the skin. CHEP signals have crucial diagnostic implications in human pain activation studies. This work proposes a novel design of a digital proportional integral (PI) controller based on Arduino microcontroller with a view to explore the suitability of an electric heating pad for use as a thermode in a custom-made, cost-effective CHEP stimulator. The purpose of PI controller is to set, regulate, and deliver desired temperatures on the surface of the heating pad in a user-defined pattern. The transfer function of the heating system has been deduced using the parametric system identification method, and the design parameters of the controller have been identified using the root locus technique. The efficiency of the proposed PI controller in circumventing the well-known integrator windup problem (error in the integral term builds excessively, leading to large transients in the controller output) in tracking the reference input and the controller effort (CE) in rejecting output disturbances to maintain the set temperature of the heating pad have been found to be superior compared with the conventional PI controller and two of the existing anti-windup models.

Magnetocardiography (MCG) measures weak magnetic fields originating due to the electrical activity of the heart. MCG offers distinct diagnostic information on the cardiac electrophysiology in a variety of dysfunctions. This list includes myocardial ischemia, which is associated with reduced blood supply to the heart, electrically manifested as changes in the ST segment of the cardiac cycle. As opposed to the conventional measurement of electrocardiogram (ECG) on subjects undergoing physical stress test to investigate these ST changes, rest MCG itself has been demonstrated to be more sensitive. Considerable interest exists among researchers to investigate MCG signals measured during physical exertion as well to explore the possibilities of improvements in its sensitivity. This paper portrays the MCG measurements of a few subjects under rest and during moderate cycling in supine posture using a non-magnetic bicycle ergometer. The work details the signal processing steps followed in processing MCG to refine the signal quality in computing the parameter, ST fluctuation score in an automated manner. Significant changes are seen on the ST fluctuation scores measured on a few healthy subjects across rest and stress conditions. These results persuade its possible use on MCG measured on subjects with ischemic heart diseases by treating this analysis as baseline measurements.