Continuous monitoring of electrode-skin impedance mismatch during bioelectric recordings

In bioelectric recordings, an electrode-skin impedance mismatch leads to a reduced common-mode rejection ratio (CMRR) of the amplifier. For this reason, the impedance of each individual electrode-skin contact is usually measured prior to a recording. The measurement circuit itself degrades the CMRR and is switched off during the bioelectric recording. In this paper, we present a new method that allows to monitor the electrode-skin impedance in a continuous way without reducing the CMRR of the amplifier. The new method is based on an additional common-mode signal that is superimposed on the bioelectric signal

Characterizing the Noise Associated with Sensor Placement and Motion Artifacts and Overcoming its Effects for Body-worn Physiological Sensors

Wearable sensors for continuous physiological monitoring have the potential to change the paradigm for healthcare by providing information in scenarios not covered by the existing clinical model. One key challenge for wearable physiological sensors is that their signal-to-noise ratios are low compared to those of their medical grade counterparts in hospitals. Two primary sources of noise are the sensor-skin contact interface and motion artifacts due to the user’s daily activities. These are challenging problems because the initial sensor placement by the user may not be ideal, the skin conditions can change over time, and the nature of motion artifacts is not predictable. The objective of this research is twofold. The first is to design sensors with reconfigurable contact to mitigate the effects of misplaced sensors or changing skin conditions. The second is to leverage signal processing techniques for accurate physiological parameter estimation despite the presence of motion artifacts. In this research, the sensor contact problem was specifically addressed for dry-contact electroencephalography (EEG). The proposed novel extension to a popular existing EEG electrode design enabled reconfigurable contact to adjust to variations in sensor placement and skin conditions over time. Experimental results on human subjects showed that reconfiguration of contact can reduce the noise in collected EEG signals without the need for manual intervention. To address the motion artifact problem, a particle filter based approach was employed to track the heart rate in cardiac signals affected by the movements of the user. The algorithm was tested on cardiac signals from human subjects running on a treadmill and showed good performance in accurately tracking heart rate. Moreover, the proposed algorithm enables fusion of multiple modalities and is also computationally more efficient compared to other contemporary approaches

Functionally Adaptive Myosite Selection using conformable HD sEMG electrodes for movement-pattern classification

Myoelectric prosthesis systems currently use advanced control schemes such as pattern recognition to classify muscle activation signals as intended movement classes. For this classification, generally, untargeted, equally spaced electrodes placed circumferentially around the muscle belly of the forearm, are used for acquisition of surface electromyogram (sEMG) for tran-radial amputee subjects. We propose a novel system, consisting of a hardware and software component. We built the hardware component in the form of a flexible and conformable high-density sEMG array. We tested the signal quality and electrode-skin contact characteristics to demonstrate the quality and conformability of the electrode array. We built the software component of the system based on separability criteria. This proposed system is called functionally adaptive myoelectrode site (myosite) selection (FAMS) and is to identify optimal myosites for pattern recognition. Our study investigates the effects of optimal myosite selection with increase in the number of movement classes and inclusion of fine motor movements. We also used myosite selection from current clinical and research procedures and compared the performances of FAMS to existing systems. Results of our study indicate that using optimal myosites selected using FAMS for movement pattern classification improves performance and this becomes more evident with increase in the number of selected myosites. The significance of using optimal myosites increases when more movement classes are included. This work also shows that the optimal myosites change spatially with the type and number of movement classes included for classification. We then explored other future applications of 1) FAMS in temporal adaptations to help prosthetic users begin early use of pattern recognition based prosthesis system and 2) extending FAMS to site selection for direct control so as to make FAMS a universal electrode interface for myoelectric prosthesis. Preliminary study results in these areas are presented in this work. The electrode design was further improved to fit inside of a prosthesis. This system has the capabilities to become an off-the-shelf universal system that can be prescribed for any myoelectric prosthesis user irrespective of their level of amputation and experience with using a myoelectric prosthesis. This system can reduce pre-prosthetic training time and facilitate early fitting. This system also removes the need for refitting every time the user changes the movement classes controlled by the prosthesis

Low Power Circuits for Smart Flexible ECG Sensors

Cardiovascular diseases (CVDs) are the world leading cause of death. In-home heart condition monitoring effectively reduced the CVD patient hospitalization rate. Flexible electrocardiogram (ECG) sensor provides an affordable, convenient and comfortable in-home monitoring solution. The three critical building blocks of the ECG sensor i.e., analog frontend (AFE), QRS detector, and cardiac arrhythmia classifier (CAC), are studied in this research. A fully differential difference amplifier (FDDA) based AFE that employs DC-coupled input stage increases the input impedance and improves CMRR. A parasitic capacitor reuse technique is proposed to improve the noise/area efficiency and CMRR. An on-body DC bias scheme is introduced to deal with the input DC offset. Implemented in 0.35m CMOS process with an area of 0.405mm2, the proposed AFE consumes 0.9W at 1.8V and shows excellent noise effective factor of 2.55, and CMRR of 76dB. Experiment shows the proposed AFE not only picks up clean ECG signal with electrodes placed as close as 2cm under both resting and walking conditions, but also obtains the distinct -wave after eye blink from EEG recording. A personalized QRS detection algorithm is proposed to achieve an average positive prediction rate of 99.39% and sensitivity rate of 99.21%. The user-specific template avoids the complicate models and parameters used in existing algorithms while covers most situations for practical applications. The detection is based on the comparison of the correlation coefficient of the user-specific template with the ECG segment under detection. The proposed one-target clustering reduced the required loops. A continuous-in-time discrete-in-amplitude (CTDA) artificial neural network (ANN) based CAC is proposed for the smart ECG sensor. The proposed CAC achieves over 98% classification accuracy for 4 types of beats defined by AAMI (Association for the Advancement of Medical Instrumentation). The CTDA scheme significantly reduces the input sample numbers and simplifies the sample representation to one bit. Thus, the number of arithmetic operations and the ANN structure are greatly simplified. The proposed CAC is verified by FPGA and implemented in 0.18m CMOS process. Simulation results show it can operate at clock frequencies from 10KHz to 50MHz. Average power for the patient with 75bpm heart rate is 13.34W