Myoelectric, Myo-Oxygenation, and Myotonometry Changes during Robot-Assisted Bilateral Arm Exercises with Varying Resistances.

Robot-assisted bilateral arm training has demonstrated its effectiveness in improving motor function in individuals post-stroke, showing significant enhancements with increased repetitions. However, prolonged training sessions may lead to both mental and muscle fatigue. We conducted two types of robot-assisted bimanual wrist exercises on 16 healthy adults, separated by one week: long-duration, low-resistance workouts and short-duration, high-resistance exercises. Various measures, including surface electromyograms, near-infrared spectroscopy, heart rate, and the Borg Rating of Perceived Exertion scale, were employed to assess fatigue levels and the impacts of exercise intensity. High-resistance exercise resulted in a more pronounced decline in electromyogram median frequency and recruited a greater amount of hemoglobin, indicating increased muscle fatigue and a higher metabolic demand to cope with the intensified workload. Additionally, high-resistance exercise led to increased sympathetic activation and a greater sense of exertion. Conversely, engaging in low-resistance exercises proved beneficial for reducing post-exercise muscle stiffness and enhancing muscle elasticity. Choosing a low-resistance setting for robot-assisted wrist movements offers advantages by alleviating mental and physiological loads. The reduced training intensity can be further optimized by enabling extended exercise periods while maintaining an approximate dosage compared to high-resistance exercises.

A VLSI Chip for the Abnormal Heart Beat Detection Using Convolutional Neural Network.

The heart is one of the human body's vital organs. An electrocardiogram (ECG) provides continuous tracings of the electrophysiological activity originated from heart, thus being widely used for a variety of diagnostic purposes. This study aims to design and realize an artificial intelligence (AI)-based abnormal heart beat detection with applications for early detection and timely treatment for heart diseases. A convolutional neural network (CNN) was employed to achieve a fast and accurate identification. In order to meet the requirements of the modularity and scalability of the circuit, modular and efficient processing element (PE) units and activation function modules were designed. The proposed CNN was implemented using a TSMC 0.18 µm CMOS technology and had an operating frequency of 60 MHz with chip area of 1.42 mm2 and maximum power dissipation of 4.4 mW. Furthermore, six types of ECG signals drawn from the MIT-BIH arrhythmia database were used for performance evaluation. Results produced by the proposed hardware showed that the discrimination rate was 96.3% with high efficiency in calculation, suggesting that it may be suitable for wearable devices in healthcare.

Short-term heart rate variability as a predictor of long-term survival in patients with chronic hemodialysis: A prospective cohort study.

BACKGROUND: Heart rate variability (HRV), a non-invasive measurement of the sympathetic-vagal balance, has been demonstrated as a predictor of long-term survival in various patient populations. However, its predictive value in patients with end-stage renal disease (ESRD) has not been evaluated in a long-term follow-up study. METHODS: Prospective data collected for 41 patients with chronic hemodialysis (age 59 ± 10 years, men 51.3%, diabetes mellitus 31%, and duration of dialysis 64 ± 50 months) who underwent a 5-minute electrocardiogram (ECG) recording as a baseline for frequency domain HRV analysis. RESULTS: During a median follow-up of 150.2 months from 2003 to 2014, 15 (35.7%) patients died (3 due to cardiac causes and 12 due to non-cardiac causes). The Cox proportional hazards model suggested that the low frequency versus high frequency signal (LF/HF) of a high ratio for the HRV and diabetes mellitus were two independent predictors of mortality (hazard ratios 3.028 and 3.494; p = 0.033 and 0.022, respectively). Less reduction in MAP during dialysis showed borderline significance of long-term survival than those with larger drop (p = 0.058). CONCLUSION: A short ECG recording and an analysis of the frequency domain of the HRV is clinically predictive of the long-term survival of patients with chronic hemodialysis.

Hardware design and implementation of a wavelet de-noising procedure for medical signal preprocessing.

In this paper, a discrete wavelet transform (DWT) based de-noising with its applications into the noise reduction for medical signal preprocessing is introduced. This work focuses on the hardware realization of a real-time wavelet de-noising procedure. The proposed de-noising circuit mainly consists of three modules: a DWT, a thresholding, and an inverse DWT (IDWT) modular circuits. We also proposed a novel adaptive thresholding scheme and incorporated it into our wavelet de-noising procedure. Performance was then evaluated on both the architectural designs of the software and. In addition, the de-noising circuit was also implemented by downloading the Verilog codes to a field programmable gate array (FPGA) based platform so that its ability in noise reduction may be further validated in actual practice. Simulation experiment results produced by applying a set of simulated noise-contaminated electrocardiogram (ECG) signals into the de-noising circuit showed that the circuit could not only desirably meet the requirement of real-time processing, but also achieve satisfactory performance for noise reduction, while the sharp features of the ECG signals can be well preserved. The proposed de-noising circuit was further synthesized using the Synopsys Design Compiler with an Artisan Taiwan Semiconductor Manufacturing Company (TSMC, Hsinchu, Taiwan) 40 nm standard cell library. The integrated circuit (IC) synthesis simulation results showed that the proposed design can achieve a clock frequency of 200 MHz and the power consumption was only 17.4 mW, when operated at 200 MHz.

A real-time fatigue monitoring and analysis system for lower extremity muscles with cycling movement.

A real-time muscle fatigue monitoring system was developed to quantitatively detect the muscle fatigue of subjects during cycling movement, where a fatigue progression measure (FPM) was built-in. During the cycling movement, the electromyogram (EMG) signals of the vastus lateralis and gastrocnemius muscles in one leg as well as cycling speed are synchronously measured in a real-time fashion. In addition, the heart rate (HR) and the Borg rating of perceived exertion scale value are recorded per minute. Using the EMG signals, the electrical activity and median frequency (MF) are calculated per cycle. Moreover, the updated FPM, based on the percentage of reduced MF counts during cycling movement, is calculated to measure the onset time and the progressive process of muscle fatigue. To demonstrate the performance of our system, five young healthy subjects were recruited. Each subject was asked to maintain a fixed speed of 60 RPM, as best he/she could, under a constant load during the pedaling. When the speed reached 20 RPM or the HR reached the maximal training HR, the experiment was then terminated immediately. The experimental results show that the proposed system may provide an on-line fatigue monitoring and analysis for the lower extremity muscles during cycling movement.

Event-related brain potentials reveal enhancing and compensatory mechanisms during dual neurocognitive and cycling tasks.

BACKGROUND: Various neurocognitive tests have shown that cycling enhances cognitive performance compared to resting. Event-related potentials (ERPs) elicited by an oddball or flanker task have clarified the impact of dual-task cycling on perception and attention. In this study, we investigate the effect of cycling on cognitive recruitment during tasks that involve not only stimulus identification but also semantic processing and memory retention. METHODS: We recruited 24 healthy young adults (12 males, 12 females; mean age = 22.71, SD = 1.97 years) to perform three neurocognitive tasks (namely color-word matching, arithmetic calculation, and spatial working memory) at rest and while cycling, employing a within-subject design with rest/cycling counterbalancing. RESULTS: The reaction time on the spatial working memory task was faster while cycling than at rest at a level approaching statistical significance. The commission error percentage on the color-word matching task was significantly lower at rest than while cycling. Dual-task cycling while responding to neurocognitive tests elicited the following results: (a) a greater ERP P1 amplitude, delayed P3a latency, less negative N4, and less positivity in the late slow wave (LSW) during color-word matching; (b) a greater P1 amplitude during memory encoding and smaller posterior negativity during memory retention on the spatial working memory task; and (c) a smaller P3 amplitude, followed by a more negative N4 and less LSW positivity during arithmetic calculation. CONCLUSION: The encoding of color-word and spatial information while cycling may have resulted in compensatory visual processing and attention allocation to cope with the additional cycling task load. The dual-task cycling and cognitive performance reduced the demands of semantic processing for color-word matching and the cognitive load associated with temporarily suspending spatial information. While dual-tasking may have required enhanced semantic processing to initiate mental arithmetic, a compensatory decrement was noted during arithmetic calculation. These significant neurocognitive findings demonstrate the effect of cycling on semantic-demand and memory retention-demand tasks.

Resistance-induced brain activity changes during cycle ergometer exercises.

BACKGROUND: EEGs are frequently employed to measure cerebral activations during physical exercise or in response to specific physical tasks. However, few studies have attempted to understand how exercise-state brain activity is modulated by exercise intensity. METHODS: Ten healthy subjects were recruited for sustained cycle ergometer exercises at low and high resistance, performed on two separate days a week apart. Exercise-state EEG spectral power and phase-locking values (PLV) are analyzed to assess brain activity modulated by exercise intensity. RESULTS: The high-resistance exercise produced significant changes in beta-band PLV from early to late pedal stages for electrode pairs F3-Cz, P3-Pz, and P3-P4, and in alpha-band PLV for P3-P4, as well as the significant change rate in alpha-band power for electrodes C3 and P3. On the contrary, the evidence for changes in brain activity during the low-resistance exercise was not found. CONCLUSION: These results show that the cortical activation and cortico-cortical coupling are enhanced to take on more workload, maintaining high-resistance pedaling at the required speed, during the late stage of the exercise period.

A VLSI Implementation of Independent Component Analysis for Biomedical Signal Separation Using CORDIC Engine.

This study aims to design and implement a very large scale integration (VLSI) chip of the extend InfoMax independent component analysis (ICA) algorithm which can separate the super-Gaussian source signals. In order to substantially reduce the circuit area, the proposed circuit utilizes the time sharing matrix multiplication array (MMA) to realize a series of matrix multiplication operations and employs the coordinate rotation digital computer (CORDIC) algorithm to calculate the hyperbolic functions sinh(θ) and cosh(θ) with the rotation of the hyperbolic coordinate system. Also, the rotation of the linear coordinate system of the CORDIC is adopted for the design of a divider used for obtaining the required function value of tanh(θ) simply by evaluating sinh(θ)/cosh(θ). Implemented in a TSMC 90-nm CMOS technology, the proposed ICA has an operation frequency of 100 MHz with 90.8 K gate counts. Furthermore, the measurement results show the ICA core can be successfully applied to separating mixed medical signals into independent sources.

Wavelet-based ECG compression by bit-field preserving and running length encoding.

Efficient electrocardiogram (ECG) compression can reduce the payload of real-time ECG transmission as well as reduce the amount of data storage in long-term ECG recording. In this paper an ECG compression/decompression architecture based on the bit-field preserving (BFP) and running length encoding (RLE)/decoding schemes incorporated with the discrete wavelet transform (DWT) is proposed. Compared to complex and repetitive manipulations in the set partitioning in hierarchical tree (SPIHT) coding and the vector quantization (VQ), the proposed algorithm has advantages of simple manipulations and a feedforward structure that would be suitable to implement on very-large-scale integrated circuits and general microcontrollers.

A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising.

In this paper, a simple moving average-based computing method for real-time QRS detection is proposed. In addition, for signal preprocessing our detection algorithm also incorporates a wavelet-based denoising procedure to effectively reduce the noise level for electrocardiogram (ECG) data. The overall computational structure of the proposed algorithm allows the QRS detection to be performed and implemented in real-time with high time- and memory-efficiency. Algorithm performance was evaluated against the MIT-BIH Arrhythmia Database. The numerical results indicated that the novel algorithm finally achieved about 99.5% of the detection rate for the standard database, and also, it could function reliably even under the condition of poor signal quality in the measured ECG data.

A quantitative cell modeling and wound-healing analysis based on the Electric Cell-substrate Impedance Sensing (ECIS) method.

In this paper, a quantitative modeling and wound-healing analysis of fibroblast and human keratinocyte cells is presented. Our study was conducted using a continuous cellular impedance monitoring technique, dubbed Electric Cell-substrate Impedance Sensing (ECIS). In fact, we have constructed a mathematical model for quantitatively analyzing the cultured cell growth using the time series data directly derived by ECIS in a previous work. In this study, the applicability of our model into the keratinocyte cell growth modeling analysis was assessed first. In addition, an electrical "wound-healing" assay was used as a means to evaluate the healing process of keratinocyte cells at a variety of pressures. Two innovative and new-defined indicators, dubbed cell power and cell electroactivity, respectively, were developed for quantitatively characterizing the biophysical behavior of cells. We then employed the wavelet transform method to perform a multi-scale analysis so the cell power and cell electroactivity across multiple observational time scales may be captured. Numerical results indicated that our model can well fit the data measured from the keratinocyte cell culture for cell growth modeling analysis. Also, the results produced by our quantitative analysis showed that the wound healing process was the fastest at the negative pressure of 125mmHg, which consistently agreed with the qualitative analysis results reported in previous works.

Cell proliferation on PVA/sodium alginate and PVA/poly(γ-glutamic acid) electrospun fiber.

To overcome the obstacles of easy dissolution of PVA nanofibers without crosslinking treatment and the poor electrospinnability of the PVA cross-linked nanofibers via electrospinning process, the PVA based electrospun hydrogel nanofibers are prepared with post-crosslinking method. To expect the electrospun hydrogel fibers might be a promising scaffold for cell culture and tissue engineering applications, the evaluation of cell proliferation on the post-crosslinking electrospun fibers is conducted in this study. At beginning, poly(vinyl alcohol) (PVA), PVA/sodium alginate (PVASA) and PVA/poly(γ-glutamic acid) (PVAPGA) electrospun fibers were prepared by electrospinning method. The electrospun PVA, PVASA and PVAPGA nanofibers were treated with post-cross-linking method with glutaraldehyde (Glu) as crosslinking agent. These electrospun fibers were characterized with thermogravimetry analysis (TGA) and their morphologies were observed with a scanning electron microscope (SEM). To support the evaluation and explanation of cell growth on the fiber, the study of 3T3 mouse fibroblast cell growth on the surface of pure PVA, SA, and PGA thin films is conducted. The proliferation of 3T3 on the electrospun fiber surface of PVA, PVASA, and PVAPGA was evaluated by seeding 3T3 fibroblast cells on these crosslinked electrospun fibers. The cell viability on electrospun fibers was conducted with water-soluble tetrazolium salt-1 assay (Cell Proliferation Reagent WST-1). The morphology of the cells on the fibers was also observed with SEM. The results of WST-1 assay revealed that 3T3 cells cultured on different electrospun fibers had similar viability, and the cell viability increased with time for all electrospun fibers. From the morphology of the cells on electrospun fibers, it is found that 3T3 cells attached on all electrospun fiber after 1day seeded. Cell-cell communication was noticed on day 3 for all electrospun fibers. Extracellular matrix (ECM) productions were found and cell-ECM adhesion was shown on day 7. The cell number was also increased on all of the crosslinked electrospun fibers. It seems that the PVA based electrospun hydrogel nanofibers prepared with post-crosslinking method can be used as scaffold for tissue engineering.

A Cycling Movement Based System for Real-Time Muscle Fatigue and Cardiac Stress Monitoring and Analysis.

In this study, we defined a new parameter, referred to as the cardiac stress index (CSI), using a nonlinear detrended fluctuation analysis (DFA) of heart rate (HR). Our study aimed to incorporate the CSI into a cycling based fatigue monitoring system developed in our previous work so the muscle fatigue and cardiac stress can be both continuously and quantitatively assessed for subjects undergoing the cycling exercise. By collecting electrocardiogram (ECG) signals, the DFA scaling exponent α was evaluated on the RR time series extracted from a windowed ECG segment. We then obtained the running estimate of α by shifting a one-minute window by a step of 20 seconds so the CSI, defined as the percentage of all the less-than-one α values, can be synchronously updated every 20 seconds. Since the rating of perceived exertion (RPE) scale is considered as a convenient index which is commonly used to monitor subjective perceived exercise intensity, we then related the Borg RPE scale value to the CSI in order to investigate and quantitatively characterize the relationship between exercise-induced fatigue and cardiac stress. Twenty-two young healthy participants were recruited in our study. Each participant was asked to maintain a fixed pedaling speed at a constant load during the cycling exercise. Experimental results showed that a decrease in DFA scaling exponent α or an increase in CSI was observed during the exercise. In addition, the Borg RPE scale and CSI were positively correlated, suggesting that the factors due to cardiac stress might also contribute to fatigue state during physical exercise. Since the CSI can effectively quantify the cardiac stress status during physical exercise, our system may be used in sports medicine, or used by cardiologists who carried out stress tests for monitoring heart condition in patients with heart diseases.

Combined heart rate variability and dynamic measures for quantitatively characterizing the cardiac stress status during cycling exercise.

In this study, we aimed to seek for different ways of measuring cardiac stress in terms of heart rate variability (HRV) and heart rate (HR) dynamics, and to develop a novel index that can effectively summarize the information reflected by these measures to continuously and quantitatively characterize the cardiac stress status during physical exercise. Standard deviation, spectral measure of HRV as well as a nonlinear detrended fluctuation analysis (DFA) based fractal-like behavior measure of HR dynamics were all evaluated on the RR time series derived from windowed electrocardiogram (ECG) data for the subjects undergoing cycling exercise. We recruited eleven young healthy subjects in our tests. Each subject was asked to maintain a fixed speed under a constant load during the pedaling test. We obtained the running estimates of the standard deviation of the normal-to-normal interval (SDNN), the high-fidelity power spectral density (PSD) of HRV, and the DFA scaling exponent α, respectively. A trend analysis and a multivariate linear regression analysis of these measures were then performed. Numerical experimental results produced by our analyses showed that a decrease in both SDNN and α was seen during the cycling exercise, while there was no significant correlation between the standard lower frequency to higher frequency (LF-to-HF) spectral power ratio of HRV and the exercise intensity. In addition, while the SDNN and α were both negatively correlated with the Borg rating of perceived exertion (RPE) scale value, it seemed that the LF-to-HF power ratio might not have substantial impact on the Borg value, suggesting that the SDNN and α may be further used as features to detect the cardiac stress status during the physical exercise. We further approached this detection problem by applying a linear discriminant analysis (LDA) to both feature candidates for the task of cardiac stress stratification. As a result, a time-varying parameter, referred to as the cardiac stress measure (CSM), is developed for quantitatively on-line measuring and stratifying cardiac stress status.

A wavelet-based heart rate variability analysis for the study of nonsustained ventricular tachycardia.

It has been reported that the sympathovagal balance (SB) can be quantified by heart rate (HR) via the low-frequency (LF) to high-frequency (HF) spectral power ratio LF/HF. In this paper, an investigation of the relationship between the autonomic nervous system (ANS) and non-sustained ventricular tachycardia (NSVT) is presented. A wavelet transform (WT)-based approach for short-time heart rate variability (HRV) assessments is proposed for this aspect of analysis. The study was conducted on an RR-interval database consisting of 87 NSVT, 61 ischemic and five normal episodes. First, instantaneous SB estimates were generated by the proposed method. Then, waveforms of the WT-based SB evolutions were quantitatively examined. Numerical results showed that while a majority of SB waveforms (about 71%) derived from the non-NSVT population (i.e., ischemic and normal) appeared to come near oscillating with certain fixed levels, approximate 75% of SB evolutions underwent significantly rapid increases prior to the onset of NSVT, suggesting that an abrupt sympathovagal imbalance might partly account for the occurrence of NSVT.

Compressed sensing technology-based spectral estimation of heart rate variability using the integral pulse frequency modulation model.

In this paper, a compressed sensing (CS)-based spectral estimation of heart rate variability (HRV) using the integral pulse frequency modulation (IPFM) model is introduced. Previous research in the literature indicated that the IPFM model is widely accepted as a functional description of the cardiac pacemaker, and thus, very useful in modeling the mechanism by which the autonomic nervous system modulates the heart rate (HR). On the other hand, recently CS becomes an emerging technology that has attracted great attention since it is capable of acquiring and reconstructing signals that are considered sparse or compressible, even when the number of measurements is small. Using the IPFM model, we here present a CS-based algorithm for deriving the amplitude spectrum of the modulating signal for HRV assessments. In fact, the application of the CS method into HRV spectral estimation is unprecedented. Numerical results produced by a real RR database of PhysioNet demonstrated that the proposed approach can robustly provide high-fidelity HRV spectral estimates, even under the situation of a degree of incompleteness in the RR data caused by ectopic or missing beats.

A reweighted ℓ1-minimization based compressed sensing for the spectral estimation of heart rate variability using the unevenly sampled data.

In this paper, a reweighted ℓ1-minimization based Compressed Sensing (CS) algorithm incorporating the Integral Pulse Frequency Modulation (IPFM) model for spectral estimation of HRV is introduced. Knowing as a novel sensing/sampling paradigm, the theory of CS asserts certain signals that are considered sparse or compressible can be possibly reconstructed from substantially fewer measurements than those required by traditional methods. Our study aims to employ a novel reweighted ℓ1-minimization CS method for deriving the spectrum of the modulating signal of IPFM model from incomplete RR measurements for HRV assessments. To evaluate the performance of HRV spectral estimation, a quantitative measure, referred to as the Percent Error Power (PEP) that measures the percentage of difference between the true spectrum and the spectrum derived from the incomplete RR dataset, was used. We studied the performance of spectral reconstruction from incomplete simulated and real HRV signals by experimentally truncating a number of RR data accordingly in the top portion, in the bottom portion, and in a random order from the original RR column vector. As a result, for up to 20% data truncation/loss the proposed reweighted ℓ1-minimization CS method produced, on average, 2.34%, 2.27%, and 4.55% PEP in the top, bottom, and random data-truncation cases, respectively, on Autoregressive (AR) model derived simulated HRV signals. Similarly, for up to 20% data loss the proposed method produced 5.15%, 4.33%, and 0.39% PEP in the top, bottom, and random data-truncation cases, respectively, on a real HRV database drawn from PhysioNet. Moreover, results generated by a number of intensive numerical experiments all indicated that the reweighted ℓ1-minimization CS method always achieved the most accurate and high-fidelity HRV spectral estimates in every aspect, compared with the ℓ1-minimization based method and Lomb's method used for estimating the spectrum of HRV from unevenly sampled RR data.

Compressed sensing for integral pulse frequency modulation (IPFM)-based heart rate variability spectral estimation.

In this paper, a Compressed Sensing (CS) based spectral analysis of Heart Rate Variability (HRV) using the Integral Pulse Frequency Modulation (IPFM) model is introduced. Previous research in literature indicated that the IPFM model is considered as a functional description of the cardiac pacemaker and thus is very useful in modeling the mechanism by which the Autonomic Nervous System (ANS) modulates the Heart Rate (HR). On the other hand, in recent years CS has attracted great attention over many aspects of signal processing applications. According to the IPFM model, we here present a CS-based algorithm for deriving the amplitude spectrum of the modulating signal for HRV assessments. In fact, the application of the CS method into HRV spectral estimation is novel and unprecedented in HRV analysis. Numerical experimental results demonstrated that the proposed approach can robustly yield accurate HRV spectral estimates, even under the situation of a degree of incompleteness in the interbeat interval or RR data caused by ectopic or missing beats.

A computational modeling and analysis in cell biological dynamics using electric cell-substrate impedance sensing (ECIS).

In this paper, a study of computational modeling and multi-scale analysis in cell dynamics is presented. Our study aims at: (1) deriving and validating a mathematical model for cell growth, and (2) quantitatively detecting and analyzing the biological interdependencies across multiple observational scales with a variety of time and frequency resolutions. This research was conducted using the time series data practically measured from a novel on-line cell monitoring technique, referred to as electric cell-substrate impedance sensing (ECIS), which allows continuously tracking the cellular behavior such as adhesion, proliferation, spreading and micromotion. First, comparing our ECIS-based cellular growth modeling analysis results with those determined by hematocytometer measurement using different time intervals, we found that the results obtained from both experimental methods consistently agreed. However, our study demonstrated that it is much easier and more convenient to operate with the ECIS system for on-line cellular growth monitoring. Secondly, for multi-scale analysis our results showed that the proposed wavelet-based methodology can effectively quantify the fluctuations associated with cell micromotions and quantitatively capture the biological interdependencies across multiple observational scales. Note that although the wavelet method is well known, its application into the ECIS time series analysis is novel and unprecedented in computational cell biology. Our analyses indicated that the proposed study on ECIS time series could provide a hopeful start and great potentials in both modeling and elucidating the complex mechanisms of cell biological systems.