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A bio-potential recorder working under 0.8 V supply voltage with a tunable low-pass filter is proposed in this paper. The prototype is implemented in TSMC 180 nm CMOS technology, featuring a power consumption of 2.27  μW, while preserving a high tolerance of power-line interference (PLI) up to 600 m V pp , a common-mode rejection ratio (CMRR) of higher than 100 dB, a THD of -65.5 dB, and a noise density of 50 nV/ √{Hz} by employing four new techniques, including 1) low noise chopper modulator, 2) feedback loop based common-mode cancellation loop (CMCL), 3) offset cancellation loop (OCL) with PMOS backgate control scheme, and 4) a very-lower transconductance (VLT) operational transconductance amplifier (OTA) using in the DC-servo-loop (DSL). The measured mid-band gain is 43.3 dB with a high-pass cut-off frequency of 1.2 Hz. The low-pass cut-off frequency can be configured from 650 Hz to 7.5 kHz. The measured input-referred integrated noise is 1.2 uVrms in the frequency band of 1-650 Hz and 4.1 uVrms in the 1 Hz-7.5 kHz frequency band, respectively, leading to a power efficiency factor (PEF) of 7.49 and 7.59.

This paper presents a 10-bit successive approximation analog-to-digital converter (ADC) that operates at an ultralow voltage of 0.3 V and can be applied to biomedical implants. The study proposes several techniques to improve the ADC performance. A pipeline comparator was utilized to maintain the advantages of dynamic comparators and reduce the kickback noise. Weight biasing calibration was used to correct the offset voltage without degrading the operating speed of the comparator. The incorporation of a unity-gain buffer improved the bootstrap switch leakage problem during the hold period and reduced the effect of parasitic capacitances on the digital-to-analog converter. The chip was fabricated using 90-nm CMOS technology. The data measured at a supply voltage of 0.3 V and sampling rate of 3 MSps for differential nonlinearity and integral nonlinearity were +0.83/-0.54 and +0.84/-0.89, respectively, and the signal-to-noise plus distortion ratio and effective number of bits were 56.42 dB and 9.08 b, respectively. The measured total power consumption was 6.6 μW at a figure of merit of 4.065 fJ/conv.-step.

This paper presents an adaptable dictionary-based feature extraction approach for spike sorting offering high accuracy and low computational complexity for implantable applications. It extracts and learns identifiable features from evolving subspaces through matched unsupervised subspace filtering. To provide compatibility with the strict constraints in implantable devices such as the chip area and power budget, the dictionary contains arrays of {-1, 0 and 1} and the algorithm need only process addition and subtraction operations. Three types of such dictionary were considered. To quantify and compare the performance of the resulting three feature extractors with existing systems, a neural signal simulator based on several different libraries was developed. For noise levels σ N between 0.05 and 0.3 and groups of 3 to 6 clusters, all three feature extractors provide robust high performance with average classification errors of less than 8% over five iterations, each consisting of 100 generated data segments. To our knowledge, the proposed adaptive feature extractors are the first able to classify reliably 6 clusters for implantable applications. An ASIC implementation of the best performing dictionary-based feature extractor was synthesized in a 65-nm CMOS process. It occupies an area of 0.09 mm 2 and dissipates up to about 10.48 μW from a 1 V supply voltage, when operating with 8-bit resolution at 30 kHz operating frequency.

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.

Neural recording systems are a central component of Brain-Machince Interfaces (BMIs). In most of these systems the emphasis is on faithful reproduction and transmission of the recorded signal to remote systems for further processing or data analysis. Here we follow an alternative approach: we propose a neural recording system that can be directly interfaced locally to neuromorphic spiking neural processing circuits for compressing the large amounts of data recorded, carrying out signal processing and neural computation to extract relevant information, and transmitting only the low-bandwidth outcome of the processing to remote computing or actuating modules. The fabricated system includes a low-noise amplifier, a delta-modulator analog-to-digital converter, and a low-power band-pass filter. The bio-amplifier has a programmable gain of 45-54 dB, with a Root Mean Squared (RMS) input-referred noise level of 2.1 μV, and consumes 90 μW . The band-pass filter and delta-modulator circuits include asynchronous handshaking interface logic compatible with event-based communication protocols. We describe the properties of the neural recording circuits, validating them with experimental measurements, and present system-level application examples, by interfacing these circuits to a reconfigurable neuromorphic processor comprising an array of spiking neurons with plastic and dynamic synapses. The pool of neurons within the neuromorphic processor was configured to implement a recurrent neural network, and to process the events generated by the neural recording system in order to carry out pattern recognition.

This paper describes an electrocardiograph (ECG) monitoring SoC using a non-volatile MCU (NVMCU) and a noise-tolerant instantaneous heartbeat detector. The novelty of this work is the combination of the non-volatile MCU for normally off computing and a noise-tolerant-QRS (heartbeat) detector to achieve both low-power and noise tolerance. To minimize the stand-by current of MCU, a non-volatile flip-flop and a 6T-4C NVRAM are used. Proposed plate-line charge-share and bit-line non-precharge techniques also contribute to mitigate the active power overhead of 6T-4C NVRAM. The proposed accurate heartbeat detector uses coarse-fine autocorrelation and a template matching technique. Accurate heartbeat detection also contributes system-level power reduction because the active ratio of ADC and digital block can be reduced using heartbeat prediction. Measurement results show that the fully integrated ECG-SoC consumes 6.14 μ A including 1.28- μA non-volatile MCU and 0.7- μA heartbeat detector.

Over the past two decades, nanopores have been a promising technology for next generation deoxyribonucleic acid (DNA) sequencing. Here, we present a hybrid semi-digital transimpedance amplifier (HSD-TIA) to sense the minute current signatures introduced by single-stranded DNA (ssDNA) translocating through a nanopore, while discharging the baseline current using a semi-digital feedback loop. The amplifier achieves fast settling by adaptively tuning a DC compensation current when a step input is detected. A noise cancellation technique reduces the total input-referred current noise caused by the parasitic input capacitance. Measurement results show the performance of the amplifier with 31.6 M Ω mid-band gain, 950 kHz bandwidth, and 8.5 fA/ √Hz input-referred current noise, a 2× noise reduction due to the noise cancellation technique. The settling response is demonstrated by observing the insertion of a protein nanopore in a lipid bilayer. Using the nanopore, the HSD-TIA was able to measure ssDNA translocation events.

The oral feeding disorder is one of the important indicators for the high risk group of neurodevelopment delay. The procedure of oral feeding requires the coordination of sucking, swallowing, and breathing activities, and it is the most complex sensorimotor process for newborn infants. Premature infants often uneasily complete the procedure of oral feeding. However, the evaluation of the oral feeding disorders and severity are usually dependent on the subjective clinical experience of the physician. Monitoring the sucking-swallowing-breathing activities directly is difficult for preterm infants. In this study, a wireless monitoring system for oral-feeding evaluation of full term and preterm infants was proposed to objectively and quantitatively evaluate the coordination of suck-swallow-respiration function during oral feeding. Moreover, the ratios of the swallowing and breathing event numbers to the sucking event number were defined to evaluate the coordination of suck-swallow-respiration function during oral feeding. Finally, the system performance was validated and the coordination of suck-swallow-respiration function for full term and preterm infants during oral feeding was also investigated.

An analog inner hair cell and auditory nerve circuit using a dual AGC model has been implemented using 0.35 micron mixed-signal technology. A fully-differential current-mode architecture is used and the ability to correct channel mismatch is evaluated with matched layouts as well as with digital current tuning. The Meddis test paradigm is used to examine the analog implementation's auditory processing capabilities and investigate the circuit's ability to correct DC mismatch. The correction techniques used demonstrate the analog inner hair cell and auditory nerve circuit's potential use in low-power, multiple-sensor analog biomimetic systems with highly reproducible signal processing blocks on a single massively parallel integrated circuit.

In this paper, the simultaneous real-time control of multiple degrees of freedom (DOF) for myoelectric systems is investigated. The goal of this study, in which ten able-bodied subjects participated, was to directly compare three control paradigms of constrained (force targeted), unconstrained (position targeted) and resisted unconstrained (position targeted) limb contractions. Artificial neural networks (ANNs) were trained for simultaneous myoelectric control of the three degrees of freedom (DOFs) (wrist flexion-extension, abduction-adduction, and pronation-supination) using mirrored bilateral contractions. In the resisted unconstrained experiment, some resistance to movement was provided using flexible wrist braces in order to increase the required level of muscle activation. The force, in constrained experiments, and position, in unconstrained and resisted unconstrained experiments, were measured. The three protocols were compared off-line using estimation accuracies (R2) and online using a real-time computer-based target acquisition test. The constrained control paradigm outperformed the unconstrained method in the abduction-adduction DOF (R(constrained)2 = 90.8 ± 0.6, R(unconstrained)2 = 85.6 ± 1.6) and pronation-supination DOF (R(constrained)2 = 88.5 ± 0.9, R(unconstrained)2 = 82.3 ± 1.6), but no significant difference was found in the flexion-extension DOF. The constrained control method outperformed unconstrained control in two real-time testing metrics including completion time and path efficiency. The constrained method results, however, were not significantly different than those of the resisted unconstrained method (with braces) in both off-line and real-time tests. This suggests that the quality of control using constrained and unconstrained contraction-based myoelectric schemes is not appreciably different when using comparable l- vels of muscle activation.

There have been substantial progresses in the technology of cardiac implantable electric devices (CIEDs) during the past decades. One of the progresses is represented by the development of a hemodynamic sensor embedded at the tip of a pacing lead that measures myocardial contractility by the analysis of myocardial mechanical vibrations occurring during the cardiac cycle. This sensor, providing continuous hemodynamic monitoring, could play an important role in clinical practice because of several clinical applications in CIEDs recipients. The objectives of this work are to report how this sensor operates and to review the main findings about its clinical applications.

The automatic emotion recognition technology is an important part of building intelligent systems to prevent the computers acting inappropriately. A novel approach for recognizing emotional state by their keystroke typing patterns on a standard keyboard was developed in recent years. However, there was very limited investigation about the phenomenon itself in the previous literatures. Hence, in our study, we conduct a controlled experiment to collect subjects' keystroke data in the different emotional states induced by facial feedback. We examine the difference of the keystroke data between positive and negative emotional states. The results prove the significance in the differences in the typing patterns under positive and negative emotions for all subjects. Our study provides an evidence for the reasonability about developing the technique of emotion recognition by keystroke.

A signal processor/compressor dedicated to implantable neural recording microsystems is presented. Signal compression is performed based on Haar wavelet. It is shown in this paper that, compared to other mathematical transforms already used for this purpose, compression of neural signals using this type of wavelet transform can be of almost the same quality, while demanding less circuit complexity and smaller silicon area. Designed in a 0.13-μm standard CMOS process, the 64-channel 8-bit signal processor reported in this paper occupies 113 μm x 110 μm of silicon area. It operates under a 1.8-V supply voltage at a master clock frequency of 3.2 MHz.

On-chip implementation of Hilbert-Huang transform (HHT) has great impact to analyze the non-linear and non-stationary biomedical signals on wearable or implantable sensors for the real-time applications. Cubic spline interpolation (CSI) consumes the most computation in HHT, and is the key component for the HHT processor. In tradition, CSI in HHT is usually performed after the collection of a large window of signals, and the long latency violates the realtime requirement of the applications. In this work, we propose to keep processing the incoming signals on-line with small and overlapped data windows without sacrificing the interpolation accuracy. 58% multiplication and 73% division of CSI are saved after the data reuse between the data windows.

In recent years, new generation spectrophotometers are increasingly used in biomedical applications. Handheld spectrophotometers, offering compactness, versatility, and low cost, have facilitated a broad array of applications in biomedical optics. However, despite the popularity and the diverse range of applications, a detailed characterization of many of these new spectrophotometers in terms of stray light and noise characteristics is missing from the literature. Such a popular instrument (USB2000) is characterized in detail with particular focus and emphasis on its noise and stray light characteristics. The results of the analysis may be useful to numerous users of this and other similar instruments in a diverse range of applications.

Biomedical signals carry fundamental information about the nature and the status of the living systems under study. A proper processing of these signals obtains useful physiological and clinical information. A closer integration between signal processing and modeling of the relevant biological systems is capable to directly attribute pathophysiological meaning to the parameters obtained from the processing and vice versa. Another issue of great interest in which BSP plays an important role is the Brain-Computer Interface (BCI) or Brain-Machine Interface (BMI) where fast and reliable signal processing approaches are fundamental for a practical implementation. The physiological mechanisms underlying these heart rate variability findings are supposed to be related to stochastic processes at the cellular level, to influence of respiration on the heart rate, and to the interactions of the multiple feedback loops regulating the cardiovascular system. Another important area in which BSP plays a pivotal role is the "computational genomics and proteomics." It is true that "traditional" biomedical engineering studies biomedical signals which are obtained at the level of the major physiological systems.

A low power biomedical digital signal processor ASIC based on hardware and software codesign methodology was presented in this paper. The codesign methodology was used to achieve higher system performance and design flexibility. The hardware implementation included a low power 32bit RISC CPU ARM7TDMI, a low power AHB-compatible bus, and a scalable digital co-processor that was optimized for low power Fast Fourier Transform (FFT) calculations. The co-processor could be scaled for 8-point, 16-point and 32-point FFTs, taking approximate 50, 100 and 150 clock circles, respectively. The complete design was intensively simulated using ARM DSM model and was emulated by ARM Versatile platform, before conducted to silicon. The multi-million-gate ASIC was fabricated using SMIC 0.18 microm mixed-signal CMOS 1P6M technology. The die area measures 5,000 microm x 2,350 microm. The power consumption was approximately 3.6 mW at 1.8 V power supply and 1 MHz clock rate. The power consumption for FFT calculations was less than 1.5 % comparing with the conventional embedded software-based solution.

Biochips have been an advanced technology for biomedical applications since the end of the 20th century. Optical detection systems have been a very important tool in biochip analysis. Microscopes are often inadequate for high resolution and big view-area detection of microarray chips, thus some new optical instruments are required. In this work, a novel digital imaging scanning system with dark-field irradiation is developed for some biomedical applications for microarray chips, characterized by analyzing genes and proteins of clinical samples with high specific, parallel, and nanoliter samples. The novel optical system has a high numerical aperture (NA=0.72), a long working distance (wd>3.0 mm), an excellent contrast and signal-to-noise ratio, a high resolving power close to 3 mum, and an efficiency of collected fluorescence more than two-fold better than that of other commercial confocal biochip scanners. An edge overlap algorithm is proposed for the image restructure of free area detection and correcting scanning position errors to a precision of 1 pixel. A novel algorithm is explored for recognizing the target from the scanning images conveniently, removing noise, and producing the signal matrix of biochip analysis. The digital imaging scanning system is equally as good for the detection of enclosed biochips as it is for the detection of biological samples on a slide surface covered with a glass cover slip or in culture solution. The clinical bacteria identification and serum antibody detection of biochips are described.

A new implantable pressure sensor for long-term monitoring of intracranial pressure is presented. The sensor is powered by telemetry and can be interrogated wirelessly. A capacitive pressure transducer, whose capacitance is converted to a frequency-encoded signal by an application-specific integrated circuit (ASIC), senses the absolute pressure. The pressure-encoded signal, the ASIC input voltage, and onboard calibration parameters are transmitted to an external reading unit. The proposed novel packaging solution is designed for long-term stability and reliability of the sensor. The accuracy of sensor at body temperature is better than 2 mbar across a pressure range of 600-1200 mbar. The sensor is 13 mm in diameter and 4.5 mm in height.

This article presents a review on biomedical signal processing. Discussions on traditional approaches, nonstationary and nonlinear systems, signal fusion, physiological modeling, and the MMM (multivariate, multiorgan and multiscale) paradigm are included.

The electronics of a general biomedical device consist of energy delivery, analog-to-digital conversion, signal processing, and communication subsystems. Each of these blocks must be designed for minimum energy consumption. Specific design techniques, such as aggressive voltage scaling, dynamic power-performance management, and energy-efficient signaling, must be employed to adhere to the stringent energy constraint. The constraint itself is set by the energy source, so energy harvesting holds tremendous promise toward enabling sophisticated systems without straining user lifestyle. Further, once harvested, efficient delivery of the low-energy levels, as well as robust operation in the aggressive low-power modes, requires careful understanding and treatment of the specific design limitations that dominate this realm. We outline the performance and power constraints of biomedical devices, and present circuit techniques to achieve complete systems operating down to power levels of microwatts. In all cases, approaches that leverage advanced technology trends are emphasized.

A novel biomedical acquisition system which can adjust its resolution by the condition of signal is presented in this paper. The resolution of sigma-delta modulator in proposed system can be automatically varied by switching its architecture and sampling rate that can acquire accurate date without additional power consumption. The modulator in this system reaches specifications from 10-bit to 16-bit resolution and consumes power from 48microW to 360microW. In the electrocardiogram and electroculogram acquisition by proposed system, it can save more than 40% and 73% power consumption comparing with the conventional acquisition system.

The task of Electrocardiographic Imaging is an ill-posed inverse problem, requiring regularization. However, it has special features, firstly because it is a "non-stationary" inverse problem, and secondly because the inherent dynamical variety (e.g., epicardial breakthroughs, arrhythmias, ischemic changes) may preclude a fruitful nontrivial process model. Importantly, its structure places it in the category of "partial inverse problems" - a theory that arises from this setting. Surprising features of the resulting regularization methodology include the ability to fashion nontrivial regularization matrices in part (and sometimes entirely) from the data. There is evidence that these theoretical results can have significant practical benefits.

Ultrasonic medical imaging courses often stop at the theory or MATLAB simulation level, since professors find it challenging to give the students the experience of designing a real-time ultrasonic system. Some of the practical problems of working with real-time data from the ultrasonic transducers can be avoided by working at lower frequencies (sonar to low ultrasound) range. To facilitate this, we have created a platform using the ease of MATLAB programming with the real-time processing capability of the low-cost Texas Instruments C6711 DSP starter kit and a 4-channel sonar array. With this platform students can design a B-mode or Color-Mode sonar system in the MATLAB environment. This paper will demonstrate how the platform can be used in the classroom to demonstrate the real-time signal processing stages including beamforming, multi-rate sampling, demodulation, filtering, image processing, echo imaging, and Doppler frequency estimation.

This article revisits the subject of short-term heart-rate and arterial-pressure variability from the perspective of model structures that can be useful in defining signal processing algorithms. We draw a general scheme of the oscillation sources and interactions that contribute to cardiovascular control mechanisms and highlight the elements that were considered in different modeling works. The origin, superposition, and interaction of respiratory high-frequency (HF) and vasomotor low-frequency (LF) rhythms is presented as the integration of supraspinal and spinal circuits, vasomotor activity, and pressure control loops. We analyze in detail the necessity of considering all relevant interactions for the algorithms designed to estimate the baroreflex sensitivity. We also pinpoint the components of cardiorespiratory coupling in relation to the analysis of data from the acoustic quantification of the left ventricular volume. Finally, we analyze the tendency to produce complex behaviors even in extremely simplified systems involving interactions between oscillatory mechanisms.

Motion artifact resulting from electrode and patient movement is a significant source of noise in ECG, EEG, EMG, and impedance pneumography recording. Noise resulting from motion is particularly troublesome in ambulatory ECG recordings, such as those made during Holter monitoring or stress tests, because the bandwidth of the motion artifact overlaps with the ECG signal bandwidth. The authors investigated the effect of an adaptive motion-artifact removal algorithm on the performance of a standard QRS detector. They made four ECG recordings on each of the three subjects while manually generating artifact. Adaptive noise removal was applied to the ECG signal using a skin-stretch signal as the noise reference. Adaptive noise removal reduced the number of false QRS detections in the records from 380 to 104, for an average reduction in false detections of 72.6%. False-detection reductions for individual records ranged from 12% to 93%.

A system for real-time acquisition, processing, display, and logging of multiple channels of the EMG waveform has been described. By designing this system around the configuration of a standard PC, custom hardware was avoided. Similarly, software was designed in a higher-level language (C), simplifying the programming burden and minimizing custom software development. Components of the software are available off the shelf. The computational capacity of generic PCs facilitates very high rates of numeric calculation, as required by some advanced EMG amplitude estimation algorithms. Computational capacity is expected to increase, facilitating ever more numerically intensive EMG algorithms that are likely to be developed in the future. The system described here has been used in the development of an adaptive window EMG amplitude-estimation algorithm. The system is extensible and can be altered for many uses related to EMG and biosignal processing.

Femoral rotational waveforms sampled at 15 Hz for a duration 20 s satisfy the sampling rate and frequency resolution requirements of such waveforms during walking. Spectral analysis provides a unique signature of the frequency composition of such signals. This identity may prove useful in characterising human gait and could be of value in future studies of walking in health and disease.

Field Programmable Gate Arrays (FPGAs) allow digital circuitry to be integrated on to a single device without the long time penalty incurred with mask programmable Application Specific Integrated Circuits (ASICs). This makes FPGA technology an attractive option to engineer involved in designing digital circuitry where space is at a premium. Furthermore, the relatively low cost of FPGAs at low volume, compared to mask programmable ASICs make them an excellent prototyping option for designers who are considering low cost volume production with mask programmable ASICs. There are several different types of FPGAs available which thus creates confusion for prospective users uncertain of their relative benefits. This paper serves as an introduction to FPGA technology and the stages involved in implementing a design, via the application of an Actel FPGA to the development of an ambulatory long term electrocardiogram (ECG) based RR interval recorder.

To meet the needs of our research programmes on auditory prostheses for the totally deaf, a 15-channel data acquisition and waveform generator system with flexible triggering, pacing and linking was developed. It allows both synchronous and asynchronous operation at high speed. Each channel of the system, which is controlled by a simple personal computer, has an on-board microcontroller, a 512 kWord signal memory, a voltage input and both voltage and current outputs. The system includes a master pacer and trigger unit with elaborate hardware and software triggering options. This paper describes the system hardware and the software used to control it. Finally, some of its application are demonstrated. The flexibility of the system makes it widely applicable in the field of biomedical engineering.

An electro-mechanical system to characterize the dynamic performance of a position measurement system was constructed. The system produced pure sine wave kinematics over the frequency range of 1-11 Hz. Synchronous measurements of the position (using infra-red light emitting diodes) and the acceleration were taken at discrete frequencies. The position signal was filtered and differentiated twice to obtain an estimated acceleration. The acceleration estimate was compared to the acceleration reading from the accelerometer, and both were compared to the theoretical acceleration. The comparison was based on extracting average features of the signal like amplitude, offset and noise. The results show that the accelerometer measurements matched the theoretical amplitude to within 1-3% over most of the range but showed significant offset drift. The acceleration estimates based on the position measurements were highly dependent on the filtering scheme, showed no significant offset but had higher levels of noise. The experimental measurements and the mathematical analysis quantitatively compared the dynamic performance of the position measurement system and the accelerometer. Such a system could be used to optimize the performance of position measurement devices, by comparing different filtering and/or differentiating schemes.