Biomedical Signal Acquisition Research Articles

A coloured Petri nets-based system for validation of biomedical signal acquisition devices

A coloured Petri nets-based system for validation of biomedical signal acquisition devices

Design of 8 -bit low power SAR ADC in 45 nm for biomedical implants

The utilisation of low power SAR (Successive Approximation Register) Analog-to-Digital Converters holds significant importance in the domain of bio-medical signal acquisition. The present study showcases the utilisation of an 8-bit CMOS SAR-ADC for integration into the analog front end of bio-signal acquisition. The focus of this technology pertains to the monitoring of implanted bio-signal devices, with a specific emphasis on ECG/EEG signals. A capacitive digital-to-analog converter (DAC) is suggested as a means to attain power consumption in the microwatt range. This approach enables comparisons to be made without any energy consumption, leading to a substantial enhancement in energy efficiency. Furthermore, a comprehensive theoretical examination of comparator offset voltages has been conducted to enhance the offset performance of the comparator operating at low supply voltage. The analysis indicates that optimization of the comparator is achieved solely through the adjustment of transistor sizes, without the implementation of any specific offset cancellation techniques. Simulations indicate that the optimization of the offset voltage to approximately 5 mV occurs when there is variation in the common-mode input voltage at a 1 V supply. The proposed Analog-to-Digital Converter (ADC) layout has been successfully executed utilizing the 45 nm Complementary Metal-Oxide-Semiconductor (CMOS) technology. The Analog-to-Digital Converter (ADC) attains a Spurious-Free Dynamic Range (SFDR) of 64.02 dB and consumes 1.9 μW of power at a sampling rate of 1.1 MHz and a supply voltage of 1 V.

A Review on Direct Digital Conversion Techniques for Biomedical Signal Acquisition

Biomedical signals such as Electrocardiogram (ECG), Electroencephalogram (EEG) and photoplethysmography (PPG) are recorded routinely to provide helpful information for early diagnosis of disease. Low power consumption is very important to allow long-term ambulatory monitoring with battery-powered systems. A direct digital conversion (DDC) technique has been proposed in recent years, which employs preamplifier and data converters, reducing the complexity of the readout chain and thus its power consumption. This paper provides a review on DDC for biopotential signals and bio-optical signal acquisition. The state-of-the-art DDC-based readout architectures together with circuit implementations are provided.

Innovative Measures of Verhulst Diagram for Emotion Recognition using Eye-Blinking Variability

Background: The human body continuously reveals the status of several organs through biomedical signals. Over time, biomedical signal acquisition, monitoring, and analysis have captured the attention of many scientists for further prediction, diagnosis, decision-making, and recognition. Recently, building an intelligent emotion recognition system has become a challenging issue using the application of signal processing. Frequently, human emotion classification was proposed utilizing the internal body status in dealing with affective provocations. However, external states, such as eye movements, have been claimed to convey practical information about the participant’s emotions. In this study, we proposed an automatic emotion recognition scheme through the analysis of a single-modal eye-blinking variability. Methods: Initially, the signal was transformed into a 2D space using the Verhulst diagram, a simple analysis based on the signal’s dynamics. Next, some innovative features were introduced to characterize the maps. Then, the extracted measures were inputted to the support vector machine (SVM) and k-nearest neighbor (kNN). The former classifier was evaluated with three kernel functions, including RBF, linear, and polynomial. The latter performances were examined with different values for k. Moreover, the classification results were assessed in two feature-set partitioning modes: a 5-fold and 10-fold cross-validation. Results: The results showed a statistically significant difference between neutral/fear and neutral/sadness for all Verhulst indices. Also, the average values of these characteristics were higher for fear and sadness than those of other emotions. Our results indicated a maximum rate of 100% for the fear/neutral classification. Therefore, the suggested Verhulst-based approach was supremely talented in emotion classification and analysis using eye-blinking signals. Conclusion: The novel biomarkers set the scene for designing a simple accurate emotion recognition system. Additionally, this experiment could fortify the territory of ocular affective computing, and open a new horizon for diagnosing or treating various emotion deficiency disorders.

Comparison between Two Time Synchronization and Data Alignment Methods for Multi-Channel Wearable Biosensor Systems Using BLE Protocol

Wireless wearable sensor systems for biomedical signal acquisition have developed rapidly in recent years. Multiple sensors are often deployed for monitoring common bioelectric signals, such as EEG (electroencephalogram), ECG (electrocardiogram), and EMG (electromyogram). Compared with ZigBee and low-power Wi-Fi, Bluetooth Low Energy (BLE) can be a more suitable wireless protocol for such systems. However, current time synchronization methods for BLE multi-channel systems, via either BLE beacon transmissions or additional hardware, cannot satisfy the requirements of high throughput with low latency, transferability between commercial devices, and low energy consumption. We developed a time synchronization and simple data alignment (SDA) algorithm, which was implemented in the BLE application layer without the need for additional hardware. We further developed a linear interpolation data alignment (LIDA) algorithm to improve upon SDA. We tested our algorithms using sinusoidal input signals at different frequencies (10 to 210 Hz in increments of 20 Hz-frequencies spanning much of the relevant range of EEG, ECG, and EMG signals) on Texas Instruments (TI) CC26XX family devices, with two peripheral nodes communicating with one central node. The analysis was performed offline. The lowest average (±standard deviation) absolute time alignment error between the two peripheral nodes achieved by the SDA algorithm was 384.3 ± 386.5 μs, while that of the LIDA algorithm was 189.9 ± 204.7 μs. For all sinusoidal frequencies tested, the performance of LIDA was always statistically better than that of SDA. These average alignment errors were quite low-well below one sample period for commonly acquired bioelectric signals.

Novel tunable current feedback instrumentation amplifier based on BBFC OP-AMP for biomedical applications with low power and high CMRR

The paper presents a novel low power, low noise and high CMRR current feedback instrumentation amplifier (CFIA) design for biomedical signal acquisition i.e. EEG/ECG applications is presented. The proposed instrumentation amplifier performance is evaluated using 0.18μm, CMOS technology with 1.8 V supply and results are obtained in Cadence EDA tool. The proposed CFIA is implemented using a dual output bandwidth boosted folded cascode operational amplifier (BBFC OP-AMP) active block. In this paper the proposed CFIA performance parameters has been considered for optimization to get better results by using the idea of signal to noise ratio (SNR) with Taguchi design of experiments (Taguchi DoE) and analysis of variance (ANOVA) to estimate the most important independent variable which control the performance of the design. The bias voltage, channel length and width of input MOS transistors have been selected to optimize the proposed circuit performance. The proposed CFIA structure have 50.33 to 69.67 dB tunable differential gain, common mode rejection ratio (CMRR) of 143.19 dB, tunable bandwidth of 148.24 kHz to 1.31 MHz, dynamic range (DR) of 74.31 dB and 297.73 dB of figure of merit (FoM). The CFIA shows an equivalent input referred noise (IRN) of 0.586 μV at 0.1 Hz to 148.24 kHz frequency range, with total power dissipation of 102.73 μW and occupies 8194μm2 of core area. The simulation results of the proposed CFIA are close to the predicted solution obtained from ANOVA. The PVT analysis and post layout simulation has been performed to check the robustness of the proposed design. The proposed design has been compared with the other existing literature which shows the competence of the design.

A 0.8-μW and 74-dB High-Pass Sigma-Delta Modulator With OPAMP Sharing and Noise-Coupling Techniques for Biomedical Signal Acquisition.

This work presents a third-order high-pass sigma-delta modulator (HPSDM) for biomedical signal acquisition. The operational amplifier (op-amp) sharing and noise-coupling techniques are adopted to reduce the required quantity of op-amps and add a noise-shaping order, which can achieve low power consumption and high resolution. A novel switched-capacitor architecture is proposed to suppress the increasing in-band noise and alleviate the circuit sensitivity to capacitor mismatch in the high-pass integrator. The proposed HPSDM was fabricated in a 0.18-μm standard CMOS process. Measurement results reveal that the proposed HPSDM has a signal-to-noise and distortion ratio (SNDR) of 75.26/74 dB in 200 Hz bandwidth and consumes 1.52/0.8 μW under 1.2/1 V supply voltage, which can achieve a peak Schreier Figure-of-Merit of 156.45/157.98 dB and a peak Walden FoM of 0.802/0.488 pJ/conv.

Fin Field-Effect Transistor (FinFET) Based Low Noise Low Power Chopper Stabilized Capacitively Coupled Amplifier for Biosignal Acquisition System

Design of low power and low noise amplifier for biomedical signal acquisition is quite challenging. In this paper, an inverter based chopper stabilized capacitively coupled amplifier is designed for biomedical applications using 14 nm FinFET technology. Single-stage inverter based amplifier which is used as a core amplifier in the proposed chopper stabilized circuit is designed and compared using different configurations of FinFET and CMOS. Gain and power dissipation is better with fully FinFET based single-stage amplifier when compared with fully CMOS based counterpart. When the FinFET based amplifier is used with capacitive feedback and chopper stabilization circuit, it provides a significant reduction of noise as shown by Noise Efficiency Factor (NEF) of 3.66. The gain in chopper stabilized amplifier is 36.7 dB and bandwidth is in the range of 9.16 Hz–7.6 kHz. The CMRR and PSRR of the designed amplifier are 96.1 dB and 98.5 dB respectively. The power dissipation of the amplifier is 1.24 μW which is relatively better. In the end, spike reduction circuitry is also used successfully to minimize the spikes in the output signal.

Design Approaches of Ultra-Low Power SAR ADC for Biomedical Systems — A Review

In recent years, implantable biomedical devices like cardiac pacemaker, defibrillators, cochlear implants, visual prosthesis etc. have gained immense importance in the personal health monitoring system. Most of these devices are battery powered. The life span of a pacemaker is expected to be between 10 and 12 years. This shows the importance of having an ultra-low power design technique to improve the reliability and battery life of the system. To achieve this, power draws from the battery must be kept low. Analog-to-Digital Convertor (ADC) is a main block in the front-end sensing unit of an implant for measurements of various biophysiological signals. This is the most power consuming unit in the system. ADC alone consumes about 30%–35% of the total power. This work surveys various successive approximation ADC designs for biomedical signal acquisition, in terms of power consumption, signal to noise distortion ratio, sampling rate, resolution and Figure of Merit. The different switching schemes for capacitive DAC are also surveyed.

Design of N-path notch filter circuit with switched-capacitor resistor

Hum noise such as power line interference is one of the critical problems in biomedical signal acquisition. Various techniques have been proposed to suppress power line interference, such as an N-path notch filter. The notch depth in the conventional N-path notch filter is limited by the number of paths. The previous 10-phase N-path notch filter circuit achieved deeper notch depth but has a problem with capacitor size. The previous circuit requires more input–output pin because the large capacitance and therefore can not be implemented on an integrated circuit. This work proposed a new N-path notch filter with a switched-capacitor resistor to enable on-chip implementation. The proposed N-path notch filter achieved deeper notch depth than previous circuits with smaller capacitors. The proposed circuit still reached notch depth deeper than 40 dB with a total capacitance of 1 nF.

Recent Trends, Construction, and Applications of Smart Textiles and Clothing for Monitoring of Health Activity: A Comprehensive Multidisciplinary Review.

In the area of biomedical signal monitoring, wearable electronics represents a dynamically growing field with a significant impact on the market of commercial products of biomedical signal monitoring and acquisition, as well as consumer electronic for vital functions monitoring. Since the electrodes are perceived as one of the most important part of the biomedical signal monitoring, they have been one of the most frequent subjects in the research community. Electronic textile (e-textile), also called smart textile represents a modern trend in the wearable electronics, integrating of functional materials with common clothing with the goal to realize the devices, which include sensors, antennas, energy harvesters and advanced textiles for self-cooling and heating. The area of textile electrodes and e-textile is perceived as a multidisciplinary field, integrating material engineering, chemistry, and biomedical engineering. In this review, we provide a comprehensive view on this area. This multidisciplinary review integrates the e-textile characteristics, materials and manufacturing of the textile electrodes, noise influence on the e-textiles performance, and mainly applications of the textile electrodes for biomedical signal monitoring and acquisition, including pressure sensors, electrocardiography, electromyography, electroencephalography and electrooculography monitoring.

Biomedical Signal Acquisition Using Sensors under the Paradigm of Parallel Computing.

There are several pathologies attacking the central nervous system and diverse therapies for each specific disease. These therapies seek as far as possible to minimize or offset the consequences caused by these types of pathologies and disorders in the patient. Therefore, comprehensive neurological care has been performed by neurorehabilitation therapies, to improve the patients’ life quality and facilitating their performance in society. One way to know how the neurorehabilitation therapies contribute to help patients is by measuring changes in their brain activity by means of electroencephalograms (EEG). EEG data-processing applications have been used in neuroscience research to be highly computing- and data-intensive. Our proposal is an integrated system of Electroencephalographic, Electrocardiographic, Bioacoustic, and Digital Image Acquisition Analysis to provide neuroscience experts with tools to estimate the efficiency of a great variety of therapies. The three main axes of this proposal are: parallel or distributed capture, filtering and adaptation of biomedical signals, and synchronization in real epochs of sampling. Thus, the present proposal underlies a general system, whose main objective is to be a wireless benchmark in the field. In this way, this proposal could acquire and give some analysis tools for biomedical signals used for measuring brain interactions when it is stimulated by an external system during therapies, for example. Therefore, this system supports extreme environmental conditions, when necessary, which broadens the spectrum of its applications. In addition, in this proposal sensors could be added or eliminated depending on the needs of the research, generating a wide range of configuration limited by the number of CPU cores, i.e., the more biosensors, the more CPU cores will be required. To validate the proposed integrated system, it is used in a Dolphin-Assisted Therapy in patients with Infantile Cerebral Palsy and Obsessive–Compulsive Disorder, as well as with a neurotypical one. Event synchronization of sample periods helped isolate the same therapy stimulus and allowed it to be analyzed by tools such as the Power Spectrum or the Fractal Geometry.

Application of Biomedical Signal Acquisition Equipment in Human Sport Heart Rate Monitoring

Aiming at exploring biomedical signal acquisition equipment used in human motion heart rate monitoring, the research on the related hardware design and signal processing method was carried out. A biomedical signal acquisition device based on photoplethysmography (PPG) is designed, and the equipment was applied to acquire PPG signals and acceleration sensor signals under different motion states. The analysis of the experimental data showed that, the fusion method of the acceleration sensing information in the motion artifact removal method is perfected. The effectiveness of the baseline drift removal algorithm, motion artifact removal algorithm and dynamic heart rate monitoring algorithm was verified by reconstructing the signal quality evaluation index. To sum up, taking MINDRAY VS-800 as a reference device, it is compared with the adaptive filtering technology in terms of signal quality, BPM detection results and algorithm complexity, and better results are finally obtained.

Experimental Study on Biomedical Signal Acquisition of Sports Fatigue

To explore the biomedical signal acquisition of sports fatigue, the Pclab-UE biomedical signal acquisition and processing system is used to collect and process heart rate variability (HRV) automatically after quiet and fatigue exercise. In the meanwhile, the experimental data are analyzed. The heart rate variability of the subjects is recorded, aiming to provide experimental evidence for the future application of HRV in the diagnosis of exercise fatigue. Moreover, it also provides a noninvasive diagnostic index for exercise fatigue and exercise training practice. The research results showed that, the HRV values in the sub-maximal exercise caused fatigue. The maximal exercise induced fatigue is significantly decreased and there are significant differences. As a result, it is summed up that we can use HRV as a quantitative analysis index for the diagnosis of sports fatigue.

Multi-Layer Basis Pursuit for Compressed Sensing MR Image Reconstruction

Compressive Sensing (CS) is a widely used technique in biomedical signal acquisition and reconstruction. The technique is especially useful for reducing acquisition time for magnetic resonance imaging (MRI) signal acquisitions and reconstruction, where effects of patient fatigue and Claustrophobia need mitigation. In addition to improving patient experience, faster MRI scans are important for time sensitive imaging, such as functional or cardiac MRI, where target movement is unavoidable. Inspired from recent research works on multi-layer convolutional sparse coding (ML-CSC) theory to model deep neural networks, this work proposes a multi-layer basis pursuit framework which combines the benefit from objective-based CS reconstructions and deep learning-based reconstruction by employing iterative thresholding algorithms for successfully training a CS-MRI restoration framework on GPU and reconstruct test images using parameters of the trained model. Extensive experiments show the effectiveness of the proposed framework on four MRI datasets in terms of faster convergence, improved PSNR/SSIM, and better restoration efficiency as compared to the state of the art frameworks with different CS ratios.

Analog front-end design for biomedical signal acquisition systems

This manuscript contains a brief overview of the work being carried out by the author under the aegis of Visvesvaraya Young Faculty Research Fellowship awarded to him by the Ministry of Electronics and Information Technology (MeitY), India.

An Energy Efficient Multi-User Asynchronous Wireless Transmitter for Biomedical Signal Acquisition.

The paper presents a novel transmitter architecture for short-range asynchronous wireless communication, applicable to simultaneous multi-user wireless acquisition of biological signals. The analog signal, provided from an analog biosensor, is transformed to time information using an Integral Pulse Frequency Modulator (IPFM) as a Time-Encoding Machine. The IPFM generates a time-encoded unipolar pulse train, maintaining the linear dependence of the output pulse distance on analog input voltage. The system enables continuous acquisition of the signals from multiple sensors in which each transmitter has unique feedback loop delay used for multi-user coding. IPFM pulses trigger the Impulse Radio Ultra-Wideband pulse generator directly, providing two ultra-wideband (UWB) pulses per each IPFM pulse. Due to the lack of internal clock signal and microprocessor-free multi-user coding, the circuitry satisfies the requirements of multi-user coding energy efficiency and size reduction, which are crucial demands in biomedical applications. The proposed Time-Encoded UWB (TE-UWB) transmitter is implemented in 0.18 [Formula: see text] CMOS technology. Measurement results of the IPFM transfer function for input voltage ranging from 0.15 to 1.5 V are presented, providing the dependence of the IPFM pulse time distance on analog input voltage and power consumption dependence on the input voltage level. For continuous monitoring operation, total power consumption of the transmitter circuitry for the maximum input voltage is 10.8 [Formula: see text], while for the lowest input voltage it increases to 40.48[Formula: see text]. The circuit occupies 0.14 [Formula: see text].

Biomedical Signal Acquisition of Hepatobiliary and Portal Vein Before and After Exercise

Biomedical Signal Acquisition of Hepatobiliary and Portal Vein Before and After Exercise

Implementation of a Cost-Effective Didactic Prototype for the Acquisition of Biomedical Signals

This paper presents the implementation of a cost-effective didactic prototype, which was designed as a tool for theoretical and practical learning in the biomedical instrumentation area for engineering students. The prototype provides integrated hardware and software components that allow online acquisition, processing, and visualization of electrocardiographic (ECG), electroencephalographic (EEG), electromyographic (EMG), and electrooculographic (EOG) signals, as well as measurements of bio-impedance from the skin. A control system using an Arduino Uno board and the PIC16F877A and PIC18F2550 microcontrollers was implemented. This control system allows selecting the type of module; the lead to be used in the ECG module; the input channel for the EEG, EMG, and EOG modules; and controlling the signal generator for the bioimpedance module. In addition, a graphical interface was developed in LabVIEW, in which all the acquired biomedical signals can be visualized in real time. It is highlighted as a novelty the modular implementation of the prototype, the incorporation of five modules in a single device and the graphical user-friendly interface. The final result is a low-cost device capable of processing and visualizing bioelectric signals through an interface in LabVIEW, which also allows the user to interact with each of the stages.

A 36 μW 1.1 mm2 Reconfigurable Analog Front-End for Cardiovascular and Respiratory Signals Recording.

This paper presents a 1.2 V 36 μW reconfigurable analog front-end (R-AFE) as a general-purpose low-cost IC for multiple-mode biomedical signals acquisition. The R-AFE efficiently reuses a reconfigurable preamplifier, a current generator (CG), and a mixed signal processing unit, having an area of 1.1mm2 per R-AFE while supporting five acquisition modes to record different forms of cardiovascular and respiratory signals. The R-AFE can interface with voltage-, current-, impedance-, and light-sensors and hence can measure electrocardiography (ECG), bio-impedance (BioZ), photoplethysmogram (PPG), galvanic skin response (GSR), and general-purpose analog signals. Thanks to the chopper preamplifier and the low-noise CG utilizing dynamic element matching, the R-AFE mitigates ${\text{1}}\text{/}f$ noise from both the preamplifier and the CG for improved measurement sensitivity. The IC achieves competitive performance compared to the state-of-the-art dedicated readout ICs of ECG, BioZ, GSR, and PPG, but with approximately 1.4×-5.3× smaller chip area per channel.