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Fulltext Characterization of Textile-Insulated Capacitive Biosensors

Ng CL, Reaz MB

Sensors (Basel), 2017 Mar 12;17(3).
PMID: 28287493 DOI: 10.3390/s17030574

Capacitive biosensors are an emerging technology revolutionizing wearable sensing systems and personal healthcare devices. They are capable of continuously measuring bioelectrical signals from the human body while utilizing textiles as an insulator. Different textile types have their own unique properties that alter skin-electrode capacitance and the performance of capacitive biosensors. This paper aims to identify the best textile insulator to be used with capacitive biosensors by analysing the characteristics of 6 types of common textile materials (cotton, linen, rayon, nylon, polyester, and PVC-textile) while evaluating their impact on the performance of a capacitive biosensor. A textile-insulated capacitive (TEX-C) biosensor was developed and validated on 3 subjects. Experimental results revealed that higher skin-electrode capacitance of a TEX-C biosensor yields a lower noise floor and better signal quality. Natural fabric such as cotton and linen were the two best insulating materials to integrate with a capacitive biosensor. They yielded the lowest noise floor of 2 mV and achieved consistent electromyography (EMG) signals measurements throughout the performance test.
Fulltext An integrated approach for platoon-based simulation and its feasibility assessment

Ng KM, Reaz MB

PLoS One, 2015;10(3):e0114406.
PMID: 25785693 DOI: 10.1371/journal.pone.0114406

Research on developing mathematical and simulative models to evaluate performance of signalized arterials is still ongoing. In this paper, an integrated model (IM) based on Rakha vehicle dynamics and LWR model is proposed. The IM which imitates actuated performance measurement in signalized arterials is described using continuous timed Petri net with variable speeds (VCPN). This enables systematic discretized description of platoon movement from an upstream signalized intersection towards a downstream signalized intersection. The integration is based on the notion that speed and travel time characteristics in a link can be provided using Rakha model. This will assist the LWR to estimate arrival profiles of vehicles at downstream intersection. One immediate benefit of the model is that platoon arrival profile obtained from the IM can be directly manipulated to estimate queues and delays at the target intersection using input-output analysis without considering the effect of shockwaves. This is less tedious as compared to analysing the LWR model through tracing trajectory of shockwave. Besides, time parameters of a platoon could be estimated for self-scheduling control approach from a cycle to cycle basis. The proposed IM is applied to a test intersection where simulated queues and average delays from the IM are compared with the platoon dispersion model (PDM) implemented in TRANSYT, cell transmission model (CTM) and HCM2000 for both under-saturated and oversaturated situations. The comparisons yielded acceptable and reasonable results, thus ascertained the feasibility and validity of the model.
Fulltext Platoon Interactions and Real-World Traffic Simulation and Validation Based on the LWR-IM

Ng KM, Reaz MB

PLoS One, 2016;11(1):e0144798.
PMID: 26731745 DOI: 10.1371/journal.pone.0144798

Platoon based traffic flow models form the underlying theoretical framework in traffic simulation tools. They are essentially important in facilitating efficient performance calculation and evaluation in urban traffic networks. For this purpose, a new platoon-based macroscopic model called the LWR-IM has been developed in [1]. Preliminary analytical validation conducted previously has proven the feasibility of the model. In this paper, the LWR-IM is further enhanced with algorithms that describe platoon interactions in urban arterials. The LWR-IM and the proposed platoon interaction algorithms are implemented in the real-world class I and class II urban arterials. Another purpose of the work is to perform quantitative validation to investigate the validity and ability of the LWR-IM and its underlying algorithms to describe platoon interactions and simulate performance indices that closely resemble the real traffic situations. The quantitative validation of the LWR-IM is achieved by performing a two-sampled t-test on queues simulated by the LWR-IM and real queues observed at these real-world locations. The results reveal insignificant differences of simulated queues with real queues where the p-values produced concluded that the null hypothesis cannot be rejected. Thus, the quantitative validation further proved the validity of the LWR-IM and the embedded platoon interactions algorithm for the intended purpose.
Fulltext Techniques of EMG signal analysis: detection, processing, classification and applications (Correction)

Reaz MB, Hussain MS, Mohd-Yasin F

Biol Proced Online, 2006;8:163.
PMID: 19565309 DOI: 10.1251/bpo124

This paper was originally published in Biological Procedures Online (BPO) on March 23, 2006. It was brought to the attention of the journal and authors that reference 74 was incorrect. The original citation for reference 74, "Stanford V. Biosignals offer potential for direct interfaces and health monitoring. Pervasive Computing, IEEE 2004; 3(1):99-103." should read "Costanza E, Inverso SA, Allen R. 'Toward Subtle Intimate Interfaces for Mobile Devices Using an EMG Controller' in Proc CHI2005, April 2005, Portland, OR, USA."
A time series based sequence prediction algorithm to detect activities of daily living in smart home

Marufuzzaman M, Reaz MB, Ali MA, Rahman LF

Methods Inf Med, 2015;54(3):262-70.
PMID: 25604028 DOI: 10.3414/ME14-01-0061

OBJECTIVES: The goal of smart homes is to create an intelligent environment adapting the inhabitants need and assisting the person who needs special care and safety in their daily life. This can be reached by collecting the ADL (activities of daily living) data and further analysis within existing computing elements. In this research, a very recent algorithm named sequence prediction via enhanced episode discovery (SPEED) is modified and in order to improve accuracy time component is included.
METHODS: The modified SPEED or M-SPEED is a sequence prediction algorithm, which modified the previous SPEED algorithm by using time duration of appliance's ON-OFF states to decide the next state. M-SPEED discovered periodic episodes of inhabitant behavior, trained it with learned episodes, and made decisions based on the obtained knowledge.
RESULTS: The results showed that M-SPEED achieves 96.8% prediction accuracy, which is better than other time prediction algorithms like PUBS, ALZ with temporal rules and the previous SPEED.
CONCLUSIONS: Since human behavior shows natural temporal patterns, duration times can be used to predict future events more accurately. This inhabitant activity prediction system will certainly improve the smart homes by ensuring safety and better care for elderly and handicapped people.
Fulltext High-speed current dq PI controller for vector controlled PMSM drive

Marufuzzaman M, Reaz MB, Rahman LF, Chang TG

ScientificWorldJournal, 2014;2014:709635.
PMID: 24574913 DOI: 10.1155/2014/709635

High-speed current controller for vector controlled permanent magnet synchronous motor (PMSM) is presented. The controller is developed based on modular design for faster calculation and uses fixed-point proportional-integral (PI) method for improved accuracy. Current dq controller is usually implemented in digital signal processor (DSP) based computer. However, DSP based solutions are reaching their physical limits, which are few microseconds. Besides, digital solutions suffer from high implementation cost. In this research, the overall controller is realizing in field programmable gate array (FPGA). FPGA implementation of the overall controlling algorithm will certainly trim down the execution time significantly to guarantee the steadiness of the motor. Agilent 16821A Logic Analyzer is employed to validate the result of the implemented design in FPGA. Experimental results indicate that the proposed current dq PI controller needs only 50 ns of execution time in 40 MHz clock, which is the lowest computational cycle for the era.
Fulltext Evolution of electroencephalogram signal analysis techniques during anesthesia

Al-Kadi MI, Reaz MB, Ali MA

Sensors (Basel), 2013;13(5):6605-35.
PMID: 23686141 DOI: 10.3390/s130506605

Biosignal analysis is one of the most important topics that researchers have tried to develop during the last century to understand numerous human diseases. Electroencephalograms (EEGs) are one of the techniques which provides an electrical representation of biosignals that reflect changes in the activity of the human brain. Monitoring the levels of anesthesia is a very important subject, which has been proposed to avoid both patient awareness caused by inadequate dosage of anesthetic drugs and excessive use of anesthesia during surgery. This article reviews the bases of these techniques and their development within the last decades and provides a synopsis of the relevant methodologies and algorithms that are used to analyze EEG signals. In addition, it aims to present some of the physiological background of the EEG signal, developments in EEG signal processing, and the effective methods used to remove various types of noise. This review will hopefully increase efforts to develop methods that use EEG signals for determining and classifying the depth of anesthesia with a high data rate to produce a flexible and reliable detection device.
A study on discrete wavelet-based noise removal from EEG signals

Asaduzzaman K, Reaz MB, Mohd-Yasin F, Sim KS, Hussain MS

Adv Exp Med Biol, 2010;680:593-9.
PMID: 20865544 DOI: 10.1007/978-1-4419-5913-3_65

Electroencephalogram (EEG) serves as an extremely valuable tool for clinicians and researchers to study the activity of the brain in a non-invasive manner. It has long been used for the diagnosis of various central nervous system disorders like seizures, epilepsy, and brain damage and for categorizing sleep stages in patients. The artifacts caused by various factors such as Electrooculogram (EOG), eye blink, and Electromyogram (EMG) in EEG signal increases the difficulty in analyzing them. Discrete wavelet transform has been applied in this research for removing noise from the EEG signal. The effectiveness of the noise removal is quantitatively measured using Root Mean Square (RMS) Difference. This paper reports on the effectiveness of wavelet transform applied to the EEG signal as a means of removing noise to retrieve important information related to both healthy and epileptic patients. Wavelet-based noise removal on the EEG signal of both healthy and epileptic subjects was performed using four discrete wavelet functions. With the appropriate choice of the wavelet function (WF), it is possible to remove noise effectively to analyze EEG significantly. Result of this study shows that WF Daubechies 8 (db8) provides the best noise removal from the raw EEG signal of healthy patients, while WF orthogonal Meyer does the same for epileptic patients. This algorithm is intended for FPGA implementation of portable biomedical equipments to detect different brain state in different circumstances.
Fulltext Reduction of the dimensionality of the EEG channels during scoliosis correction surgeries using a wavelet decomposition technique

Al-Kadi MI, Reaz MB, Ali MA, Liu CY

Sensors (Basel), 2014;14(7):13046-69.
PMID: 25051031 DOI: 10.3390/s140713046

This paper presents a comparison between the electroencephalogram (EEG) channels during scoliosis correction surgeries. Surgeons use many hand tools and electronic devices that directly affect the EEG channels. These noises do not affect the EEG channels uniformly. This research provides a complete system to find the least affected channel by the noise. The presented system consists of five stages: filtering, wavelet decomposing (Level 4), processing the signal bands using four different criteria (mean, energy, entropy and standard deviation), finding the useful channel according to the criteria's value and, finally, generating a combinational signal from Channels 1 and 2. Experimentally, two channels of EEG data were recorded from six patients who underwent scoliosis correction surgeries in the Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM) (the Medical center of National University of Malaysia). The combinational signal was tested by power spectral density, cross-correlation function and wavelet coherence. The experimental results show that the system-outputted EEG signals are neatly switched without any substantial changes in the consistency of EEG components. This paper provides an efficient procedure for analyzing EEG signals in order to avoid averaging the channels that lead to redistribution of the noise on both channels, reducing the dimensionality of the EEG features and preparing the best EEG stream for the classification and monitoring stage.
Fulltext A novel vehicle stationary detection utilizing map matching and IMU sensors

Amin MS, Reaz MB, Nasir SS, Bhuiyan MA, Ali MA

ScientificWorldJournal, 2014;2014:597180.
PMID: 25276855 DOI: 10.1155/2014/597180

Precise navigation is a vital need for many modern vehicular applications. The global positioning system (GPS) cannot provide continuous navigation information in urban areas. The widely used inertial navigation system (INS) can provide full vehicle state at high rates. However, the accuracy diverges quickly in low cost microelectromechanical systems (MEMS) based INS due to bias, drift, noise, and other errors. These errors can be corrected in a stationary state. But detecting stationary state is a challenging task. A novel stationary state detection technique from the variation of acceleration, heading, and pitch and roll of an attitude heading reference system (AHRS) built from the inertial measurement unit (IMU) sensors is proposed. Besides, the map matching (MM) algorithm detects the intersections where the vehicle is likely to stop. Combining these two results, the stationary state is detected with a smaller timing window of 3 s. A longer timing window of 5 s is used when the stationary state is detected only from the AHRS. The experimental results show that the stationary state is correctly identified and the position error is reduced to 90% and outperforms previously reported work. The proposed algorithm would help to reduce INS errors and enhance the performance of the navigation system.
Fulltext Design of high speed and low offset dynamic latch comparator in 0.18 µm CMOS process

Rahman LF, Reaz MB, Yin CC, Ali MA, Marufuzzaman M

PLoS One, 2014;9(10):e108634.
PMID: 25299266 DOI: 10.1371/journal.pone.0108634

The cross-coupled circuit mechanism based dynamic latch comparator is presented in this research. The comparator is designed using differential input stages with regenerative S-R latch to achieve lower offset, lower power, higher speed and higher resolution. In order to decrease circuit complexity, a comparator should maintain power, speed, resolution and offset-voltage properly. Simulations show that this novel dynamic latch comparator designed in 0.18 µm CMOS technology achieves 3.44 mV resolution with 8 bit precision at a frequency of 50 MHz while dissipating 158.5 µW from 1.8 V supply and 88.05 µA average current. Moreover, the proposed design propagates as fast as 4.2 nS with energy efficiency of 0.7 fJ/conversion-step. Additionally, the core circuit layout only occupies 0.008 mm2.
Fulltext Designing a ring-VCO for RFID transponders in 0.18 μm CMOS process

Jalil J, Reaz MB, Bhuiyan MA, Rahman LF, Chang TG

ScientificWorldJournal, 2014;2014:580385.
PMID: 24587731 DOI: 10.1155/2014/580385

In radio frequency identification (RFID) systems, performance degradation of phase locked loops (PLLs) mainly occurs due to high phase noise of voltage-controlled oscillators (VCOs). This paper proposes a low power, low phase noise ring-VCO developed for 2.42 GHz operated active RFID transponders compatible with IEEE 802.11 b/g, Bluetooth, and Zigbee protocols. For ease of integration and implementation of the module in tiny die area, a novel pseudodifferential delay cell based 3-stage ring oscillator has been introduced to fabricate the ring-VCO. In CMOS technology, 0.18 μm process is adopted for designing the circuit with 1.5 V power supply. The postlayout simulated results show that the proposed oscillator works in the tuning range of 0.5-2.54 GHz and dissipates 2.47 mW of power. It exhibits a phase noise of -126.62 dBc/Hz at 25 MHz offset from 2.42 GHz carrier frequency.
Fulltext Surface electromyography signal processing and classification techniques

Chowdhury RH, Reaz MB, Ali MA, Bakar AA, Chellappan K, Chang TG

Sensors (Basel), 2013;13(9):12431-66.
PMID: 24048337 DOI: 10.3390/s130912431

Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.
Fulltext Active inductor based fully integrated CMOS transmit/ receive switch for 2.4 GHz RF transceiver

Bhuiyan MA, Zijie Y, Yu JS, Reaz MB, Kamal N, Chang TG

An Acad Bras Cienc, 2016 May 31;88(2):1089-98.
PMID: 27254443 DOI: 10.1590/0001-3765201620150123

Modern Radio Frequency (RF) transceivers cannot be imagined without high-performance (Transmit/Receive) T/R switch. Available T/R switches suffer mainly due to the lack of good trade-off among the performance parameters, where high isolation and low insertion loss are very essential. In this study, a T/R switch with high isolation and low insertion loss performance has been designed by using Silterra 0.13µm CMOS process for 2.4GHz ISM band RF transceivers. Transistor aspect ratio optimization, proper gate bias resistance, resistive body floating and active inductor-based parallel resonance techniques have been implemented to achieve better trade-off. The proposed T/R switch exhibits 0.85dB insertion loss and 45.17dB isolation in both transmit and receive modes. Moreover, it shows very competitive values of power handling capability (P1dB) and linearity (IIP3) which are 11.35dBm and 19.60dBm, respectively. Due to avoiding bulky inductor and capacitor, the proposed active inductor-based T/R switch became highly compact occupying only 0.003mm2 of silicon space; which will further trim down the total cost of the transceiver. Therefore, the proposed active inductor-based T/R switch in 0.13µm CMOS process will be highly useful for the electronic industries where low-power, high-performance and compactness of devices are the crucial concerns.
Fulltext A machine learning model for early detection of diabetic foot using thermogram images

Khandakar A, Chowdhury MEH, Ibne Reaz MB, Md Ali SH, Hasan MA, Kiranyaz S, et al.

Comput Biol Med, 2021 10;137:104838.
PMID: 34534794 DOI: 10.1016/j.compbiomed.2021.104838

Diabetes foot ulceration (DFU) and amputation are a cause of significant morbidity. The prevention of DFU may be achieved by the identification of patients at risk of DFU and the institution of preventative measures through education and offloading. Several studies have reported that thermogram images may help to detect an increase in plantar temperature prior to DFU. However, the distribution of plantar temperature may be heterogeneous, making it difficult to quantify and utilize to predict outcomes. We have compared a machine learning-based scoring technique with feature selection and optimization techniques and learning classifiers to several state-of-the-art Convolutional Neural Networks (CNNs) on foot thermogram images and propose a robust solution to identify the diabetic foot. A comparatively shallow CNN model, MobilenetV2 achieved an F1 score of ∼95% for a two-feet thermogram image-based classification and the AdaBoost Classifier used 10 features and achieved an F1 score of 97%. A comparison of the inference time for the best-performing networks confirmed that the proposed algorithm can be deployed as a smartphone application to allow the user to monitor the progression of the DFU in a home setting.
A nomogram-based diabetic sensorimotor polyneuropathy severity prediction using Michigan neuropathy screening instrumentations

Haque F, Ibne Reaz MB, Chowdhury MEH, Md Ali SH, Ashrif A Bakar A, Rahman T, et al.

Comput Biol Med, 2021 12;139:104954.
PMID: 34715551 DOI: 10.1016/j.compbiomed.2021.104954

BACKGROUND: Diabetic Sensorimotor polyneuropathy (DSPN) is one of the major indelible complications in diabetic patients. Michigan neuropathy screening instrumentation (MNSI) is one of the most common screening techniques used for DSPN, however, it does not provide any direct severity grading system.
METHOD: For designing and modeling the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials were used. Different Machine learning-based feature ranking techniques were investigated to identify the important MNSI features associated with DSPN diagnosis. A multivariable logistic regression-based nomogram was generated and validated for DSPN severity grading using the best performing top-ranked MNSI features.
RESULTS: Top-10 ranked features from MNSI features: Appearance of Feet (R), Ankle Reflexes (R), Vibration perception (L), Vibration perception (R), Appearance of Feet (L), 10-gm filament (L), Ankle Reflexes (L), 10-gm filament (R), Bed Cover Touch, and Ulceration (R) were identified as important features for identifying DSPN by Multi-Tree Extreme Gradient Boost model. The nomogram-based prediction model exhibited an accuracy of 97.95% and 98.84% for the EDIC test set and an independent test set, respectively. A DSPN severity score technique was generated for MNSI from the DSPN severity prediction model. DSPN patients were stratified into four severity levels: absent, mild, moderate, and severe using the cut-off values of 17.6, 19.1, 20.5 for the DSPN probability less than 50%, 75%-90%, and above 90%, respectively.
CONCLUSIONS: The findings of this work provide a machine learning-based MNSI severity grading system which has the potential to be used as a secondary decision support system by health professionals in clinical applications and large clinical trials to identify high-risk DSPN patients.

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