Designing of a biopotential amplifier for the acquisition and processing of subvocal electromyography signals

Designing of a biopotential amplifier for the acquisition and processing of subvocal electromyography signals

36

Reddy Vamsi1, Suraj K. Nayak1, Anilesh Dey2, Arindam Bit3, Biswajit Mohapatra4, Haladhar Behera1 and Kunal Pal1 1 Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela, India, 2Department of Electronics and Communication Engineering, Narula Institute of Technology, Kolkata, India, 3Department of Biomedical Engineering, National Institute of Technology, Raipur, India, 4Vesaj Patel Hospital, Rourkela, India

Introduction In this tremendously growing era of digital technologies, the integration of the medical devices with smart technologies has been addressing the insurmountable necessities of the differently abled people. The World Health Organisation (WHO) (2011) had adapted the International Classification of Functioning, Disability and Health as the conceptual framework to define the term “disability” in the World Report on Disability. Disability is the generalized term for impairment, limitations in activities, and participation on restrictions, referring to the negative interaction between a healthy person and that individual’s contextual factors. The Irish census and the disability survey of 2006 state that 9.3% of the total population in Ireland is suffering from various types of disabilities. According to WHO (2011) report on Zambia in 2006, the majority of the differently abled population suffers from mobility-related disabilities. As defined in the Disability Act (1995) of India, “a person with a disability is anyone who suffers at least 40% impairment from a medical issue such as blindness, low vision, leprosy, hearing problems, locomotor disability, mental retardation or mental illness” (Census of India, 2011). The most common disabilities in India include ocular disability, locomotor disability, mental disability, and disability due to speech and hearing. In the developing countries, many people neither have access to sophisticated treatment facilities nor have the awareness about the growing technology to resolve the challenges due to the impairment of a specific function in the human body. Movement-related disability was found to be relatively dominant in the distinguished viewpoint of the report, which was made to estimate the distribution of the disabled population by the type of disability in India during 2011 (Fig. 36.1). The report pointed out that 20% Bioelectronics and Medical Devices. DOI: https://doi.org/10.1016/B978-0-08-102420-1.00043-1 Copyright © 2019 Elsevier Ltd. All rights reserved.

914

Bioelectronics and Medical Devices

Figure 36.1 Distribution of disabled population by type of disability in India (Census of India, 2011).

(approximately) of the disabled population suffers from a movement-related disability, and this constitutes the most common disabled population. The biomedical industry has been inclined to the development of rehabilitation devices, especially for the aforementioned motor-disabled individuals. Distinct technologies such as functional electrical stimulation, powered wheelchairs, etc., have emerged over the past decade by the continuous integration of the aptitudes of the assistive devices and the user. Enhanced integration is possible by upgrading the mechanics of the assistive devices and amending the physical interface of the user. Bilateral control between the assistive technology and the user can be a major patron for the development of emerging technologies. A biosignal can be defined as the descriptor of a physiological phenomenon occurring in the human body (McAdams, 2006; Schultz et al., 2017). The biosignals like electroencephalogram (EEG), electrooculography (EOG), and electromyography (EMG) have been widely explored for the development of various human computer interface-based assistive technologies in recent years (Quitadamo et al., 2017; Ramkumar, Kumar, Rajkumar, Ilayaraja, & Shankar, 2018). The EEG signal represents the electrical activity of the neurons occurring within the brain. It is a small voltage signal with a magnitude of about 10 100 μV, measured with respect to the scalp (Horlings, Datcu, & Rothkrantz, 2008; Vaid, Singh, & Kaur, 2015). Its

Designing of a biopotential amplifier for the acquisition and processing

915

frequency lies in the range of 0 100 Hz. The EEG signals have been reported to provide information about the mental conditions of a person (Horlings et al., 2008) and have been employed in developing control systems for neuro-prosthetics (Lotte et al., 2018; McFarland & Wolpaw, 2017). However, the EEG electrodes have to be placed on the scalp around the head that reduces the patient compliance. Also, the EEG signal analysis is relatively more complicated among the three aforementioned biosignals. The EOG signal represents the electrical activity of the corneo-retinal potential that exists between the anterior and posterior of the human eye. The EOG signals are preferred for the development of rehabilitative devices because of the low cost of production of the EOG bioamplifier and the ease of usage (Ramkumar et al., 2018). Despite the advantages, the EOG has its own set of disadvantages such as the unstable corneo-retinal potential, which varies as the individuals experience fatigue (Banerjee et al., 2012). The EMG signals are the graphical representation of the biopotential generated by the muscles (Zawawi et al., 2018). The EMG signals can also be defined as an indicator of the electrical activity of the muscle. They can be acquired from the various parts of the body (e.g., upper and lower limbs, head, neck, subvocal region, and face). These signals have been studied for controlling rehabilitative devices like prosthetics (Tavakoli, Benussi, & Lourenco, 2017) and electricpowered wheelchairs (Joraimee, Tarmizi, Redhwan, Hairy, & Azinee, 2018; Maeda & Ishibashi, 2017). Among the EMG signals acquired from the different parts of the body, the subvocal electromyogram (svEMG) signals have gained popularity in recent years (Meltzner et al., 2018). The svEMG signals, acquired from the volunteers while performing a specific task, can be used to develop a bigger net of rehabilitative technologies for the differently abled people. Despite being affected by various types of diseases, the svEMG signals remain intact (Jorgensen & Binsted, 2005). However, the number of studies attempted on the analysis of the svEMG signals was found to be limited. The vague apprehensions regarding the deterioration of the svEMG signals by sensual factors (like age, vocal cord functioning, etc.) severely limit the creative efforts required to explore the real potential of the svEMG signals. In this study, we report the development of a biopotential amplifier to acquire the svEMG signals, which can be used to control the assistive devices. The svEMG signals, acquired while uttering different words, were processed and classified with adequate accuracy. This study also highlights the real potential of svEMG signals in the development of rehabilitative devices and biocontrol systems.

Literature review Among the various biosignals, the EMG signals have been widely employed for the control of the rehabilitative devices (e.g., electric-powered wheelchairs and artificial limbs), which are specially engaged for providing the independence to the physically impaired people (Al-Timemy, Bugmann, & Escudero, 2018; Jang, Kim, Lee, & Choi, 2016; Li et al., 2014). These applications can be attributed to its

916

Bioelectronics and Medical Devices

noninvasive behavior and also to availability of various patterns of EMG signals even during different motor impairments. In the early years of this century, the svEMG signal classification was applied to control a modified web browser interface (Jorgensen & Binsted, 2005). The svEMG signals were acquired from the sublingual region underneath the jaw and were processed using a specialized complex dual quad tree wavelet transform. Six subvocally pronounced control word-sets were used during the acquisition. In this study, the acquisition of svEMG was performed by pronouncing a feature set of six control words, 17 vowels, 10 digits, and 23 consonant phonemes. Major challenge mentioned in this study for svEMG-based research was the voicing feature (Jorgensen & Binsted, 2005). Subsequent to the above study, Lam, Mak, and Leong (2005) proposed a methodology for speech synthesis, which was based on the surface electromyogram (sEMG) signals acquired from the chin and cheek. The sEMG signals were acquired, and parallel speech recordings were transformed into the frequency domain to extract multiple features. This study chose short-time Fourier transform and linear predictive coding coefficients as sEMG and speech features, respectively. The sEMG features were converted into speech features by employing a neural network classifier on a frame-by-frame basis. The processed parameters supplemented in reconstructing the original speech. Mendes, Robson, Labidi, and Barros (2008) proposed a subvocal speech recognition system based on sEMG signals acquired from the subvocal region. The researchers used a subvocal speech database, which consists of the EMG signals extracted while pronouncing the Portuguese vowels. The feature extraction was done by independent component analysis. The recorded signals were classified using neural networks (Mendes et al., 2008). Champaty, Biswal, Pal, and Tibarewala (2014) reported the development of a biopotential amplifier for the acquisition of the svEMG signals. The time domain and discrete wavelet transform (DWT)-based features were extracted from the acquired svEMG signals. The classification of the features using random forest (RF) method provided a classification accuracy of more than 90%. Based on the results, the authors suggested that the proposed system can be used for controlling rehabilitative devices. Meltzner et al. (2018) proposed the use of specially designed miniaturized sensors for acquiring EMG signals from the subvocal region (i.e., muscles of face and neck responsible for the speech generation) so that a silent speech recognition system can be developed. The acquired signals were subjected to a number of signal processing algorithms for the synthesis of speech. The proposed system was used in a number of subvocal speech experiments and a word recognition rate of 91.1% could be achieved. Thus the authors proposed that the developed system may be used as an alternate way of communication for patients with speech impairment and covert communication of military personnel (Meltzner et al., 2018). Taking note of the above-mentioned facts, in this study, we report the development of a biopotential amplifier for the acquisition of the svEMG signals, followed by their classification using artificial neural network (ANN).

Designing of a biopotential amplifier for the acquisition and processing

917

Materials and software A laptop (Ideapad S510p, Lenovo, China), printed circuit board (PCB) (Anand Circuits, India), instrumentation amplifiers (INA128P, Texas Instruments, United States), operational amplifiers (OP07CP, Analog Devices, United States), Ag/AgCl disposable electrodes (BPL Medical Technologies Private Limited, India), lead wires (MEDKE, mainland, China), DC 9 V batteries (NIPPO, India), NI USB-6008 data acquisition device (National Instruments, United States), LabVIEW (V17.0, National Instruments, United States), Statistica (trial version 13.2, Statsoft Inc., United States), MATLAB (R2015a Math Works, Inc., United States), and Eagle (trial version, Autodesk, United States) were used in this study.

Methods Designing of a subvocal electromyogram biopotential amplifier The dynamic range of the amplitude of a svEMG signal is confined to 1 10 mV (Pradhan et al., 2016; Uvanesh et al., 2016). Hence, the svEMG signals require a substantial amplification for the acquisition and further processing. A svEMG biopotential amplifier was developed for the restrained acquisition of the svEMG signals. It was provided with an overall gain of B15,840 V/V in three stages (Fig. 36.2), which was indeed a major modification of the amplifier developed by our group earlier (Champaty et al., 2014). The first stage gain of 50 V/V was provided by an INA128P instrumentation amplifier. The output signal of the instrumentation amplifier was carried through the integrator (fc  10 Hz) circuit to eliminate the DC offset voltage, if any. The capacitor in the integrated circuit plays a vital role in eliminating DC offset and associated noise. Such capacitors typically

Figure 36.2 Circuit diagram of svEMG biopotential amplifier.

918

Bioelectronics and Medical Devices

operate at a frequency range lower than 10 kHz. Below 10 kHz, the equivalent series resistance of electrolytic capacitor increases, so Tantalum capacitors were incorporated in the integrator circuit instead of the electrolytic capacitors. The second and third stage gains of 48 and 6.6 V/V, respectively, were implemented using OP07CP operational amplifiers in noninverting configuration. The bandwidth of the acquired svEMG signal was limited by incorporating a first-order high-pass filter (fc  20 Hz) before the second stage gain amplifier and an antialiasing first-order low-pass filter (fc  2000 Hz) after the third stage gain amplifier. Proper grounding of the volunteer plays an important role in the designing of a bioamplifier as it helps to decrease the interference noise (e.g., electromagnetic interference) by reducing the common mode gain. An OP07CP operational amplifiers-based actively driven ground (ADG) circuit was implemented in our proposed bioamplifier using a buffer circuit, followed by an integrator circuit (Chi et al., 2010). The output of the ADG circuit was connected to the reference electrode.

Development of the printed circuit board Printed circuit board design The design layout of the PCB for the EMG amplifier was prepared in Eagle software. First, a schematic sheet was opened, and all the necessary components were added to the graphical user interface of Eagle software. The components were then connected as per the amplifier design and the nets were inserted at appropriate locations in the schematic to specify paths and junctions. An electrical rule check was performed to analyze the errors in the schematic, if any. A board outline was created within the software, and the entire schematic diagram was transferred onto it. All the components were positioned on the board as per the predetermined design of the PCB, considering the ease of soldering, perception of the design, and also the rectangular-edged connections to avoid greater heat dissipations. The tracks between the positioned components on the board were routed. The design rule check was performed to ensure that all the connections were appropriately routed. Text legends were added to the PCB layout for providing additional details, such as the circuit name. The board model was then printed on a glossy sheet for the construction of the PCB.

Printed circuit board construction The construction of PCB for the svEMG biopotential amplifier was performed using the carbon transfer-copper etching method (Nayak et al., 2015). A reinforced phenolic resin-based single-sided copper-clad board (with a bonded copper foil) was cut with desired dimensions. The copper side of the board was cleaned with a sandpaper or steel wool to remove the pre-formed oxidized layer. The PCB layout on the glossy sheet was adjusted and placed on the single-sided copper-clad board in such a way that the carbon lining of the design on the glossy sheet touched the copper side of the board. The arrangement was gently heated to transfer the carbon lining to the copper side of the PCB. Ferric chloride solution was prepared by

Designing of a biopotential amplifier for the acquisition and processing

919

diluting 10.5 g of ferric chloride with 250 mL of water. The PCB board was then dipped in the prepared solution, and it was shaken until the unprinted portion of the copper side was etched out. Thereafter, the PCB was cleaned with a scrubber in running water. This resulted in the formation of the copper track. Holes were drilled carefully at the specified terminals. An additional soldering layer was uniformly applied to the copper track to avoid further oxidation. Subsequently, the components were mounted onto the PCB, according to the circuit design. At first, the bases for the integrated circuits (ICs) and the headers for power supply and input and output terminals were mounted. This was followed by the soldering of the resistors, capacitors, and jumper wires. The ICs were then inserted into their corresponding bases. The PCB prototype was finally tested for the proper acquisition of the svEMG signals using a set of three electrodes.

Acquisition of subvocal electromyogram signals The acquisition of the EMG signals was carried out using NI USB-6008 data acquisition device and an in-lab developed LabVIEW program. A proffered healthy individual was instructed about the specific protocol to be followed while acquiring the data, and a written consent was received from him. Prior approval was received from the Institute Ethical Clearance committee for the acquisition of the EMG signals. The EMG signals were recorded while the individual was performing the hand movements for testing purposes. A similar procedure was emphasized to acquire the EMG signals from the subvocal region while uttering the four directional commands: LEFT, RIGHT, START, and STOP. The electrodes were positioned on the right and the left anterior area of the throat, around 0.25 cm below the chin and 1.5 cm from the right and the left of the larynx. The proffered healthy individual was suggested to repeat the utterance of each command 10 times during the acquisition of the svEMG signals. The signals were stored in a laptop in .lvm file format using the “Write to Measurement” palette of LabVIEW for further analysis.

Processing and feature extraction of subvocal electromyogram signals The acquired svEMG signals were associated with noises like the power line interference and basal noise. The svEMG signals of 4 seconds duration, consisting of a single svEMG peak of the proffered volunteer, were subjected to digital band-stop filters (fcL 5 45 Hz and fcH 5 55 Hz) using an in-lab developed LabVIEW program for the elimination of the power line interference. The rectification and smoothening of the signals were carried out to obtain the envelope of the acquired signals. The basal noise was removed by applying a threshold of 0.05 during the extraction of the envelope. The threshold signal was multiplied with the initially filtered signal, and the resulting signal was used to estimate the statistical parameters. The calculated statistical parameters included arithmetic mean (AM), root mean square (RMS) values, variance (VAR), standard deviation (SD), kurtosis, mode, skewness,

920

Bioelectronics and Medical Devices

Figure 36.3 Block diagram representation of svEMG signal processing and classification. svEMG, Subvocal electromyogram.

median, and summation. The flowchart of the steps used for the processing and the analysis of the svEMG signals are given in Fig. 36.3.

Statistical analysis and classification using ANN The statistical analysis of the extracted svEMG signal parameters was performed using different tests, namely, classification and regression tree (CART), boosted tree (BT), random forest (RF), and breakdown and one-way analysis of variance (ANOVA). These methods helped to identify the statistically significant parameters among all the extracted parameters. The important parameters identified during these statistical tests were provided as categorical inputs to multilayer perceptron (MLP)-based ANN classifiers. The MLP network has three customized layers: an input layer, one or more hidden layers, and an output layer. A typical representation of the MLP network is given by MLP a-b-c, where a, b, c represents the number of categorical inputs, number of perceptrons in the hidden layers, and number of output classes, respectively. The MLP classifier was implemented using Statistica Automated Neural Networks. Back propagation algorithm was used to train the MLP networks (Gurney, 2014).

Results and discussion Development of a subvocal electromyogram biopotential amplifier Initially, a svEMG biopotential amplifier was developed on a breadboard. The overall theoretical gain of the subvocal biopotential amplifier was B15,840 V/V with 50, 48, and 6.6 V/V gains in three stages, respectively. A sinusoidal signal of 1 mV

Designing of a biopotential amplifier for the acquisition and processing

921

at 100 Hz frequency was fed to the biopotential amplifier using a function generator, and the output voltage was measured to be 15.4 V. So, the practical gain was determined to be B15,400 V/V, with an error of 2.7%. The above testing ensured the reliability and the functionality of the developed biopotential amplifier.

Frequency characteristics of the antialiasing low-pass filter The frequency characteristics of the first-order antialiasing low-pass filter, used after the third gain stage of the biopotential amplifier, are shown in Fig. 36.4. The low-pass filter (fc 5 2000 Hz) consisted of resistance (R1) of 6.8 kΩ and capacitance (C1) of 0.01 μF. This antialiasing low-pass filter was introduced to suppress the frequency components of the acquired signal above the maximum-allowed frequency (i.e., half of the sampling rate).

Development of the printed circuit board The designing of the PCB layout for the svEMG biopotential amplifier circuit was performed using Eagle software. The design was optimized both in terms of the positioning of the components and proper spatial appearance. The PCB was constructed from the designed layout using carbon transfer-copper etching method on a reinforced phenolic resin-based single-sided copper-clad board. Fig. 36.5 represents the developed PCB of the svEMG biopotential amplifier. The three-pin connector of ice blue color was meant for the power supply (negative-ground-positive starting

Figure 36.4 Frequency characteristics of antialiasing first-order low-pass filter.

922

Bioelectronics and Medical Devices

Figure 36.5 PCB prototype of svEMG biopotential amplifier. PCB, Printed circuit board; svEMG, subvocal electromyogram.

from the left). The PCB also contained a three-pin and a two-pin red-colored connector. The red-colored three-pin connector was meant for connecting the electrodes, whereas the two-pin connector was provided for connecting the output of the svEMG biopotential amplifier to the analog input channel and ground terminal of the data acquisition system.

Acquisition and processing of subvocal electromyogram signals The Ag/AgCl disposable electrodes were used for the acquisition of the svEMG signals. The combination of high efficiency and low half-cell potential in these electrodes helps in minimizing the motion artifacts (Pradhan et al., 2016). The two electrodes were placed on the right and the left anterior area of the throat around 0.25 cm below the chin and 1.5 cm from the right and left of the larynx (Fig. 36.6). The amplitude range of the svEMG signals were enhanced by providing a significant gain (B15,400 V/V) through the svEMG biopotential amplifier. The undesired portion of the acquired svEMG signal was eliminated and a 4-second portion of each svEMG signal was extracted for all the four commands: LEFT, RIGHT, START, STOP, such that the physiological activity of the volunteer lies within that 4-second time window. The processing of the acquired signals comprises several phases, namely, filtering, rectification, smoothening, thresholding, and signal extraction (Fig. 36.7). In the filtering phase, digital filters were applied to the preprocessed signals in order to eliminate the DC offset, power line interference

Designing of a biopotential amplifier for the acquisition and processing

923

Figure 36.6 Positioning of electrodes at the right and the left anterior area of the throat at the subvocal region.

(50 Hz noise), basal noise, and EMG swallowing muscle fatigue. A third-order Butterworth high-pass digital filter (fc 5 10 Hz) was applied to flatten the response of the signal in the passband and thereby reduce the DC offset in the acquired svEMG signal. Successively, a third-order Butterworth band-stop infinite impulse response filter, with lower and higher cutoff frequencies of 45 and 55 Hz, was introduced to eliminate the power line interference (50 Hz noise). The filtered signal was rectified by squaring the filtered signal to obtain the power spectrum of the acquired svEMG signals. The data points of the svEMG signals were smoothened by applying a rectangular moving average smoothing filter with a half-width moving average value of 2000. The smoothening process reduces the relatively high amplitude data points to the corresponding average of the adjacent low amplitude data points and vice versa. Despite the analog and digital filtering, the acquired svEMG signals possessed a small amount of noise. Therefore thresholding was applied in order to eliminate all the leftover noise. A limit of 0.05 was applied to deliberately eliminate the basal noise. The filtered signal and threshold signal were then multiplied to obtain the svEMG signal free from baseline noise. This signal was used to extract the statistical parameters, namely, AM, RMS values, VAR, SD, kurtosis, mode, skewness, median, and summation using a LabVIEW program. The aforementioned parameters were later used for statistical analysis and classification.

924

Bioelectronics and Medical Devices

Figure 36.7 Schematic representation of svEMG signals at various stages: (A) filtered signal, (B) rectified signal, (C) smoothened signal, (D) threshold signal, and (E) extracted signal obtained by multiplying (A) and (D). svEMG, Subvocal electromyogram.

Statistical analysis and classification using ANN The statistical parameters acquired from the svEMG signals were analyzed by various data mining techniques such as CART, BT, RF, and breakdown and one-way ANOVA. The CART method is regarded as the primitive decision tree based analysis method, which uses recursive partitioning and pruning mechanisms to form the binary decision tree (Lawrence & Wright, 2001). The BT method is another decision tree method, which uses the stochastic gradient boosting algorithms to improve

Table 36.1 Statistically significant features of the extracted subvocal electromyogram signals. Methods

CART

BT

RF breakdown and one-way ANOVA

Mean 6 SD

Parameters

Kurtosis VAR SD RMS Mode Skewness SM Mode AM RMS VAR SD SM Mode AM RMS SD VAR Mode SM

PI

Category left

Category right

Category start

Category stop

3.977 6 2.247 0.015 6 0.005 0.122 6 0.023 0.139 6 0.029 0.001 6 0.000 1.588 6 0.494 1403.18 6 214.28 0.0017 6 0.000 0.066 6 0.018 0.139 6 0.029 0.015 6 0.005 0.122 6 0.023 1403.18 6 214.28 0.001 6 0.000 0.066 6 0.018 0.139 6 0.029 0.122 6 0.023 0.015 6 0.005 0.001 6 0.000 1403.18 6 214.28

3.322 6 0.016 0.027 6 0.008 0.164 6 0.029 0.187 6 0.034 0.002 6 0.000 1.456 6 0.008 1803.00 6 337.60 0.002 6 0.000 0.090 6 0.0168 0.182 6 0.034 0.027 6 0.008 0.164 6 0.029 1803.00 6 337.67 0.002 6 0.000 0.090 6 0.016 0.187 6 0.034 0.164 6 0.029 0.027 6 0.008 0.002 6 0.000 1803.00 6 337.60

3.204 6 0.090 0.045 6 0.017 0.209 6 0.045 0.241 6 0.051 0.002 6 0.000 1.404 6 0.061 2410.45 6 473.70 0.002 6 0.000 0.120 6 0.023 0.241 6 0.051 0.045 6 0.017 0.209 6 0.045 2410.41 6 473.70 0.002 6 0.000 0.120 6 0.023 0.241 6 0.051 0.209 6 0.045 0.045 6 0.017 0.002 6 0.000 2410.45 6 473.70

3.168 6 0.078 0.056 6 0.030 0.228 6 0.068 0.266 6 0.076 0.003 6 0.0008 1.385 6 0.066 2745.90 6 720.73 0.003 6 0.000 0.137 6 0.036 0.266 6 0.076 0.056 6 0.030 0.228 6 0.068 2745.946 6 720.73 0.003 6 0.000 0.137 6 0.036 0.266 6 0.0769 0.228 6 0.068 0.056 6 0.030 0.003 6 0.000 2745.95 6 720.74

P value

1.000 0.990 0.990 0.970 0.960 0.950 1.000 1.000 1.000 0.980 0.980 0.980 1.000 0.970 1E 2 6 12E 2 6 34E 2 6 91E 2 6 11E 2 6 1E 2 6

AM, Arithmetic mean; ANOVA, analysis of variance; BT, boosted tree; CART, classification and regression tree; PI, predictor importance; RF, random forest; RMS, root mean square deviation; SD, standard deviation; SM, summation; VAR, variance.

926

Bioelectronics and Medical Devices

its performance (De’Ath, 2007). The RF method is regarded as the most widely used decision tree based method, which differs from the CART method in the sense that the RF trees are generated non-deterministically, and only a subset of the available parameters is used to form the tree instead of using the entire parameter set (Hau, 2015). The one-way ANOVA is a linear statistical method that is used to find the difference among the mean values of the parameters computed from two or more populations for one dependent variable (Ross & Willson, 2017). It is regarded as a generalization of the t test (Heiberger & Neuwirth, 2009). The statistically significant parameters, identified using the given statistical methods, are shown in Table 36.1. These important parameters (i.e., AM, RMS value, SD, VAR, and kurtosis) were provided as categorical inputs to the MLP networks. In the neural networks, more numbers of perceptrons chosen for the hidden layer correlate to the ease of exchange of weights while transferring the data from different neurons to classify the data (Mendes et al., 2008). For implementing the MLP networks, the “Tanh” function was selected as the activation function for hidden layer neurons and the “Identity” function was used as the output activation function. Different networks with their corresponding classification efficiencies for the said hidden and output activation functions are mentioned in Table 36.2. It can be easily observed from Table 36.2 that with the increase in the number of perceptrons in the hidden layer from 3 to 7, the overall classification efficiency was increased accordingly, with the same hidden and output activation functions. The MLP network (MLP 5-3-4) provided an efficiency of 82.5%, and it was further increased to 87.5% for MLP 5-4-4, 92.5% for both MLP 5-5-4 and MLP 5-6-4 networks, with a corresponding increase in the number of perceptrons. The MLP 5-7-4 provided a maximum classification efficiency of 97.50% (Table 36.3). Later, the efficiency values declined gradually upon the increase in the number of perceptrons in the hidden layer.

Table 36.2 Different multilayer perceptron (MLP) networks with their corresponding classification efficiency. Sl. no.

MLP network

Hidden function

Output function

Overall performance

1 2 3 4 5 6 7 8 9

5-3-4 5-4-4 5-5-4 5-6-4 5-7-4 5-8-4 5-9-4 5-10-4 5-11-4

Tanh Tanh Tanh Tanh Tanh Tanh Tanh Tanh Tanh

Identity Identity Identity Identity Identity Identity Identity Identity Identity

82.500 87.500 92.500 92.500 97.500 96.420 95.000 95.000 92.500

Note: Bold value represents the highest overall performance.

Designing of a biopotential amplifier for the acquisition and processing

927

Table 36.3 Confusion matrix of multilayer perceptron 5-7-4.

Total Correct Incorrect Correct (%) Incorrect (%)

Category left

Category right

Category start

Category stop

Overall

10.000 10.000 0.000 100.00

10.000 10.000 0.000 100.000

10.000 9.000 1.000 90.000

10.000 10.000 0.000 100.000

40.000 39.000 1.000 97.500

0.000

0.000

10.000

0.000

2.500

Conclusion To understand the real potential of svEMG signals in controlling the assistive devices, a biopotential amplifier was developed. The proposed biopotential amplifier provided a practical gain of B15,400 V/V in three stages. A PCB was designed for the amplifier using carbon transfer-copper etching method, and the svEMG signals were acquired. The acquired svEMG signals were processed, and various statistical parameters were computed. Multiple methods, namely, CART, BT, RF, and breakdown and one-way ANOVA were used to analyze the statistical parameters. The significant parameters identified using the above-mentioned techniques were fed as categorical inputs to MLP networks for classifying the svEMG signals. The MLP 5-7-4 network provided a maximum classification efficiency of 97.50%. A comparison of the classification efficiencies was presented by varying the number of perceptrons in the hidden layer. The classification accuracy of the svEMG signals acquired using the proposed biopotential amplifier suggested that it can act as a potential candidate for the development of svEMG signal-based assistive device control systems. The proposed study enhances the vision of biocontrol systems for biomedical applications.

References Al-Timemy, A., Bugmann, G., & Escudero, J. (2018). Adaptive windowing framework for surface electromyogram-based pattern recognition system for transradial amputees. Sensors, 18(8), 2402. Banerjee, A., Chakraborty, S., Das, P., Datta, S., Konar, A., Tibarewala, D., et al. (2012). Single channel electrooculogram (EOG) based interface for mobility aid. In: Paper presented at the Intelligent Human Computer Interaction (IHCI), 2012 4th international conference on. Census of India. Census of India 2011 data on disability. (2011). Retrieved from ,http:// www.censusindia.gov.in/2011census/Disability_Data/Disability_2011_Data_Release_ Dec_2013_PPT%20(27.12.13).ppt..

928

Bioelectronics and Medical Devices

Champaty, B., Biswal, B. K., Pal, K., & Tibarewala, D. (2014). Random forests based subvocal electromyogram signal acquisition and classification for rehabilitative applications. In: Paper presented at the Automation, Control, Energy and Systems (ACES), 2014 first international conference on. Chi, Y. M., Ng, P., Kang, E., Kang, J., Fang, J., & Cauwenberghs, G. (2010). Wireless noncontact cardiac and neural monitoring. In: Paper presented at the wireless health 2010. De’Ath, G. (2007). Boosted trees for ecological modeling and prediction. Ecology, 88(1), 243 251. Gurney, K. (2014). An introduction to neural networks. CRC press. Hau, C. C. (2015). Handbook of pattern recognition and computer vision. World Scientific. Heiberger, R. M., & Neuwirth, E. (2009). One-way ANOVA. R through excel (pp. 165 191). Springer. Horlings, R., Datcu, D., & Rothkrantz, L. J. (2008). Emotion recognition using brain activity. In: Paper presented at the proceedings of the 9th international conference on computer systems and technologies and workshop for PhD students in computing. Jang, G., Kim, J., Lee, S., & Choi, Y. (2016). EMG-based continuous control scheme with simple classifier for electric-powered wheelchair. IEEE Transactions on Industrial Electronics, 63(6), 3695 3705. Joraimee, M., Tarmizi, I., Redhwan, A., Hairy, B., & Azinee, S. (2018). Powered electric wheelchair controlled by real-time electromyography. Advanced Science Letters, 24(6), 4183 4187. Jorgensen, C., & Binsted, K. (2005). Web browser control using EMG based sub vocal speech recognition. In: Paper presented at the null. Lam, Y.-M., Mak, M.-W., & Leong, P. H.-W. (2005). Speech synthesis from surface electromyogram signal. Paper presented at the Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005. Lawrence, R. L., & Wright, A. (2001). Rule-based classification systems using classification and regression tree (CART) analysis. Photogrammetric Engineering and Remote Sensing, 67(10), 1137 1142. Li, Z., Wang, B., Sun, F., Yang, C., Xie, Q., & Zhang, W. (2014). sEMG-based joint force control for an upper-limb power-assist exoskeleton robot. IEEE Journal of Biomedical and Health Informatics, 18(3), 1043 1050. Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., et al. (2018). A review of classification algorithms for EEG-based brain computer interfaces: A 10 year update. Journal of Neural Engineering, 15(3), 031005. Maeda, Y., & Ishibashi, S. (2017). Operating instruction method based on EMG for omnidirectional wheelchair robot. In: Paper presented at the Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS), 2017 joint 17th world congress of international. McAdams, E. (2006). Bioelectrodes. In Encyclopedia of medical devices and instrumentation. Hoboken, NJ: John Wiley and Sons, Inc. McFarland, D., & Wolpaw, J. (2017). EEG-based brain-computer interfaces. Current Opinion in Biomedical Engineering, 4, 194 200. Meltzner, G. S., Heaton, J. T., Deng, Y., De Luca, G., Roy, S. H., & Kline, J. C. (2018). Development of sEMG sensors and algorithms for silent speech recognition. Journal of Neural Engineering, 15, 046031. Mendes, J. A., Robson, R. R., Labidi, S., & Barros, A. K. (2008). Subvocal speech recognition based on EMG signal using independent component analysis and neural network MLP. In: Paper presented at the image and signal processing, 2008. CISP’08. Congress on.

Designing of a biopotential amplifier for the acquisition and processing

929

Nayak, S., Biswal, D., Champaty, B., Pal, K., Anis, A., Mohapatra, B., et al. (2015). Development of a simultaneous acquisition system for ECG, PCG and body temperature signals. In: Paper presented at the India Conference (INDICON), 2015 annual IEEE. Pradhan, A., Nayak, S. K., Pande, K., Ray, S. S., Pal, K., Champaty, B., et al. (2016). Acquisition and classification of EMG using a dual-channel EMG biopotential amplifier for controlling assistive devices. In: Paper presented at the India Conference (INDICON), 2016 IEEE annual. Quitadamo, L., Cavrini, F., Sbernini, L., Riillo, F., Bianchi, L., Seri, S., et al. (2017). Support vector machines to detect physiological patterns for EEG and EMG-based human computer interaction: A review. Journal of Neural Engineering, 14(1), 011001. Ramkumar, S., Kumar, K. S., Rajkumar, T. D., Ilayaraja, M., & Shankar, K. (2018). A review-classification of electrooculogram based human computer interfaces. Biomedical Research, 29, 1078 1084. Ross, A., & Willson, V. L. (2017). One-way ANOVA. Basic and advanced statistical tests (pp. 21 24). Springer. Schultz, T., Wand, M., Hueber, T., Krusienski, D. J., Herff, C., & Brumberg, J. S. (2017). Biosignal-based spoken communication: A survey. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 25(12), 2257 2271. Tavakoli, M., Benussi, C., & Lourenco, J. L. (2017). Single channel surface EMG control of advanced prosthetic hands: A simple, low cost and efficient approach. Expert Systems with Applications, 79, 322 332. Uvanesh, K., Nayak, S. K., Champaty, B., Thakur, G., Mohapatra, B., Tibarewala, D., et al. (2016). Classification of surface electromyogram signals acquired from the forearm of a healthy volunteer. Classification and clustering in biomedical signal processing (pp. 315 333). IGI Global. Vaid, S., Singh, P., & Kaur, C. (2015). EEG signal analysis for BCI interface: A review. In: Paper presented at the Advanced Computing & Communication Technologies (ACCT), 2015 Fifth international conference on. World Health Organisation (WHO). World report on disability. (2011). Retrieved from ,http://www.who.int/disabilities/world_report/2011/report.pdf.. Zawawi, T. T., Abdullah, A., Jopri, M., Sutikno, T., Saad, N., & Sudirman, R. (2018). A review of electromyography signal analysis techniques for musculoskeletal disorders. Indonesian Journal of Electrical Engineering and Computer Science, 11, 1136 1146.