Butterworth Bandpass Filter Research Articles

Focal mechanism and source determination of the Padang Lawas earthquake april 30, 2020, using moment tensor inversion

The earthquake focal mechanism is one of the essential parameters in understanding the characteristics of an earthquake source. For example, the earthquake mainshock at the Padang Lawas Regency and South Tapanuli Regency on April 30, 2020, caused some damage to houses, schools, and mosques, especially in the South Tapanuli Regency. BMKG released the first information that the earthquake was 1.17N, 99.53E, 24 kilometers northwest of Padang Lawas, North Sumatra province, at a depth of 10 kilometers, Thursday (30/4/2020) at 15.20.25 Local Time with a magnitude of 5.6. The purpose of this study is to determine the focal mechanism of the Padang Lawas earthquake mainshock on April 30, 2021, and determine the fault nodal plane that best suits local tectonic conditions. We used the moment tensor inversion method by using waveform velocity data from the BMKG mini regional station seismic network installed in 2019 and close to the epicenter, namely TTSM, PLSM, LTSM, and RRSI. We also used the velocity model from AK135. In this study, we applied the filter technique using a Butterworth bandpass filter with a lower limit of 0.05 Hz, an upper limit of 0.10 Hz, and a cut frequency of 0.13 Hz. The result of the moment tensor inversion shows the variance of the observation and calculation data is 0.3824. The earthquake source parameters show that the Nodal I plane with strike/dip/slip values is 100.1/88.3/7.5 and the Nodal II plane with strike/dip/slip values is 9.9/82.5/178.3. The value of Seismic Moment (Mo) is 0.4653E+17 Nm or Mw 5. Based on the distribution of aftershocks and the mechanism pattern of the Tapanuli earthquake source on April 30, 2020, it is consistent with the nodal plane II. The mechanism is in the form of a strike-slip fault and corresponds to the movement mechanism of the Sumatran fault system in the Toru segment. Therefore, the tectonic conditions in the southern part of Padang Lawas Regency and South Tapanuli Regency are classified as active. Thus, the government and society need to be aware and make mitigation efforts against the impact of the next earthquake in the future.

Automatic Diagnosis of Epileptic Seizures in EEG Signals Using Fractal Dimension Features and Convolutional Autoencoder Method

This paper proposes a new method for epileptic seizure detection in electroencephalography (EEG) signals using nonlinear features based on fractal dimension (FD) and a deep learning (DL) model. Firstly, Bonn and Freiburg datasets were used to perform experiments. The Bonn dataset consists of binary and multi-class classification problems, and the Freiburg dataset consists of two-class EEG classification problems. In the preprocessing step, all datasets were prepossessed using a Butterworth band pass filter with 0.5–60 Hz cut-off frequency. Then, the EEG signals of the datasets were segmented into different time windows. In this section, dual-tree complex wavelet transform (DT-CWT) was used to decompose the EEG signals into the different sub-bands. In the following section, in order to feature extraction, various FD techniques were used, including Higuchi (HFD), Katz (KFD), Petrosian (PFD), Hurst exponent (HE), detrended fluctuation analysis (DFA), Sevcik, box counting (BC), multiresolution box-counting (MBC), Margaos-Sun (MSFD), multifractal DFA (MF-DFA), and recurrence quantification analysis (RQA). In the next step, the minimum redundancy maximum relevance (mRMR) technique was used for feature selection. Finally, the k-nearest neighbors (KNN), support vector machine (SVM), and convolutional autoencoder (CNN-AE) were used for the classification step. In the classification step, the K-fold cross-validation with k = 10 was employed to demonstrate the effectiveness of the classifier methods. The experiment results show that the proposed CNN-AE method achieved an accuracy of 99.736% and 99.176% for the Bonn and Freiburg datasets, respectively.

The effect of horseshoes and surfaces on horse and jockey centre of mass displacements at gallop.

Horseshoes influence how horses' hooves interact with different ground surfaces, during the impact, loading and push-off phases of a stride cycle. Consequently, they impact on the biomechanics of horses' proximal limb segments and upper body. By implication, different shoe and surface combinations could drive changes in the magnitude and stability of movement patterns in horse-jockey dyads. This study aimed to quantify centre of mass (COM) displacements in horse-jockey dyads galloping on turf and artificial tracks in four shoeing conditions: 1) aluminium; 2) barefoot; 3) GluShu; and 4) steel. Thirteen retired racehorses and two jockeys at the British Racing School were recruited for this intervention study. Tri-axial acceleration data were collected close to the COM for the horse (girth) and jockey (kidney-belt), using iPhones (Apple Inc.) equipped with an iOS app (SensorLog, sample rate = 50 Hz). Shoe-surface combinations were tested in a randomized order and horse-jockey pairings remained constant. Tri-axial acceleration data from gallop runs were filtered using bandpass Butterworth filters with cut-off frequencies of 15 Hz and 1 Hz, then integrated for displacement using Matlab. Peak displacement was assessed in both directions (positive 'maxima', negative 'minima') along the cranio-caudal (CC, positive = forwards), medio-lateral (ML, positive = right) and dorso-ventral (DV, positive = up) axes for all strides with frequency ≥2 Hz (mean = 2.06 Hz). Linear mixed-models determined whether surfaces, shoes or shoe-surface interactions (fixed factors) significantly affected the displacement patterns observed, with day, run and horse-jockey pairs included as random factors; significance was set at p<0.05. Data indicated that surface-type significantly affected peak COM displacements in all directions for the horse (p<0.0005) and for all directions (p≤0.008) but forwards in the jockey. The largest differences were observed in the DV-axis, with an additional 5.7 mm and 2.5 mm of downwards displacement for the horse and jockey, respectively, on the artificial surface. Shoeing condition significantly affected all displacement parameters except ML-axis minima for the horse (p≤0.007), and all displacement parameters for the jockey (p<0.0005). Absolute differences were again largest vertically, with notable similarities amongst displacements from barefoot and aluminium trials compared to GluShu and steel. Shoe-surface interactions affected all but CC-axis minima for the jockey (p≤0.002), but only the ML-axis minima and maxima and DV-axis maxima for the horse (p≤0.008). The results support the idea that hoof-surface interface interventions can significantly affect horse and jockey upper-body displacements. Greater sink of hooves on impact, combined with increased push-off during the propulsive phase, could explain the higher vertical displacements on the artificial track. Variations in distal limb mass associated with shoe-type may drive compensatory COM displacements to minimize the energetic cost of movement. The artificial surface and steel shoes provoked the least CC-axis movement of the jockey, so may promote greatest stability. However, differences between horse and jockey mean displacements indicated DV-axis and CC-axis offsets with compensatory increases and decreases, suggesting the dyad might operate within displacement limits to maintain stability. Further work is needed to relate COM displacements to hoof kinematics and to determine whether there is an optimum configuration of COM displacement to optimise performance and minimise injury.

Evaluation of the airport runway flexible pavement macro-texture using digital image processing technique (DIPT)

ABSTRACT In this paper, we have explored and compared different digital image processing techniques on 2D Images of asphalt pavement for evaluating the macro-texture. Compared to the conventional tests, image processing provides a fast, reliable, and low-cost method with less computational complexity. The images were acquired using a digital camera on a fixed tripod at one angle (90°). We have studied three main digital image processing methods: Butterworth band-pass filter, edge pixels count and fuzzy logic edge detection. The derived parameters were compared with the measured mean texture depth using the sand patch test that was conducted at the same spots where images were captured in the airfield. In our analysis, both regression models of edge pixel count and fuzzy logic edge detection methods showed a good correlation (R 2 = 0.75; 0.76) with MTD data.

Improving Motor Imagery EEG Signals Classification Accuracy with CSP by Available Machine Learning Approach

In brain computer interface (BCI) systems, the electroencephalography (EEG) signals give a pathway to a motor disabled person to communicate outside using the brain signal and a computer. EEG signals of different motor imagery (MI) movements can be differentiated using an effective classification technique to aid a motor disabled patient. The purpose of this paper is to classify two different types of MI movement tasks, movement of the left hand and movement of the right foot EEG signals accurately. For this purpose we have used a publicly available dataset. Since the feature extraction for classification is an important task, so we have used popular common spatial pattern (CSP) method for spatial feature extraction. Two different machine learning classifiers named support vector machine (SVM) and K-nearest neighbor (KNN) have been used to verify the proposed method. We got the highest average results 95.55%, 98.73% and 92.38% in case of SVM and 93.5%, 98.73% and 90.15% in case of KNN for classification accuracy, sensitivity, and specificity, respectively when a Butterworth band-pass filter passed through [10–30] Hz. On the other hand accuracy came to 89.4% in [10-30] Hz when applying CSP for feature extraction and fisher linear discriminant analysis (FLDA) for classification on this dataset earlier.&#x0D; Journal of Engineering Science 12(2), 2021, 67-77

Electronic Tuneable Grounded and Floating FDNR using FTFNTA

ABSTRACT In this manuscript, an electronically tunable resistor-less grounded and floating FDNR (Frequency-Dependent Negative Resistance) is presented. The proposed circuit uses two FTFNTAs (Four Terminal Floating Nullor Transconductance Amplifier) and three capacitors for the design of floating FDNR; while only one FTFNTA and two capacitors are used for the design of grounded FDNR. The electronic tunability is achieved by altering the bias current of the FTFNTA. The workability of the proposed FDNR is demonstrated through the implementation of 4th order Butterworth band pass filter (BPF) and 2nd order low pass filter (LPF). The PSPICE simulation results are included to verify the theoretical interpretation.

QZSS L5 Bandwidth Frequency Filter Design for QZSS Receiver

The purpose of this study was to design QZSS L5 Bandwidth Frequency Filter for QZSS Receiver. We presented the configuration of the Butterworth bandpass filter by using PSpice as the tool for the simulation. Bandpass filter frequency of the QZSS L5 with bandwidth is designed in 25 MHz using the Butterworth filter which is presented in this article. These designed circuits are composed of the 3rd order. Moreover, the combination between low pass filter and high pass filter is performed as a bandpass filter. In conclusion, this designed filter on the frequency of QZSS L5 is to be useful by using the Butterworth bandpass filter.

Analysis of Frequency Bands of Uterine Electromyography Signals for the Detection of Preterm Birth.

In this work, an attempt has been made to analyze the influence of the frequencies bands in uterine electromyography (uEMG) signals on the detection of preterm birth. The signals recorded from the women's abdomen during pregnancy are considered in this study. The signals are subjected to preprocessing using digital bandpass Butterworth filter and decomposed into different frequency bands namely, 0.3-1.0 Hz (F1), 1.0-2.0 Hz (F2) and 2.0-3.0Hz (F3). Spectral features namely, peak magnitude, peak frequency, mean frequency and median frequency are extracted from the power spectrum. Classification models namely, k-nearest neighbor, support vector machine and random forest are employed to distinguish the term and preterm conditions. The results show that the features extracted from these frequency bands are able to differentiate term and preterm condition. Particularly, the frequency band F3 performs better than other frequency bands. The features associated with these frequencies along with random forest classification model achieves a maximum accuracy of 75.2%. Thus, these measures could be used to accurately detect the preterm birth well in advance.

Objectively Assessing Sports Concussion Utilizing Visual Evoked Potentials.

A portable system capable of measuring steady-state visual-evoked potentials (SSVEP) was developed to provide an objective, quantifiable method of electroencephalogram (EEG) testing following a traumatic event. In this study, the portable system was used on 65 healthy rugby players throughout a season to determine whether SSVEP are a reliable electrophysiological biomarker for concussion. Preceding the competition season, all players underwent a baseline SSVEP assessment. During the season, players were re-tested within 72 h of a match for either test-retest reliability or post-injury assessment. In the case of a medically diagnosed concussion, players were reassessed again once deemed recovered by a physician. The SSVEP system consisted of a smartphone housed in a VR-frame delivering a 15 Hz flicker stimulus, while a wireless EEG headset recorded occipital activity. Players were instructed to stare at the screen's fixation point while remaining seated and quiet. Electrodes were arranged according to the 10-20 EEG-positioning nomenclature, with O1-O2 being the recording channels while P1-P2 the references and bias, respectively. All EEG data was processed using a Butterworth bandpass filter, Fourier transformation, and normalization to convert data for frequency analysis. Players SSVEP responses were quantified into a signal-to-noise ratio (SNR), with 15 Hz being the desired signal, and summarized into respective study groups for comparison. Concussed players were seen to have a significantly lower SNR compared to their baseline; however, post-recovery, their SNR was not significantly different from the baseline. Test-retest indicated high device reliability for the portable system. An improved portable SSVEP system was also validated against an established EEG amplifier to ensure the investigative design is capable of obtaining research quality EEG measurements. This is the first study to identify differences in SSVEP responses in amateur athletes following a concussion and indicates the potential for SSVEP as an aid in concussion assessment and management.

Classification of sleep apnea based on EEG sub-band signal characteristics

Sleep apnea syndrome (SAS) is a disorder in which respiratory airflow frequently stops during sleep. Alterations in electroencephalogram (EEG) signal are one of the physiological changes that occur during apnea, and can be used to diagnose and monitor sleep apnea events. Herein, we proposed a method to automatically distinguish sleep apnea events using characteristics of EEG signals in order to categorize obstructive sleep apnea (OSA) events, central sleep apnea (CSA) events and normal breathing events. Through the use of an Infinite Impulse Response Butterworth Band pass filter, we divided the EEG signals of C3-A2 and C4-A1 into five sub-bands. Next, we extracted sample entropy and variance of each sub-band. The neighbor composition analysis (NCA) method was utilized for feature selection, and the results are used as input coefficients for classification using random forest, K-nearest neighbor, and support vector machine classifiers. After a 10-fold cross-validation, we found that the average accuracy rate was 88.99%. Specifically, the accuracy of each category, including OSA, CSA and normal breathing were 80.43%, 84.85%, and 95.24%, respectively. The proposed method has great potential in the automatic classification of patients' respiratory events during clinical examinations, and provides a novel idea for the development of an automatic classification system for sleep apnea and normal events without the need for expert intervention.

Fractal Based Feature Extraction Method for Epileptic Seizure Detection in Long-Term EEG Recording

One of the most common brain disorders is epilepsy. A person who has epilepsy is not able to have normal days like the others. It’s characterized by more than two unprovoked seizures. However, the faster detection and treatment of epileptic seizures, the quicker reduction of the disease abnormal level. Neurologists are still diagnosing, detecting, and testing a seizure manually by observing the Electroencephalogram (EEG) signals. This takes a very long time because of the irregularity of EEG signals. Hence, a Computer-Aided Diagnosis (CAD) is developed by many scientists to help neurologists in detecting seizures automatically. In this research, a CAD system was developed at CHB-MIT dataset. The EEG signals were processed at several stages through this system, namely pre-processing, decomposition, feature extraction, and classification. In pre-processing, the EEG signals were uniformed by selecting the most appropriate channels and filtered using Butterworth Bandpass Filter (BPF) to remove noise. The process continued to the decomposition and feature extraction stage using Empirical Mode Decomposition (EMD) and fractal dimension-based methods, i.e. Higuchi, Katz, and Sevcik, respectively. Then, the features were classified by Support Vector Machine (SVM). The proposed method achieved the highest accuracy at 94.72% on the Chb07 record. Meanwhile, the average accuracy was 81.2% for all records. The proposed study is expected to be applied for the detection of seizure onset in a real-time system.

Additional Damping Control of a Hybrid Multi-infeed DC System with a Wind Farm

Background: In a hybrid multi-infeed direct current (HMIDC) system, the interaction between the AC and DC systems will have an influence on the damping characteristics of the system, while a doubly-fed induction generator (DFIG) -based wind turbine connected to the grid complicates the coupling between AC and DC. Objective: Based on the basic principles of wind power generation and low-frequency oscillation (LFO), a DFIG -based wind turbine is respectively connected to the LCC-HVDC side and VSC-HVSC side of an HMIDC system to study the influence of the oscillation mode on a hybrid system with different wind power access locations, and two kinds of additional DC damping controllers are designed to suppress the LFO. Comparing the effects of different additional DC damping controllers on the suppression of the LFO in the system. Methods: First, the total least squares- estimation of signal parameters via rotational invariance techniques (TLS-ESPRIT) is used to obtain the system’s oscillation mode. Second, the transfer function is determined by the Butterworth bandpass filter. Third, the LCC-HVDC and VSC-HVDC additional damping controllers are designed based on the H2/H∞ hybrid control theory. Results: The designed additional DC damping controller has a good suppression effect on LFO in the HMIDC system and can meet the system damping deficit. The effect of the LCC-HVDC additional damping controller is better than that of VSC-HVDC. Conclusion: Through a simulation analysis on PSCAD/EMTDC simulation software, the effectiveness of the designed additional DC damping controller is verified.

Experimental and numerical simulation of the piston engine fueled with alternative fuel blends: CFD approach

PurposeThe purpose of this paper is to find the pressure and the knocking phenomena. To get the pressure values, the butterworth bandpass filter was used and the potential of knocking was found by using peak-to-peak pressure values and also the species concentration. Cooled exhaust gas recirculation was the method used to minimize the knocking occurrence in the engine. Moreover, the effect of premixed methanol and start of engine (SOI) on knocking were also determined.Design/methodology/approachThis paper deals with the compression ignition engine to investigate the unfavorable knocking behavior. The tests were carried out with the 3D model of engine fueled with waste cooking oil blended with TiO2. A number of tests were taken to find the pressure variation and the species concentration at eight different locations in the computational model.FindingsIn doing the tests, the positive intended outcome was achieved. From results, it is clear that the SOI and premixed methanol mitigated the knocking process.Originality/valueThe species concentration and pressure in the form of filtered signal were proved to be the ideal methods for evaluating the knocking event in the engine.

Comparison between Butterworth Bandpass and Stationary Wavelet Transform Filter for Electroencephalography Signal

The Electroencephalography (EEG) signals able to obtain the information from the brain signals. Reduce the noise of the raw EEG data can improve the accuracy of the result. The pre-processing step of the raw EEG data can generate a clean signal and improve the accuracy. The purpose of this paper is to compare the Butterworth bandpass (BB) and stationary wavelet transform (SWT) method for the pornography addiction EEG data. The data was collected from Yayasan Kita dan Buah Hati (YKBH), Jakarta, Indonesia, using the Brain Maker EEG machine with 19 channels. We used mean square error (MSE) and peak-to-noise ratio (PSNR) to compare the quantitative value for the filtered EEG signals. The result shows that the BB filter is more effective in removing the noise and keep the original information.

Multi-variable classification model for valve internal leakage based on acoustic emission time–frequency domain characteristics and random forest

To use acoustic-emission technology to detect leaks inside valves, the necessary first step is to model the valve-internal-leakage acoustic-emission signal (VILAES) mathematically. A multi-variable classification model that relates the VILAES characteristics and the leakage rate under varying pressure is built by combining time-frequency domain characteristics and the random-forest method. A Butterworth bandpass filter is used to preprocess the VILAES from a liquid medium, and the best frequency band for filtering is determined as being 140 kHz-180 kHz. Then, (i) the standard deviation, (ii) root mean square, (iii) wavelet packet entropy, (iv) peak standard-deviation probability density, and (v) spectrum area are calculated as the VILAES characteristics, and six parameters-the pressure and the five VILAES characteristics-are used as the inputs for the random-forest classification model. Analysis shows that the five VILAES characteristics increase with an increase in the leakage rate. The multi-variable classification model is established by random forest to determine whether the valve leakage is small, medium, or large. The random forest uses many decision trees to predict the final result. For the same experimental data, the accuracy and operating time of the multi-variable classification model are compared with those of a support-vector-machine classification method for the bandpass and wavelet packet filtering preprocessing methods. The results show that the modeling method based on the combination of time-frequency characteristics and random forest has shorter operating time and higher accuracy.

An Efficacy of Spectral Features with Boosted Decision Tree Algorithm for Automatic Heart Sound Classification

This research work aims to classify the audio signals received from heart into normal/abnormal. The heart sound perceived has been referred as phonocardiogram (PCG) signals. An attempt has been made to identify a set of features that provide more accurate results for classifying PCG under designated categories using a variant of decision tree algorithm. After applying 6th order butter worth band-pass filter on PCG signals, the new features, viz. Tonnetz, Spectral contrast, and Chroma have been extracted. Further, XGBOOST, a variant of the decision tree has been used for classifying unsegmented PCG signals. The benchmark datasets, PhysioNet 2016, and PASCAL 2011 have been taken for validating the proposed methodology presented here. PhysioNet 2016 is comprised of sub-datasets, viz. A–F which contain a total of 3,240 PCG recordings, whereas the PASCAL 2011 contains 415 heart sound signals. The proposed approach considers a new feature set in conjunction with the existing ones; and it has resulted in mean accuracy, sensitivity, and specificity scores as 95.2, 94.22 and 96.18 respectively.

Development of Unified Neuro-Affective Classification Tool (UNACT)

Brain signals have been analysed to understand the affective state of different cognitive and mental conditions. For example, through the analysis, we can visualize the changes of emotion while driving, identify an autistic kid, understand the conditions that stimulate attention while studying, and many more, because emotion has a strong impact on cognitive processes in humans’ activities. This can be done through a machine learning technique, which includes data acquisition, pre-processing, feature extraction, and training. However, no existing tool integrates all supervised machine learning processes for affective state classification, which makes the process tedious and time-consuming for an analyst by doing programming. Therefore, this project aims to develop a brain analysis tool, namely Unified Neuro-Affective Classification Tool (UNACT). It consists of 3 main functions including training, classifying, and analysis. In the study of affective state electroencephalogram (EEG) signals have used, which measures brain signals. UNACT uses the Butterworth Bandpass filter for EEG signal filtering, the Power Spectral Density method for feature extraction, and the Multi-layer perceptron (MLP) for emotion classification. This tool can be used by a non-technical person to perform affective-emotional state analysis without having programming knowledge.

Development of Gas-Liquid Slug Flow Measurement Using Continuous-Wave Doppler Ultrasound and Bandpass Power Spectral Density

This paper addresses the issues of slug detection and characterization in air-water two-phase flow in a vertical pipeline. A novel non-invasive measurement technique using continuous-wave Doppler ultrasound (CWDU) and bandpass power spectral density (BPSD) is proposed for multiphase flow applications and compared with the more established gamma-ray densitometry measurement. In this work, analysis using time-frequency analysis of the CWDU is performed to infer the applicability of the BPSD method for observing the slug front and trailing bubbles in a multiphase flow. The CWDU used a piezo transmitter/receiver pair with an ultrasonic frequency of 500 kHz. Signal processing on the demodulated signal of Doppler frequency was done using the Butterworth bandpass filter on the power spectral density which reveals slugs from background bubbles. The experiments were carried out in the 2” vertical pipeline-riser at the process system engineering laboratory at Cranfield University. The 2-inch test facility used in this experiment is made up of a 54.8 mm internal diameter and 10.5 m high vertical riser connected to a 40 m long horizontal pipeline. Taylor bubbles were generated using a quick-closing air valve placed at the bottom of the riser underwater flow, with rates of 0.5 litres/s, 2 litres/s, and 4 litres/s. The CWDU spectrum of the measured signal along with the BPSD method is shown to describe the distinctive nature of the slugs.

COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images

Coronavirus (COVID-19) epidemic outbreak has devastating effects on daily lives and healthcare systems worldwide. This newly recognized virus is highly transmissible, and no clinically approved vaccine or antiviral medicine is currently available. Early diagnosis of infected patients through effective screening is needed to control the rapid spread of this virus. Chest radiography imaging is an effective diagnosis tool for COVID-19 virus and follow-up. Here, a novel hybrid multimodal deep learning system for identifying COVID-19 virus in chest X-ray (CX-R) images is developed and termed as the COVID-DeepNet system to aid expert radiologists in rapid and accurate image interpretation. First, Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Butterworth bandpass filter were applied to enhance the contrast and eliminate the noise in CX-R images, respectively. Results from two different deep learning approaches based on the incorporation of a deep belief network and a convolutional deep belief network trained from scratch using a large-scale dataset were then fused. Parallel architecture, which provides radiologists a high degree of confidence to distinguish healthy and COVID-19 infected people, was considered. The proposed COVID-DeepNet system can correctly and accurately diagnose patients with COVID-19 with a detection accuracy rate of 99.93%, sensitivity of 99.90%, specificity of 100%, precision of 100%, F1-score of 99.93%, MSE of 0.021%, and RMSE of 0.016% in a large-scale dataset. This system shows efficiency and accuracy and can be used in a real clinical center for the early diagnosis of COVID-19 virus and treatment follow-up with less than 3 s per image to make the final decision.

0.5-V High Linear and Wide Tunable OTA for Biomedical Applications

This paper presents a low-voltage nano-power multiple-input operational transconductance amplifier (MI-OTA) with high linearity performance and increased input voltage swing. The enhanced performances are achieved thanks to employing several techniques as the bulk-driven, source-degeneration, self-cascode and negative conductance along with the concept of the input signal attenuation formed by multiple-input MOS transistor. The MI-OTA is widely tunable that serves for biological signals processing. A 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> -order Butterworth band-pass filter (BPF) for electrocardiogram (ECG) signal processing with 55.8 dB dynamic rang is presented. The MI-OTA circuit is designed for 0.5V voltage supply and offers a 0.22% total harmonic distortion (THD) for 0.2V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pp</sub> input signal with total power consumption of 13.4nW. Extensive simulation results including Monte Carlo analysis and process, voltage, temperature (PVT) corners using the 0.18μm CMOS technology from TSMC confirm the characteristics of the proposed MI-OTA and the filter.