
Mohammad ShahbakhtiKaunas University of Technology · Institute of Biomedical Engineering
Mohammad Shahbakhti
PhD
Researcher at Artinis Medical Systems B.V.
About
43
Publications
9,141
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Introduction
Accomplished data scientist with a PhD in Electrical and Electronics Engineering and nearly a decade of experience in academic and industrial research. Expertise in developing innovative algorithms to analyse neural data during alterations of consciousness and across cognitive states. Currently a scientific researcher at Artinis Medical Systems (since 2022) where I have led the development of four ready-to-use algorithms for neural data analysis.
Additional affiliations
September 2014 - September 2018
Mehr Private Hospital
Position
- Head of Department
Education
September 2018 - June 2020
September 2009 - September 2013
Publications
Publications (43)
Objective: Electroencephalographic (EEG) signals, pivotal for Brain-Computer Interfacing (BCI), are prone to several undesired artifacts that may jeopardize the accurate brain activity analysis. This paper proposes a robust automated low complexity algorithm based on the Stationary Wavelet Transform (SWT) to remove electrical shift and linear trend...
Objective:
Recent advances in development of low-cost single-channel electroencephalography (EEG) headbands have opened new possibilities for applications in health monitoring and brain-computer interface (BCI) systems. These recorded EEG signals, however, are often contaminated by eye blink artifacts that can yield the fallacious interpretation o...
Objective:
Blink-related features derived from electroencephalography (EEG) have recently arisen as a meaningful measure of drivers cognitive state. Combined with band power features of low-channel prefrontal EEG data, blink-derived features enhance the detection of driver drowsiness. Yet, it remains unanswered whether synergy of combined blink an...
Objective: Although a wide range of researches have shown the utility of electroencephalography (EEG) for the sleep monitoring, the majority of them reported a low sensitivity for classification of wakefulness from sleep stage I. This paper, therefore, validates a reliable nonlinear feature set for discriminating the wakefulness from sleep stage I...
Objective:
The driver fatigue detection using multi-channel electroencephalography (EEG) has been extensively addressed in the literature. However, the employment of a single prefrontal EEG channel should be prioritized as it provides users with more comfort. Furthermore, eye blinks from such channel can be analyzed as the complementary informatio...
Based on prior research indicating a decrease in the spectral slope of electroencephalogram (EEG) during anesthesia induction and an increase during recovery, we propose Slope Entropy (SlopEn), which uniquely emphasizes variations in signal slope, as a new index for monitoring the depth of anesthesia (DoA). The performance of SlopEn is investigated...
Objectives: Commercial systems for monitoring the depth of anesthesia (DoA) are often financially inaccessible to developing countries. As an alternative, a wearable single frontal electroencephalogram (EEG) device can be utilized. Nonetheless, most studies addressing DoA monitoring utilizing just one frontal EEG channel rely on nonlinear features...
While entropy metrics derived from electroencephalography (EEG) have shown significant promise in monitoring the depth of anesthesia (DoA), the applicability of fuzzy entropy (FuzzEn) initially proposed to address the limitations of conventional entropy metrics regarding the sample size and class boundaries, remains unexplored in this context. This...
Despite promising results reported in the literature for mental workload assessment using electroencephalography (EEG), most of the proposed methods rely on employing multiple EEG channels, limiting their practicality. However, the advent of wearable EEG technology provides the possibility of mental workload assessment for real-life applications. Y...
Timely preterm labor prediction plays an important role for increasing the chance of neonate survival, the mother's mental health, and reducing financial burdens imposed on the family. The objective of this study is to propose a method for the reliable prediction of preterm labor from the electrohysterogram (EHG) signals based on different pregnanc...
Background: Near-infrared spectroscopy (NIRS) relative concentration signals contain ‘noise’ from physiological processes such as respiration and heart rate. Simultaneous assessment of NIRS and respiratory rate (RR) using a single sensor would facilitate a perfectly time-synced assessment of (cerebral) physiology. Our aim was to extract respiratory...
Objective:
The employment of wearable systems for continuous monitoring of vital signs is increasing. However, due to substantial susceptibility of conventional bio-signals recorded by wearable systems to motion artifacts, estimation of the respiratory rate (RR) during physical activities is a challenging task. Alternatively, functional Near-Infra...
Objective:
Respiration is recognized as a systematic physiological interference in functional near-infrared spectroscopy (fNIRS). However, it remains unanswered as to whether it is possible to estimate the respiratory rate (RR) from such interference. Undoubtedly, RR estimation from fNIRS can provide complementary information that can be used alon...
Although detection of the driver fatigue using a single electroencephalography (EEG) channel has been addressed in literature, the gender differentiation for applicability of the model has not been investigated heretofore. Motivated accordingly, we address the detection of driver fatigue based the gender-segregated datasets, where each of them cont...
Objective:
The early prediction of preterm labor can significantly minimize premature delivery complications for both the mother and infant. The aim of this research is to propose an automatic algorithm for the prediction of preterm labor using a single electrohysterogram (EHG) signal.
Method:
The proposed method firstly employs empirical mode d...
VME-DWT algorithm for elimination of eye blink from short segment of single EEG channel
Objective
Electroencephalographic (EEG) signals, pivotal for Brain–Computer Interfacing (BCI), are prone to several undesired artifacts that may jeopardize the accurate brain activity analysis. This paper proposes a robust automated low complexity algorithm based on the Stationary Wavelet Transform (SWT) to remove electrical shift and linear trend...
This file contains the data and code for elimination of electrical shift and linear trends from EEG signals.
The electroencephalogram signal (EEG) often suffers from various artifacts and noises that have physiological and non-physiological origins. Among these artifacts, eye blink, due to its amplitude is considered to have the most influence on EEG analysis. In this paper, a low complexity approach based on Stationary Wavelet Transform (SWT) and skewnes...
Electrohysterogram (EHG) signal represents electrical activity of uterine collected from abdominal surface of pregnant women. It has been proven that EHG analysis could be a suitable way to predict preterm labor and consequently to prevent it. The aim of this paper is to present an efficient low computational complexity algorithm to detect preterm...
Neonatal death can be prevented by early
prediction of pre-term labor. During the last decade, uterine
electrohysterography (EHG) signal has been considered as a noninvasive method for this aim. There is a wide range of researches
which investigated EHG signals for diagnosis of pre-term labor.
In this article, features have been extracted by Discre...
Electrocardiogram (ECG) signals might be affected by various artifacts and noises that have biological and external sources. Baseline wander (BW) is a low-frequency artifact that may be caused by breathing, body movements and loose sensor contact. In this paper, a novel method based on empirical mode decomposition (EMD) for removal of baseline nois...
Communicated by Cecilia Pennetta Electrocardiogram (ECG) signals might be affected by various artifacts and noises that have biological and external sources. Baseline wander (BW) is a low-frequency artifact that may be caused by breathing, body movements and loose sensor contact. In this paper, a novel method based on empirical mode decomposition (...
Recording the electrical current of the cortex is called electroencephalography (EEG). EEG signals can be affected by high and low frequency noises which are caused due to muscular activity (EMG), Power line interference, eye blinks and etc. In this paper, we introduce an adaptive wavelet method for elimination of high frequency noises from EEG. Th...
Prediction of preterm labor is of great importance to reduce neonatal death. Analysis of electrohysterogram (EHG) could be considered as a proper tool for this aim. In this paper, the statistical and non-linear features have been extracted from EHG signals and then Support Vector machine (SVM) has been applied for classification between term and pr...
An electrocardiogram (ECG) often contains various types of noises and artifacts that might lead to wrong analysis. Recently, many techniques based on discrete wavelet transform (DWT) for ECG noise elimination have been proposed. Determination of the number of decomposition levels, which could vary with the sampling rate (frequency sampling), is one...
The electrocardiographic signal (ECG) is a major artifact during recording the surface electromyography (SEMG). Removal of this artifact is one of the important tasks before SEMG analysis for biomedical goals. In this paper, the application of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for elimination of ECG artifact fr...
Parkinson’s disease (PD) is the most common disease of motor system degeneration that occurs when the dopamine-producing cells are damaged in substantia nigra. To detect PD, various signals have been investigated, including EEG, gait and speech. Since approximately 90 percent of the people with PD suffer from speech disorders, speech analysis is co...
Parkinson's disease (PD) was described by James Parkinson first time and it is now recognized as the second common neurological disorder after Alzheimer. Since most of the people with PD suffer form speech disorder, it is believed that speech analysis can be considered as the easiest way for PD detection. In this research, we try to use extracted f...
Parkinson's disease (PD) is a neurodegenerative brain disorder that occurs when approximately 60% to 80% of the dopamine-producing cells are damaged. PD is the second common neurodegenerative disorder after Alzheimer. PD could be diagnosed by various signals such as EEG, gait and speech. Approximately, 90 percent of people with PD suffer from speec...
EEG signals collected from cortex are often contaminated with the eye electrical activity, which increase the difficulty in analyzing the EEG and to obtaining clinical information. This paper describes a novel method, adaptive filtering without using an extra channel, for elimination of blink from EEG. The artifact reference is extracted from blink...
Electroencephalograph (EEG) is the measurement of the electrical activity of the brain, obtained by recording from electrodes placed on the cortex. It is often affected with various artifacts that have biological and external sources. Eye electrical activity might be considered as the most common artifact, which increase the difficulty in analyzing...
The electroencephalographic signals (EEG) are rather weak and contaminated with different artifacts that have biological and external sources. Among these artifacts, blinks and eye movements are the most common of them. In this paper, we introduce a new method, Empirical Mode Decomposition (EMD), for removal of blink contamination from EEG signal....