En
  • دکتری (1389)

    مهندسی پزشکی - بیوالکتریک

    دانشگاه تربیت مدرس،

  • کارشناسی‌ارشد (1384)

    مهندسی پزشکی - بیوالکتریک

    دانشگاه تهران، ایران

  • پردازش سیگنال ‌های حیاتی‌
  • پردازش سیگنال اولتراسوند
  • پردازش سیگنالهای پزشکی

    زمینه های پژوهشی: پردازش سیگنال های حیاتی و اولتراسوند

    کارشناس مسئول: مهندس بهروز پهلوانی

    هیات علمی همکار: بابک محمدزاده اصل

    تلفن: 02182884331

    مکان: بلوک 6، طبقه 4، اتاق 41

بابک محمدزاده اصل مدرک کارشناسی ارشد و دکترای خود را در رشته مهندسی پزشکی- بیو الکتریک به ترتیب از دانشگاه تهران در سال 1384 و دانشگاه تربیت مدرس در سال 1389 دریافت کرد. او از سال 1390 به عنوان عضو هیأت علمی در گروه مهندسی پزشکی دانشگاه تربیت مدرس مشغول به کار شده و در سال 1395 به درجه دانشیاری ارتقا یافته است. زمینه های تحقیقاتی مورد علاقه ایشان پردازش سیگنال های حیاتی و پردازش سیگنال اولتراسوند می باشند.

ارتباط

رزومه

Detection of sleep apnea using deep neural networks and single-lead ECG signals

A Zarei, H Beheshti, BM Asl
Journal Papers , , {Pages }

Abstract

Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based features of EEG signals

Asghar Zarei, Babak Mohammadzadeh Asl
Journal PapersComputers in Biology and Medicine , Volume 131 , 2021 April 1, {Pages 104250 }

Abstract

Background and objectiveEpilepsy is a prevalent disorder that affects the central nervous system, causing seizures. In the current study, a novel algorithm is developed using electroencephalographic (EEG) signals for automatic seizure detection from the continuous EEG monitoring data.MethodsIn the proposed methods, the discrete wavelet transform (DWT) and orthogonal matching pursuit (OMP) techniques are used to extract different coefficients from the EEG signals. Then, some non-linear features, such as fuzzy/approximate/sample/alphabet and correct conditional entropy, along with some statistical features are calculated using the DWT and OMP coefficients. Three widely-used EEG datasets were utilized to assess the performance of the proposed

Rest-fMRI Based Comparison Study between Autism Spectrum Disorder and Typically Control Using Graph Frequency Bands

Alireza Talesh Jafadideh, Baback Mohammadzadeh Asl
Journal PapersbioRxiv , 2021 January 1, {Pages }

Abstract

Graph signal processing is a subset of signal processing enabling the analysis of functional magnetic resonance imaging (fMRI) data in brain topological domain. One of the most important and highly interested tool of GSP is graph Fourier transform (GFT) by which brain signals can be analyzed in different graph frequency bands. In this paper, the resting-state fMRI (rfMRI) data is analyzed using GFT tool in order to discover new knowledge about the autism spectrum disorder (ASD) and find features discriminating between ASD and typically control (TC) subjects. For ASD group, the signal concentration in both low and high frequency bands is decreased by increasing the age in most of the brain well-known networks. The ASD in comparison to TC sho

Automatic detection of non-apneic sleep arousal regions from polysomnographic recordings

Jamileh Karimi, Babak Mohammadzadeh Asl
Journal PapersBiomedical Signal Processing and Control , Volume 66 , 2021 April 1, {Pages 102394 }

Abstract

A signal processing/machine learning (ML), data-driven approach for classifying targeted sleep arousal regions of polysomnography (PSG) signals is presented focusing on feature subset selection and consensus methods, deploying ensemble techniques. The targeted regions are the regions where RERA and Non-RERA-Non-Apnea events are present. The sensor independent and sensor-based features in time and frequency domain were derived from the PSG signals. To reduce the feature space dimension, a combination of feature selection strategies and a method of rank aggregation was applied to rank the features. Aiming to find a feature set, which conveys the most discriminative information of detection in designated learning models, the Non-Dominated Sort

User Parameter-Free Minimum Variance Beamformer in Medical Ultrasound Imaging

Ali Salari, Babak Mohammadzadeh Asl
Journal PapersIEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control , 2021 March 12, {Pages }

Abstract

Minimum variance beamformer (MVB) is the well-known adaptive beamformer in medical ultrasound imaging. Accurate estimation of the covariance matrix has a great effect on the performance of the MVB. In adaptive ultrasound imaging, parameters such as the subarray length, the number of samples used for temporal averaging, and the value of diagonal loading (DL) have the main role in the true estimation of the covariance matrix. The optimal values for these parameters are different from one scenario to another one. So, the MVB is not a parameter-free method, and its behavior is scenario-dependent. In the field of telecommunications and radar, the shrinkage method was proposed to determine the DL factor, but no method has been provided yet to det

Weighted delay-and-sum beamformer for breast cancer detection using microwave imaging

Seyed Abbas Shah Karam, Declan O’Loughlin, Barbara Luz Oliveira, Martin O’Halloran, Babak Mohammadzadeh Asl
Journal PapersMeasurement , 2021 March 20, {Pages 109283 }

Abstract

Many similarities exist between ultrasound and microwave imaging, however, ultrasound is typically performed with a handheld transducer whereas antennas are placed around the subject of imaging for microwave breast imaging. Hence, ultrasound beamformers are not always directly applicable for microwave imaging. Signals traverse different paths experiencing different delays, attenuations, and even different expected shapes of signal due to dispersion. Besides, each signal may contain the response from a different point of the boundary of the object. In this work, increased coherencies between the signals of certain antenna pairs are shown statistically and these characteristics exploited to develop a novel beamformer which weights the receive

Automatic Sleep Stage Classification Using Temporal Convolutional Neural Network and New Data Augmentation Technique from Raw Single-Channel EEG

Ebrahim Khalili, Babak Mohammadzadeh Asl
Journal PapersComputer Methods and Programs in Biomedicine , 2021 March 27, {Pages 106063 }

Abstract

Background and objective: This paper presents a new framework for automatic classification of sleep stages using a deep learning algorithm from single-channel EEG signals. Each segmented EEG signal appended with its label of stages is fed into a deep learning model to create an automatic sleep stage classification. This is one of the most important problems that is critical to the realization of monitoring patients with sleep disorder.Methods: In the present study, a neural network architecture is introduced utilizing Convolutional Neural Networks (CNNs) to extract features, followed by Temporal Convolutional Neural Network to extract the temporal features from the extracted features vector of CNN. Finally, the performance of our model is i

A new extension of DMAS ultrasound nonlinear beamformer using the third degree terms with low computational complexity

L Eslami, F Makouei, MS Ziksari, SAS Karam, BM Asl
Journal Papers , , {Pages }

Abstract

Super-Resolution Ultrasound Imaging with Low Number of Frames Enhanced by Adaptive Beamforming

R Tasbaz, BM Asl
Journal Papers , , {Pages }

Abstract

Improvement of Microbubbles Localization Using Adaptive Beamforming in Super-Resolution Ultrasound Imaging

R Tasbaz, BM Asl
Journal Papers , , {Pages }

Abstract

Automatic detection of epileptic seizures using Riemannian geometry from scalp EEG recordings

A Shariat, A Zarei, SA Karvigh, BM Asl
Journal Papers , , {Pages }

Abstract

A Comparison Study between Autism Spectrum Disorder and Typically Control in Graph Frequency Bands Using Graph and Triadic Interaction Metrics

AT Jafadideh, BM Asl
Journal Papers , , {Pages }

Abstract

Subspace-Based Blood Power Spectral Capon Combined with Wiener Postfilter to Provide a High-Quality Velocity Waveform with Low Mathematical Complexity

Fatemeh Makouei, Babak Mohammadzadeh Asl
Journal PapersUltrasound in Medicine & Biology , 2020 May 6, {Pages }

Abstract

In Doppler analysis, the power spectral density (PSD), which accounts for the axial velocity distribution of the blood scatterers, is estimated. The conventional spectral estimator is Welch's method, which suffers from frequency leakage at small observation window length. The performance of adaptive techniques such as blood power Capon (BPC) has been promising at the cost of higher computation complexity. Reducing the computational complexity while retaining the benefits of BPC would be necessary for real-time implementation. The purpose of the work described here was to investigate whether it is possible to decrease the computation load in BPC and still obtain acceptable results. The computation complexity in BPC is owing primarily to the

Eigenspace-based minimum variance beamformer combined with sign coherence factor: Application to linear-array photoacoustic imaging

Sadaf Shamekhi, Vijitha Periyasamy, Manojit Pramanik, Mohammad Mehrmohammadi, Babak Mohammadzadeh Asl
Journal PapersUltrasonics , 2020 May 22, {Pages 106174 }

Abstract

Photoacoustic (PA) imaging combining the advantages of high resolution of ultrasound imaging and high contrast of optical imaging provides images with good quality. PA imaging often suffers from disadvantages such as clutter noises and decreased signal-to-noise-ratio at higher depths. One studied method to reduce clutter noises is to use weighting factors such as coherence factor (CF) and its modified versions that improve resolution and contrast of images. In this study, we combined the Eigen-space based minimum variance (EIBMV) beamformer with the sign coherence factor (SCF) and show the ability of these methods for noise reduction when they are used in combination with each other. In addition, we compared the proposed method with delay-a

Automatic classification of apnea and normal subjects using new features extracted from HRV and ECG-derived respiration signals

Asghar Zarei, Babak Mohammadzadeh Asl
Journal PapersBiomedical Signal Processing and Control , Volume 59 , 2020 May 1, {Pages 101927 }

Abstract

A novel framework for automatic detection of obstructive sleep apnea (OSA) is introduced in which a symbolic dynamics method, alphabet entropy, along with other well-known features such as fuzzy/approximate and sample entropy are calculated from ECG-derived respiration (EDR) and heart rate variability (HRV) signals. In addition, six different algorithms are employed in the extraction of the EDR signal from a single-lead ECG, and the results are compared. The sequential feature selection method is applied to pick the most effective features. Finally, the picked features are fed into the different classifiers to classify OSA patients and normal subjects. The Physionet Apnea-ECG and Fantasia datasets are utilized to assess the proposed OSA det

Performance Evaluation of the Spectral Autocorrelation Function and Autoregressive Models for Automated Sleep Apnea Detection Using Single-Lead ECG Signal

Asghar Zarei, Babak Mohammadzadeh Asl
Journal PapersComputer Methods and Programs in Biomedicine , 2020 June 26, {Pages 105626 }

Abstract

Background and objective: This paper addresses the automated recognition of obstructive sleep apnea (OSA) from the analysis of single-lead ECG signals. This is one of the most important problems that is, critical to the realization of monitoring patients with sleep apnea.Methods: In the present study, a novel solution based on autoregressive (AR) modeling of the single-lead ECG, and spectral autocorrelation function as an ECG feature extraction method is presented. The more effective features are opted by sequential forward feature selection (SFFS) technique and fed into the random forest for binary classification between the apnea and normal events.Results: Experimental results on Apnea-ECG database proved that the introduced algorithm res

Adaptive Transverse Blood Velocity Estimation in Medical Ultrasound: A Simulation Study

Fatemeh Makouei, Babak Mohammadzadeh Asl
Journal PapersUltrasonics , 2020 June 15, {Pages 106209 }

Abstract

Undoubtedly, highly valuable information about vascular anomalies is attained by the examination of the blood flow profile. The chief drawback of the conventional medical ultrasound in preparation of the blood periodogram is the measurement system shortcoming at the beam to flow angles near 90?. Recently, a method based on transverse oscillation (TO) approach, known as “Fourth-order estimation”, has been developed to directly estimate the transverse power spectral density (PSD) of the fully transverse blood flow. One of the basic requirements to accomplish acceptable PSDs by this technique is the sufficiently large observation window. In this paper, two adaptive approaches for efficient estimation of the velocity spectrum of a fully tra

3-D Synthetic Aperture High Volume Rate Tensor Velocity Imaging Using 1024 Element Matrix Probe

Fatemeh Makouei, Babak Mohammadzadeh Asl, Lasse Thurmann, Borislav Gueorguiev Tomov, Matthias Bo Stuart
Conference Papers2020 IEEE International Ultrasonics Symposium (IUS) , 2020 September 7, {Pages 04-Jan }

Abstract

Tensor velocity imaging (TVI) in a 3-D volume is investigated through simulations and experiments conducted using a Vermon 3 MHz 1024 element phased array (Vermon S.A., Tours, France) along with the experimental scanner SARUS. The flow-rig with a parabolic flow profile was positioned along the direction. The data is acquired by a recursive interleaved synthetic aperture sequence with 5 emissions at a pulse repetition frequency of 5 kHz to produce estimates at a volume rate of 500 Hz. A directional transverse oscillations velocity estimator based on cross-correlation is used to estimate the velocity components in the three Cartesian directions. The simulations were performed at peak velocities from 0.1 m/s to 1 m/s. At m/s, the esti

Seizure Onset Detection Based on Detection of Changes in Brain Activity Quantified by Evolutionary Game Theory Model

Ramtin Hamavar, Babak Mohammadzadeh Asl
Journal PapersComputer Methods and Programs in Biomedicine , 2020 December 17, {Pages 105899 }

Abstract

Background and Objective: Epilepsy is one of the most common diseases of the nervous system, affecting about 1% of the world’s population. The unpredictable nature of the epilepsy seizures deprives the patients and those around them of living a normal life. Therefore, the development of new methods that can help these patients will increase the life quality of these people and can bring a lot of economic savings in the health sector.Methods: In this study, we introduced a new framework for seizure onset detection. Our framework provides a new modelling for brain activity using evolutionary game theory and Kalman filter. If the patterns in the electroencephalogram (EEG) signal violate the predicted patterns by the proposed model, using a n

Minimum Variance Combined with Modified Delay Multiply and Sum Beamforming for Plane Wave Compounding.

Mahsa Sotoodeh Ziksari, Babak Mohammadzadeh Asl
Journal PapersIEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control , 2020 December 10, {Pages }

Abstract

Plane wave compounding is an active topic of research in ultrasound imaging because it is a promising technique for ultrafast ultrasound imaging. Unfortunately, due to the data-independent nature of the traditional compounding method, it imposes a fundamental limit on the image quality. To address this issue, adaptive beamformers have been implemented in the compounding procedure. In this paper, a new adaptive beamformer for the 2-D dataset obtained from multiple plane wave transmissions is investigated. In the proposed scheme, the Minimum Variance (MV) weights are applied to the backscattered echoes. Then the final image is obtained by employing a modified version of the Delay Multiply and Sum (DMAS) beamformer in the coherent compounding.

دروس نیمسال جاری

  • كارشناسي ارشد
    پردازش سيگنال هاي ديجيتال (3 واحد)
    دانشکده مهندسی برق و کامپیوتر، گروه مهندسي پزشكي
  • كارشناسي ارشد
    پردازش سيگنال هاي ديجيتال (3 واحد)

دروس نیمسال قبل

  • كارشناسي ارشد
    پردازش سيگنال هاي ديجيتال (3 واحد)
    دانشکده مهندسی برق و کامپیوتر، گروه مهندسي پزشكي
  • كارشناسي ارشد
    پردازش سيگنال هاي ديجيتال (3 واحد)
  • 1397
    الماسي, كژال
    بهبود كارايي شكل دهنده هاي پرتو بر مبناي همدوسي در تصويربرداري اولتراسوند و فتوآكوستيك
  • 1397
    طاس باز, رضا
  • 1397
    مولائي, اميرمحمد
  • 1397
    ميرقادري, زهراسادات
  • 1397
    حاج حسني, داود
  • 1398
    رمضاني مقدم, سحر
  • 1398
    شايسته, حانيه
  • 1398
    رشيدي, سمندخت
  • 1396
    ستوده زيكساري, مهسا
    بهبود شكل‌دهي پرتو در تصويربرداري اولتراسوند خيلي سريع
  • 1396
    طالش جفاديده, عليرضا
  • 1397
    زارعي, اصغر
  • عضو اصلی هیات مدیره انجمن علمی مهندسی پزشکی ایران- دبیر انجمن
  • سردبیر شماره ویژه مهندسی پزشکی در مجله برق مدرس
  • مدیر گروه مهندسی پزشکی- بیوالکترونیک
  • عضو اصلی هیات مدیره انجمن علمی مهندسی پزشکی ایران
  • عضوکارگروه تخصصی مهندسی پزشکی وزارت عتف
  • عضو شورای نظارت و ارزیابی دانشکده مهندسی برق و کامپیوتر
  • معاون پژوهشی دانشکده مهندسی برق و کامپیوتر
    داده ای یافت نشد

مهم

جدید

    اطلاعیه ای درج نشده است