Mojtaba Jafaritadi

Mojtaba Jafaritadi
Stanford University | SU · Department of Radiology

Ph.D.
Postdoctoral Research Fellow at Stanford University

About

46
Publications
11,786
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
790
Citations
Citations since 2017
31 Research Items
758 Citations
2017201820192020202120222023050100150
2017201820192020202120222023050100150
2017201820192020202120222023050100150
2017201820192020202120222023050100150
Additional affiliations
January 2017 - December 2019
University of Turku
Position
  • Project Researcher
January 2017 - July 2019
University of Turku
Position
  • Researcher
June 2013 - April 2015
University of Turku
Position
  • Project Researcher

Publications

Publications (46)
Article
Full-text available
Gyrocardiography (GCG) is a new non-invasive technique for assessing heart motions by using a sensor of angular motion – gyroscope – attached to the skin of the chest. In this study, we conducted simultaneous recordings of electrocardiography (ECG), GCG, and echocardiography in a group of subjects consisting of nine healthy volunteer men. Annotatio...
Article
Positron emission tomography (PET) is a non-invasive imaging technique which may be considered as the state of art for the examination of cardiac inflammation due to atherosclerosis. A fundamental limitation of PET is that cardiac and respiratory motions reduce the quality of the achieved images. Current approaches for motion compensation involve g...
Article
In this paper, a novel method to detect atrial fibrillation from a seismocardiogram (SCG) is presented. The proposed method is based on linear classification of the spectral entropy and a heart rate variability index computed from the SCG. The performance of the developed algorithm is demonstrated on data gathered from 13 patients in clinical setti...
Article
Heart rate monitoring helps in assessing the functionality and condition of the cardiovascular system. We present a new real-time applicable approach for estimating beat-to-beat time intervals and heart rate in seismocardiograms acquired from a tri-axial microelectromechanical accelerometer. Seismocardiography (SCG) is a non-invasive method for hea...
Preprint
Full-text available
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in...
Conference Paper
We introduce similarity weighted aggregation, a principled and efficient method for regularized weight aggregation in federated learning. Our method is adapted to non-IID collaborators and is simultaneously cost-efficient. This is the first method to propose a sliding-window to select the collaborators, to the best of our knowledge. We demonstrate...
Article
Full-text available
Joint effusion due to elbow fractures are common among adults and children. Radiography is the most commonly used imaging procedure to diagnose elbow injuries. The purpose of the study was to investigate the diagnostic accuracy of deep convolutional neural network algorithms in joint effusion classification in pediatric and adult elbow radiographs....
Chapter
We introduce similarity weighted aggregation, a principled and efficient method for regularized weight aggregation in federated learning. Our method is adapted to non-IID collaborators and is simultaneously cost-efficient. This is the first method to propose a sliding-window to select the collaborators, to the best of our knowledge. We demonstrate...
Article
Objective: The purpose of this research is to develop a new deep learning framework for detecting atrial fibrillation (AFib), one of the most common heart arrhythmias, by analyzing the heart's mechanical functioning as reflected in seismocardiography (SCG) and gyrocardiography (GCG) signals. Jointly, SCG and GCG constitute the concept of mechanoca...
Chapter
One of the most important subjects for societies is human health services, which aims to determine the appropriate, accurate and robust diagnosis of the disorder for patients to get the adequate treatment as quickly as possible. Since this diagnosis is always a challenging process, support from other areas such as statistics and computer science ar...
Article
Full-text available
Noise and motion artifacts in Positron emission tomography (PET) scans can interfere in diagnosis and result in inaccurate interpretations. PET gating techniques effectively reduce motion blurring, but at the cost of increasing noise, as only a subset of the data is used to reconstruct the image. Deep convolutional neural networks (DCNNs) could com...
Article
Full-text available
Background Dual-gating reduces respiratory and cardiac motion effects but increases noise. With motion correction, motion is minimized and image quality preserved. We applied motion correction to create end-diastolic respiratory motion corrected images from dual-gated images. Methods [ ¹⁸ F]-fluorodeoxyglucose ([ ¹⁸ F]-FDG) PET images of 13 subjec...
Article
Full-text available
We present a novel method for estimating respiratory motion using inertial measurement units (IMUs) based on microelectromechanical systems (MEMS) technology. As an application of the method we consider the amplitude gating of positron emission tomography (PET) imaging, and compare the method against a clinically used respiration motion estimation...
Article
Objective: Assessment of cardiac time intervals (CTIs) is essential for monitoring cardiac performance. Recently, gyrocardiography (GCG) has been introduced as a non-invasive technology for cardiac monitoring. GCG measures the chest's angular precordial vibrations caused by myocardium wall motion using a gyroscope sensor attached to the sternum. I...
Article
Full-text available
Timely diagnosis of cardiovascular diseases (CVD) is crucial to prevent morbidity and mortality. Atrial fibrillation (AFib) and heart failure (HF) are two prevalent cardiac disorders that are associated with a high risk of morbidity and mortality, especially if they are concurrently present. Current approaches fail to screen many at-risk individual...
Article
Full-text available
There is an unmet clinical need for a low cost and easy to use wearable devices for continuous cardiovascular health monitoring. A flexible and wearable wristband, based on microelectromechanical sensor (MEMS) elements array was developed to support this need. The performance of the device in cardiovascular monitoring was investigated by (i) compar...
Article
Full-text available
Smartphone mechanocardiography (sMCG) is a new technique that allows patients to record a cardiac rhythm strip using a smartphone built-in tri-axial accelerometer and gyroscope. In this study, we investigated how a self-applied sMCG can reliably contribute to the differentiation of atrial fibrillation (AFib) from the sinus rhythm (SR). A study samp...
Article
Full-text available
Dual cardiac and respiratory gating is a well-known technique for motion compensation in nuclear medicine imaging. In this study, we present a new data fusion framework for dual cardiac and respiratory gating based on multidimensional microelectromechanical (MEMS) motion sensors. Our approach aims at robust estimation of the chest vibrations, that...
Thesis
Full-text available
Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evalua...
Article
Full-text available
Atrial fibrillation (AFib) is the most common sustained heart arrhythmia and is characterized by irregular and excessively frequent ventricular contractions. Early diagnosis of AFib is a key step in the prevention of stroke and heart failure. In this study, we present a comprehensive time-frequency pattern analysis approach for automated detection...
Article
Full-text available
We describe a home health monitoring solution with cardiac beat-to-beat detection using accelerometer and gyroscope signal fusion. The proposed method measures both the precordial translational and rotational motions of the chest using miniaturized inertial sensors. The algorithm removes motion artefacts, selects the best axis from multi-axial acce...
Article
Full-text available
Cardiac translational and rotational vibrations induced by left ventricular motions are measurable using joint seismocardiography (SCG) and gyrocardiography (GCG) techniques. Multi-dimensional non-invasive monitoring of the heart reveals relative information of cardiac wall motion. A single inertial measurement unit (IMU) allows capturing cardiac v...
Conference Paper
Ballistocardiography (BCG) is seeing a new renaissance mainly due to access of new miniaturized and sensitive MEMS accelometers and gyroscopes that provides us a new tool for unobstrusive measurement of cardiac signals. These signal, however, suffer from high signal morphology variability and commonly signals are at least partly of low quality. A c...
Poster
Introduction Smartphones and mobile health applications (‘’app”) are widely used for non-clinical applications, such as fitness and well-being. However, the other clinical usefulness of smartphones has not been thoroughly investigated. We address a new smartphone mechanocardiography platform based on the use of miniaturized built-in inertial sensor...
Conference Paper
This paper describes a method for estimation of heart rate (HR) and heart rate variability (HRV) with accelerometers and gyroscopes. We denote this joint seismocardiography (SCG) and gyrocardiography (GCG) approach as SCG/GCG. In principle, SCG which is a well known method measures the linear mechanical movements of the heart and GCG is a new techn...
Conference Paper
This study presents a new technique which allows identification of individual heartbeats from seismocardiograms (SCG) with high accuracy. Our method is electrocardiogram (ECG) independent and designed based upon S-transform and Shannon energy. The S-transform which is a time-frequency (TF) representation first provides frequency-dependent resolutio...
Conference Paper
The pumping action of the heart is performed by contraction of the myocardium fibers. We present a non-invasive technique named gyrocardiography (GCG) that comprises a sensor of angular motion, gyroscope, configured to obtain three-dimensional angular velocity signals. A tri-axial micro electromechanical (MEMS) gyroscope sensor was attached to the...
Conference Paper
Heart rate variability (HRV), the variation in the beat-to-beat heart rate, is a key indicator of the cardiovascular condition of an individual. The purpose of this study was to cross-validate the beat-by-beat time variations in seismocardiography (SCG) with electrocardiography (ECG) for determining ultra-short term HRV indices. Twenty healthy youn...
Conference Paper
Heart rate variability (HRV), the variation in the beat-to-beat heart rate, is a key indicator of the cardiovascular condition of an individual. The purpose of this study was to cross-validate the beat-by-beat time variations in seismocardiography (SCG) with electrocardiography (ECG) for determining ultra-short term HRV indices. Twenty healthy youn...
Poster
Background: Respiratory motion (RM) and cardiac motion (CM) degrade the image quality and quantitative accuracy of positron emission tomography (PET). Motion artifacts due to RM and CM might cause blurring in myocardial perfusion and incorrect assessment of tumor size and activity in cardiac PET imaging and oncology such as lung metastasis imaging....
Conference Paper
Systolic time intervals (STI) have significant diagnostic values for a clinical assessment of the left ventricle in adults. This study was conducted to explore the feasibility of using seismocardiography (SCG) to measure the systolic timings of the cardiac cycle accurately. An algorithm was developed for the automatic localization of the cardiac ev...
Conference Paper
Systolic time intervals (STI) have significant diagnostic values for a clinical assessment of the left ventricle in adults. This study was conducted to explore the feasibility of using seismocardiography (SCG) to measure the systolic timings of the cardiac cycle accurately. An algorithm was developed for the automatic localization of the cardiac ev...
Article
Full-text available
Both respiratory and cardiac motions reduce the quality and consistency of medical imaging specifically in nuclear medicine imaging. Motion artifacts can be eliminated by gating the image acquisition based on the respiratory phase and cardiac contractions throughout the medical imaging procedure. Electrocardiography (ECG), 3-axis accelerometer, and...

Network

Cited By

Projects

Projects (4)
Archived project
In the proposed project, NOSTROKES, microelectromechanical (MEMS) pressure, gyroscope and accelerometer sensors are used for noninvasive diagnosis and long-term monitoring of atrial fibrillation. A goal of the research was to develop accurate and reliable methods for determining of the heart rhythm and mechanism of AFib without electrocardiography (ECG), only using smartphone MCG. On the other hand, by employing ECG-calibrated patient-specific computational models, the considered MEMS-based measurements are used for long-term monitoring of the clinical status of a chronic AFib patient. Because seismocardiography, a method previously established based on accelerometers, found not very accurate in detecting AFib, we investigated adding Gyrocardiography modality as well as sensor fusion and machine learning techniques to improve AFib diagnostic accuracy. Jointly, these two methods (SCG+GCG) constitute the concept of mechanocardiography (MCG). Almost all modern smartphones contain accurate accelerometers and gyroscopes to detect axis- and angular orientation, and subsequently they can be used to collect MCG data without any additional hardware. This feature of modern smartphones allowed me to investigate the reliability of smartphone MCG detection of AFib against the gold standard electrocardiography (ECG) recordings in hospitalized patients. The results showed over 95% accuracy for mobile phone detection of atrial fibrillation.
Archived project
In the proposed project, NOSTROKES, microelectromechanical (MEMS) pressure, gyroscope and accelerometer sensors are used for noninvasive diagnosis and long-term monitoring of atrial fibrillation. A goal of the research was to develop accurate and reliable methods for determining of the heart rhythm and mechanism of AFib without electrocardiography (ECG), only using smartphone MCG. On the other hand, by employing ECG-calibrated patient-specific computational models, the considered MEMS-based measurements are used for long-term monitoring of the clinical status of a chronic AFib patient. Because seismocardiography, a method previously established based on accelerometers, found not very accurate in detecting AFib, we investigated adding Gyrocardiography modality as well as sensor fusion and machine learning techniques to improve AFib diagnostic accuracy. Jointly, these two methods (SCG+GCG) constitute the concept of mechanocardiography (MCG). Almost all modern smartphones contain accurate accelerometers and gyroscopes to detect axis- and angular orientation, and subsequently they can be used to collect MCG data without any additional hardware. This feature of modern smartphones allowed me to investigate the reliability of smartphone MCG detection of AFib against the gold standard electrocardiography (ECG) recordings in hospitalized patients. The results showed over 95% accuracy for mobile phone detection of atrial fibrillation.
Project
Our aim is to decrease disclosure risks by developing new data sharing and privacy protection models based on explainable AI methods. We will focus on computational anonymity and semantic anonymization techniques including synthetic data generators, by which new artificial medical image data is generated and used to substitute the original images. This way we build an image data model from the original data set and, subsequently, a new (protected) image set is randomly generated using the model. Synthetic medical image data helps overcome privacy issues and tackle the insufficient number of labelled data. Our ultimate goal is to reach to the maximum anonymity and unlinkability of the patient information through an AI-based anonymity system.