Prash Ganesan is a Postdoctoral Research Fellow at Stanford Cardiovascular Medicine. His research is focused on developing novel bioengineering approaches using signal processing and machine learning for improving personalized ablation therapy for patients with atrial fibrillation. He was previously a research fellow at the US National Institutes of Health working on conditions such as cervical cancer and Pneumonia using Deep Learning methods. In 2019, he was awarded the "Impactful Bioengineering Research" scholarship for his research on atrial fibrillation therapy. He is a co-inventor of patents on novel mapping approaches for atrial fibrillation. In his free time, he enjoys hiking, reading biographies and non-fictional books, and songwriting.
Honors & Awards
Best Poster Award, Karlshruhe Institute of Technology, Germany (2021)
Young Investigator Award Finalist, Asia-Pacific Heart Rhythm Society (2021)
The Provost Honorary Recognition for Publishing, Florida Atlantic University (2019)
3-Minute Thesis Winner, Department of Electrical Engineering, Florida Atlantic University (2017)
Best Paper Award Finalist, IEEE Engineering in Medicine and Biology Society (2016)
Boards, Advisory Committees, Professional Organizations
Member, American Heart Association (2020 - Present)
Member, Heart Rhythm Society (2019 - Present)
Fellowship, US National Institutes of Health, Artificial Intelligence in Medicine (2018)
PhD, Florida Atlantic University, Electrical Engineering (2019)
MS, Rochester Institute of Technology, Electrical Engineering (2015)
BE, Anna University, Electronics Engineering (2013)
Sanjiv Narayan, Postdoctoral Faculty Sponsor
Prasanth Ganesan, Behnaz Ghoraani. "United States Patent US10398338B2 Systems and methods for guiding a multi-pole sensor catheter to locate cardiac arrhythmia sources", National Institutes of Health, Florida Atlantic University Board of Trustees, Oct 6, 2017
Prasanth Ganesan, Behnaz Ghoraani. "United States Patent US10398346B2 Systems and methods for localizing signal resources using multi-pole sensors", National Institutes of Health, Florida Atlantic University Board of Trustees, May 15, 2017
Atrial fibrillation signatures on intracardiac electrograms identified by deep learning.
Computers in biology and medicine
2022; 145: 105451
BACKGROUND: Automatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet suboptimally groups AF, flutter or tachycardia (AT) together as 'high rate events'. This may delay or misdirect therapy.OBJECTIVE: We hypothesized that deep learning (DL) can accurately classify AF from AT by revealing electrogram (EGM) signatures.METHODS: We studied 86 patients in whom the diagnosis of AF or AT was established at electrophysiological study (25 female, 65±11 years). Custom DL architectures were trained to identify AF using N=29,340 unipolar and N=23,760 bipolar EGM segments. We compared DL to traditional classifiers based on rate or regularity. We explained DL using computer models to assess the impact of controlled variations in shape, rate and timing on AF/AT classification in 246,067 EGMs reconstructed from clinical data.RESULTS: DL identified AF with AUC of 0.97±0.04 (unipolar) and 0.92±0.09 (bipolar). Rule-based classifiers misclassified 10-12% of cases. DL classification was explained by regularity in EGM shape (13%) or timing (26%), and rate (60%; p<0.001), and also by a set of unipolar EGM shapes that classified as AF independent of rate or regularity. Overall, the optimal AF 'fingerprint' comprised these specific EGM shapes, >15% timing variation, <0.48 correlation in beat-to-beat EGM shapes and CL<190ms (p<0.001).CONCLUSIONS: Deep learning of intracardiac EGMs can identify AF or AT via signatures of rate, regularity in timing or shape, and specific EGM shapes. Future work should examine if these signatures differ between different clinical subpopulations with AF.
View details for DOI 10.1016/j.compbiomed.2022.105451
View details for PubMedID 35429831
TARGETING SYNCHRONIZED ELECTROGRAM ISLANDS WITHIN ATRIAL FIBRILLATION FOR ABLATION
ELSEVIER SCIENCE INC. 2022: 3
View details for Web of Science ID 000781026600004
A MORPHOLOGICAL OPERATION-BASED APPROACH TO AUTOMATICALLY SEPARATE AND LABEL LEFT ATRIUM BODY AND PULMONARY VEINS
ELSEVIER SCIENCE INC. 2022: 1244
View details for Web of Science ID 000781026601343
UNSUPERVISED MACHINE LEARNING IDENTIFIES PHENOTYPES FOR ATRIAL FIBRILLATION THAT PREDICT ACUTE ABLATION SUCCESS
ELSEVIER SCIENCE INC. 2022: 51
View details for Web of Science ID 000781026600052
Deep learning model calibration for improving performance in class-imbalanced medical image classification tasks.
1800; 17 (1): e0262838
In medical image classification tasks, it is common to find that the number of normal samples far exceeds the number of abnormal samples. In such class-imbalanced situations, reliable training of deep neural networks continues to be a major challenge, therefore biasing the predicted class probabilities toward the majority class. Calibration has been proposed to alleviate some of these effects. However, there is insufficient analysis explaining whether and when calibrating a model would be beneficial. In this study, we perform a systematic analysis of the effect of model calibration on its performance on two medical image modalities, namely, chest X-rays and fundus images, using various deep learning classifier backbones. For this, we study the following variations: (i) the degree of imbalances in the dataset used for training; (ii) calibration methods; and (iii) two classification thresholds, namely, default threshold of 0.5, and optimal threshold from precision-recall (PR) curves. Our results indicate that at the default classification threshold of 0.5, the performance achieved through calibration is significantly superior (p < 0.05) to using uncalibrated probabilities. However, at the PR-guided threshold, these gains are not significantly different (p > 0.05). This observation holds for both image modalities and at varying degrees of imbalance. The code is available at https://github.com/sivaramakrishnan-rajaraman/Model_calibration.
View details for DOI 10.1371/journal.pone.0262838
View details for PubMedID 35085334
Identifying Atrial Fibrillation Mechanisms for Personalized Medicine.
Journal of clinical medicine
2021; 10 (23)
Atrial fibrillation (AF) is a major cause of heart failure and stroke. The early maintenance of sinus rhythm has been shown to reduce major cardiovascular endpoints, yet is difficult to achieve. For instance, it is unclear how discoveries at the genetic and cellular level can be used to tailor pharmacotherapy. For non-pharmacologic therapy, pulmonary vein isolation (PVI) remains the cornerstone of rhythm control, yet has suboptimal success. Improving these therapies will likely require a multifaceted approach that personalizes therapy based on mechanisms measured in individuals across biological scales. We review AF mechanisms from cell-to-organ-to-patient from this perspective of personalized medicine, linking them to potential clinical indices and biomarkers, and discuss how these data could influence therapy. We conclude by describing approaches to improve ablation, including the emergence of several mapping systems that are in use today.
View details for DOI 10.3390/jcm10235679
View details for PubMedID 34884381
CONSISTENT SPATIOTEMPORAL VECTORS IN ATRIAL FIBRILLATION PREDICT RESPONSE TO ABLATION
ELSEVIER SCIENCE INC. 2021: 334
View details for Web of Science ID 000647487500334
CLASSIFICATION OF INDIVIDUAL ATRIAL INTRACARDIAC ELECTROGRAMS BY DEEP LEARNING
ELSEVIER SCIENCE INC. 2021: 3217
View details for Web of Science ID 000647487503241
PROBING MACHINE LEARNING TO SEPARATE ATRIAL FIBRILLATION FROM OTHER ARRHYTHMIAS
ELSEVIER SCIENCE INC. 2021: 3410
View details for Web of Science ID 000647487503431
MACHINE LEARNING CLASSIFIES INTRACARDIAC ELECTROGRAMS OF ATRIAL FIBRILLATION FROM OTHER ARRHYTHMIAS
ELSEVIER SCIENCE INC. 2021: 279
View details for Web of Science ID 000647487500279
Three dimensional reconstruction to visualize atrial fibrillation activation patterns on curved atrial geometry.
2021; 16 (4): e0249873
BACKGROUND: The rotational activation created by spiral waves may be a mechanism for atrial fibrillation (AF), yet it is unclear how activation patterns obtained from endocardial baskets are influenced by the 3D geometric curvature of the atrium or 'unfolding' into 2D maps. We develop algorithms that can visualize spiral waves and their tip locations on curved atrial geometries. We use these algorithms to quantify differences in AF maps and spiral tip locations between 3D basket reconstructions, projection onto 3D anatomical shells and unfolded 2D surfaces.METHODS: We tested our algorithms in N = 20 patients in whom AF was recorded from 64-pole baskets (Abbott, CA). Phase maps were generated by non-proprietary software to identify the tips of spiral waves, indicated by phase singularities. The number and density of spiral tips were compared in patient-specific 3D shells constructed from the basket, as well as 3D maps from clinical electroanatomic mapping systems and 2D maps.RESULTS: Patients (59.4±12.7 yrs, 60% M) showed 1.7±0.8 phase singularities/patient, in whom ablation terminated AF in 11/20 patients (55%). There was no difference in the location of phase singularities, between 3D curved surfaces and 2D unfolded surfaces, with a median correlation coefficient between phase singularity density maps of 0.985 (0.978-0.990). No significant impact was noted by phase singularities location in more curved regions or relative to the basket location (p>0.1).CONCLUSIONS: AF maps and phase singularities mapped by endocardial baskets are qualitatively and quantitatively similar whether calculated by 3D phase maps on patient-specific curved atrial geometries or in 2D. Phase maps on patient-specific geometries may be easier to interpret relative to critical structures for ablation planning.
View details for DOI 10.1371/journal.pone.0249873
View details for PubMedID 33836026
Atrial fibrillation source area probability mapping using electrogram patterns of multipole catheters
BIOMEDICAL ENGINEERING ONLINE
2020; 19 (1): 27
Catheter ablation therapy involving isolation of pulmonary veins (PVs) from the left atrium is performed to terminate atrial fibrillation (AF). Unfortunately, standalone PV isolation procedure has shown to be a suboptimal success with AF continuation or recurrence. One reason, especially in patients with persistent or high-burden paroxysmal AF, is known to be due to the formation of repeating-pattern AF sources with a meandering core inside the atria. However, there is a need for accurate mapping and localization of these sources during catheter ablation.A novel AF source area probability (ASAP) mapping algorithm was developed and evaluated in 2D and 3D atrial simulated tissues with various arrhythmia scenarios and a retrospective study with three cases of clinical human AF. The ASAP mapping analyzes the electrograms collected from a multipole diagnostic catheter that is commonly used during catheter ablation procedure to intelligently sample the atria and delineate the trajectory path of a meandering repeating-pattern AF source. ASAP starts by placing the diagnostic catheter at an arbitrary location in the atria. It analyzes the recorded bipolar electrograms to build an ASAP map over the atrium anatomy and suggests an optimal location for the subsequent catheter location. ASAP then determines from the constructed ASAP map if an AF source has been delineated. If so, the catheter navigation is stopped and the algorithm provides the area of the AF source. Otherwise, the catheter is navigated to the suggested location, and the process is continued until an AF-source area is delineated.ASAP delineated the AF source in over 95% of the simulated human AF cases within less than eight catheter placements regardless of the initial catheter placement. The success of ASAP in the clinical AF was confirmed by the ablation outcomes and the electrogram patterns at the delineated area.Our analysis indicates the potential of the ASAP mapping to provide accurate information about the area of the meandering repeating-pattern AF sources as AF ablation targets for effective AF termination. Our algorithm could improve the success of AF catheter ablation therapy by locating and subsequently targeting patient-specific and repeating-pattern AF sources inside the atria.
View details for DOI 10.1186/s12938-020-00769-0
View details for Web of Science ID 000531635800001
View details for PubMedID 32370754
View details for PubMedCentralID PMC7201756
- Re-evaluating The Multiple Wavelet Hypothesis for Atrial Fibrillation. Heart rhythm 2020
Locating Atrial Fibrillation Rotor and Focal Sources Using Iterative Navigation of Multipole Diagnostic Catheters
CARDIOVASCULAR ENGINEERING AND TECHNOLOGY
2019; 10 (2): 354–66
Multi-polar diagnostic catheters are used to construct the 3D electro-anatomic mapping of the atrium during atrial fibrillation (AF) ablation procedures; however, it remains unclear how to use the electrograms recorded by these catheters to locate AF-driving sites known as focal and rotor source types. The purpose of this study is to present the first algorithm to iteratively navigate a circular multi-polar catheter to locate AF focal and rotor sources without the need to map the entire atria.Starting from an initial location, the algorithm, which was blinded to the location and type of the AF source, iteratively advanced a Lasso catheter based on its electrogram characteristics. The algorithm stopped the catheter when it located of an AF source and identified the type. The efficiency of the algorithm is validated using a set of simulated focal and rotor-driven arrhythmias in fibrotic human 2D and 3D atrial tissue.Our study shows the feasibility of locating AF sources with a success rate of greater than 95.25% within average 7.56 ± 2.28 placements independently of the initial position of the catheter and the source type.The algorithm could play a critical role in clinical electrophysiology laboratories for mapping patient-specific ablation of AF sources located outside the pulmonary veins and improving the procedure success.
View details for DOI 10.1007/s13239-019-00414-5
View details for Web of Science ID 000468445200013
View details for PubMedID 30989616
View details for PubMedCentralID PMC6527788
Iterative navigation of multipole diagnostic catheters to locate repeating-pattern atrial fibrillation drivers
JOURNAL OF CARDIOVASCULAR ELECTROPHYSIOLOGY
2019; 30 (5): 758–68
Targeting repeating-pattern atrial fibrillation (AF) sources (reentry or focal drivers) can help in patient-specific ablation therapy for AF; however, the development of reliable and accurate tools for locating such sources remains a major challenge. We describe iterative catheter navigation (ICAN) algorithm to locate AF drivers using a conventional circular Lasso catheter.At each step, the algorithm analyzes 10 bipolar electrograms recoded at a given catheter location and the history of previous catheter movements to determine if the source is inside the catheter loop. If not, it calculates new coordinates and selects a new position for the catheter. The process continues until a source is located. The algorithm was evaluated in a computer model of atrial tissue with various degrees of fibrosis under a broad range of arrhythmia scenarios. The latter included slow and fast reentry, macroreentry, figure-of-eight reentry, and fibrillatory conduction. Depending on the initial distance of the catheter from the source and scenario, it took about 3 to 16 steps to localize an AF source. In 94% of cases, the identified location was within 4 mm from the source, independently of the initial position of the catheter. The algorithm worked equally well in the presence of patchy fibrosis, low-voltage areas, fragmented electrograms, and dominant-frequency gradients.AF repeating-pattern sources can be localized using circular catheters without the need to map the entire tissue. The proposed algorithm has the potential to become a useful tool for patient-specific ablation of AF sources located outside the pulmonary veins.
View details for DOI 10.1111/jce.13872
View details for Web of Science ID 000471046800018
View details for PubMedID 30725499
View details for PubMedCentralID PMC6554033
Assessment of Data Augmentation Strategies Toward Performance Improvement of Abnormality Classification in Chest Radiographs.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
2019; 2019: 841–44
Image augmentation is a commonly performed technique to prevent class imbalance in datasets to compensate for insufficient training samples, or to prevent model overfitting. Traditional augmentation (TA) techniques include various image transformations, such as rotation, translation, channel splitting, etc. Alternatively, Generative Adversarial Network (GAN), due to its proven ability to synthesize convincingly-realistic images, has been used to perform image augmentation as well. However, it is unclear whether GAN augmentation (GA) strategy provides an advantage over TA for medical image classification tasks. In this paper, we study the usefulness of TA and GA for classifying abnormal chest X-ray (CXR) images. We first trained a progressive-growing GAN (PG-GAN) to synthesize high-resolution CXRs for performing GA. Then, we trained an abnormality classifier using three training sets individually - training set with TA, with GA and with no augmentation (NA). Finally, we analyzed the abnormality classifier's performance for the three training cases, which led to the following conclusions: (1) GAN strategy is not always superior to TA for improving the classifier's performance; (2) in comparison to NA, however, both TA and GA leads to a significant performance improvement; and, (3) increasing the quantity of images in TA and GA strategies also improves the classifier's performance.
View details for DOI 10.1109/EMBC.2019.8857516
View details for PubMedID 31946026
Performance Evaluation of a Generative Adversarial Network for Deblurring Mobile-phone Cervical Images
IEEE. 2019: 4487–90
View details for Web of Science ID 000557295304212
Assessment of Data Augmentation Strategies Toward Performance Improvement of Abnormality Classification in Chest Radiographs
IEEE. 2019: 841–44
View details for Web of Science ID 000557295301063
Performance Evaluation of a Generative Adversarial Network for Deblurring Mobile-phone Cervical Images.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
2019; 2019: 4487–90
Visual examination forms an integral part of cervical cancer screening. With the recent rise in smartphone-based health technologies, capturing cervical images using a smartphone camera for telemedicine and automated screening is gaining popularity. However, such images are highly prone to image corruption, typically out-of-focus target or camera shake blur. In this paper, we applied a generative adversarial network (GAN) to deblur mobile-phone cervical (MC) images, and we evaluate the deblur quality using various measures. Our evaluation process is three-fold: first, we calculate the peak signal to noise ratio (PSNR) and the structural similarity (SSIM) of a test dataset with ground truth availability. Next, we calculate the perception based image quality evaluator (PIQE) score of a test dataset without ground truth availability. Finally, we classify a dataset of blurred and the corresponding deblurred images into normal/abnormal MC images. The resulting change in classification accuracy was our final assessment. Our evaluation experiments show that deblurring of MC images can potentially improve the accuracy of both manual and automated cancerous lesion screening.
View details for DOI 10.1109/EMBC.2019.8857124
View details for PubMedID 31946862
Developing an Iterative Tracking Algorithm to Guide a Catheter Towards Atrial Fibrillation Rotor Sources in Simulated Fibrotic Tissue
Locating atrial fibrillation (AF) rotor sources can help target ablation therapy for AF. Our aim was to develop a catheter-tracking algorithm to locate AF rotor sources using a conventional 20-electrode circular catheter. We simulated rotor-driven arrhythmias in homogeneous and fibrotic human atrial tissue and evaluated the algorithm for different initial catheter positions. The algorithm guided and detected a rotor with a success rate of greater than 97.9% independently of the initial position of the catheter with an accuracy of greater than 2.3±1.4 mm.
View details for DOI 10.22489/CinC.2018.129
View details for Web of Science ID 000482598700049
View details for PubMedID 31681821
View details for PubMedCentralID PMC6824711
Development of a Rotor-Mapping Algorithm to Locate Ablation Targets During Atrial Fibrillation
IEEE. 2018: 41–44
Catheter ablation therapy involving isolation of pulmonary veins (PVs) remains the cornerstone procedure to treat AF. However, due to the sub-optimal success rates of PV isolation, there is a need for new ablation techniques to locate AF ablation targets known as rotors, outside of the PVs. In this paper, we developed a novel rotor-mapping algorithm that uses a conventional diagnostic catheter, Lasso, to locate a rotor source. The algorithm, called the Region of Rotor (ROR) Mapping, utilizes the characteristics of local bipolar electrograms to navigate the catheter's iterative placements while generating a map, overlaid on the atrial anatomy, that displays the potential rotor region. We evaluated the developed ROR mapping algorithm using a 2D simulation of AF on a tissue with heterogeneous conduction properties. The results demonstrated a significant success rate of 93% in accurately locating the region of the rotor with a mean distance of 1.4mm from the ground truth trajectory. The algorithm could play a critical role in mapping non-PV AF ablation targets and improving the outcome of AF ablation.
View details for Web of Science ID 000459872900011
View details for PubMedID 31693015
View details for PubMedCentralID PMC6830728
Simulation of Spiral Waves and Point Sources in Atrial Fibrillation with Application to Rotor Localization
IEEE. 2017: 379–84
Cardiac simulations play an important role in studies involving understanding and investigating the mechanisms of cardiac arrhythmias. Today, studies of arrhythmogenesis and maintenance are largely being performed by creating simulations of a particular arrhythmia with high accuracy comparable to the results of clinical experiments. Atrial fibrillation (AF), the most common arrhythmia in the United States and many other parts of the world, is one of the major field where simulation and modeling is largely used. AF simulations not only assist in understanding its mechanisms but also help to develop, evaluate and improve the computer algorithms used in electrophysiology (EP) systems for ablation therapies. In this paper, we begin with a brief overeview of some common techniques used in simulations to simulate two major AF mechanisms - spiral waves (or rotors) and point (or focal) sources. We particularly focus on 2D simulations using Nygren et al.'s mathematical model of human atrial cell. Then, we elucidate an application of the developed AF simulation to an algorithm designed for localizing AF rotors for improving current AF ablation therapies. Our simulation methods and results, along with the other discussions presented in this paper is aimed to provide engineers and professionals with a working-knowledge of application-specific simulations of spirals and foci.
View details for DOI 10.1109/CBMS.2017.161
View details for Web of Science ID 000424864800078
View details for PubMedID 29629398
View details for PubMedCentralID PMC5886720
Characterization of Electrograms from Multipolar Diagnostic Catheters during Atrial Fibrillation.
BioMed research international
2015; 2015: 272954
Atrial fibrillation (AF) is the most common arrhythmia in USA with more than 2.3 million people affected annually. Catheter ablation procedure is a method for treatment of AF, which involves 3D electroanatomic mapping of the patient's left atrium (LA) by maneuvering a conventional multipolar diagnostic catheter (MPDC) along the LA endocardial surface after which pulmonary vein (PV) isolation is performed, thus eliminating the AF triggers originating from the PVs. However, it remains unclear how to effectively utilize the information provided by the MPDC to locate the AF-sustaining sites, known as sustained rotor-like activities (RotAs). In this study, we use computer modeling to investigate the variations in the characteristics of the MPDC electrograms, namely, total conduction delay (TCD) and average cycle length (CL), as the MPDC moves towards a RotA source. Subsequently, a study with a human subject was performed in order to verify the predictions of the simulation study. The conclusions from this study may be used to iteratively direct an MPDC towards RotA sources thus allowing the RotAs to be localized for customized and improved AF ablation.
View details for DOI 10.1155/2015/272954
View details for PubMedID 26581316
View details for PubMedCentralID PMC4637153