DESC:FORM 2 THE PATENTS ACT, 1970 (39 of 1970) & THE PATENT RULES, 2003 COMPLETE SPECIFICATION (See Section 10 and Rule 13) Title of invention: METHOD AND SYSTEM FOR DETERMINING PROGRESSION OF ATRIAL FIBRILLATION BASED ON HEMODYNAMIC METRICS Applicant: Tata Consultancy Services Limited A company Incorporated in India under the Companies Act, 1956 Having address: Nirmal Building, 9th Floor, Nariman Point, Mumbai 400021, Maharashtra, India The following specification particularly describes the invention and the manner in which it is to be performed. CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY The present application claims priority from Indian provisional patent application no. 202221039572, filed on July 10, 2022. The entire contents of the aforementioned application are incorporated herein by reference. TECHNICAL FIELD The disclosure herein generally relates to the field of health monitoring, and, more particularly, to a method and system for determining progression of atrial fibrillation (AF) based on hemodynamic metrics. BACKGROUND Atrial fibrillation (AF) is a form of cardiac arrhythmia causing rapid and disorganized beating of an atrium. The AF is mostly triggered by electrical impulses originating in roots of pulmonary veins in a left atrium (LA), and effects the LA functionality in general. Prevalence of the AF is predominantly increasing in older population and though the AF in isolation is not life threatening, it is often accompanied with or can autonomously cause other cardiovascular diseases. The most significant effect associated with the AF is thromboembolism due to a blood stasis inside the LA. Such thromboembolic events are generally precursor to vascular dementia, stroke and infraction leading to heart failure. Hence understanding disease etiology and its manifestation is crucial to predict the thromboembolic event and aid in early diagnosis and management of the AF. Conventionally the AF is detected mainly through electrocardiogram (ECG). However, several clinical studies suggests that hemodynamic analysis on the LA and a left atrial appendage (LAA) are crucial for risk stratification of the thromboembolic events. A Computational fluid dynamics (CFD) model is a potent tool that could evaluate subject specific structural changes associated with the LA remodeling and link with the hemodynamic analysis and predict chances of the thromboembolic events. Conventionally the CFD models of the AF have mostly concentrated on an image segmentation pipeline to recreate the related atrium geometry and use simple motion model to estimate cardiac rhythm (pulsatile behavior) of a heart of a subject and computed wall shear stress related variables. However, the effect of the AF on a cardiovascular system has not been modeled. Further some of the conventional CFD models express pressure flow dynamical variation during the AF and its effect on the cardiovascular system through lumped parameter modelling. However, these models do not involve evaluation of associated hemodynamic metrics. Further multiscale conventional approach involving both the CFD, and arterial hemodynamics was implemented to study the flow distribution in aortic circulation due to the AF variations and its relation to stroke but the effect on LA dynamics have not been considered. SUMMARY Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a method for determining progression of atrial fibrillation is provided. The method includes receiving a plurality of medical scan images of a heart of a subject. Further the method includes creating a three-dimensional Computational Fluid Dynamics (3D-CFD) model of a Left Atrium (LA), from the received plurality of medical scan images. Furthermore, the method includes performing discretization on the 3D-CFD model, into a plurality of regions of interest, generating a plurality of high density meshes. Furthermore, the method includes modelling a zero-dimensional (0D) lumped cardiovascular hemodynamic model, to generate a plurality of cardiovascular parameters, wherein the plurality of cardiovascular parameters comprises, a systemic artery flow, a pulmonary artery flow, pressure flow dynamics at the LA (LA dynamics) and pressure flow dynamics at a left ventricle (LV dynamics), a left atrium compliance corresponding to the LA dynamics and, a left ventricle compliance corresponding to the LV dynamics. Upon modelling the zero-dimensional (0D) lumped cardiovascular hemodynamic model, the method performs modelling of an Atrial Fibrillation (AF), by the 0D lumped cardiovascular hemodynamic model along with a rhythm generator, to generate cardiac rhythms for a normal sinus rhythm condition and AF conditions, wherein the AF conditions comprises a high frequency AF (HF-AF) rhythm condition and, a LA remodeled AF rhythm condition. Furthermore, the method comprises generating by the 0D lumped cardiovascular hemodynamic model, the plurality of cardiovascular parameters, for the LA and the LV, by using the generated cardiac rhythms corresponding to the normal sinus rhythm condition and the AF conditions. Furthermore, the method comprises constructing a plurality of blood inflow boundary conditions at a bilateral pulmonary vein inlets and a plurality of blood outflow boundary conditions at a mitral valve outlet, by using the plurality of cardiovascular parameters of the 0D lumped cardiovascular hemodynamic model, generating a boundary conditions imposed 3D-CFD model. Further the method includes calculating a mitral flow blood velocity, on the plurality of high density meshes, by performing a CFD analysis, on the boundary conditions imposed 3D-CFD model. Finally, the method extracts a plurality of LA hemodynamic metrics of wall shear stress (WSS) that are possible indicators for progression of Atrial Fibrillation (AF), from the calculated mitral flow blood velocity, wherein the plurality of LA hemodynamic metrics comprising of time average wall shear stress (TAWSS), oscillatory shear index (OSI) and endothelial cell activation potential (ECAP) are indicators for progression of the AF. In another aspect, a system for determining progression of atrial fibrillation is provided is provided. The system includes receiving a plurality of medical scan images of a heart of a subject. Further the system includes creating a three-dimensional Computational Fluid Dynamics (3D-CFD) model of a Left Atrium (LA), from the received plurality of medical scan images. Furthermore, the system includes performing discretization on the 3D-CFD model, into a plurality of regions of interest, generating a plurality of high density meshes. Furthermore, the system includes modelling a zero-dimensional (0D) lumped cardiovascular hemodynamic model, to generate a plurality of cardiovascular parameters, wherein the plurality of cardiovascular parameters comprises, a systemic artery flow, a pulmonary artery flow, pressure flow dynamics at the LA (LA dynamics) and pressure flow dynamics at a left ventricle (LV dynamics), a left atrium compliance corresponding to the LA dynamics and, a left ventricle compliance corresponding to the LV dynamics. Upon modelling the zero-dimensional (0D) lumped cardiovascular hemodynamic model, the system performs modelling of an Atrial Fibrillation (AF), by the 0D lumped cardiovascular hemodynamic model along with a rhythm generator, to generate cardiac rhythms for a normal sinus rhythm condition and AF conditions, wherein the AF conditions comprises a high frequency AF (HF-AF) rhythm condition and, a LA remodeled AF rhythm condition. Furthermore, the system comprises generating by the 0D lumped cardiovascular hemodynamic model, the plurality of cardiovascular parameters, for the LA and the LV, by using the generated cardiac rhythms corresponding to the normal sinus rhythm condition and the AF conditions. Furthermore, the system comprises constructing a plurality of blood inflow boundary conditions at a bilateral pulmonary vein inlets and a plurality of blood outflow boundary conditions at a mitral valve outlet, by using the plurality of cardiovascular parameters of the 0D lumped cardiovascular hemodynamic model, generating a boundary conditions imposed 3D-CFD model. Further the system includes calculating a mitral flow blood velocity, on the plurality of high density meshes, by performing a CFD analysis, on the boundary conditions imposed 3D-CFD model. Finally, the system extracts a plurality of LA hemodynamic metrics of wall shear stress (WSS) that are possible indicators for progression of Atrial Fibrillation (AF), from the calculated mitral flow blood velocity, wherein the plurality of LA hemodynamic metrics comprising of time average wall shear stress (TAWSS), oscillatory shear index (OSI) and endothelial cell activation potential (ECAP) are indicators for progression of the AF. In yet another aspect, there are provided one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause a method for determining progression of atrial fibrillation. The method includes receiving a plurality of medical scan images of a heart of a subject. Further the method includes creating a three-dimensional Computational Fluid Dynamics (3D-CFD) model of a Left Atrium (LA), from the received plurality of medical scan images. Furthermore, the method includes performing discretization on the 3D-CFD model, into a plurality of regions of interest, generating a plurality of high density meshes. Furthermore, the method includes modelling a zero-dimensional (0D) lumped cardiovascular hemodynamic model, to generate a plurality of cardiovascular parameters, wherein the plurality of cardiovascular parameters comprises, a systemic artery flow, a pulmonary artery flow, pressure flow dynamics at the LA (LA dynamics) and pressure flow dynamics at a left ventricle (LV dynamics), a left atrium compliance corresponding to the LA dynamics and, a left ventricle compliance corresponding to the LV dynamics. Upon modelling the zero-dimensional (0D) lumped cardiovascular hemodynamic model, the method performs modelling of an Atrial Fibrillation (AF), by the 0D lumped cardiovascular hemodynamic model along with a rhythm generator, to generate cardiac rhythms for a normal sinus rhythm condition and AF conditions, wherein the AF conditions comprises a high frequency AF (HF-AF) rhythm condition and, a LA remodeled AF rhythm condition. Furthermore, the method comprises generating by the 0D lumped cardiovascular hemodynamic model, the plurality of cardiovascular parameters, for the LA and the LV, by using the generated cardiac rhythms corresponding to the normal sinus rhythm condition and the AF conditions. Furthermore, the method comprises constructing a plurality of blood inflow boundary conditions at a bilateral pulmonary vein inlets and a plurality of blood outflow boundary conditions at a mitral valve outlet, by using the plurality of cardiovascular parameters of the 0D lumped cardiovascular hemodynamic model, generating a boundary conditions imposed 3D-CFD model. Further the method includes calculating a mitral flow blood velocity, on the plurality of high density meshes, by performing a CFD analysis, on the boundary conditions imposed 3D-CFD model. Finally, the method extracts a plurality of LA hemodynamic metrics of wall shear stress (WSS), that are possible indicators for progression of Atrial Fibrillation (AF), from the calculated mitral flow blood velocity, wherein the plurality of LA hemodynamic metrics comprising of time average wall shear stress (TAWSS), oscillatory shear index (OSI) and endothelial cell activation potential (ECAP) are indicators for progression of the AF. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles: FIG. 1 is a functional block diagram of a system for determining progression of an atrial fibrillation (AF) based on hemodynamic metrics, according to some embodiments of the present disclosure. FIG. 2 illustrates a pipeline for a three-dimensional Computational Fluid Dynamics (3D-CFD) model and a zero-dimensional (0D) lumped cardiovascular hemodynamic model, for the system, for determining progression of the atrial fibrillation based on the hemodynamic metrics, in accordance with some embodiments of the present disclosure. FIG. 3A and FIG. 3B are exemplary flow diagrams for a processor implemented method for determining progression of the atrial fibrillation (AF) based on the hemodynamic metrics, in accordance with some embodiments of the present disclosure. FIG. 4 illustrates a plot for blood inflow boundary conditions at four bilateral pulmonary vein inlets, for a normal sinus rhythm condition and AF conditions, in accordance with some embodiments of the present disclosure. FIG. 5 illustrates a plot for a mitral flow blood velocity, for the normal sinus rhythm condition and the AF conditions, in accordance with some embodiments of the present disclosure. FIG. 6 illustrates a plot for the mitral flow blood velocity of LA, for the normal sinus rhythm condition and the AF conditions, in accordance with some embodiments of the present disclosure. FIG. 7 illustrates a plot for pressure dynamics and volume dynamics plot of the LA, for the normal sinus rhythm condition and the AF conditions, in accordance with some embodiments of the present disclosure. DETAILED DESCRIPTION OF EMBODIMENTS Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope being indicated by the following embodiments described herein. In Atrial Fibrillation (AF) apart from high frequency fibrillation of arterial wall, the AF is characterized with lack of active contraction of LA, referred to as atrial kick as discussed in literature (e.g., “D.Gupta, A.Shah, R.Giugliano, C.Ruff, et al. Left atrial structure and function in atrial fibrillation, European Heart Journal, vol:35(22), pp:1457-1465, 2014.”). Changes in LA dynamics over prolonged period, as in case of a persistent AF cause structural and functional remodelling of the LA. Especially a structure in the LA known as a left atrial appendage (LAA) is known to aid in blood stasis due to the improper LA contraction and alteration in interatrial blood flow dynamics which consequently increases stroke risk. Conventionally the AF is detected mainly through ECG. However, several clinical studies suggests that hemodynamic analysis on the LA and the LAA are crucial for risk stratification of thromboembolic events. Computational fluid dynamics (CFD) is a potent tool that could evaluate subject specific structural changes associated with the LA remodeling and link with hemodynamic effect and predict chances of the thromboembolic events. Conventionally in the CFD models, the effect of AF on a cardiovascular system has not been modeled. Further in some conventional CFD models, express the pressure flow dynamical variation during the AF, but the effect of the AF on the cardiovascular system has not been modeled. Further in these models, evaluation of associated hemodynamic metrics and effect on the LA dynamics have not been considered. The method and system for determining progression of the AF based on the hemodynamic metrics disclosed herein, analyzes the effect of the AF on various cardiovascular parameters like pressure flow dynamics at the LA and pressure flow dynamics at the LV as well as effect on the LA wall stress parameters for the AF variations. A 3D-CFD model is modelled from a plurality of medical scan images of a heart of a subject, and the AF variations are incorporated in a zero-dimensional (0D) lumped cardiovascular hemodynamic model along with a rhythm generator that generates the AF specific cardiac compliance and cardiac rhythms. The method and system enable improvement in understanding the AF progression that leads to the thromboembolic events. An implementation of the method and system for determining progression of the atrial fibrillation (AF) based on the hemodynamic metrics is described further in detail with reference to FIGS. 1 through 7. Referring now to the drawings, and more particularly to FIG. 1 through 7, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method. FIG. 1 is a functional block diagram of a system 100 for determining progression of the AF based on the hemodynamic metrics, according to some embodiments of the present disclosure. In an embodiment the system 100 includes or is otherwise in communication with one or more hardware processors 104, communication interface device(s) or input/output (I/O) interface(s) 106 (also referred as interface(s)), and one or more data storage devices or memory 102 operatively coupled to the one or more hardware processors 104. The one or more processors 104 may be one or more software processing components and/or hardware processors. In an embodiment, the hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) is/are configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices (e.g., smartphones, tablet phones, mobile communication devices, and the like), workstations, mainframe computers, servers, a network cloud, and the like. The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server. The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic-random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, a database 108 is comprised in the memory 102, wherein the database 108 comprises information of the plurality of medical scan images. The memory 102 further comprises of a plurality of cardiovascular parameters, the hemodynamic metrics, a plurality of blood inflow boundary conditions and a plurality of blood outflow boundary condition, LA wall properties, and cardiac rhythms. The memory 102 further comprises plurality of module such as the 3D-CFD model, an Electrophysiology (EP) (not shown) module, a simplified Central Nervous system (CNS) (not shown), the 0D lumped cardiovascular hemodynamic model along with the rhythm generator and the like as shown in FIG. 2 depicting process overview of the system 100. The above-mentioned technique(s) are implemented as at least one of a logically self-contained part of a software program, a self-contained hardware component, and/or, a self-contained hardware component with a logically self-contained part of a software program embedded into each of the hardware component (e.g., hardware processor 104 or memory 102) that when executed perform the method described herein. The memory 102 further comprises (or may further comprise) information pertaining to input(s)/output(s) of each step performed by the systems and methods of the present disclosure. In other words, input(s) fed at each step and output(s) generated at each step are comprised in the memory 102 and can be utilized in further processing and analysis. Functions of the components of system 100 are explained in conjunction with diagrams depicted in FIG. 2, FIG. 3A and, FIG 3B for determining progression of the AF based on the hemodynamic metrics. In an embodiment, the system 100 comprises one or more data storage devices or the memory 102 operatively coupled to the processor(s) 104 and is configured to store instructions for execution of steps of the method depicted in FIG. 3A and FIG 3B by the processor(s) or one or more hardware processors 104. The steps of the method of the present disclosure will now be explained with reference to the components or blocks of the system 100 as depicted in FIG. 1, the pipeline of 3D-CFD model and the 0D in FIG. 2 and, the steps of the exemplary flow diagrams as depicted in FIG. 3A and FIG 3B. Although process steps, method steps, techniques or the like may be described in a sequential order, such processes, methods, and techniques may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps to be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously. FIG. 2, with reference to FIG.1, illustrates a pipeline for the 3D-CFD model and the 0D lumped cardiovascular hemodynamic model, for the system 100 for determining progression of the AF based on the hemodynamic metrics, in accordance with some embodiments of the present disclosure. The system 100, in FIG. 2, includes a CT/MRI images block comprising of the plurality of medical scan images of the heart of the subject. From the received plurality of the medical scan images, the 3D-CFD model is created, as shown in a segmentation and 3D-CFD Modelling block in FIG. 2 of the present disclosure. In a mesh generation block in FIG. 2 of the present disclosure, the system 100 generates a plurality of high density meshes, by performing discretization on the 3D-CFD model. The system 100 for determining progression of the AF based on the hemodynamic metrics, includes the 0D lumped cardiovascular hemodynamic model block and the rhythm generator block. The AF is modelled by the 0D lumped cardiovascular hemodynamic model along with the rhythm generator, to generate the cardiac rhythms for a normal sinus rhythm condition and AF conditions respectively. Further the plurality of cardiovascular parameters for the LA and the LV are generated from the generated cardiac rhythms corresponding to the normal sinus rhythm condition and the AF conditions respectively. The plurality of cardiovascular parameters for the LA and the LV corresponds to a Left Atrium dynamics block, and a Left Ventricle dynamics block are shown in the FIG. 2 of the present disclosure. At boundary conditions block in FIG. 2 of the present disclosure, the plurality of blood inflow boundary conditions and the plurality of blood outflow boundary conditions are created by using the plurality of cardiovascular parameters of the 0D lumped cardiovascular hemodynamic model generating a boundary conditions imposed 3D-CFD model. Further at a wall property block in the FIG. 2 of the present disclosure, a CFD analysis is performed on the plurality of high density meshes of the 3D-CFD model, to calculate a mitral flow blood velocity, by using the boundary conditions imposed 3D-CFD model. The hemodynamic metrics comprises a plurality of LA hemodynamic metrics (LA wall properties) and a plurality of LV hemodynamic metrics. Further the plurality of LA hemodynamic metrics of wall shear stress (WSS) are calculated from the mitral flow blood velocity that are possible indicators for progression of the AF, as shown in the blood flow and wall stress analysis block in FIG. 2 of the present disclosure. The LV hemodynamic metrics such as an ejection fraction (EF), a stroke volume (SV) and a mean arterial pressure (MAP) are extracted from the Left Ventricle dynamics block as shown in FIG. 2 of the present disclosure. FIG. 3A and FIG. 3B, with reference to FIGS 1-2, are exemplary flow diagrams for a processor implemented a method 300 for determining progression of the AF based on the hemodynamic metrics, in accordance with some embodiments of the present disclosure. At step 302 of the present disclosure, the one or more hardware processors 104 receive, the plurality of medical scan images of the heart of the subject. The plurality of medical scan images can include Computed Tomography (CT) scan images or Magnetic Resonance Imaging (MRI) scan images. Referring to steps of method 300, at step 304 of the present disclosure, one or more hardware processors 104 create, the three-dimensional Computational Fluid Dynamics (3D-CFD) model of the LA, from the received plurality of medical scan images. The method identifies a plurality of substructures, of the heart by a slice selection, in an axial, a coronal and a sagittal plane from the received plurality of medical scan images. Further the method identifies a plurality of path lines that are connecting centres of the plurality of substructures. Further the method performs segmentation, by using the plurality of the path lines, to generate a plurality of segmented sections. A cross sectional areas of the plurality of substructures are segmented based on selection of a slice criterion corresponding to optimal substructures of the LA. Then the method performs, a loft operation, on to the plurality of segmented sections and generates the 3D-CFD model of the LA. The loft operation performed on the plurality of segmented sections smoothens the 3D-CFD model of the LA for the CFD analysis. The generated 3D-CFD model of the LA comprising of four bilateral Pulmonary Veins (PVs) inlets, a LA chamber and outlet to a left ventricle (LV) via a mitral valve. The four bilateral PVs are a Right Superior Pulmonary Vein (RSPV), a Right Inferior Pulmonary Vein (RIPV), a Left superior Pulmonary Vein (LSPV) and a Left inferior Pulmonary Vein (LIPV). Referring to steps of method 300, at step 306 of the present disclosure, one or more hardware processors 104 perform discretization, on the 3D-CFD model, into a plurality of regions of interest, generating the plurality of high density meshes. At step 308 of the method 300, one or more hardware processors 104 model, the 0D lumped cardiovascular hemodynamic model, to generate the plurality of cardiovascular parameters. The plurality of cardiovascular parameters comprises, a systemic artery flow, a pulmonary artery flow, the pressure flow dynamics at the LA (LA dynamics), the pressure flow dynamics at the LV (LV dynamics), a left atrium compliance corresponding to the LA dynamics, and a left ventricle compliance corresponding to the LV dynamics. The 0D lumped cardiovascular hemodynamic model comprises of the Electrophysiology (EP) module (not shown), and the simplified Central Nervous system (CNS) (not shown). The 0D lumped cardiovascular hemodynamic model (i) regulates the systemic artery flow and, the pulmonary artery flow (ii) captures and replicates the LA dynamics and the LV dynamics, and the volume dynamics during a cardiac cycle. The LA dynamics and the LV dynamics replication of the 0D lumped cardiovascular hemodynamic model, at the LA and the LV that are expressed by a state space as: P_la =1/(C_la (t) ) [(P_la-P_pa)/R_p -U_(mi ) X (P_la-P_lv)/R_mi -C_la (t) P_la ] (1) P_la =1/(C_lv (t) ) [U_(mi ) X (P_la-P_lv)/R_mi -U_(ao ) X (P_lv-P_sa)/R_ao -C_lv (t) P_lv ] (2) where P_la, P_lv, P_sa, and P_pa are pressure variables, capturing the LA dynamics and the LV dynamics at the LA and the LV, the systemic artery flow, the pulmonary artery flow respectively; R_mi, R_ao are valvular resistance across the mitral valve, and aortic valves; R_p is a vascular resistance; C_la, C_la are LA compliance and LV compliance respectively; U_(mi ), U_(ao ) are control inputs for opening and closing of heart valves and t is a function of time that varies with each cardiac cycle. The method for determining progression of the atrial fibrillation based on hemodynamic metrics, extracts LV hemodynamic metrics such as, the EF, the MAP and the stroke volume from the pressures variables that captured pressure flow dynamics at the LV (the LV dynamics), the systemic artery flow, the pulmonary artery flow respectively, to analyze general cardiovascular health of the heart. Referring to steps of method 300, at step 310 of the present disclosure, the one or more hardware processors 104 model, the AF, by the 0D lumped cardiovascular hemodynamic model along with the rhythm generator, to generate the cardiac rhythms (pulsating behavior) for the normal sinus rhythm condition and the AF conditions respectively. The AF conditions includes a high frequency AF (HF-AF) rhythm condition and, a LA remodeled AF rhythm condition. The method of the present disclosure sequentially activates heart chambers of the 0D lumped cardiovascular hemodynamic model, by a plurality of time-varying compliance functions incorporated by the rhythm generator that generates AF specific compliance functions. The time varying compliance functions across right atrium C_ra (t), left atrium C_la (t), right ventricle C_rv (t) and, left ventricle C_lv (t), incorporated by the rhythm generator is mathematically represented as: C_ra (t)=C_(min,ra)+0.5 × (C_(max,ra)-C_(min,ra))u(t) (3) C_la (t) =C_(min,la)+0:5 × C_(max,la)-C_(min,la))u(t-d_la) (4) C_i (t) ) =Ci × u_v (t-d), i ? {lv,rv} (5) where C_(min,ra), C_(min,la) are minimum values of a right atrium (RA) compliance and a LA compliance respectively; C_(max,ra), C_(max,la) are maximum values of the RA compliance and the LA compliance respectively; C_i ;i ? {lv,rv} is a systolic compliance across the LV and the RV; d_la and d represents the delay in activation of the LA and the LV with respect to the RA; u(t) and u_v (t) define activation functions at a predefined time (t) in the LA, the RA , and the LV and the RV respectively. The activation function at the t is mathematically expressed as: u(t)={¦(0, 0=t