Cardiac Beat Research Articles

P874Mitral and aortic flow adaptation to 58-days head-down bed-rest assessed by PC-MRI, and effectiveness of high-intensity jump training countermeasure

Aims: Prolonged immobilization generates cardiac deconditioning, a cardiovascular disease risk factor, and efficient countermeasures (CM) are needed to prevent it. We aimed to assess by Phase-Contrast (PC) MRI the effects of long-term strict head-down (-6 degrees) bed-rest (BR) deconditioning and the effectiveness of high-intensity jump training CM on aortic and mitral flow. Methods: 23 males (29±6 years, 181±6 cm, 77±7 kg) were enrolled. The experiment was conducted at Envihab research facility as part of the European Space Agency BR studies. Participants were randomly allocated to the jump training group (JUMP, n=12) or to the control group (CTRL, n=11). A typical training session consisted of 4x10 countermovement jumps and 2x10 hops in a horizontal sledge jump system, with 5–6 sessions/week. PC-MRI images (3T Biograph mMR) with interleaved 3-directional velocity encoding (VENC: x and y: 80 cm/s; z: 150 cm/s) were obtained (spatial resolution 1.4x1.4 mm2) at the level of the aortic root, and of the mitral plane, before (PRE) and after 58-days (HDT58) of BR. The resulting planar magnitude data and 3-directional velocity images were semi-automatically analysed with previously validated custom software to compute the following parameters: cardiac output (CO), stroke volume (SV), flow rate (Qpeak), systolic duration (Sys) and heart beat duration (RR), rapid filling (Efill) and inflow rate (Epeak). Results: In CTRL, compared to baseline values, at HDT58 a significant (p<.05, paired t-test) RR (14%) and systolic (10%) shortening, with a decrease in CO (8%), SV (22%), Qpeak (12%), Efilling (26%), Epeak (26%) were observed. In CM, only RR was shortened (8%), together with a decrease in SV (12%), Qpeak (7.5%), Efilling (11%), Epeak (15%). Conclusions: This is the first study addressing aortic and mitral flow using PC-MRI during BR. Cardiac adaptation to deconditioning due to prolonged immobilization resulted in a reduction of aortic outflow and mitral inflow. The applied CM appeared only partially effective in opposing this phenomenon. This information could be useful to better understand physiologic changes in patients undergoing long periods of immobilization, and to improve countermeasures to reduce cardiac deconditioning. Acknowledgement/Funding: This research was supported by the Italian Space Agency (contract 2013-064-R.0, recipient EG Caiani)

Wavelet and kernel dimensional reduction on arrhythmia classification of ECG signals

Electrocardiogram (ECG) monitoring is continuously required to detect cardiac ailments. At times it is challenging to interpret the differences in the P- QRS-T curve. The proposed approach aims to show the excellence of kernel capabilities of Kernel Principal Component Analysis (KPCA) and Kernel Independent Component Analysis (KICA) in the wavelet domain. In this work, experiments are performed using five different categories of cardiac beats. The supervised classifiers like feed-forward neural network (FNN), backpropagation neural network (BPNN), and K nearest neighbor (KNN) statistically evaluates the impact of discrete wavelet with KPCA and KICA on extracted beats. The performance evaluation also compares the outcomes with existing techniques. The obtained results justify the supremacy of the combination of wavelet, kernel, and KNN approach, yielding a 99.7 % classification success rate. The five-fold crossvalidation scheme is used for measuring the efficacy of classifiers.

Functional video-based analysis of 3D cardiac structures generated from human embryonic stem cells

Human embryonic stem cells (hESCs) differentiated into cardiomyocytes (CM) often develop into complex 3D structures that are composed of various cardiac cell types. Conventional methods to study the electrophysiology of cardiac cells are patch clamp and microelectrode array (MEAs) analyses. However, these methods are not suitable to investigate the contractile features of 3D cardiac clusters that detach from the surface of the culture dishes during differentiation. To overcome this problem, we developed a video-based motion detection software relying on the optical flow by Farnebäck that we call cBRA (cardiac beat rate analyzer). The beating characteristics of the differentiated cardiac clusters were calculated based on the local displacement between two subsequent images. Two differentiation protocols, which profoundly differ in the morphology of cardiac clusters generated and in the expression of cardiac markers, were used and the resulting CM were characterized. Despite these differences, beat rates and beating variabilities could be reliably determined using cBRA. Likewise, stimulation of β-adrenoreceptors by isoproterenol could easily be identified in the hESC-derived CM. Since even subtle changes in the beating features are detectable, this method is suitable for high throughput cardiotoxicity screenings.

Mechano-electric finite element model of the left atrium

Mechanical stretch plays a major role in modulating atrial function, being responsible for beat-by-beat responses to changes in chamber preload, enabling a prompt regulation of cardiac function. Mechano-electric coupling (MEC) operates through many mechanisms and has many targets, making it experimentally difficult to isolate causes and effects especially under sinus conditions where effects are more transient and subtle. Therefore, modelling is a powerful tool to help understand the role of MEC with respect to the atrial electromechanical interaction. We propose a cellular-based computational model of the left atrium that includes a strongly coupled MEC component and mitral flow component to account for correct pressure generation in the atrial chamber as a consequence of blood volume and contraction. The method was applied to a healthy porcine left atrium. Results of the strongly coupled simulation show that strains are higher in the areas adjacent to the mitral annulus, the rim of the appendage, around the pulmonary venous trunks and at the location of the Bachmann's bundle, approximately between the mitral annulus and the region where the venous tissue transitions into atrial. These are regions where arrhythmias are likely to originate. The role of stretch-activated channels was very small for sinus rhythm for the single cardiac beat simulation, although tension development was very sensitive to stretch. The method could be applied to investigate potential therapeutic interventions acting on the mechano-electrical properties of the left atrium.

Classification of heart rhythm disorders using instructive features and artificial neural networks

Accurate detection of the heart rhythm disorders at an early stage is helpful for improving survival rate. This paper presents an automated detection and classification methods of cardiac arrhythmia by time-frequency analysis of the recorded ECG signals from MIT-BIH database. The discrete wavelet transform has been used to eliminate noises in order to enhance the quality of signals and adaptive thresholding-based Hilbert transform has been used to find precise R-peaks. Temporal, morphological and statistical features were extracted from each heartbeat and has been used as input to the classifier to detect five cardiac arrhythmia beats. The results show less detection error rate of 0.17% in detecting QRS complex. The MLP-BP, RBF-NN, and the PNN classifiers provide an average accuracy of 98.72%, 99.77% and 99.16% respectively. The result indicates the efficiency of the proposed method in classifying ECG beats which is useful in diagnosis of cardiac arrhythmias.

A novel controller based on state-transition models for closed-loop vagus nerve stimulation: Application to heart rate regulation.

Vagus nerve stimulation (VNS) is an established adjunctive therapy for pharmacologically refractory epilepsy and depression and is currently in active clinical research for other applications. In current clinical studies, VNS is delivered in an open-loop approach, where VNS parameters are defined during a manual titration phase. However, the physiological response to a given VNS configuration shows significant inter and intra-patient variability and may significantly evolve through time. VNS closed-loop approaches, allowing for the optimization of the therapy in an adaptive manner, may be necessary to improve efficacy while reducing side effects. This paper proposes a generic, closed-loop control VNS system that is able to optimize a number of VNS parameters in an adaptive fashion, in order to keep a control variable within a specified range. Although the proposed control method is completely generic, an example application using the cardiac beat to beat interval (RR) as control variable will be developed in this paper. The proposed controller is based on a state transition model (STM) that can be configured using a partially or a fully-connected architecture, different model orders and different state-transition algorithms. The controller is applied to the adaptive regulation of heart rate and evaluated on 6 sheep, for 13 different targets, using partially-connected STM with 10 states. Also, partially and fully-connected STM defined by 30 states were applied to 7 other sheep for the same 10 targets. Results illustrate the interest of the proposed fully-connected STM and the feasibility of integrating this control system into an implantable neuromodulator.

Detection of atrial fibrillation using an earlobe photoplethysmographic sensor

Atrial fibrillation (AF) is the most common cardiac arrhythmia in the world, associated with increased risk of thromboembolic events and an increased mortality rate. In addition, a significant portion of AF patients are asymptomatic. Current AF diagnostic methods, often including a body surface electrocardiogram or implantable loop recorder, are both expensive and invasive and offer limited access within the general community. Objective: We tested the feasibility of the detection of AF using a photoplethysmographic signal acquired from an inexpensive, non-invasive earlobe photoplethysmographic sensor. This technology can be implemented into wearable devices and would enable continuous cardiac monitoring capabilities, greatly improving the rate of asymptomatic AF detection. Approach: We conducted a clinical study of patients going through electrical cardioversion for AF treatment. Photoplethysmographic recordings were taken from these AF patients before and after their cardioversion procedure, along with recordings from a healthy control group. Using these recordings, cardiac beats were identified and the inter-systolic interval was calculated. The inter-systolic interval was used to calculate four parameters to quantify the heart rate variability indicative of AF. Receiver operating characteristic curves were used to calculate discriminant thresholds between the AF and non-AF cohorts. Main results: The parameter with the greatest discriminant capability resulted in a sensitivity and specificity of 90.9%. These results are comparable to expensive ECG-based and invasive implantable loop recorder AF detection methods. Significance: These results demonstrate that using a non-invasive earlobe photoplethysmographic signal is a viable and inexpensive alternative to ECG-based AF detection methods, and an alternative that could be invaluable in detecting subclinical AF.

An autonomous medical monitoring system: Validation on arrhythmia detection.

In this paper, we present a generic platform for autonomous medical monitoring and diagnostics. We validated the platform in the context of arrhythmia detection with publicly available databases. The big advantage of this platform is its capacity to deal with various types of physiological signals. Many pre-processing steps are performed to bring the input information into a uniform state that will be explored by a machine learning algorithm. Since this block plays a crucial role in the entire processing pipeline, three different methods were evaluated for detection and classification of anomalies. The results presented in this work are validated on cardiac beats, where the highest accuracy was obtained on the classification of normal beats (94%). On the other hand, atrial fibrillation and premature ventricular contraction beats were classified with an accuracy of 78%.

Mechanical ventilatory modes and cardioventilatory phase synchronization in acute respiratory failure patients

Cardioventilatory phase synchronization was studied in ten critically ill patients admitted in intensive care unit (ICU) for acute respiratory failure under two mechanical ventilatory modes: (i) pressure controlled ventilation (PCV); (ii) pressure support ventilation (PSV). The two modalities were administered to the same patient in different times in a random order. Cardioventilatory phase interactions were typified by plotting the relative position of a heartbeat, detected from the electrocardiogram and collected in n groups, within m ventilatory cycles as a function of the progressive cardiac beat number via the synchrogram. n:m phase synchronized patterns were detected by computing the variability of each phase group. The percent duration of the recording featuring phase synchronization was assessed as a measure of the strength of phase synchrony and tested against situations of full phase desynchronization between cardiac and ventilatory rhythms. Indexes quantifying the variability of the cardiac and ventilatory activities were computed as well. Findings proved that: (i) a significant presence of n:m phase synchronized patterns was detected in PCV; (ii) the strength of n:m phase synchronization was stronger during PCV than PSV; (iii) different strengths of cardioventilatory phase synchronization detected during PCV and PSV were found in presence of similar heart and ventilatory rates and alike variability. We conclude that mechanical ventilation can induce a significant presence of cardioventilatory phase synchronized patterns and this amount depends on the mode of mechanical ventilation. Future studies should test the eventual link of the level of phase coordination between heart and mechanical ventilation to a clinical outcome to understand whether featuring a certain degree of cardioventilatory phase synchronization is beneficial for the critical patient in ICU.

A non-invasive approach to investigation of ventricular blood pressure using cardiac sound features

Heart sounds (HSs) are produced by the interaction of the heart valves, great vessels, and heart wall with blood flow. Previous researchers have demonstrated that blood pressure can be predicted by exploring the features of cardiac sounds. These features include the amplitude of the HSs, the ratio of the amplitude, the systolic time interval, and the spectrum of the HSs. A single feature or combinations of several features have been used for prediction of blood pressure with moderate accuracy.Experiments were conducted with three beagles under various levels of blood pressure induced by different doses of epinephrine. The HSs, blood pressure in the left ventricle and electrocardiograph signals were simultaneously recorded. A total of 31 records (18 262 cardiac beats) were collected. In this paper, 91 features in various domains are extracted and their linear correlations with the measured blood pressures are examined. These features are divided into four groups and applied individually at the input of a neural network to predict the left ventricular blood pressure (LVBP).The analysis shows that non-spectral features can track changes of the LVBP with lower standard deviation. Consequently, the non-spectral feature set gives the best prediction accuracy. The average correlation coefficient between the measured and the predicted blood pressure is 0.92 and the mean absolute error is 6.86 mmHg, even when the systolic blood pressure varies in the large range from 90 mmHg to 282 mmHg.Hence, systolic blood pressure can be accurately predicted even when using fewer HS features. This technique can be used as an alternative to real-time blood pressure monitoring and it has promising applications in home health care environments.

Caracterización del patrón respiratorio de pacientes con insuficiencia cardíaca crónica mediante análisis de dinámica simbólica conjunta

This work proposes a method to characterize the respiratory pattern of patients with chronic heart failure (CHF) to determine non-periodic breathing (nPB), periodic breathing (PB) and Cheyne-Stokes respiration (CSR) through non-linear, symbolic analysis of biological signals. A total of 43 patients were examined for their cardiorespiratory profiles, their ECG and respiratory pattern signals were processed, analyzed and studied for parameters that could be of potential use in clinical decision making, specifically in patient classification. Patients in the study were characterized through their cardiorespiratory signals, applying joint symbolic dynamics (JSD) analysis to cardiac beat and respiratory interval durations. The most statistically significant parameters across all groups were identified through a Kruskal-Wallis two tailed test (α = 0.05) and a linear discriminant analysis (LDA) classification method based on such parameters was developed. The best result achieved with this classification method uses 10 features to discriminate patients with a 97.67% Accuracy (Acc). The best features to discriminate among groups are related to cardiorespiratory interaction rather than just respiration patterns alone. Results further support the idea that abnormal breathing patterns derive from physiological abnormalities in chronic heart failure.

Measurement of electrocardiograms in a bath through tap water utilizing capacitive coupling electrodes placed outside the bathtub wall

BackgroundTaking a bath sometimes poses a risk for subjects with chronic cardiopulmonary disorders, due to the thermal effect and water pressure on his/her body. The ECG measurement would be helpful for the early recognition of abnormal cardiac beats and respiratory conditions. This paper describes a new attempt to improve on previous bathtub ECG measurement techniques that had electrodes placed inside the bathtub that were intrusive to the subjects’ bathing experience. This study is concerned with the initial development of a method to measure an electrocardiogram (ECG) through tap water without conscious awareness of the presence of electrodes that are placed outside the bathtub wall.MethodsA configuration of capacitive coupling electrodes placed outside the bathtub was designed so that the electrodes could be hidden. The capacitive coupling was made from the electrodes to the water through the bathtub wall. Two electrodes with an active shielding amplifier covered further by an electromagnetic shield were fixed to the outside surface of the bathtub wall, near the bather’s right scapula and left foot. The potential difference between these two electrodes, similar to the bipolar lead-II ECG, was amplified to obtain raw signals inclusive of ECG/QRS components. Respiration intervals were also derived from ECG/RR intervals. Comparison experiments between this bathtub method and conventional direct methods with spot-electrodes and a chest-band sensor were made using 10 healthy male volunteers (22.2 ± 0.98 years).ResultsThe ECG signal was detectable through tap water as well as water with differing conductivity resulting from mixing bathwater additives with the water. ECG signals and respiration curves derived from ECG/RR intervals were successfully obtained in all subjects. The intervals of the ECG/RR and respiration obtained by the bathtub system and by the direct method were respectively agreed well with each other.ConclusionThe ECG signal, in particular ECG/QRS components, were successfully detected utilizing capacitive coupling electrodes placed outside the bathtub wall. Also, the ECG/RR and respiration intervals were determined with reasonable accuracy as compared with the conventional direct methods.

Premature ventricular complex-induced cardiomyopathy

Premature ventricular complex (PVC)-induced cardiomyopathy is due to frequent PVCs and it can be potentially reversible in selected patients resolves after catheter ablation.PVCs burden is importantly associated with impaired left ventricular(LV) function.Necessary evaluation is important for PVCs patients.For patients with high PVC burdens above 15%-25% of the total cardiac beats in 24 hours, medical treatment should be used to reduce PVC burden and to improve LV function.Catheter ablation of PVCs is recommended for selected patients with high PVC burdens associated with a decline in LV systolic function. Key words: Premature ventricular complex; Cardiomyopathy; Child

Changes of Femoral Photolethysmographic Waveform Characteristics in Anesthetized Dogs with Increased Blood Pressure Induced by Epinephrine.

Background: Blood pressure (BP) has been proven to play an important role in changes of the morphology of a pulse waveform. However, the extent of change of the morphology because of BP signaling has yet to be accurately confirmed.Objectives: This study aims to disclose the accurate effect of BP on the changes in the morphology of the pulse waveform.Methods: Two dogs' invasive intraventricular BP (varied by ejecting different doses of epinephrine) and their femoral arterial pulse waveform (FAPW) signals were synchronously recorded. For each BP increase, a normalized single cardiac beat pulse from the FAPW signal was fitted by five Gaussian curves and the changes in the Gaussian parameters (height, peak position, and time support) were observed.Results: The height parameter increased while the position and time support parameters decreased with increasing systolic BP (SBP). The height ratio and the peak intervals between the first two components decreased with increasing SBP.Conclusions: These results may contribute to the better understanding of the underlying changes of arterial pulse properties at different BP levels and demonstrate the potential application value of the Gaussian fitting method for clinically assessing pulse morphology and evaluating the well-being of artery system.

Cardiac arrhythmia beat classification using DOST and PSO tuned SVM

Background and objectiveThe increase in the number of deaths due to cardiovascular diseases (CVDs) has gained significant attention from the study of electrocardiogram (ECG) signals. These ECG signals are studied by the experienced cardiologist for accurate and proper diagnosis, but it becomes difficult and time-consuming for long-term recordings. Various signal processing techniques are studied to analyze the ECG signal, but they bear limitations due to the non-stationary behavior of ECG signals. Hence, this study aims to improve the classification accuracy rate and provide an automated diagnostic solution for the detection of cardiac arrhythmias. MethodsThe proposed methodology consists of four stages, i.e. filtering, R-peak detection, feature extraction and classification stages. In this study, Wavelet based approach is used to filter the raw ECG signal, whereas Pan–Tompkins algorithm is used for detecting the R-peak inside the ECG signal. In the feature extraction stage, discrete orthogonal Stockwell transform (DOST) approach is presented for an efficient time-frequency representation (i.e. morphological descriptors) of a time domain signal and retains the absolute phase information to distinguish the various non-stationary behavior ECG signals. Moreover, these morphological descriptors are further reduced in lower dimensional space by using principal component analysis and combined with the dynamic features (i.e based on RR-interval of the ECG signals) of the input signal. This combination of two different kinds of descriptors represents each feature set of an input signal that is utilized for classification into subsequent categories by employing PSO tuned support vector machines (SVM). ResultsThe proposed methodology is validated on the baseline MIT-BIH arrhythmia database and evaluated under two assessment schemes, yielding an improved overall accuracy of 99.18% for sixteen classes in the category-based and 89.10% for five classes (mapped according to AAMI standard) in the patient-based assessment scheme respectively to the state-of-art diagnosis. The results reported are further compared to the existing methodologies in literature. ConclusionsThe proposed feature representation of cardiac signals based on symmetrical features along with PSO based optimization technique for the SVM classifier reported an improved classification accuracy in both the assessment schemes evaluated on the benchmark MIT-BIH arrhythmia database and hence can be utilized for automated computer-aided diagnosis of cardiac arrhythmia beats.

Beat-to-beat heart rate detection by smartphone's accelerometers: validation with ECG.

9 healthy male volunteers were studied in supine (SUP) and in standing (ST) posture. A smartphone (iPhone6, Apple) was positioned on the thorax (POS1) to acquire SCG signal. While supine, a second smartphone was positioned on the navel (POS2). The SCG signal was recorded for 3 minutes during spontaneous respiration, synchronous with 3-leads ECG. Using a fully automated algorithm based on amplitude thresholding after rectification, the characteristic peak of the SCG signal (IVC) was detected and used to compute beat-to-beat heart duration, to be compared with the corresponding RR intervals extracted from the ECG. A 100% feasibility of the approach resulted for POS1 in SUP, while 89% in POS2, and 78% for POS1 in ST. In supine, for each smartphones' position, the automated algorithm correctly identified the cardiac beats with >98% accuracy. Linear correlation (r2) with RR was very high (>0.98) in each posture and position, with no bias and narrow limits of agreement. The obtained results proved the feasibility of the proposed approach and the robustness of the applied algorithm in measuring the beat-to-beat heart rate from smartphone-derived SCG, with high accuracy compared to conventional ECG-derived measure.

Three-Dimensional Echocardiographic Assessment of Left Heart Chamber Size and Function with Fully Automated Quantification Software in Patients with Atrial Fibrillation

Echocardiographic determination of left heart chamber volumetric parameters by using manual tracings during multiple beats is tedious in atrial fibrillation (AF). The aim of this study was to determine the usefulness of fully automated left chamber quantification software with single-beat three-dimensional transthoracic echocardiographic data sets in patients with AF. Single-beat full-volume three-dimensional transthoracic echocardiographic data sets were prospectively acquired during consecutive multiple cardiac beats (≥10 beats) in 88 patients with AF. In protocol 1, left ventricular volumes, left ventricular ejection fraction, and maximal left atrial volume were validated using automated quantification against the manual tracing method in identical beats in 10 patients. In protocol 2, automated quantification-derived averaged values from multiple beats were compared with the corresponding values obtained from the indexed beat in all patients. Excellent correlations of left chamber parameters between automated quantification and the manual method were observed (r=0.88-0.98) in protocol 1. The time required for the analysis with the automated quantification method (5min) was significantly less compared with the manual method (27min) (P<.0001). In protocol 2, there were excellent linear correlations between the averaged left chamber parameters and the corresponding values obtained from the indexed beat (r=0.94-0.99), and test-retest variability of left chamber parameters was low (3.5%-4.8%). Three-dimensional transthoracic echocardiography with fully automated quantification software is a rapid and reliable way to measure averaged values of left heart chamber parameters during multiple consecutive beats. Thus, it is a potential new approach for left chamber quantification in patients with AF in daily routine practice.

Slope Transit Time (STT): A Pulse Transit Time Proxy requiring Only a Single Signal Fiducial Point.

A novel pulse transit time proxy measurement, slope transit time (STT), is proposed in this letter. STT is based on geometrical considerations of the arriving photoplethysmographic cardiac waveform and its computation requires only the measurement of a single point on each cardiac beat arriving at the peripheral site. This novel transit time is explained conceptually and its implementation illustrated through its application to signals from respiratory effort, Müller maneuver, and obstructive sleep apnea trials.

Autonomic cardiac and cardiorespiratory responses in volleyball athletes compared to recreationally trained individuals

Objective: The aim of this study was to investigate the autonomic cardiac modulation between professional volleyball players (VP) and recreational aerobically trained individuals (RAT). Methods: Twentyeight men were divided into two groups, (VP; n = 14) and (RAT; n = 14) and each group was subjected to a sub-maximal progressive ramp treadmill test. Cardiac cycle beat to beat (RR) intervals, was obtained through electrocardiogram recording with the test being stopped when the individual reached 85% of their maximum age-predicted heart rate (HR). The heart rate variables were calculated from ECG using MATLABbased algorithms. Results: At rest, RAT showed lower values of the mean value of RR interval, the root mean square successive difference of all RR intervals (r-MSSD), low frequency (LF) and total power spectral (TP) when compared to VP. No significant difference between groups was noted for high frequency (HF) and index LF/HF. After exercise, no significant differences were measured between groups. Mean values of VO2, VE/VO2, and VE showed no significant difference at rest and post effort. Conclusion: The variables such as frequency, intensity and volume may to interfere the autonomic cardiac modulation and possibly indicating a superior protection against exercise-induced ventricular arrhythmias and lower risk of sudden cardiac deaths

Hybrid firefly and Particle Swarm Optimization algorithm for the detection of Bundle Branch Block

Abnormal Cardiac beat identification is a key process in the detection of heart ailments. This work proposes a technique for the detection of Bundle Branch Block (BBB) using hybrid Firefly and Particle Swarm Optimization (FFPSO) technique in combination with Levenberg Marquardt Neural Network (LMNN) classifier. BBB is developed when there is a block along the electrical impulses travel to make heart to beat. ECG feature extraction is a key process in detecting heart ailments. Our present study comes up with a hybrid method combining the two meta-heuristic optimization methods, Firefly algorithm (FFA) and Particle Swarm Optimization (PSO), for feature optimization of ECG (BBB and normal) patterns. One of the major controlling forces is the light intensity attraction of FFA algorithm that models the optimum solution. The light intensity attraction process of the FFA algorithm depends on random directions for search, this may delay in achieving the global optimization solution. The hybrid technique FFPSO, integrates the concepts from FF algorithm and PSO and creates new individuals. In the FFPSO method the local search is performed through the modified light intensity attraction step with PSO operator. The FFPSO features are compared with the classical FF, PSO features. The FFPSO feature values are given as the input to the Levenberg Marquardt Neural Network (LM NN) classifier. It has been observed that the performance of the classifier is improved with the help of the optimized features.