Abnormal Heartbeats Research Articles

A unique feature extraction using MRDWT for automatic classification of abnormal heartbeat from ECG big data with Multilayered Probabilistic Neural Network classifier

This paper employs a novel adaptive feature extraction techniques of electrocardiogram (ECG) signal for detection of cardiac arrhythmias using multiresolution discrete wavelet transform from ECG big data. In this paper, five types ECG arrhythmias including normal beats have been classified. The MIT-BIH database of 48 patient records is utilized for detection and analysis of cardiac arrhythmias. Proposed feature extraction utilizes Daubechies as wavelet function and extracts 21 feature points which include the QRS complex of ECG signal. The Multilayered Probabilistic Neural Network (MPNN) classifier is proposed as the best-suited classifier for the proposed feature. Total 1700 ECG betas were tested using MPNN classifier and compared with other three classifiers Back Propagation (BPNN), Multilayered Perceptron (MLP) and Support Vector Machine (SVM). The system efficiency and performance have been evaluated using seven types of evaluation criteria: precision (PR), F-Score, positive predictivity (PP), sensitivity (SE), classification error rate (CER) and specificity (SP). The overall system accuracy, using MPNN technique utilizing the proposed feature, obtained is 99.53% whereas using BPNN, MLP and SVM provide 97.94%, 98.53%, and 99%. The processing time using MPNN classifier is only 3 s which show that the proposed techniques not only very accurate and efficient but also very quick.

ASSESSMENT OF CARDIAC HEART FAILURE AND CARDIAC ARTERY DISEASE BY THE HIGHER ORDER SPECTRA

Cardiac diseases are major reason of death in the world populace and the numeral of cases is upsurging every year. Due to cardiac artery disease (CAD), the strength of heart muscles becomes weak and heart pumping is disturbed which may eventually lead to abnormal heart beat and heart failure. Therefore, the beginning stage detection of CAD and cardiac heart failure (CHF) are of prime importance. In this work, we have used a non-invasive diagnosis method as higher order spectra (HOS) for assessment of cardiac diseases. The method indicates whether or not a cardiac heart disease is present, by assessing the cardiac health of subjects using extracted features from heart rate variability (HRV) signals. This assessment is based on 10 spectra nonlinear features. These features were extracted from HRV signals by using the HOS method. For this study, the R-R interval data (i.e. HRV signals) were taken from the standard database of cardiac heart failure (CHF), CAD patients, healthy young (YNG) and Self recorded of healthy young (SELF_YNG) subjects. Statistical assessments were performed on the group of database sets as YNG-CAD, YNG-CHF, SELF_YNG-CAD and Self_YNG-CHF subjects. A Wilcoxon rank sum test ([Formula: see text]-value) was used to statistically compare the features extracted by HOS for group of data sets. It indicates whether or not the same features of individual classes of HRV data sets are dissimilar. The results depicted that the all features are very significant ([Formula: see text]) except the phase entropy (PHE) feature which is not significant for CAD-CHF, SELF_YNG-CAD and SELF_YNG-CHF group of subjects. While in the case of YNG-CAD group of subjects, features like first-order spectral moment of amplitudes of diagonal elements (H3), PHE and logarithmic amplitudes of diagonal elements (H2) are significant ([Formula: see text]) and excluding these features, the remaining features are very significant except MM and H1 which are not significant. The results also depicted that the mean value of sum of logarithmic amplitude (H1), H2, normalized entropy (P1), normalized squared entropy (P2) and PHE features of healthy YNG subjects are having higher values than that of CAD and CHF patients. While weighted center of bi-spectrum (WCOB2) and FLAT spectrum features are lower than CAD and CHF patients compared to YNG subjects. In case of CAD and CHF patients, all the features of CAD patients are having higher values compared to CHF except P1, P2 and WCOB1.

A Novel Adaptive Feature Extraction for Detection of Cardiac Arrhythmias Using Hybrid Technique MRDWT & MPNN Classifier from ECG Big Data

Abstract The efficient automatic detection of cardiac arrhythmia using a hybrid technique from ECG big data has been proposed with novel feature extraction technique using Multiresolution Discrete Wavelet Transform (MRDWT) and Multilayer Probabilistic Neural Network (MPNN) classifier. Big Data of ECG signals have been selected from MIT–BIH arrhythmia database for detection of two types of arrhythmias LBBB (Left Bundle Branch Block) and RBBB (Right Bundle Branch Block). The proposed technique can accurately detect and classify LBBB and RBBB along with normal heartbeat. A novel and hybrid method of detection of cardiac arrhythmia have four main stages: denoising of raw ECG, baseline wander removal, proposed feature extraction, and detection of abnormal heartbeats using MPNN neural classifier. 8600 ECG beats were selected, including 4200 normal and 4400 abnormal beats (2200 LBBB and 2200 RBBB) were utilized for testing the proposed technique. The detection outcome using MPNN was compared with other two neural classifiers: Feed Forward Neural Network (FFNN) and Back Propagation Neural Network (BPNN) classifiers. The accuracy and efficiency of classifiers performance were attained in terms of CER (Classification Error Rate), SP (Specificity), Se (Sensitivity), Pr (Precision), PPr (Positive Predictivity) and F-Score. The system performance is achieved with 96.22%, 97.15% and 99.07% overall accuracy using FFNN, BPNN and MPNN. The average percentage of classification error rate (CER) using MPNN classifier is lowest 0.62% whereas FFNN and BPNN show 2.2% and 1. 90% average CER.

Windkessel model of hemodynamic state supported by a pulsatile ventricular assist device in premature ventricle contraction

BackgroundCounter-pulsation control (CPC) by ventricular assist devices (VADs) is believed to reduce cardiac load and increase coronary perfusion. However, patients with VADs have a higher risk of arrhythmia, which may cause the CPC to fail. Consequently, CPC has not been applied by VADs in clinical practice. The phase-locked loop (PLL) algorithm for CPC is readily implemented in VADs; however, it requires a normal, consistent heartbeat for adequate performance. When an arrhythmia occurs, the algorithm maintains a constant pumping rate despite the unstable heartbeat. Therefore, to apply the PLL algorithm to CPC, the hemodynamic effects of abnormal heartbeats must be analyzed.ObjectivesThis study sought to predict the hemodynamic effects in patients undergoing CPC using VADs, based on electrocardiogram (ECG) data, including a wide range of heart rate (HR) changes caused by premature ventricular contraction (PVC) or other reasons.MethodsA four-element Windkessel hemodynamic model was used to reproduce the patient’s aortic blood pressure in this study. ECG data from 15 patients with severe congestive heart failure were used to assess the effect of the CPC on the patients’ hemodynamic state. The input and output flow characteristics of the pulsatile VAD (LibraHeart I, Cervika, Korea) were measured using an ultrasound blood flow meter (TS410, Transonic, USA), with the aortic pressure maintained at 80–120 mmHg. All other patient conditions were also reproduced.ResultsIn patients with PVCs or normal heartbeats, CPC controlled by a VAD reduced the cardiac load by 20 and 40%, respectively. When the HR was greater for other reasons, such as sinus tachycardia, simultaneous ejection from the heart and VAD was observed; however, the cardiac load was not increased by rapid cardiac contractions resulting from decreased left ventricle volume. These data suggest that the PLL algorithm reduces the cardiac load and maintains consistent hemodynamic changes.

ECG Decision Support System based on feedforward Neural Networks

Abstract The success of an Electrocardiogram (ECG) Decision Support System (DSS) requires the use of an optimum machine learning approach. For this purpose, this paper investigates the use of three feedforward neural networks; the Multilayer Perceptron (MLP), the Radial Basic Function Network (RBF), and the Probabilistic Neural Network (PNN) for recognition of normal and abnormal heartbeats. Feature sets were based on ECG morphology and Discrete Wavelet Transformer (DWT) coefficients. Then, a correlation between features was applied. After that, networks were configured and consequently used for the ECG classification. Next, with respect to the performance criteria fixed by the DSS users, a comparative study between them was deduced. Results show that for classifying the MIT-BIH arrhythmia database signals, the RBF (ACC = 99.9%) was retained as the most accurate network, the PNN (Tr_ttime = 0.070 s) as the rapidest network in the training stage and the MLP (Test_time = 0.096 s) as the rapidest network in testing stage.

Neurophysiological development of newborn pigs: effect of the sow

The objective of the present study was to determine the effect of sow parity on neonatal piglet health and vitality at birth. We evaluated 1505 neonate piglets, which were born of York–Landrace sows with the following parity distribution: primiparous (n = 202), second (n = 207), third (n = 211), fourth (n = 222), fifth (n = 225), sixth (n = 218) and seventh parity (n = 220). Piglets born to primiparous and seventh-parity sows had the lowest and highest birthweights respectively, and showed the most marked imbalances in blood gas exchanges, acid–base balance and energy profiles, as well as the highest percentage of severe meconium staining of the skin and the lowest vitality scores (P < 0.05). In contrast, the neonates from the fourth-parity sows had the highest vitality scores, required less time to reach the mother’s teat, and had the highest percentage of adhered umbilical cords and newborns with dyspnoea, apnoea and abnormal heartbeat (P < 0.05). The results of this study suggest that during eutocic farrowings, the sow’s parity number has the following effects on newborn piglets: reduced vigour and longer latencies to begin breathing, stand and take the teat. These effects are due to the presence of imbalances in gas exchanges, the acid–base balance and energy profiles that occurred when the mother was a primiparous or older sow. These signs indicate that the newborn piglet survived a process of intrapartum asphyxia.

Wearable cardioverter defibrillators in schools: A guide for parents and educators.

Keeping children safe is a goal for everyone. To stay safe, some children need a wearable cardioverter defibrillator (WCD). WCDs protect individuals who are at risk for life-threatening arrhythmias. The LifeVest® (ZOLL, Pittsburgh, PA, USA) is a WCD that identifies, records, and treats potentially life-threatening abnormal heartbeats, or arrhythmias. The WCD will provide electrical energy to the heart with up to five high-energy shocks if a potentially dangerous cardiac arrhythmia is detected. This type of arrhythmia, if not treated within a short time period (less than 2 minutes), can result in disruption of blood flow to the brain and other vital organs (that is, a sudden cardiac arrest), and death can occur within minutes. A shock delivered by the LifeVest can terminate an arrhythmia and restore a normal heart rhythm and blood flow to the body. In December 2015, the Food and Drug Administration approved the LifeVest for patients under 18 years old. The purpose of this guide is to serve as a resource for parents and educators, to promote awareness of the LifeVest, and to answer questions about the safety of the LifeVest in schools.

Altered physiochemical properties in industrially synthesized ZnO nanoparticles regulate oxidative stress; induce in vivo cytotoxicity in embryonic zebrafish by apoptosis

This study investigates the in vivo cytotoxicity of ZnO nanoparticles synthesized at industrial scale with embryonic Zebrafish. Industrial synthesis of ZnO nanoparticles was mimicked at lab scale by high energy ball milling technique by milling bulk ZnO particles for 15 h. Synthesized 7 h and 10 h ZnO nanoparticles showed significant alteration of size, zeta potential and optical properties in comparison to Bulk ZnO. Mortality and hatching rate in Zebrafish embryos were influenced by these alterations. Size and charge dependent effect of ZnO nanoparticles exposure on physiology and development of Zebrafish embryos were evident by malfunctioned organ development and abnormal heartbeat rate. Similar dependency on quenching of ROS due to influential hydrogen bond interaction with glycine residue of Sod1 oxidative stress protein and increased apoptosis were observed in cells. The study revealed the mechanism of cytotoxicity in exposed embryonic Zebrafish as an effect of accumulation and internalization inside cells instigating to generation of hypoxic condition and interference with the normal adaptive stress regulation signaling pathways leading towards enhanced apoptosis. The study revealed hidden size and charge dependent in vivo cytotoxicity mechanism of ZnO nanoparticles in Zebrafish embryos insight of the environmental and clinical importance of attention on industrially synthesized ZnO nanoparticles.

A deep convolutional neural network model to classify heartbeats

The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats.

Case 2: Herpes Simplex Virus Infection in a Preterm Infant and Complications

This preterm infant is the product of a dichorionic, diamniotic twin pregnancy born at 27 5/7 weeks’ gestation to a 22-year-old woman via an emergency cesarean delivery for an abnormal fetal heartbeat. The woman’s pregnancy was complicated with prolonged rupture of membranes 8 days before delivery, gestational diabetes mellitus, and a history of depression. She denies a history of sexually transmitted infections, including herpes simplex virus (HSV). She received ampicillin, amoxicillin, and azithromycin as well as 2 doses of betamethasone before delivery. The male infant has Apgar scores of 3, 4, and 8 at 1, 5, and 10 minutes after birth, respectively. An endotracheal tube is placed 4 minutes after birth, and the infant is admitted to the NICU. He receives 1 dose of surfactant and is in stable condition with noninvasive ventilation. The infant receives ampicillin and gentamicin for 7 days for presumed sepsis. His complete blood cell count and C-reactive protein are within normal limits, with a negative initial blood culture. His screening head ultrasonography result is negative for intraventricular hemorrhage on day 7 after birth. At 9 days of age, the infant develops respiratory distress and a diffuse vesicular rash on his axilla, neck, back, and chest. Cerebrospinal fluid (CSF) analysis reveals a white blood cell count of 54/μL (0.5×109/L), red blood cell count …

Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network

Our cardiovascular system weakens and is more prone to arrhythmia as we age. An arrhythmia is an abnormal heartbeat rhythm which can be life-threatening. Atrial fibrillation (Afib), atrial flutter (Afl), and ventricular fibrillation (Vfib) are the recurring life-threatening arrhythmias that affect the elderly population. An electrocardiogram (ECG) is the principal diagnostic tool employed to record and interpret ECG signals. These signals contain information about the different types of arrhythmias. However, due to the complexity and non-linearity of ECG signals, it is difficult to manually analyze these signals. Moreover, the interpretation of ECG signals is subjective and might vary between the experts. Hence, a computer-aided diagnosis (CAD) system is proposed. The CAD system will ensure that the assessment of ECG signals is objective and accurate. In this work, we present a convolutional neural network (CNN) technique to automatically detect the different ECG segments. Our algorithm consists of an eleven-layer deep CNN with the output layer of four neurons, each representing the normal (Nsr), Afib, Afl, and Vfib ECG class. In this work, we have used ECG signals of two seconds and five seconds’ durations without QRS detection. We achieved an accuracy, sensitivity, and specificity of 92.50%, 98.09%, and 93.13% respectively for two seconds of ECG segments. We obtained an accuracy of 94.90%, the sensitivity of 99.13%, and specificity of 81.44% for five seconds of ECG duration. This proposed algorithm can serve as an adjunct tool to assist clinicians in confirming their diagnosis.

The impact of situation-background-assessment-recommendation (SBAR) on safety attitudes in the obstetrics department

ObjectivePrevious studies evaluating the situation-background-assessment-recommendation (SBAR) have been shown to increase effective nurse–physician communication and collaboration. The purpose of this study is to evaluate the impact of the SBAR technique on safety attitudes in the obstetrics department. Materials and MethodsThis study implemented the SBAR Collaborative Communication Education course and was conducted in a medical center from February 2012 to March 2015, which included an educational session on fetal heart rate monitoring, a case-based discussion, and a video demonstration on traditional and SBAR communication. The nurses in the obstetrics department were requested to report their clinical findings and recommendations using a novel SBAR list when abnormal fetal heart beat tracings occurred. All obstetric nurses were requested to complete the Chinese-version of the Safety Attitudes Questionnaire before and after the SBAR educational course. The primary outcome was to evaluate the effect of the SBAR technique on the safety attitudes of the obstetrics department. The secondary outcome was to evaluate the effect of the SBAR technique on the 5-minute Apgar score for neonates. ResultsMost values, including teamwork climate, safety climate, job satisfaction, and working conditions, significantly improved at both postintervention surveys compared with the preintervention survey. There were no significant differences in the number of the neonates with less than seven 5-minute Apgar scores between the pre- and postintervention periods. ConclusionThe SBAR technique, which uses a novel structured handover list, is a feasible tool for nurse–obstetrician communication, and it may improve most dimensions of safety attitudes in the obstetrics department.

A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm

Background and objectivesDetection of the R-peak pertaining to the QRS complex of an ECG signal plays an important role for the diagnosis of a patient's heart condition. To accurately identify the QRS locations from the acquired raw ECG signals, we need to handle a number of challenges, which include noise, baseline wander, varying peak amplitudes, and signal abnormality. This research aims to address these challenges by developing an efficient lightweight algorithm for QRS (i.e., R-peak) detection from raw ECG signals. MethodsA lightweight real-time sliding window-based Max-Min Difference (MMD) algorithm for QRS detection from Lead II ECG signals is proposed. Targeting to achieve the best trade-off between computational efficiency and detection accuracy, the proposed algorithm consists of five key steps for QRS detection, namely, baseline correction, MMD curve generation, dynamic threshold computation, R-peak detection, and error correction. Five annotated databases from Physionet are used for evaluating the proposed algorithm in R-peak detection. Integrated with a feature extraction technique and a neural network classifier, the proposed ORS detection algorithm has also been extended to undertake normal and abnormal heartbeat detection from ECG signals. ResultsThe proposed algorithm exhibits a high degree of robustness in QRS detection and achieves an average sensitivity of 99.62% and an average positive predictivity of 99.67%. Its performance compares favorably with those from the existing state-of-the-art models reported in the literature. In regards to normal and abnormal heartbeat detection, the proposed QRS detection algorithm in combination with the feature extraction technique and neural network classifier achieves an overall accuracy rate of 93.44% based on an empirical evaluation using the MIT-BIH Arrhythmia data set with 10-fold cross validation. ConclusionsIn comparison with other related studies, the proposed algorithm offers a lightweight adaptive alternative for R-peak detection with good computational efficiency. The empirical results indicate that it not only yields a high accuracy rate in QRS detection, but also exhibits efficient computational complexity at the order of O(n), where n is the length of an ECG signal.

Preliminary study of abnormal heart beat detection using capacitive electrocardiograph during sleep

Preliminary study of abnormal heart beat detection using capacitive electrocardiograph during sleep

Preliminary study of abnormal heart beat detection using capacitive electrocardiograph during sleep

Preliminary study of abnormal heart beat detection using capacitive electrocardiograph during sleep

Sleep Apnea Detection using Cardioid Based Graph

In this study, the development of Cardioid based graph electrocardiogram heart abnormalities classification technique is presented. ECG signals in this work were attained from a public online database UCD Sleep Apnea database (UCDB) with sampling rate of 250 Hz. Each recording has 60 seconds of electrocardiogram signals. Unique features were extracted using the Pan Tompkins algorithm, later Cardioid based graph was formed as the result of the differentiation process. The various shapes of closed-loop created were then observed. From the Cardioid loop, we evaluated the area and standard deviation to differentiate between normal and abnormal heartbeats. As a result, the area, standard deviation, and mean values of abnormal heartbeat were twice the value of a normal heartbeat thus indicating the differences between two types of heart morphologies. Thus, the output of the study suggests the proof-of-concept of our proposed mechanisms to detect heart abnormalities and has the potential to act as an alternative to the current techniques.

Development of a Photoplethysmogram Based Heart Abnormality Detection Technique

In this study, the development of Cardioid based graph photoplethysmogram heart abnormality detection technique is presented. PPG signals in this work were collected from an online public repository called MIMIC II Waveform Database, Version 3 Part 1 with sampling rate of 250 Hz. Each recording has one minute of PPG signals. Distinctive features were extracted, and then the Cardioid based graph was plotted as the result of the differentiation of the signals. In addition, the different shapes of closed-loop created were then observed and assessed. From the Cardioid loop, the area and standard deviation were computed to distinguish between normal and abnormal heartbeats. Based on the results, these values for abnormal heartbeat were higher than the value of normal heartbeat thus signifying the differences between two categories of heart conditions. Therefore, the results of this study suggest the capability of the proposed mechanisms to determine heart abnormality and act as an alternative to the current detection system.

ECG Based Heart Arrhythmia Detection Using Wavelet Coherence and Bat Algorithm

Atrial fibrillation (AF) is a type of heart abnormality, during the AF electrical discharges in the atrium are rapid, results in abnormal heart beat. The morphology of ECG changes due to the abnormalities in the heart. This paper consists of three major steps for the detection of heart diseases: signal pre-processing, feature extraction and classification. Feature extraction is the key process in detecting the heart abnormality. Most of the ECG detection systems depend on the time domain features for cardiac signal classification. In this paper we proposed a wavelet coherence (WTC) technique for ECG signal analysis. The WTC calculates the similarity between two waveforms in frequency domain. Parameters extracted from WTC function is used as the features of the ECG signal. These features are optimized using Bat algorithm. The Levenberg Marquardt neural network classifier is used to classify the optimized features. The performance of the classifier can be improved with the optimized features.

Efficient Fine Arrhythmia Detection Based on DCG P-T Features.

Due to the high mortality associated with heart disease, there is an urgent demand for advanced detection of abnormal heart beats. The use of dynamic electrocardiogram (DCG) provides a useful indicator of heart condition from long-term monitoring techniques commonly used in the clinic. However, accurately distinguishing sparse abnormal heart beats from large DCG data sets remains difficult. Herein, we propose an efficient fine solution based on 11 geometrical features of the DCG PQRST(P-T) waves and an improved hierarchical clustering method for arrhythmia detection. Data sets selected from MIT-BIH are used to validate the effectiveness of this approach. Experimental results show that the detection procedure of arrhythmia is fast and with accurate clustering.

Patient-Centered Outcomes Research: Early Evidence From A Burgeoning Field.

Patient-Centered Outcomes Research: Early Evidence From A Burgeoning Field.