Electrocardiogram Machine Research Articles

Electrocardiogram machine learning for detection of cardiovascular disease in African Americans: the Jackson Heart Study.

AimsAlmost half of African American (AA) men and women have cardiovascular disease (CVD). Detection of prevalent CVD in community settings would facilitate secondary prevention of CVD. We sought to develop a tool for automated CVD detection.Methods and resultsParticipants from the Jackson Heart Study (JHS) with analysable electrocardiograms (ECGs) (n=3679; age, 6212 years; 36% men) were included. Vectorcardiographic (VCG) metrics QRS, T, and spatial ventricular gradient vectors magnitude and direction, and traditional ECG metrics were measured on 12-lead ECG. Random forests, convolutional neural network (CNN), lasso, adaptive lasso, plugin lasso, elastic net, ridge, and logistic regression models were developed in 80% and validated in 20% samples. We compared models with demographic, clinical, and VCG input (43 predictors) and those after the addition of ECG metrics (695 predictors). Prevalent CVD was diagnosed in 411 out of 3679 participants (11.2%). Machine learning models detected CVD with the area under the receiver operator curve (ROC AUC) 0.690.74. There was no difference in CVD detection accuracy between models with VCG and VCG + ECG input. Models with VCG input were better calibrated than models with ECG input. Plugin-based lasso model consisting of only two predictors (age and peak QRS-T angle) detected CVD with AUC 0.687 [95% confidence interval (CI) 0.6250.749], which was similar (P=0.394) to the CNN (0.660; 95% CI 0.5970.722) and better (P<0.0001) than random forests (0.512; 95% CI 0.4930.530).ConclusionsSimple model (age and QRS-T angle) can be used for prevalent CVD detection in limited-resources community settings, which opens an avenue for secondary prevention of CVD in underserved communities.

WITHDRAWN: Early detection of cardiovascular diseases using deep convolutional neural network & fourier wavelet transform

CVD (Cardiovascular) Diseases is leading cause of human deaths globally. The increasing threats of CVD can be early detected with various medical tests including electrocardiogram (ECG), and also 2D Echo, Stress Test. As ECG is non-invasive, clinical therapeutic agent, so with the help of an ECG signal, early detection of CVD is possible and proper medication can be provided for human life. All these signals from different equipment can, however, be non-stationary and repetitive which takes more time to process and exhausting for physical examination. Moreover, Heart Signal from ECG machine is not a stationary indicator, the discrepancies might not be repeated and may demonstrate up at various periods, hence there is need to adopt computer aided model for fast and accurate prognosis of CVD’s. Also now a day’s various smart devices are available in market which captures the heart rate through pulse detection and also needs fast processing of that heart rate signals for accurately identifying the ECG Signal Wavelengths which further helps in identification of CVD’s. Through this study, we are proposing CVD detection by using Deep Neural Network (DNN) with the help of HRV (Heart Rate Variability). The experimental result had shown that we had achieved 99% accuracy by using our DNN model. Earlier Mental stress detection methods using machine learning methodologies like Support Vector Machine (SVM), Decision Tree (DT) or Random Forest (RF) which gives the accuracy in the range of 80%-85%. In comparison to that we had achieved the highest accuracy rate because we had used Fourier wavelet transform mechanism on ECG signal to detect the variance in HRV (Heart Rate Variability). We had also used 2 additional intermediately layer for training the DNN model and implemented four-fold average accuracy and precision which had given us better result. We had used the combination of MIT-BIH Arrhythmia Database; BIDMC Congestive Heart Failure Database & MIT-BIH Normal Sinus Rhythm Database ECG Dataset. This dataset was resampled with rate of 128 Hz and data length was truncated to a common length of 65,536 samples. This study thus demonstrated the improvement of DNN on CVD detection with the highest accuracy of deep learning model as of now.

Detecting Myocardial Infarction by Electrocardiogram Machine Learning Models with Greater Accuracy; A Technical Advance Article

Background Electrocardiogram (ECG) interpretation is based on the understanding of cardiac electrical patterns. Machine learning (ML) techniques have been used to interpret ECGs, however, there is a lacuna in models able to identify the timing and affected cardiac territories with high accuracy. We aimed to utilize machine learning techniques coupled with relevant medical knowledge to create a machine learning model to detect MI with greater accuracy along with affected territory and timing. Methods A dataset containing 452 ECGs with 279 features from the University of California, Irvine, Machine Learning Repository was utilized. Three machine learning classification models namely Bootstrap Aggregation Decision Trees (BADT), Random Forest (RF) and Multi-layer Perceptron (MLP) were fed with ECG features selected based on the medical knowledge, categorized as normal, acute ischemic changes, old anterior MI and old inferior MI. Results The RF, BADT and MP models identified normal, acute ischemia, old anterior and old inferior MI with overall accuracies of 91.67% (95% CI: 84.24 – 96.33%), 89.58 (95% CI: 81.68 – 94.89%) and 85.42 (95% CI: 76.74 – 91.79%) respectively. All 3 models identified old anterior MIs with 100% sensitivity and specificity and RF model also identified old inferior MIs with same accuracy. Conclusion Machine learning models trained utilizing medical knowledge on the ECG changes in myocardial infarction can achieve greater accuracy in detecting MI along with affected area and timing. This study can be expanded by using a more extensive data set, to include more detail regarding timing and territories, as well as the detection and classification of arrhythmias.

ECG Restitution Analysis and Machine Learning to Detect Paroxysmal Atrial Fibrillation: Insight from the Equine Athlete as a Model for Human Athletes.

Atrial fibrillation is the most frequent arrhythmia in both equine and human athletes. Currently, this condition is diagnosed via electrocardiogram (ECG) monitoring which lacks sensitivity in about half of cases when it presents in paroxysmal form. We investigated whether the arrhythmogenic substrate present between the episodes of paroxysmal atrial fibrillation (PAF) can be detected using restitution analysis of normal sinus-rhythm ECGs. In this work, ECG recordings were obtained during routine clinical work from control and horses with PAF. The extracted QT, TQ, and RR intervals were used for ECG restitution analysis. The restitution data were trained and tested using k-nearest neighbor (k-NN) algorithm with various values of neighbors k to derive a discrimination tool. A combination of QT, RR, and TQ intervals was used to analyze the relationship between these intervals and their effects on PAF. A simple majority vote on individual record (one beat) classifications was used to determine the final classification. The k-NN classifiers using two-interval measures were able to predict the diagnosis of PAF with area under the receiving operating characteristic curve close to 0.8 (RR, TQ with k ≥ 9) and 0.9 (RR, QT with k ≥ 21 or TQ, QT with k ≥ 25). By simultaneously using all three intervals for each beat and a majority vote, mean area under the curves of 0.9 were obtained for all tested k-values (3–41). We concluded that 3D ECG restitution analysis can potentially be used as a metric of an automated method for screening of PAF.

The Importance of Environmental Screening in a Methicillin-Resistant Staphylococcus aureus (MRSA) Outbreak Investigation in a Transplant Unit

Background: Methicillin-resistant Staphylococcus aureus (MRSA) colonization conveys a higher risk of invasive infection. The transplant cohort is a group of immunocompromised patients who are at higher risk of infection. We conducted an outbreak investigation of hospital-acquired MRSA colonization within the transplant unit, which led to the discovery of positive isolates within our environment and to changes in our hospital disinfection policies. Methods: Our transplant unit consists of 8 single, positive-pressure rooms housed separately at the side of a larger ward. Staffing from this unit differs from the rest of the shared ward that houses up to 60 patients. As part of hospital screening, we found that a patient admitted for a stem-cell transplant had acquired nosocomial MRSA colonization. Given the unusual occurrence of such an event, a root-cause analysis was conducted. Results: A meeting was convened together with nursing, medical staff, and ancillary staff. Identified areas of potential transmission were deemed equipment, staff, and patients, and screening was performed. Shared equipment included the portable electrocardiogram (ECG) machines and portable x-ray machines and boards. In particular, ECG machines were shared with the adjoining nontransplant oncology ward. The usual practice was to clean the machine after use but not prior to the next use. This was deemed a possible exposure risk in view of a recent MRSA outbreak in a separate section of the ward. Positive isolates were found on both the x-ray and ECG machines. All healthcare workers were screened and were negative for MRSA. Furthermore, 7 patients admitted during the same time period were also screened for MRSA and were negative. Given the concurrent outbreak within the ward, pulsed-field gel electrophoresis was performed for all MRSA isolates obtained and the outbreak strain. These were found to be nonclonal (Table 1). Work processes for both the cleaning of ECG and x-ray machines were enhanced and modified. Hand hygiene measures to ward and radiology staff were reinforced. Thus far, no further cases have been detected. Conclusions: The environment is an important part of outbreak investigation. Shared equipment is often overlooked during day to day processes but should not be neglected. This can result in changes to hospital disinfection policy.Funding: NoneDisclosures: Indumathi Venkatachalam reports receiving honoraria for speaking engagements for bioMérieux and Pfizer and serving on an expert panel for MSD Pharma.

Poor Agreement but Good Predictive Value Between Automated and Manual QTc Intervals in Pediatric Emergency Department Electrocardiograms.

QTc interval is significant because prolongation may lead to ventricular dysrhythmia. Computerized electrocardiogram machines typically measure QT interval length and, using an algorithm assessment of multiple leads, calculate a QTc value. Manual measurement of the QT interval used to calculate the QTc value is more time-consuming but potentially more accurate. In this study, we compare the automated QTc calculation with the QTc value calculated using manual QT measurements. We prospectively obtained 350 resting 12-lead electrocardiograms (ECGs) in children aged 2 to 14 years in an academic pediatric emergency department. Manual measurement of the QT interval was performed and the QTc was calculated using the 2 most commonly used correction methods, Bazzet and Fridericia formulas. The paired values were used to perform a Bland-Altman analysis and create a receiver operating characteristic curve. Bland-Altman analysis determined that QT-automated and QTc-Bazett had an average difference of 3.8 milliseconds, with a standard deviation of 86 milliseconds (95% confidence interval = -161 to 176). An automated QTc value of 455 milliseconds was sensitive to detect manual QTc values of greater than 480 milliseconds. In children with resting ECGs, there is a poor agreement between the automated QTc produced by a computerized electrocardiogram and the QTc value obtained using manual QT measurement. Statistically and clinically relevant discrepancy between the automated QTc and QTc values calculated after manual QT measurement was present. Automated QTc values may be used as a screening tool to detect prolonged QTc, but for accurate determination of QTc, manual measurement is necessary.

A comparative study to evaluate the clinical utility and performance of a new hand-held mobile electrocardiogram device

Background: Electrocardiogram (ECG) is a non-invasive test which can provide clue for the presence of cardiac diseases. Simple, handheld devices, sufficiently miniaturized are useful for a widespread use. New devices, however, need to be compared with the standard ones for their performance in the real-world practice. Here in we report clinical utility of a handheld device.Methods: KardioscreenTM is a mobile and handheld device. It’s been approved for safety and performance standards and it has been certified for ‘Conformite Europeenne’ (CE). Using this device, a comparative blinded study with a conventional and commercially available standard 12 lead ECG machine was one. 604 ECGs recorded from 302 patients with various clinical disorders were coded and analyzed by two blinded observers. A third cardiologist adjudicated the reports. The reports were then correlated for the ECG patterns generated and with the clinical diagnosis. Computer generated measurements of various durations and intervals were also analyzed and compared. Regression analysis was used to compare the values. SPSS 21 software was used to analyze the data.Results: Kardioscreen device could provide recordings to diagnose including ST elevation (99%), non-ST elevation myocardial infarction (94.1%), chamber-hypertrophy (87%), conduction blocks (99%), and arrhythmias (96.4%), with good correlations with the comparator for pattern recognition. Also, computer generated measurements were significantly correlated with the comparator (R=0.96 for HR, R=0.82 for QRSd, R=0.86 for QT/QTc, R=0.76 for PR).Conclusions: The Kardioscreen device is a reliable tool for electrocardiographic diagnosis of common clinical cardiac disorders.

Cloud-based ECG monitoring using event-driven ECG acquisition and machine learning techniques.

An approach is proposed for the detection of chronic heart disorders from the electrocardiogram (ECG) signals. It utilizes an intelligent event-driven ECG signal acquisition system to achieve a real-time compression and effective signal processing and transmission. The experimental results show that grace of event-driven nature an overall 2.6 times compression and bandwidth utilization gain is attained by the suggested solution compared to the counter classical methods. It results in a significant reduction in the complexity and execution time of the post denoising, features extraction and classification processes. The overall system precision is studied in terms of the classification accuracy, the F-measure, the area under the ROC curve (AUC) and the Kappa statistics. The best classification accuracy of 94.07% is attained. It confirms that the designed event-driven solution realizes a computationally efficient automatic diagnosis of the cardiac arrhythmia while achieving a high precision decision support for cloud-based mobile health monitoring.

National Instruments LabVIEW Biomedical Toolkit for Measuring Heart Beat Rate and ECG LEAD II Features

Electrocardiogram (ECG) is undoubtedly the gold method for assessing heart activities. However, the ECG machine for measuring ECG is mostly only available in health care center especially in the third-world countries. Therefore, this research proposed to utilize the product of National Instruments i.e. LabVIEW Biomedical Toolkit and data acquisition device as an alternative approach to acquire ECG features and heart beat rate (HBR). As for performances verification, the NI-LabVIEW instruments are tested simultaneously with two types of ECG machine which are Philips PageWriter TC 30 and KenZ ECG 108. The results indicated that the ECG features and HBR obtained from NI-LABVIEW instruments are agreed well with the measurements attained from both of the ECG machines. Hence, this study proved that NI-LabVIEW instruments are highly potent for measuring ECG features and HBR. Moreover, the computerized NI-LabVIEW instruments with the self-interpretation function can always outperform most of the conventional ECG machines that are still required cardiologists for manually interpreting the results printed on the ECG paper.

Evaluation of the potential for QTc prolongation with avelumab

PurposeTo report integrated electrocardiogram (ECG) summary and exposure–QTc analyses for avelumab, a human immunoglobulin G1 monoclonal antibody that binds programmed cell death 1 ligand 1, to assess potential effects on cardiac repolarization.MethodsData were pooled from three-phase 1/2 studies of patients with advanced solid tumors who received avelumab monotherapy (22,000 ECGs from 1818 patients). All analyses used 12-lead singlet ECGs taken using local ECG machines before and approximately 2 h after avelumab infusion on prespecified days. The exposure–QTc and outlier analyses used locally read ECGs; since larger variability is known to be associated with local reading, outlier ECGs were subsequently reevaluated by central read. QTc derived from Fridericia’s formula (QTcF) and a project-specific formula (QTcP) were analyzed. Multivariable linear mixed-effects models were used to describe the relationship between serum concentration of avelumab and QTc absolute value or change from baseline (ΔQTc).ResultsExposure–QTc models showed that the effect of avelumab on QTc or ΔQTc was minimal and not statistically significant for both QTcP and QTcF. In addition, models including avelumab concentration and diphenhydramine premedication use did not show a clinically meaningful effect on the QT interval. The frequency of QTc outliers in both short and long ranges was overestimated by local reads. Six patients (0.3%) were QTc outliers; all had either received concomitant medication known to cause QT prolongation or had a preexisting cardiac condition.ConclusionAvelumab does not have any clinically relevant effect on cardiac repolarization.

A Mobile Cloud-Based Health Promotion System for Cardiovascular Diseases

In this paper, we propose a mobile cloud-based health promotion system targeting clinical cardiovascular diseases for use with both the Windows and Android operating systems. The prototype system can provide health promotion recommendations by physicians via so-called mobile cloud-based health promotion. These recommendations are based on users’ own the systolic and diastolic blood pressure (BP), pulse, sleep time, electrocardiogram (ECG), climate, diet, and movement records, utilizing remote video conferences, and asynchronous messaging with users. More specifically, the system integrates cloud-based BP and ECG healthcare module, a remote video technique, a video recording module, a message module, and a cloud-based climate, diet, and movement module. Furthermore, it includes a cloud-based cardiovascular disease electronic bookshelf module to provide healthcare education, as well as a cloud-based health promotion module, which utilizes physicians’ preventive health recommendations. We demonstrate the use of this system for cloud-based health promotion and healthcare procedures for cardiovascular diseases. The system integrated BP and ECG machines, which obtain data on systolic and diastolic BP, pulse, sleep time, and ECG signals, utilize a universal serial bus connection to send these vital signs to a computer, laptop, tablet, or smart phone, from which they are transferred to a health promotion center using Wi-Fi, or third and fourth generations mobile communication technologies. This mobile health promotion system can be used in the self-management of various chronic and cardiovascular diseases, anywhere and anytime. The aims of this system are to improve costs, time, efficiency, health management and monitoring, and quality of care.

Atrial Fibrillation Detection Based on EEMD and XGBoost

The electrocardiogram (ECG) is non-invasive, inexpensive and widely used in several applications, implemented to detect the physical condition and disease of the human body. Atrial fibrillation (AF) is the most common of many different forms of sustained arrhythmia. Therefore, early diagnosis of AF may help to improve doctor’s diagnostic efficiency and is essential to prevent further progression of Atrial fibrillation to other heart disease and stroke complications. With the popularity of the machine learning and deep learning, more and more researchers apply them in image recognition, speech recognition and so on. Naturally, there are also many studies which achieve the purpose of diagnosing diseases, such as detection of arrhythmia, biometric identification based on ECG signals and machine learning or deep learning. A novel approach to detect AF from ECG signals was developed on this study, we used great filter EEMD (Ensemble Empirical Mode Decomposition) and classifier XGBoost (eXtreme Gradient Boosting) to detect normal rhythm, AF and other rhythm. Finally, the great performance was achieved with an average F1 score of 0.84 and accuracy of 0.86.

Cost-effectiveness and screening performance of ECG handheld machine in a population screening programme: The Belgian Heart Rhythm Week screening programme.

Overall, 40% of patients with atrial fibrillation are asymptomatic. The usefulness and cost-effectiveness of atrial fibrillation screening programmes are debated. We evaluated whether an atrial fibrillation screening programme with a handheld electrocardiogram (ECG) machine in a population-wide cohort has a high screening yield and is cost-effective. We used a Markov-model based modelling analysis on 1000 hypothetical individuals who matched the Belgian Heart Rhythm Week screening programme. Subgroup analyses of subjects ≥65 and ≥75 years old were performed. Screening was performed with one-lead ECG handheld machine Omron® HeartScan HCG-801. In both overall population and subgroups, the use of the screening procedure diagnosed a consistently higher number of diagnosed atrial fibrillation than not screening. In the base-case scenario, the screening procedure resulted in 106.6 more atrial fibrillation patient-years, resulting in three fewer strokes, 10 more life years and five more quality-adjusted life years (QALYs). The number needed-to-screen (NNS) to avoid one stroke was 361. In subjects ≥65 years old, we found 80.8 more atrial fibrillation patient-years, resulting in three fewer strokes, four more life-years and five more QALYs. The NNS to avoid one stroke was 354. Similar results were obtained in subjects ≥75 years old, with a NNS to avoid one stroke of 371. In the overall population, the incremental cost-effectiveness ratio for any gained QALY showed that the screening procedure was cost-effective in all groups. In a population-wide screening cohort, the use of a handheld ECG machine to identify subjects with newly diagnosed atrial fibrillation was cost-effective in the general population, as well as in subjects ≥65 and subjects ≥75 years old.

Abstract 201: Evaluating Time from Electrocardiogram (ECG) Acquisition to ST-Elevation Myocardial Infarction (STEMI) Activation for Inpatients: Deployment of a Preliminary Automated ECG Reading System

Background: Inpatients with STEMI experience lengthier symptom-to-intervention times, decreased likelihood of invasive management, and an estimated 3-10-fold increased mortality risk compared to outpatients. Launched in October 2018, the Chest Pain - MI Registry™ now includes inpatient STEMIs, further motivating improvement of care for this vulnerable population. We investigated whether deployment of an automated ECG reading system across the inpatient enterprise at a large academic hospital is associated with improvement in time from ECG acquisition to STEMI activation. Methodology: From May 2018-December 2018, a pre-existing automatic ECG interpretation software on all inpatient ECG machines (Philips PageWriter TC70) was activated at our institution. The interpretation level on the software was changed from “none” to “measurements and interpretations.” This change affected all inpatient ECGs. A protocol for response to acute STEMI auto-interpretations was developed, which instructed all ECG technicians to hand ECGs ordered “stat” directly to a member of the patient’s clinical team within 5 minutes of acquisition. A run chart ( Figure ) was analyzed per the IHI “Four Rules for Interpreting Run Charts.” Baseline and intervention samples were assessed by Wilcoxon rank-sum test. Results: From May 2016-May 2018, median time from ECG acquisition to STEMI activation for inpatients at our institution was 23 minutes (N=14) [range 5, 90] with mean time of 35 minutes (+/- 30 per SD). During the subsequent seven-month study period, median time from ECG acquisition to STEMI activation was 21 minutes (N=5) [range 11, 64] with mean time of 34 minutes (+/- 26 per SD, p = 0.36). Of note, there was no reported increased workload or consultation to cardiology due to erroneous preliminary automated ECG reading. Conclusions: Following deployment of an automated inpatient ECG reading system for seven months, there was no significant decrease in the time from ECG acquisition to STEMI activation. Due to the relative infrequency of inpatient STEMIs and the resulting small number of cases during the study period, our results may be under-powered (power of 6.4%) to detect an improvement in STEMI activation times. This data suggests more aggressive and efficient strategies are required for timely diagnosis of acute inpatient STEMI.

Morphological Arrhythmia Automated Diagnosis Method Using Gray-Level Co-Occurrence Matrix Enhanced Convolutional Neural Network

Electrocardiogram (ECG) signal represents the electrical activity of the heart and playing an increasingly important role for practitioners to diagnose heart diseases. Widely available ECG data and machine learning algorithms present an opportunity to improve the accuracy of automated arrhythmia diagnosis. However, a comprehensive evaluation of morphological arrhythmias for the ECG analysis across a wide variety of diagnostic classes is still a complex task. This paper presents a generic morphological arrhythmias classification method designed for robust and accurate detection of the ECG heartbeat. In this method, the gray-level co-occurrence matrix (GLCM) is employed for features vector description because of its extraordinary statistical feature extraction ability. In addition, the convolutional neural network (CNN) approach is utilized to automatically classification from the generated 3D multi-scale GLCM. Obtained results from the experiments demonstrate that the proposed method in this paper is quite suitable for morphological arrhythmias detection. These findings demonstrate that the GLCM description can efficiently extract the shape features vector for a broad range of distinct arrhythmias from the lead ECGs with high diagnostic performance. The experimental results of the recognition for morphological arrhythmias show the feasibility and effectiveness of the proposed method and could be used to reduce the rate of the misdiagnosed computerized ECG interpretations.

One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine

In this research, a person identification system has been simulated using electrocardiogram (ECG) signals as biometrics. Ten adult people were participated as the subjects in this research taken from their signal ECG using the one-lead ECG machine. A total of 65 raw ECG waves from the 10 subjects were analyzed. This raw signal is then processed using the Hjorth Descriptor and Sample Entropy (SampEn) to get the signal features. Support Vector Machine (SVM) algorithm was used as the classifier for the subject authentication based upon the record of ECG signal. The results of the research showed that the highest accuracy value of 93.8% was found in Hjorth Descriptor. Compared to SampEn, this method is quite promising to be implemented for having a good performance and fewer features.

Subcutaneous ICD screening with the Boston Scientific ZOOM programmer versus a 12-lead ECG machine.

The subcutaneous implantable cardioverter-defibrillator (S-ICD) requires preimplant screening to ensure appropriate sensing and reduce risk of inappropriate shocks. Screening can be performed using either an ICD programmer or a 12-lead electrocardiogram (ECG) machine. It is unclear whether differences in signal filtering and digital sampling change the screening success rate. Subjects were recruited if they had a transvenous single-lead ICD without pacing requirements or were candidates for a new ICD. Screening was performed using both a Boston Scientific ZOOM programmer (Marlborough, MA, USA) and General Electric MAC 5000 ECG machine (Fairfield, CT, USA). A pass was defined as having at least one lead that fit within the screening template in both supine and sitting positions. A total of 69 subjects were included and 27 sets of ECG leads had differing screening results between the two machines (7%). Of these sets, 22 (81%) passed using the ECG machine but failed using the programmer and five (19%) passed using the ECG machine but failed using the programmer (P<0.001). Four subjects (6%) passed screening using the ECG machine but failed using the programmer. No subject passed screening with the programmer but failed with the ECG machine. There can be occasional disagreement in S-ICD patient screening between an ICD programmer and ECG machine, all of whom passed with the ECG machine but failed using the programmer. On a per lead basis, the ECG machine passes more subjects. It is unknown what the inappropriate shock rate would be if an S-ICD was implanted. Clinical judgment should be used in borderline cases.

National instruments labVIEW and video imaging technique for health status monitoring

It is essential to monitor military health status regularly for reducing the risk of cardiovascular diseases (CVD). Physiological parameters such as heart beat rate, blood pressure and electrocardiogram (ECG) features are the most vital indicators for CVD. The standard ECG machines normally used to obtain heart beat rate and ECG features in health facilities are bulky, expensive and the heart’s rhythms and activities are recorded on a piece of ECG paper which requires professional cardiologist for interpretation. In this study, National Instruments (NI) LabVIEW Biomedical Workbench with Data Acquisition Device (DAQ) is proposed as an alternative method to receive, save, handle and extract ECG features because of its simplicity, transferability and reliability.

Design of Android Application for Telemedicine System to Improve Public Health Services

One of the services provided by health facilities in Indonesia is the heart condition’s examination using Electrocardiogram machine. This service is important to provide because heart disease is one of the 10 most common causes of death in Indonesia. The electrocardiogram machine produces a print out that contains a graph of patient’s heart condition status. A cardiologist reads an ECG Scan to diagnose whether the patient’s heart condition is normal or abnormal. Health facilities that has a cardiologist is a secondary health facility located in the city but in the primary health facility in the village, although the examination by electrocardiogram machine can be done, the patient still have to wait for the diagnosis result because the print out result is sent first to the secondary health facility which has a cardiologist. Therefore it is necessary to design an Android Application for supporting telemedicine system as an effort to accelerate diagnostic service so that patient in primary health facility can also get immediate medical treatment.

Maternal heart rate variability during different trimesters of pregnancy

Background: Under normal circumstances, change in heart rate variability (HRV) in pregnancy projecting alterations within the regulation of the autonomic nervous system (ANS). Many previous works have shown an impact of gestation on the role of ANS in control of heart rate in the western country; however, it is poorly understood in Indian population, but then again a definitive this study is beneficial in relating to the influence on maternal autonomic cardiovascular control in some aspects of physiology in normal pregnancy. Aims and Objective: The aim of our study was to analyze the cardiac autonomic control throughout different stage of pregnancy with HRV. Materials and Methods: This study (n = 200) comprised normal non-pregnant and pregnant participants in the age of 20–30 years old, were divided randomly into Group 0 (n = 50) - control group comprised non-pregnant, healthy women. Study group (n = 150) classified into three subgroups; Group 1 (n = 50) - participants in their first trimester, Group 2 (n = 50) - participants in their second trimester, and Group 3 (n = 50) - participants in their third trimester. The electrocardiographic R-wave-R-wave interval HRV was measured on a beat-to-beat basis with the help of an electrocardiogram machine (BPL Cardiart 1087/MK-V) for 5 min. Result: Mean RR interval was decreased significantly in the 3rd trimester of pregnant study group when compared to non-pregnant control groups. Root of the mean of the sum of the squares of differences, NN50, and pNN50 were increased significantly in the first trimester of the pregnant study group when compared with non-pregnant control groups. Low frequency (LF) and LF (nu) significantly decreased, high frequency (HF) and HF (nu) significantly increased in the 1st trimester and significantly altered in 2nd trimester as well as 3rd trimester as vice versa. LF/HF ratio was significantly decreased in the 1st trimester and significantly increased in the 2nd trimester as well as 3rd trimester of pregnant study group when compared to non-pregnant control groups. Conclusion: The cardiac autonomic nervous activity in pregnancy showed a higher parasympathetic dominance in the 1st trimeste r and decreased gradually in 2nd trimester as well as 3rd trimester, and there was a lower sympathetic activity in the 1st trimester and increased gradually in the 2nd trimester as well as 3rd trimester. These changes throughout normal pregnancy may have an advantage in the initial identification of abnormal physiological function.