ECG Wave Research Articles

Smart Hospital for Heart Disease Prediction using IoT

The Internet of Things (IoT) is inter communication of embedded devices using various network technologies. The IoT technology is all set to become the upcoming trend in the future. We are proposing a healthcare monitoring system consisting of ECG Sensors. The parameters which are having a significant amount of importance are sensed by the ECG sensors which are vital for remote monitoring of patient. A mobile app observation is used to continuously monitor the ECG of the patient and various data extraction techniques are performed on the ECG wave to extract attributes to correctly predict heart diseases. .Data mining with its various algorithms reduce the extra efforts and time required to conduct various tests to detect diseases.. Data is collected from ECG sensors. The data is stored onto s storage medium where data mining algorithms are performed on the data collected. These algorithms predict whether the patient has any heart disease. The results can be referred by the doctors for diagnosis purpose. By using IOT technology and data mining algorithms the predication of heart disease is going to do in system.

Energy Efficient FIR Filter for Biomedical Application using FPGA

This research work proposes a low power Finite Impulse Response (FIR) digital filter system using Field Programmable Gate Array (FPGA) to filter biomedical signals. A major issue regarding such implementations is associated with computational complexity that may hinder its practical application. Several researches that aim to overcome this problem have been found by using various designs like booth multiplier, distributive arithmetic design, reduce adder graph (RAG), common sub-expression elimination method etc. This work has achieved the target of power line frequency of 50Hz noise cancellation. It is shown that the proposed design using RAG can give faster speed and require less hardware resources than that for the Direct Structure-I design. The performance of the proposed method is also compared with several existing research works to study its efficacy and it is shown that proposed design can achieve nearly 2% area delay product and 43% less power consumptions in implementation of an FIR low-pass filter. The designed filter is also tested on ECG waves as example of biomedical signal from samples of the MIT-BIH Arrhythmia database corrupted with power frequency noise. The entire system has been implemented on the ALTERA DE-II FPGA education board by synthesizing Verilog HDL using Quartus II tool.

Electrical Remodeling of Ventricular Repolarization Abnormality after Treatment in Pheochromocytoma: U Wave Finding in a Retrospective Analysis.

Background Pheochromocytoma is a rare neuroendocrine tumor, clinically characterized by high blood pressure, palpitations, and headache. It is often associated with abnormalities of the ventricular repolarization phase; the dispersion of ventricular repolarization is the basis for ventricular arrhythmias (torsion de point, ventricular tachycardia or ventricular fibrillation). Objectives Analysis of abnormal ventricular repolarization focused on the presence and amount of U wave in patients affected by pheochromocytoma and its modification after surgery. Materials and Methods We reviewed pathology records of 722 patients admitted for adrenal nodule or suspected chromaffin-cell tumor and identified 39 patients affected by pheochromocytoma. Metanephrine, normetanephrine, and 3-methoxytyramine have been assessed by determining concentrations in 24-hour urine collection. Standard 12-lead electrocardiogram records have been reviewed with analysis of heart rate, P wave, PR interval, QRS duration, QTc, and U wave. Then we selected and compared 22 patients of 39 affected by pheochromocytoma, with both clinical and electrocardiographic data before and after surgery. Results In our cohort of 39 patients affected by pheochromocytoma, we found U wave in ECG, before treatment, in 82.8 percent of patients, while only 37.0 percent after treatment (p<0.001) and we observed a statistically significant correlation between this wave and the urinary metanephrine. After surgery, in the selected 22 patients, we observed a clear significant reduction in systemic blood pressure, fasting glucose, metanephrine, normetanephrine, and 3-methoxytyramine. We found a significant reduction of U wave presence and leads involved in these patients after surgery (90.9% versus 9%). We observed a linear correlation between the amount of U waves in 12-lead electrocardiogram and metanephrine (r2=0.333, p=0.015), 3-methoxytyramine levels (r2=0.458, p=0.006), and tumor size (r2=0.429, p=0.003). Conclusions In our retrospective analysis, patients affected by pheochromocytoma presented U wave in electrocardiogram. The presence and amount of U wave were associated with the metanephrine levels and the tumor size with significant reduction after surgical removal.

Relationship between Mathematical Parameters of Modified Van der Pol Oscillator Model and ECG Morphological Features

The mathematical model describes the electrical and mechanical activity of the cardiac conduction system thought set of differential equations. By changing the value of parameters included in these equations, it is possible to change the amplitude and the period of ECG waves. Although this model is a powerful tool for modeling the electrical activity of the heart, its use is often limited to those familiar with the differential equations that describe the system. The purpose of this work is to provide a system that allows generating an ECG signal using Ryzhii model without knowing the details of differential equations. First, we provide the relationships between the ECG wave features and the model parameters; then we generalize them through fitting neural networks. Finally, putting in series fitting neural network and heart model, we provide a system that allows generating a synthetic signal by setting as input only the morphological ECG feature. We computed numerical simulation in Simulink environment and implemented the fitting neural networks in Matlab. Results show that non-linear trends characterize the correlation functions between ECG morphological features and model parameters and that the fitting neural networks can generalized this trend by providing the model parameters given in input the respectively ECG feature.

Protective effect of omeprazole and lansoprazole on β-receptor stimulated myocardial infarction in Wistar rats.

We investigated the effect of omeprazole (OPZ) and lansoprazole (LPZ) on the pathophysiology of myocardial necrosis in rats by inspecting a series of indicators like hemodynamic parameters, biochemical estimations and histopathological changes in the myocardial tissue. Rats received either OPZ, LPZ (50mg/kg/day, p.o.) individually for 7days with concurrent administration of isoproterenol (ISO) (150mg/kg, s.c.) on 6th and 7th day of study period to induce myocardial infarction. On the 8th day after measuring hemodynamic parameters, rats were killed and parameters were evaluated. ECG waves were found to be normal in the treatment group. ISO control rats revealed escalation in the oxidative stress as evidenced by depletion in the content of SOD, GSH, catalase and increase in the level of MDA and NO as compared with the normal rats. Treatment with OPZ and LPZ significantly reduced the ROS, indicated by an increase in the endogenous antioxidants and a decrease in NO and MDA levels. ISO control rats showed a significant elevation in the levels of pro-inflammatory cytokine TNF-α as compared to the normal and treatment group of rats. Administration of OPZ and LPZ does not exhibit any significant toxicity. Our findings reveal that multiple doses of OPZ and LPZ may have distinctly minimized the ISO-induced myocardial necrosis by declining the hmodynamic parameters, oxidative stress and pro-inflammatory cytokine TNF-α in myocardial infarcted rats.

Development of unconstrained ECG and pulse wave simultaneously measurement system

Development of unconstrained ECG and pulse wave simultaneously measurement system

Baseline Troponin T level in stroke and its association with stress cardiomyopathy.

BackgroundDifferential diagnosis of elevated high sensitive Troponin T (hsTnT) in acute ischemic stroke includes myocardial infarction (MI) and neurogenic stunned myocardium (NSM). The aim of this study was to identify factors associated with baseline hsTnT levels and MI or NSM in acute ischemic stroke.MethodsWe studied 204 consecutive patients of the prospective acquired Bern Stroke Database with acute ischemic stroke diagnosed by brain MR. All patient histories and cardiac examinations were reviewed retrospectively. Volumetry of lesions on diffusion and perfusion weighted brain imaging (circular singular value decomposition, Tmax >6sec) was performed. Voxel based analysis was performed to identify brain areas associated with hsTnT elevation. Linear regression analysis was used to identify predictors of baseline hsTnT levels and myocardial infarction.ResultsElevated hsTnT was observed in 58 of the 204 patients (28.4%). The mean age was 68.3 years in the normal hsTnT group and 69.7 years in the elevated hsTnT group. Creatinine (p<0.001, OR 6.735, 95% CI 58.734–107.423), baseline NIHSS score (p = 0.029, OR 2.207, 95% CI 0.675–12.096), ST segment depression (p = 0.025, OR 2.259, 95% CI 2.419–35.838), and negative T waves in baseline ECG (p = 0.002, OR 3.209, 95% CI 13.007–54.564) were associated with hsTnT elevation, while infarct location and size were not. Coronary angiography was performed in 30 of the 204 patients (14.7%) and myocardial infarction was diagnosed in 7 of them (23.3%). Predictive factors for myocardial infarction could not be identified.ConclusionElevated baseline baseline hsTnT was associated with NIHSS, creatinine, ST segment depression and inverted T waves, but not with stroke location or size. None of the factors was helpful to differentiate MI and NSM. Therefore, ancillary investigations such as coronary angiography, cardiac MRI or both may be needed to solve the differential diagnosis.

Prognosis and first diagnostic ECG in STEMI patients referred to the emergency medical system for primary PCI

BackgroundPathological Q waves (QWs) in the first ECG recorded at hospital admission has been found to correlate with myocardial damage and mortality in STEMI patients. We investigated the association between new QWs recorded in the pre-hospital setting and adverse outcome during the hospital stay. MethodsA pre-hospital ECG was recorded in 248 patients with STEMI who underwent primary PCI. Patients were divided into two groups based on the presence (n = 44, QWs) or absence (n = 204, non-QWs) of new QWs. ResultsPatients with new QWs had a higher prevalence of anterior infarct, cardiogenic shock and a lower LV ejection fraction. In-hospital mortality was higher in patients with new QWs. The percentage of patients with new QWs increased progressively with increasing pain to ECG time. ConclusionsNew QWs provide rapid prognostic information in the pre-hospital phase of STEMI by identifying patients at risk of adverse outcome during the hospital stay.

Mechanical Microsensors for Pulse Measurement

Health parameters measurement is the focus of many researches because of their wide application area. Mechanical sensors have also many interests, especially if their sensitivity is enough high to measure small deformations. Their application can then address pulse measurement or muscle and tendons movements (fig. 1). Mechanical sensor are widely developed. Low temperature technologies allow to fabricate them on many types of substrates including flexible substrates. These sensors require the use of piezoresistive materials. We previously develop mechanical sensors based on microcrystalline silicon resistances. Microsensors are fabricated on 25 µm thick Kapton. The piezoresistive material is N-type doped microcrystalline silicon deposited by PECVD. The main problem of this thin substrate is the stress induced during the process that deforms and creates cracks in each layer (fig. 2). In order to avoid this phenomenon, various possibilities were studied, first using different structures with nitride silicon layers. A specific cleaning process was also defined and the design of a shadow mask that reduce the surface of the microcrystalline silicon deposit was proposed. The combination of the three methods made that our samples are flatten and the cracks practically disappear. Different designs were then studied in order to obtain a good sensitivity to deformation. Resistances are characterized under mechanical stresses. Their response to variable deformations was measured. In order to increase the sensitivity of the sensor, a specific electronic circuit is used, including a variable amplification of the measured signal (typically equal to 7). Sensors are placed on a thin layer which shape can be change with pressure variation. Several deformations can then be applied. Sensors are able to response to variable frequencies and different excitation waves like simple delta pulse or complicated signals like the ECG wave. The sensor response to deformation is studied with those different signals and at different frequencies (fig. 3). Several geometries are compared with the aim of obtaining the most sensitive design. The sensitivity to deformation as well as the response time are studied. This shows the feasibility and development of a highly sensitive system for small deformation measurements. Figure 1

Mechanical Microsensors for Pulse Measurement

This paper presents a low cost flexible sensor that has been characterized by a homemade circuit. The technological process of the sensor was carried out at low temperature (180 °C) allowing to fabricate them on many types of substrates including flexible. As piezoresistive materials, N-type doped microcrystalline silicon deposited by PECVD was used. In order to obtain a good sensitivity to deformation, different designs were studied. Sensors are able to response to variable frequencies and different excitation waves such as a simple delta pulse or complex signals like the ECG wave. Several geometries are compared with the aim of obtaining the most sensitive design. This shows the feasibility and development of a highly sensitive system for small deformation measurements.

The Design of Wireless Portable Electrocardiograph Monitoring System Based on ZigBee

The emerging trend in wireless hospitals requires the development of bio-signal acquisition devices to be easily integrated into a single clinical routine. The wired devices used in hospitals and clinics, which makes setup very inconvenient, costly, and difficult to manage patients. It is crucial to develop and design a clinical diagnostic system that takes bio-signal from the body of the patient and transmits wirelessly to the physicians/hospital. In this paper, we have designed a novel portable system for ECG signal acquisition, processing, and wireless transmission using a ZigBee module. Our portable design enhances mobility, small in size, and very cheap for long term monitoring of cardiac patients. The results show that the ECG wave successfully transmitted to the physician, and heartbeat values are visible to the patient. Furthermore, the results of the ECG wave are unclear when the position of electrodes is dislocated.

Textile Concentric Ring Electrodes: Influence of Position and Electrode Size on Cardiac Activity Monitoring

Continuous ECG monitoring can play an important role in the rapid detection of pathological signatures and arrhythmias. Current systems use electrodes with limitations in wearable long-term applications and spatial selectivity. In this work, two sizes of textile concentric ring electrodes (TCRE42 and TCRE50) were developed and tested for monitoring cardiac activity. The low-cost devices were found to be easy to implement and to potentially have the advantages of textile electrodes for being lightweight, stretchable, adjustable, washable, and long-lasting. Both TCREs yielded similar signal detectability of different ECG waves. The optimal P wave recording area (OPA) for detecting signals was on the upper right chest. Although the absolute signal amplitude of TCRE records was smaller than that of lead II, normalized amplitude in the OPA is similar for T wave and higher for P wave. The TCREs also allowed better analysis of P wave morphology and were able to detect more right and left atrial depolarization waves. TCRE48 showed slightly better detectability, normalized amplitude, and spatial selectivity than TCRE50 within the OPA. The authors consider the TCRE suitable for use in ubiquitous mobile health care systems, especially for atrial activity monitoring and diagnosis.

Reliability analysis of an integrated device of ECG, PPG and pressure pulse wave for cardiovascular disease

Cardiovascular diseases (CVD) are the leading cause of death globally and the mortality has been increasing in recent two decades. Most of cardiovascular diseases are preventable by early and long-term monitoring. The common option of physical examinations for CVD includes ECG, photoplethysmography (PPG), pressure pulse wave (PPW). An integrated device was designed and developed by gathering three physiological signals of ECG, PPG, and PPW synchronusly in this research. The tri-modal device was tested for reliability and stability on the synchronism of R-R intervals, distribution of peaks, and noise suppression. The experiment results displayed that the white noise and dark noise was well controlled. In the test of synchronism, PPG and PPW of the P-P intervals show a good linearity in correspondence with the ECG of R-R intervals in cardiac cycle. There is no significant difference in peak distribution of three modal signals from 4 volunteers. Thus, the equipment has excellent properties of anti-electromagnetic interference, removing movement artifacts and light tightness and may be developed to a promising tool for multi-modal diagnosis in CVD.

Heart failure complicating myocardial infarction. A report of the Peruvian Registry of ST-elevation myocardial infarction (PERSTEMI).

The aim of this study is to determine the incidence, associated factors, and 30-day mortality of patients with heart failure (HF) after ST elevation myocardial infarction (STEMI) in Peru. Observational, cohort, multicentre study was conducted at the national level on patients enrolled in the Peruvian registry of STEMI, excluding patients with a history of HF. A comparison was made with the epidemiological characteristics, treatment, and 30 day-outcome of patients with (Group 1) and without (Group 2) heart failure after infarction. Of the 388 patients studied, 48.7% had symptoms of HF, or a left ventricular ejection fraction <40% after infarction (Group 1). Age>75 years, anterior wall infarction, and the absence of electrocardiographic signs of reperfusion were the factors related to a higher incidence of HF. The hospital mortality in Group 1 was 20.6%, and the independent factors related to higher mortality were age>75 years, and the absence of electrocardiographic signs of reperfusion. Heart failure complicates almost 50% of patients with STEMI, and is associated with higher hospital and 30-day mortality. Age greater than 75 years and the absence of negative T waves in the post-reperfusion ECG are independent factors for a higher incidence of HF and 30-day mortality.

Statistical method for ECG analysis and diagnostic

In this paper, a statistical method of ECG analysis and diagnostic is proposed. This method is based on three parts: data simplification using multiscaled PCA, faults detection and localisation by introducing classic linear PCA. The studied data is presented as a multivariate matrix. The variables of this matrix are extracted from the ECG waves characteristics: waves amplitudes and segments measurements. The developed approach allows detecting arrhythmias and heart beat troubles. Comparing the results obtained by this approach and the data of the expert, we approve the performance of our study.

Automatic feature extraction of ECG signal based on adaptive window dependent differential histogram approach and validation with CSE database

A very simple and novel idea based on adaptive window dependent differential histogram approach has been proposed for automatic detection and identification of ECG waves with its characteristic features. To facilitate the estimation of the waves, the normalised signal has been divided into a few small windows by an adaptive window selection technique. By counting the number of changes between successive samples as frequency, the differential histogram has been plotted. Some of the zones having an area more than a pre-defined threshold are depicted as QRS zones. The local maxima of these zones are referred as the R-peaks. T and P peaks are also detected. Baseline point and clinically significant time plane features have been computed and validated with reference values of the CSE database. The proposed technique achieved better performance in comparison with CSE groups. Its accuracy is achieved in sensitivity (99.86%), positive productivity (99.76%) and detection accuracy (99.8%).

EKG artefakati u urgentnoj medicini

Artefacts are technical errors on electrocardiogram that may be present in all leads or in some of them or in just one. They may be subtle with no interference with correct interpretation, but they can also be significant enough to make ECG recording absolutely useless and delay diagnose and timely therapy. On the other hand, artefacts can be misinterpreted and that way patient can be exposed to unnecessary or risky therapeutic procedures. There are two groups of ECG artefacts: the first group originates from external causes and the second group originates from patient's body. The first group can be caused by electromagnetic interference (alternating current from electrical installation, electrical devices in/on patient, light) or by malfunction of the cables and/or electrodes (damaged cables and electrodes, their movement during transport, wrong connecting, 'offset signals', detached electrodes, gel lacking or inappropriate filters). The second group comes from muscles, skin and adnexa. Muscle contractions are often the cause of artefacts, voluntary movements as much as involuntary. Skin and adnexa can cause ECG artefacts in two ways: through poor contact with electrodes or through simple stretching of the epidermis which generates electric potential (turning of the patient in bed for example). Rare form of artefact caused by radial artery pulsations directly under the electrode was also described; it is so called electromechanical association. Artefacts can present in different ways on ECG recording. The ECG line can be thick, irregular, discontinued, wandering or pseudo isoelectric. Artefacts can imitate any of the ECG waves, extra systoles, tachycardia of narrow or wide complexes, long QT interval, and elevation/depression of the ST segment or T wave changes. Removal of the artefacts requires manipulating with patient, auxiliary equipment and ECG machine itself.

Statistical method for ECG analysis and diagnostic

In this paper, a statistical method of ECG analysis and diagnostic is proposed. This method is based on three parts: data simplification using multiscaled PCA, faults detection and localisation by introducing classic linear PCA. The studied data is presented as a multivariate matrix. The variables of this matrix are extracted from the ECG waves characteristics: waves amplitudes and segments measurements. The developed approach allows detecting arrhythmias and heart beat troubles. Comparing the results obtained by this approach and the data of the expert, we approve the performance of our study.

Automatic feature extraction of ECG signal based on adaptive window dependent differential histogram approach and validation with CSE database

A very simple and novel idea based on adaptive window dependent differential histogram approach has been proposed for automatic detection and identification of ECG waves with its characteristic features. To facilitate the estimation of the waves, the normalised signal has been divided into a few small windows by an adaptive window selection technique. By counting the number of changes between successive samples as frequency, the differential histogram has been plotted. Some of the zones having an area more than a pre-defined threshold are depicted as QRS zones. The local maxima of these zones are referred as the R-peaks. T and P peaks are also detected. Baseline point and clinically significant time plane features have been computed and validated with reference values of the CSE database. The proposed technique achieved better performance in comparison with CSE groups. Its accuracy is achieved in sensitivity (99.86%), positive productivity (99.76%) and detection accuracy (99.8%).

Detection of myocardial infarction in 12 lead ECG using support vector machine

In this paper, we propose myocardial infarction (MI) detection using 12-lead ECG data and analysis of each lead with the help of composite lead. This composite lead is used to detect ECG wave components and clinical wave intervals in all the 12-lead ECG. The four clinical features such as P duration, QRS duration, ST-T complex interval and QT interval are globally determined from average beats of all the 12-lead ECG. Then peak to peak amplitude, area, mean, standard deviation, skewness and kurtosis are determined for P duration, QRS duration and ST-T complex interval of average beats of all the 12-lead ECG. These 220 (4 + 6 × 3 × 12) parameters are used for myocardial infarction detection. The standard 12-lead ECG data of 60 myocardial infarction subjects and 60 healthy controls (HC) cases are obtained from Physikalisch-Technische Bundesanstalt (PTB) database and tested with support vector machine (SVM) classifier. The MI detection sensitivity, specificity and accuracy are 96.66%, 100% and 98.33% respectively. To reduce the computational complexity, feature dimension reduction is important. Therefore, proposed method applies Principal Component Analysis (PCA) reduction technique. In this proposed method, 220 features are reduced to 14 features, using these 14 features, MI detection achieved by SVM classifier is: sensitivity 96.66%, specificity 96.66% and accuracy 96.66%.