Electrocardiography Machine Research Articles

The Relationship between Body Mass Index and Frontal QRS-T Angle in Pregnant Women Undergoing Cesarean Section with Spinal Anesthesia.

Background and objectives: The frontal QRS-T angle is a novel parameter of myocardial repolarization. Weight gain during pregnancy and physiological changes during a cesarian section may affect the frontal QRS-T angle. We aimed to assess the effect of body mass index (BMI) on the frontal QRS-T angle in pregnant women undergoing cesarean section with spinal anesthesia. Method and materials: This study included 90 pregnant women. BMI was calculated for all pregnant women. The study population was divided into two groups: BMI < 30 (n = 66) and BMI ≥ 30 (n = 24). QT interval measurements and the frontal QRS-T angle were obtained from the report of an electrocardiography machine. Results: It was found that the pre-operative and post-operative frontal QRS-T angle (p = 0.045 and p = 0.007) and QTc interval (p = 0.037 and p < 0.001) were higher in pregnant women with a BMI ≥ 30 than in pregnant women with a BMI < 30. In addition, when compared to pre-operative values, the post-operative frontal QRS-T angle (from 24.0 [20.0-41.5] to 34.5 [19.5-50.0], p = 0.031) and QTc interval (from 420.6 ± 13.3 to 431.7 ± 18.3, p = 0.010) were increased in the BMI ≥ 30 group, whereas no significant post-operative increase was observed in the BMI < 30 group. In correlation analysis, BMI was positively correlated with the frontal QRS-T angle and QTc interval. Conclusions: The frontal QRS-T angle and QTc interval were importantly increased in pregnant women with a BMI ≥ 30 than in pregnant women with a BMI < 30. Also, after cesarean section operation with spinal anesthesia, the frontal QRS-T angle and QTc were increased significantly in the BMI ≥ 30 group, whereas no significant change was observed in the BMI < 30 group. Therefore, it is suggested to perform close post-operative monitoring in pregnant women with a BMI ≥ 30 undergoing cesarean section with spinal anesthesia.

The Relationship between Frontal QRS-T Angle and Vitamin D Deficiency.

Background and Objectives: A deficiency in serum 25-hydroxyvitamin D levels is associated with a number of cardiovascular situations, such as high blood pressure, heart failure, atherosclerotic heart disease, and peripheral artery disease. The frontal QRS-T angle has recently been proposed as a marker of ventricular repolarization. A wider frontal QRS-T angle has been positively correlated with adverse cardiac events. The objective of our study was to examine the association between serum 25-hydroxyvitamin D level and the frontal QRS-T angle. Materials and Methods: A total of 173 consecutive patients aged 18-60 years undergoing routine cardiology check-up evaluation, and not receiving concurrent vitamin D treatment were included in the study. Patients were classified in three groups, depending on their vitamin D levels, and categorized as follows: Group 1-deficient (<20 ng/mL), Group 2-insufficient (20-29 ng/mL), or Group 3-optimal (≥30 ng/mL). The frontal QRS-T angle was determined using the automated reports generated by the electrocardiography machine. Results: The average age of participants was 45.8 (±12.2) years, and 55.5% of participants were female (p < 0.001). Individuals with low vitamin D concentrations exhibited a wider frontal QRS-T angle. It was determined that vitamin D level is an independent predictive factor for the frontal QRS-T angle. Conclusions: As the levels of 25-hydroxyvitamin D decrease, repolarization time assessed by frontal QRS-T angle is widened. Our findings indicate that lower concentrations of vitamin D may increase the susceptibility to ventricular arrhythmia.

Pattern of Arrhythmia among Ischemic Stroke Patients on 24-hour Ambulatory Electrocardiography (Holter ECG) in South-South Nigeria

Background: The 24-hour Holter electrocardiogram is a valuable tool for the identification of ischaemic alterations, cardiac arrhythmias in hypertension, stroke and other cardiovascular conditions. Electrocardiographic (ECG) abnormalities and cardiac arrhythmias have been detected in 50% of stroke patients. However, literature in this area of research is limited in our environment. This study is aimed at describing the pattern of arrhythmia observed in patients with ischaemic stroke using 24-hour Holter ECG in our locality. Methods: A total of 22 stroke subjects whose age ranged from 41 and 83 years were evaluated in GoodHeart Medical Consultants hospital for 24-hour ambulatory ECG (Holter ECG) from January 2015 – December 2016. The Holter ECG data was acquired using Schiller type (MT-101) Holter ECG machine. Results: The study population consisted of 59.1% females and 40.9% males, with an average age of 66.95±12.32 years. Arrhythmias were more prevalent among females compared to males. However, bradycardia was more prevalent in males. Ventricular ectopics were the most common type of arrhythmia across both genders, with a higher prevalence in the older adult age group of 61-80 years. Conclusion: The most common arrhythmia observed among the stroke subjects in Southern Nigeria is ventricular ectopics. This could be due to increase in age and severe neurological deficit which are independent predictors of arrhythmia onset and this was notably seen among the older subjects in the study. Extended cardiac monitoring such as Holter ECG is a well-accepted method for the identification of ECG abnormalities and the implementation of prompt treatment to reduce mortality.

Electrocardiographic Abnormalities in Adult Homozygous Sickle Cell Disease Patients in Nnamdi Azikiwe University Teaching Hospital, Nnewi, South-Eastern Nigeria

Background; Sickle cell anaemia is the most common monogenic hematologic disorder in Nigeria. Cardiovascular pathology, resulting in electrocardiographic abnormalities are a common finding in adult sickle cell patients.&#x0D; Objective; This study investigated the pattern of electrocardiographic (ECG) abnormalities in steady state adult sickle cell anaemia (HbSS) patients and compared the findings with that of age and sex matched Haemoglobin AA(HbAA) controls.&#x0D; Methodology; Fifty steady state adult HbSS participants were recruited from the haematology clinic at Nnamdi Azikiwe University Teaching Hospital while fifty age and sex matched HbAA controls were recruited from the hospital community. Participants underwent resting 12 lead ECG using a Schiller CARDIOVIT AT-1 ECG machine. Data was analyzed using Statistical Package for Social Sciences version 21.&#x0D; Results; The prevalence of ECG abnormalities amongst the adult sickle cell participants and controls was 70% and 10% respectively. ECG abnormalities were more common amongst male sickle cell participants and left ventricle hypertrophy was the most frequent ECG abnormality detected.&#x0D; Conclusion; There is a high prevalence of ECG abnormalities amongst adult sickle cell anaemia patients. Regular Electrocardiography is recommended for early detection of these abnormalities for early treatment and improved quality of life.&#x0D; Keywords: Electrocardiographic Abnormalities, Sickle Cell Disease Patients, Nnamdi Azikiwe University, Haemoglobin AA

Evaluation of Alterations in Basic Cardiac Functions in Operators of Artisanal Refineries in Rivers State, Nigeria

The impact of numerous artisanal refineries on the populace in Niger Delta region has been a great source of mainly health, economic and environmental concerns. Thus, the present study comparatively assessed the alterations of basic cardiac functions that may be associated with the operation of artisanal refineries (OAR) in Rivers State of Nigeria. This cross-sectional survey used a multi-stage sampling technique (including purposive and snowball tools) to recruit 203 (including 120 OAR-exposed and 83 non-OAR exposed) consenting and apparently healthy adult male and female subjects. With the use of the electrocardiographic (ECG) machine, the basic ECG indices were recorded from the study subjects. Quantitative data obtained from the subjects were subjected to statistical analyses using the Statistical Package for Social Sciences (SPSS) version 21.0. The result indicated significantly (P&lt;0.0) elevated heart rates in the OAR-exposed subjects when compared to their non-OAR-exposed counterparts. Amongst the various ECG features, the exposed subjects predominantly presented with left atrial enlargement (LAE) (male exposed=57% and female exposed =46.3%) as against none in the unexposed group. And there was no significant association of the incidences of Abnormal/clinical ECG Features between the actively exposed (OAR operators) and passively exposed (residents in same city who are non-OAR operators) subjects. In conclusion, the operation of artisanal refineries in our locale may potentially predispose its operators to significant elevation of heart rate and incidences of abnormal ECG features.

Prevalence and predictors of false positive QTc prolongation by the automated measurement.

Corrected QT (QTc) is an important electrocardiographic (ECG) interval. Physicians rely on automated QTc provided by ECG machines while the manual method is the recommended method. We sought to assess the prevalence and predictors of false positive QTc prolongation by the automated measurement. Consecutive ECGs were retrieved from the ECG database at King Khaled University Hospital. Manual QT was measured by a trained physician using the tangent method and was corrected for heart rate and QRS duration. Automated QTc measurement was recorded by the ECG machine. "Long QT (LQT)" was defined as QTc≥470 ms for males and ≥480 for females. False positive LQT was defined as LQT by automated QTc but not manual QTc. Pre-determined factors were included in a multivariate logistic regression to assess predictors of false positive LQT. A total of 567 ECGs were included in this study. Automated QTc was longer than manual QTc (440 ms [±35] vs. 417 ms [±35], respectively) which resulted in a high negative predictive value (NPV) (99%) and a low positive predictive value (PPV) (32%). Male gender and abnormal rhythm were found to be independently associated with false positive LQT (OR = 1.9 [95% 1.1-3.5], p = 0.03 and OR = 3.1 [95% 1.2-8.3], p = 0.02; respectively). Automated QTc measurement is unreliable for detecting long QT, necessitating manual verification and further research to enhance its accuracy.

Prediction of ECG fiducial parameters from PPG signals for the analysis of cardiovascular diseases: A novel Gaussian process regression-based approach

PurposeCardiologists use 12-lead electrocardiograph (ECG) and prolonged continuous Holter monitoring to detect abnormalities in heart rhythm, which could diagnose or hint at different cardiac diseases. However, the availability of a costly ECG machine and the expertise to interpret ECG is suboptimal in our country. The current state of the art proposes mitigating the cost and easy diagnosis by physicians with the use of low cost photoplethysmograph (PPG), which will deliver ECG information in terms of ECG fiducial parameters. MethodsThe MIMIC-III matched paired datasets of PPG/Lead-II ECG signals with the six classes (right bundle branch block (RBBB), hypertension (HYPER), arrhythmia-atrial fibrillation (ARYTH-ATRFAB), dilated cardiomyopathy (DCM), complete heart block (CHB) and NORMAL) associated cardiovascular diseases (CVDs) syndromes of 30 patients with 5 minutes duration each were taken from Physionet. The proposed work tested by predicting ECG fiducial parameters (P, Q, R, S and T) through Gaussian process regression(GPR)-based predictor by using the PPG fiducial parameters with a training and testing ratio of 70:30. The performance was analyzed by different performance evaluation parameters e.g., RMSE, rRMSE, etc. ResultsAlthough the shape of the ECG may differ in various ways from that of the normal ECG, the methodology can predict the ECG fiducials from PPG for a variety of CVD patients with a performance accuracy of 95–99% by evaluating through seven performance metrics. ConclusionThe ECG fiducial parameters can be used to analyze CVDs captured from a low-cost, handy, skin-compatible continuous and non-invasive optical recording device well known as a PPG sensor.

Contemporary wearable and handheld technology for the diagnosis of cardiac arrhythmias in Singapore.

Twelve-lead electrocardiography (ECG) remains the gold standard for the diagnosis of cardiac arrhythmias. It provides a snapshot of the cardiac electrical activity while the leads are attached to the patient. As medical training is required to use the ECG machine, its use remains restricted to the clinic and hospital settings. These aspects limit the usefulness of 12-lead ECG in the diagnosis of cardiac arrhythmias, especially in individuals with short-lasting and infrequent paroxysmal symptoms. The introduction of ECG recording features in wearable and handheld smart devices has changed the paradigm of cardiac arrhythmia diagnosis, empowering patients to record their ECG as and when symptoms occur. This review describes contemporary ambulatory heart rhythm monitors commonly available in Singapore and their expanding role in the diagnosis of cardiac rhythm abnormalities.

Standardized Database of 12-Lead Electrocardiograms with a Common Standard for the Promotion of Cardiovascular Research: KURIAS-ECG.

Electrocardiography (ECG)-based diagnosis by experts cannot maintain uniform quality because individual differences may occur. Previous public databases can be used for clinical studies, but there is no common standard that would allow databases to be combined. For this reason, it is difficult to conduct research that derives results by combining databases. Recent commercial ECG machines offer diagnoses similar to those of a physician. Therefore, the purpose of this study was to construct a standardized ECG database using computerized diagnoses. The constructed database was standardized using Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Observational Medical Outcomes Partnership-common data model (OMOP-CDM), and data were then categorized into 10 groups based on the Minnesota classification. In addition, to extract high-quality waveforms, poor-quality ECGs were removed, and database bias was minimized by extracting at least 2,000 cases for each group. To check database quality, the difference in baseline displacement according to whether poor ECGs were removed was analyzed, and the usefulness of the database was verified with seven classification models using waveforms. The standardized KURIAS-ECG database consists of high-quality ECGs from 13,862 patients, with about 20,000 data points, making it possible to obtain more than 2,000 for each Minnesota classification. An artificial intelligence classification model using the data extracted through SNOMED-CT showed an average accuracy of 88.03%. The KURIAS-ECG database contains standardized ECG data extracted from various machines. The proposed protocol should promote cardiovascular disease research using big data and artificial intelligence.

Acquired Cardiac Dextroposition: A Step in the Right Direction?

Despite tremendous advancements in electrocardiography machine algorithms, accurately diagnosing dextrocardia, pseudodextrocardia, and limb lead reversal remains a serious challenge. We present the case of a patient with acquired cardiac dextroposition, or “pseudodextrocardia,” in which the electrocardiography machine algorithm incorrectly interpreted the finding as “dextrocardia vs limb lead reversal.” (Level of Difficulty: Advanced.)

Automatic classification of arrhythmias using multi-branch convolutional neural networks based on channel-based attention and bidirectional LSTM

Cardiac arrhythmia is an abnormal rhythm of the heartbeat and can be life-threatening Electrocardiogram (ECG) is a technology that uses an electrocardiograph machine to record a graph of the changes in electrical activity produced by the heart at each cardiac cycle. ECG can generally be used to check whether the examinee has arrhythmia, ion channel disease, cardiomyopathy, electrolyte disorder and other diseases. To reduce the workload of doctors and improve the accuracy of ECG signal recognition, a novel and lightweight automatic ECG classification method based on Convolutional Neural Network (CNN) is proposed. The multi-branch network with different receptive fields is used to extract the multi-spatial deep features of heartbeats. The Channel Attention Module (CAM) and Bidirectional Long Short-Term Memory neural network (BLSTM) module are used to filter redundant ECG features. CAM and BLSTM are beneficial for distinguishing different categories of heartbeats. In the experiments, a four-fold cross-validation technique is used to improve the generalization capability of the network, and it shows good performance on the testing set. This method divides heartbeats into five categories according to the American Advancement of Medical Instrumentation (AAMI) criteria, which is validated in the MIT-BIH arrhythmia database. The sensitivity of this method to Ventricular Ectopic Beat (VEB) is 98.5% and the F1 score is 98.2%. The precision of the Supraventricular Ectopic Beat (SVEB) is 91.1%, and the corresponding F1 score is 90.8%. The proposed method has high classification performance and a lightweight feature. In a word, it has broad application prospects in clinical medicine and health testing.

A Balanced OTA ECG Amplifier and Its Development Directions

Electrocardiograph (ECG) is a technology that uses an electrocardiograph to record the electrical activity change pattern generated by each cardiac cycle of the heart from the body surface. It is a simple test that can be used to check heart's rhythm and electrical activity of human. The amplifying and filtering of the ECG signal is the most important part of ECG signal procedures and this paper shows a balanced OTA that can fully remove the 50hz noise from the power line. Due to cost performance for the real situation in the hospital and industrial cost, the balanced OTA filter in this paper is simple and cannot guarantee a long stable use when it is implemented on an ECG machine with other models. This paper will show two different ways to increase gain and power efficiency which are using fully differential OTA and using higher bandwidth.

An optimisation approach for determining the efficiency of vital medical devices in intensive care units with COVID-19 patients using Apriori algorithm

Improving the process of strategic management in hospitals preparation and equipping the intensive care units (ICUs) and the availability of medical devices plays an important role for knowing consumer behaviour and need. This cross-sectional study was performed in the ICU of Farhikhtegan Hospital, Tehran, Iran for a period of six months. During these months, ten medical devices have been used 5,497 times. These devices include: ventilator, oxygen cylinder, infusion pump, electrocardiography machine, vital signs monitor, oxygen flowmeter, wavy mattress, ultrasound sonography machine, ultrasound echocardiography machine, and dialysis machine. The Apriori algorithm showed that four devices: ventilator, oxygen cylinder, vital signs monitoring device, oxygen flowmeter are the most used ones by patients. These devices are positively correlated with each other and their confidence is over 80% and their support is 73%. For validating the results, we have used equivalence class clustering and bottom-up lattice traversal (ECLAT) algorithm in our dataset.

Heart Rhythms Recorded Among Pupils in Enugu, South East Nigeria

Electrocardiography (ECG) is an important non-invasive tool used to detect cardiac arrhythmias. Abnormal, and indeed dangerous, rhythms may exist among apparently healthy school children. Regular cardiovascular screening may help reveal such abnormal rhythms and thus help to prevent unexpected adverse events, including sudden death. The aim of this study was to ascertain the different heart rhythms that occur in pupils in Enugu metropolis. A descriptive cross-sectional research approach was adopted. Three hundred and fourty-one healthy looking pupils between age six and twelve years were recruited. Questionnaires were administered; medical general examination and electrocardiography, using a 12-lead ECG machine, were done on each of the pupils. At the end of the study, no structural evidence of heart disease was identified in any of the pupils. There was normal sinus rhythm in 79.1% of the pupils, however, in 20.9% of them abnormal rhythm was seen. The abnormal rhythms noted were: premature complexes (8.44%), atrial tachycardia (5.64%), first degree atrioventricular block (5.63%), bradycardia (2.82%) and incomplete right bundle block (2.82%). This study showed that among apparently healthy school children, abnormal heart rhythms are present, and these rhythms could be missed in absence of cardiovascular screening. As the reported cases of sudden death during sports in children and young adults keep rising, it is important to encourage routine cardiovascular screening to aid early detection and prevention of possible abnormalities.

Design and Application of Home Wireless Electrocardiograph Machine Based on Internet

This study introduces the design and application of home wireless electrocardiograph(ECG) machine based on Internet. The world's first three-lead dry electrode mobile electrocardiograph machine has been developed, on the basis of the successful development of dry electrodes. Moreover, it is not only chips filtering, but also wireless, as a result it is applied to ECG monitoring and diagnosis of patients. Compared with traditional electrocardiograph machine, the machine is very convenient and comes into the home, ECG Machines is connected to mobile phones by Bluetooth, wireless upload, therefore we recommend to achieve remote monitoring and early warning and reduce sudden death, to achieve Internet medical by using Internet technology, people can be self-test. It is playing an increasingly important role and it is an inevitable machine to improve the success rate of diagnosis, monitoring and first aid.

Remote Monitoring System of Heart Conditions for Elderly Persons with ECG Machine Using IOT Platform

These days, heart illnesses are viewed as the essential purposes behind unforeseen passing. Along these lines, different clinical gadgets have been created by designers to analyze and examine different infections. Clinical consideration has gotten one of the main issues for the two individuals and government considering enthusiastic advancement in human people and clinical use. Numerous patients experience the ill effects of heart issues making some basic dangers their life, consequently they need ceaseless observing by a conventional checking framework for example, Electrocardiographic (ECG) which is the main procedure utilized in estimating the electrical movement of the heart, this method is accessible just in the emergency clinic which is exorbitant and far for distant patients. The improvement of far-off advancements enables to develop an association of related devices by methods for the web. The proposed ECG checking framework comprises of AD8382 ECG sensor to peruse patient's information, Arduino Uno, ESP8266 Wi-Fi module, and site page. The usage of the proposed ECG medical care framework empowers the specialist to screen the patient's distantly utilizing IoT http application library utilized in Arduino ide compiler to such an extent that it can send that information to website page made, on imagining the patient's ECG signal without human presence site page itself can book arrangement for that persistent, if it is anomalous. The observing cycle should be possible at whenever and anyplace without the requirement for the emergency clinic.

Comparison of frequency domain parameters of heart rate variability between women with polycystic ovarian disease and apparently healthy women.

ABSTRACTBackground:Polycystic ovary syndrome (PCOS) patients shows common features like increased insulin resistance and adiposity, which have been known to correlate with sympathetic hyperactivity.Objective:The aim of this study is to analyze the characteristics of heart rate variability in women with PCOS. To compare frequency domain parameters of heart rate variability (HRV) between women with PCOS and apparently healthy women. To study the impact of cardiometabolic parameters such as BMI and blood pressure on frequency-domain HRV parameters.Methods:A total of 30 women with PCOS aged 20 to 40 years (as per Rotterdam criteria) were enrolled as cases and 30 age-matched women having normal ovulatory cycles were enrolled as controls. HRV was recorded using an electrocardiography machine (ECG) machine. The following frequency-domain parameters were assessed: Total power, Very low frequency (VLF), VLF%, Low Frequency (LF), LF%, LF nu, High frequency (HF), HF%, HF nu, LF/HF ratio, short-term variability (SD1), and long-term variability (SD2), respectively.Results:Mean age of cases was 28.03 ± 5.33 years. Mean BMI of PCOS women was 25.39 ± 2.69 kg/m2. A total of 18 (60%) had BMI >25 kg/m2. Cases had significantly higher BMI, waist hip ratio, and blood pressure as compared with controls. None of the controls had BMI >25 kg/m2. Majority of cases (66.7%) had systolic blood pressure/diastolic blood pressure (SBP/DBP) >130/85 mmHg as compared with only 6 (20%) of controls (P < 0.001). For different frequency domain parameters, no statistically significant difference between two groups was observed for VLF and LF. Mean VLF%, LF%, LF (nu), and LF/HF were significantly higher in cases as compared with controls. For all, the other mean value was significantly lower in cases as compared with that of controls (P < 0.05).Conclusions:Autonomic nervous system is affected by PCOS status of women, and sympathetic hyperactivity is seen.

Digital ECG Phantom Design to Represent the Human Heart Signal for Early Test on ECG Machine in Hospital

Electrocardiograph (ECG) is a diagnostic tool that can record the electrical activity of the human heart. By analyzing the resulting waveforms of the recorded electrical activity of the heart, it is possible to record and diagnose disease. Given the importance of the ECG recording device, it is necessary to check the function of the ECG recording device, namely by performing a device calibration procedure using the Phantom ECG which aims to simulate the ECG signal. The purpose of this research is to check the ECG device during repairs, besides that the Electrocardiograph (EKG) tool functions for research purposes on ECG signals or for educational purposes. Electrocardiograph (EKG) simulator or often called Phantom ECG is in principle a signal generator in the form of an ECG like signal or a recorded ECG signal. This device can be realized based on microcontroller and analog circuit. The advantage of this simulator research is that the ECG signal displayed is the original ECG recording and has an adequate ECG signal database. ECG This simulator also has the advantage of providing convenience for research on digital signal processing applications for ECG signal processing. In its application this simulator can be used as a tool to study various forms of ECG signals. Based on the measurement results, the error value at BPM 30 and 60 is 0.00% at the sensitivity of 0.5mV, 1.0mV, and 2.0mV, then the measurement results for the error value at BPM 120 are 0.33% and at the BPM 180 value, the error value is 0.22%. From these results, it can be concluded that the highest error value is at BPM 120 with sensitivities of 0.5mV, 1.0mV, and 2.0mV.

Deep-learning model for screening sepsis using electrocardiography

BackgroundSepsis is a life-threatening organ dysfunction and a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, screening for the occurrence of sepsis is difficult. Herein, we propose a deep learning-based model (DLM) for screening sepsis using electrocardiography (ECG).MethodsThis retrospective cohort study included 46,017 patients who were admitted to two hospitals. A total of 1,548 and 639 patients had sepsis and septic shock, respectively. The DLM was developed using 73,727 ECGs from 18,142 patients, and internal validation was conducted using 7774 ECGs from 7,774 patients. Furthermore, we conducted an external validation with 20,101 ECGs from 20,101 patients from another hospital to verify the applicability of the DLM across centers.ResultsDuring the internal and external validations, the area under the receiver operating characteristic curve (AUC) of the DLM using 12-lead ECG was 0.901 (95% confidence interval, 0.882–0.920) and 0.863 (0.846–0.879), respectively, for screening sepsis and 0.906 (95% confidence interval (CI), 0.877–0.936) and 0.899 (95% CI, 0.872–0.925), respectively, for detecting septic shock. The AUC of the DLM for detecting sepsis using 6-lead and single-lead ECGs was 0.845–0.882. A sensitivity map revealed that the QRS complex and T waves were associated with sepsis. Subgroup analysis was conducted using ECGs from 4,609 patients who were admitted with an infectious disease, and the AUC of the DLM for predicting in-hospital mortality was 0.817 (0.793–0.840). There was a significant difference in the prediction score of DLM using ECG according to the presence of infection in the validation dataset (0.277 vs. 0.574, p < 0.001), including severe acute respiratory syndrome coronavirus 2 (0.260 vs. 0.725, p = 0.018).ConclusionsThe DLM delivered reasonable performance for sepsis screening using 12-, 6-, and single-lead ECGs. The results suggest that sepsis can be screened using not only conventional ECG devices but also diverse life-type ECG machines employing the DLM, thereby preventing irreversible disease progression and mortality.

Smartphone-based ECG signals encryption for transmission and analyzing via IoMTs

Electrocardiography (ECG) has long been a common method of assessing and diagnosing cardiovascular problems (CaDs). The heart’s electrical activity is visualized in the form of a waveform and its analysis can turn out to provide valuable insights about the functioning and normalcy of a healthy heart or detect a wide range of possible heart risks. This article deals with the Internet of Medical Things (IoMT) based real time healthcare monitoring system which diagnoses the ECG signals remotely and hence manifests essential heart conditions. An electrocardiograph machine captures medical-grade patient heart data. The outcome of the project includes detecting the heart rate, the various interval and segment analysis of the signal, cardiac axis detection as well as prompting the various possible conditions in case of anomalies in the heart activities. In such cases, the system can send an automatic ECG report from the patient to the doctor safely by encrypting this report. It can be further modified to generate a call in case of critical condition and provide urgent medical assistance. During the ECG analysis, the article presented clearly results represented by finding different values for different patients starting from QRS width 96, PR Interval 133, and P amplitude 2.81, and ending last patient test QRS width 93, PR Interval 112, and P amplitude 1.57.