Electrocardiogram Monitoring Devices Research Articles

An Electrocardiogram Monitoring Device Based on STM32

Cardiovascular diseases remain the leading cause of morbidity and mortality, particularly in aging populations, with rising rates of heart-related incidents in middle-aged and even younger individuals. Currently available electrocardiogram (ECG) monitoring technologies, such as bedside monitors are bulky and cumbersome, limiting their potential accessibility for continuous, real-time monitoring. Therefore, we propose a portable digital ECG monitoring system that can accurately measure heart rate, analyze the ECG in real-time, and wirelessly send data to the cloud platform. The device comprises a custom-designed circuit to capture ECG signals, with analog-to-digital conversion and heart rate measurement performed by an STM32F429 microcontroller. In addition, real-time monitoring is achieved through the L610-4G module, which transmits data to Tencent Cloud, enabling users to access their ECG data remotely while being notified by instant alerts for abnormal heart rates. Moreover, healthcare professionals can view historic ECG data for further clinical analysis of patient progression and make evidence-based decisions. The proposed system features portability and accuracy, enabling the user to access extended ECG data easily. It provides the ability to minimize redundant clinical assessments, thus offering considerable advantages to patients and healthcare practitioners.

Comparing simultaneous hybrid ablation with stand-alone thoracoscopic surgical ablation for the treatment of non-paroxysmal atrial fibrillation: a prospective randomized controlled trial.

Advanced atrial fibrillation (AF) is currently a dilemma for electrophysiologists when choosing a minimally invasive treatment strategy. Previous studies have demonstrated the outcome of either catheter ablation or thoracoscopic surgical ablation (SA) is unsatisfactory in these patients. Whether hybrid ablation (HA) could improve outcomes in these patients is unknown. The purpose of this study was to evaluate the clinical efficacy of HA for the treatment of advanced AF. A randomized controlled trial was designed to enrol patients with persistent AF (PerAF) and enlarged left atrium or long-standing persistent AF (LSPAF) who were randomized to HA or thoracoscopic SA at a 1:1 ratio. The primary endpoint was freedom from any recurrence of AF off antiarrhythmic drugs (AADs) 12 months after operation. The primary endpoint was monitored by 7-day electrocardiogram monitoring devices. One hundred patients were enrolled. The mean age was 58.5 ± 7.6 years, and the mean left atrial diameter (LAD) was 50.1 ± 6.1 mm. At 12 months, freedom from AF off AADs was recorded in 71.4% (35/49) of patients in HA group and 45.8% (22/48) in SA group [odds ratio 2.955, 95% confidence interval (1.275-6.848), P = 0.014]. HA significantly reduced patients' AF burden (30.2% in SA group and 14.8% in HA group, P = 0.048) and the LAD (mean differences: -5.53 ± 4.97 mm in HA group and -3.27 ± 5.20 mm in SA group, P = 0.037) at 12 months after operation. In patients with PerAF and enlarged left atrium or LSPAF, HA achieved better freedom from AF after 1 year of follow-up compared with thoracoscopic SA.

Application of remote electrocardiogram monitoring systems in chest pain centers for patients with high-risk chest pain.

Chinese chest pain centers (CPCs) have been expanding and maturing for the past decade, but patient wait times for pre-hospital care remain long. To demonstrate that the remote electrocardiogram (ECG) monitoring system can ensure more efficient treatment for patients with ST-elevation myocardial infarction (STEMI) in CPCs, we compared patients with high-risk chest pain who used remote ECG monitoring systems to those who used conventional ECGs in retrospective cohort study. Based on the inclusion and exclusion criteria, 290 patients who visited our CPC between June 2019 and March 2022 with acute chest pain and a diagnosis of STEMI as well as patients who had undergone an emergency primary percutaneous coronary intervention were selected. Among them, 73 patients with STEMI had employed remote real-time dynamic 12-lead ECG monitoring devices, while 217 patients with STEMI (i.e., the controls) had used conventional ECG monitoring. The effectiveness of treatment procedures for the two groups was investigated. As statistical measures, the symptom onset-to-wire times, first medical contact (FMC)-to-wire times, door-to-wire times, major adverse cardiac events in hospital, and the troponin T levels were analyzed. Compared with the control group, the patients with remote real-time dynamic 12-lead ECG monitoring devices showed shorter times for both symptom onset-to-wire (234.8 ± 95.8 min vs. 317.6 ± 129.6 min, P= 0.0321) and from symptom onset-to-FMC (170.5 ± 86.3 min vs. 245.3 ± 115.6min, P= 0.0287); this group also had a lower 30-day mortality rate (2.73% vs. 4.14%, P= 0.003). The differences between the two groups were statistically significant (P< 0.05). With remote real-time dynamic 12-lead ECG monitoring equipment, myocardial ischemia can be treated more quickly, leading to fewer possible cardiac events and a better prognosis.

Hierarchical deep learning for autonomous multi-label arrhythmia detection and classification on real-world wearable electrocardiogram data.

Arrhythmia detection and classification are challenging because of the imbalanced ratio of normal heartbeats to arrhythmia heartbeats and the complicated combinations of arrhythmia types. Arrhythmia classification on wearable electrocardiogram monitoring devices poses a further unique challenge: unlike clinically used electrocardiogram monitoring devices, the environments in which wearable devices are deployed are drastically different from the carefully controlled clinical environment, leading to significantly more noise, thus making arrhythmia classification more difficult. We propose a novel hierarchical model based on CNN+BiLSTM with Attention to arrhythmia detection, consisting of a binary classification module between normal and arrhythmia heartbeats and a multi-label classification module for classifying arrhythmia events across combinations of beat and rhythm arrhythmia types. We evaluate our method on our proprietary dataset and compare it with various baselines, including CNN+BiGRU with Attention, ConViT, EfficientNet, and ResNet, as well as previous state-of-the-art frameworks. Our model outperforms existing baselines on the proprietary dataset, resulting in an average accuracy, F1-score, and AUC score of 95%, 0.838, 0.906 for binary classification, and 88%, 0.736, 0.875 for multi-label classification. Our results validate the ability of our model to detect and classify real-world arrhythmia. Our framework could revolutionize arrhythmia diagnosis by reducing the burden on cardiologists, providing more personalized treatment, and achieving emergency intervention of patients by allowing real-time monitoring of arrhythmia occurrence.

Development of Machine Learning Model for VO2max Estimation Using a Patch-Type Single-Lead ECG Monitoring Device in Lung Resection Candidates.

A cardiopulmonary exercise test (CPET) is essential for lung resection. However, performing a CPET can be challenging. This study aimed to develop a machine learning model to estimate maximal oxygen consumption (VO2max) using data collected through a patch-type single-lead electrocardiogram (ECG) monitoring device in candidates for lung resection. This prospective, single-center study included 42 patients who underwent a CPET at a tertiary teaching hospital from October 2021 to July 2022. During the CPET, a single-lead ECG monitoring device was applied to all patients, and the results obtained from the machine-learning algorithm using the information extracted from the ECG patch were compared with the CPET results. According to the Bland-Altman plot of measured and estimated VO2max, the VO2max values obtained from the machine learning model and the FRIEND equation showed lower differences from the reference value (bias: -0.33 mL·kg-1·min-1, bias: 0.30 mL·kg-1·min-1, respectively). In subgroup analysis, the developed model demonstrated greater consistency when applied to different maximal stage levels and sexes. In conclusion, our model provides a closer estimation of VO2max values measured using a CPET than existing equations. This model may be a promising tool for estimating VO2max and assessing cardiopulmonary reserve in lung resection candidates when a CPET is not feasible.

Knowing Your Heart Condition Anytime

Electrocardiogram (ECG) monitoring has been widely explored in detecting and diagnosing cardiovascular diseases due to its accuracy, simplicity, and sensitivity. However, medical- or commercial-grade ECG monitoring devices can be costly for people who want to monitor their ECG on a daily basis. These devices typically require several electrodes to be attached to the human body which is inconvenient for continuous monitoring. To enable low-cost measurement of ECG signals with off-the-shelf devices on a daily basis, in this paper, we propose a novel ECG sensing system that uses acceleration data collected from a smartphone. Our system offers several advantages over previous systems, including low cost, ease of use, location and user independence, and high accuracy. We design a two-tiered denoising process, comprising SWT and Soft-Thresholding, to effectively eliminate interference caused by respiration, body, and hand movements. Finally, we develop a multi-level deep learning recovery model to achieve efficient, real-time and user-independent ECG measurement on commercial mobile phones. We conduct extensive experiments with 30 participants (with nearly 36,000 heartbeat samples) under a user-independent scenario. The average errors of the PR interval, QRS interval, QT interval, and RR interval are 12.02 ms, 16.9 ms, 16.64 ms, and 1.84 ms, respectively. As a case study, we also demonstrate the strong capability of our system in signal recovery for patients with common heart diseases, including tachycardia, bradycardia, arrhythmia, unstable angina, and myocardial infarction.

Research on fatigue identification methods based on low-load wearable ECG monitoring devices

The identification of fatigue in personal workers in particular environments can be achieved through early warning techniques. In order to prevent excessive fatigue of medical workers staying in infected areas in the early phase of the coronavirus disease pandemic, a system of low-load wearable electrocardiogram (ECG) devices was used as intelligent acquisition terminals to perform a continuous measurement ECG collection. While machine learning (ML) algorithms and heart rate variability (HRV) offer the promise of fatigue detection for many, there is a demand for ever-increasing reliability in this area, especially in real-life activities. This study proposes a random forest-based classification ML model to identify the four categories of fatigue levels in frontline medical workers using HRV. Based on the wavelet transform in ECG signal processing, stationary wavelet transform was applied to eliminate the main perturbation of ECG in the motion state. Feature selection was performed using ReliefF weighting analysis in combination with redundancy analysis to optimize modeling accuracy. The experimental results of the overall fatigue identification achieved an accuracy of 97.9% with an AUC value of 0.99. With the four-category identification model, the accuracy is 85.6%. These results proved that fatigue analysis based on low-load wearable ECG monitoring at low exertion can accurately determine the level of fatigue of caregivers and provide further ideas for researchers working on fatigue identification in special environments.

TinyML-Based Classification in an ECG Monitoring Embedded System

Recently, the development of the Internet of Things (IoT) has enabled continuous and personal electrocardiogram (ECG) monitoring. In the ECG monitoring system, classification plays an important role because it can select useful data (i.e., reduce the size of the dataset) and identify abnormal data that can be used to detect the clinical diagnosis and guide further treatment. Since the classification requires computing capability, the ECG data are usually delivered to the gateway or the server where the classification is performed based on its computing resource. However, real-time ECG data transmission continuously consumes battery and network resources, which are expensive and limited. To mitigate this problem, this paper proposes a tiny machine learning (TinyML)-based classification (i.e., TinyCES), where the ECG monitoring device performs the classification by itself based on the machine-learning model, which can reduce the memory and the network resource usages for the classification. To demonstrate the feasibility, after we configure the convolutional neural networks (CNN)-based model using ECG data from the Massachusetts Institute of Technology (MIT)-Beth Israel Hospital (BIH) arrhythmia and the Physikalisch Technische Bundesanstalt (PTB) diagnostic ECG databases, TinyCES is validated using the TinyML-supported Arduino prototype. The performance results show that TinyCES can have an approximately 97% detection ratio, which means that it has great potential to be a lightweight and resource-efficient ECG monitoring system.

Arrhythmia and Heart Rate Variability during Long Interdialytic Periods in Patients on Maintenance Hemodialysis: Prospective Observational Cohort Study.

Sudden cardiac death among hemodialysis patients is related to the hemodialysis schedule. Mortality is highest within 12 h before and after the first hemodialysis sessions of a week. We investigated the association of arrhythmia occurrence and heart rate variability (HRV) using an electrocardiogram (ECG) monitoring patch during the long interdialytic interval in hemodialysis patients. This was a prospective observational study with 55 participants on maintenance hemodialysis for at least six months. A patch-type ECG monitoring device was applied to record arrhythmia events and HRV during 72 h of a long interdialytic period. Forty-nine participants with sufficient ECG data out of 55 participants were suitable for the analysis. The incidence of supraventricular tachycardia and ventricular tachycardia did not significantly change over time. The square root of the mean squared differences of successive NN intervals (RMSSD), the proportion of adjacent NN intervals differing by >50 ms (pNN50), and high-frequency (HF) increased during the long interdialytic interval. The gap in RMSSD, pNN50, HF, and the low-frequency/high-frequency (LF/HF) ratio between patients with and without significant arrhythmias increased significantly over time during the long interdialytic interval. The daily changes in RMSSD, pNN50, HF, and the LF/HF ratio were more prominent in patients without significant arrhythmias than in those with significant arrhythmias. The electrolyte fluctuation between post-hemodialysis and subsequent pre-hemodialysis was not considered in this study. The study results suggest that the decreased autonomic response during interdialytic periods in dialysis patients is associated with poor cardiac arrhythmia events.

Internet of Things-Based ECG and Vitals Healthcare Monitoring System.

Health monitoring and its associated technologies have gained enormous importance over the past few years. The electrocardiogram (ECG) has long been a popular tool for assessing and diagnosing cardiovascular diseases (CVDs). Since the literature on ECG monitoring devices is growing at an exponential rate, it is becoming difficult for researchers and healthcare professionals to select, compare, and assess the systems that meet their demands while also meeting the monitoring standards. This emphasizes the necessity for a reliable reference to guide the design, categorization, and analysis of ECG monitoring systems, which will benefit both academics and practitioners. We present a complete ECG monitoring system in this work, describing the design stages and implementation of an end-to-end solution for capturing and displaying the patient's heart signals, heart rate, blood oxygen levels, and body temperature. The data will be presented on an OLED display, a developed Android application as well as in MATLAB via serial communication. The Internet of Things (IoT) approaches have a clear advantage in tackling the problem of heart disease patient care as they can transform the service mode into a widespread one and alert the healthcare services based on the patient's physical condition. Keeping this in mind, there is also the addition of a web server for monitoring the patient's status via WiFi. The prototype, which is compliant with the electrical safety regulations and medical equipment design, was further benchmarked against a commercially available off-the-shelf device, and showed an excellent accuracy of 99.56%.

Abstract 12483: Accurate QT Correction for Reliable Cardiac Risk Assessment

Introduction: In the electrocardiogram (ECG), the QT interval is one of the most important cardiac risk measures. For reliable interpretation, the QT interval must be corrected for the heart rate. Several approximative QT correction (QTc) models and methods have been developed. The most commonly used methods are the power-law formulas of Bazett and Fridericia, which are known to fail in various situations. It is generally accepted that no universally applicable QTc formulas can be constructed. Methods: We have developed a model-free QT correction method that minimizes the information transfer from the RR intervals (heart rate) to the QT intervals. This yields a reliable estimate for the true QTc. The resulting QTc values are compared to the conventional methods including Bazett, Fridericia, Hodges, and Framingham, and the individual QT correction. We use the ECG data of PhysioNet and the Telemetric and Holter ECG Warehouse (THEW). Results: Our method provides superior reliability and stability for QTc assessment. For healthy subjects in the PhysioNet database, the proportion of false positives for all the QTc intervals is only 3% with our method (Bazett: 16%; Fridericia: 10%). The pooled standard deviation is only 13 ms (Bazett: 32 ms; Fridericia: 28 ms; individual QTc: 24 ms). The figure shows a combined point cloud of RR and QT(c) intervals for the six longest recordings of the PhysioNet database. Conclusions: We find excellent potential in our QT correction to become the method of choice in QTc assessment. The method is applicable to all the ECG monitoring devices for clinical use, and it can be utilized in all the phases of drug development involving QT measurements.

Utility of a novel wearable electrode embedded in an undershirt for electrocardiogram monitoring and detection of arrhythmias

BackgroundA 12-lead electrocardiogram (ECG) and Holter ECG have been established as gold standards for detection of arrhythmias. Recently, wearable ECG monitoring devices have been available. Our purpose of the present study was to investigate whether a novel wearable electrode embedded in an undershirt is useful for ECG monitoring and detection of arrhythmias.MethodsWe studied 31 consecutive hospitalized patients who underwent catheter ablation of tachyarrhythmias. Patients equipped a wearable electrode and a lead CM5 of Holter ECG simultaneously, and total heart beats, maximum heart rate (HR), mean HR, minimum HR, detections of arrhythmias, such as atrial fibrillation, non-sustained ventricular tachycardia, and premature ventricular contractions (Lown’s grade >II), were compared between the two methods using a Holter ECG analysis software.ResultsMedian recording time of ECG by wearable electrodes was 12.6 hours. Strong correlations between the two methods were observed in total heart beats (R = 0.999, P <0.001), maximum HR (R = 0.997, P <0.001), mean HR (R = 0.999, P <0.001), minimum HR (R = 0.989, P <0.001) and QRS duration (R = 0.900, P <0.001). Bland-Altman analysis showed excellent concordance between each parameter measured by two methods. In addition, the detection of atrial fibrillation (nine events), non-sustained ventricular tachycardia (two events), and premature ventricular contractions of Lown’s grade >II (five events) were concordant in two methods. In addition, there were no significant difference in parameters of time-domain and frequency-domain analyses of heart rate variability between the two methods.ConclusionsThe usefulness of a novel electrode embedded in an undershirt is equivalent to that of a Holter ECG in monitoring the ECG and detection of arrythmias.

Mobile Electrocardiogram Monitoring System with Cloud-Based Approach

Cardiovascular disease (CVD) is a heart related disease and is the top silent killer in worldwide. Frequent electrocardiogram (ECG) monitoring for patients with CVD is vital to check if arrhythmia occurs. Though there are lots of commercialized portable ECG monitoring device available, many of them are dedicated to professional clinical staff with complex user interface, or only targeted to specific arrhythmia for home monitoring with lack of data management system. In this study, an in-house developed ECG monitoring device was integrated with an Android-based mobile application through Bluetooth wireless communication and implements Google Cloud Technology. The Android-based mobile app supports main features of real-time ECG graph display, automated location detector, data management system and simple arrhythmia detection. The ECG graph displays acquired ECG signal in real-time by ­in-house ECG acquisition unit through Bluetooth wireless communication and stored as text files in phone’s local memory. Firebase Authentication and Firebase Storage based on Google Cloud technology are implemented for data management system development. This allows users and administrators to upload or access data securely through online Google Cloud Platform. Three types of heart rhythm, namely normal sinus rhythm, bradycardia and tachycardia can be classified based on heart rate analysis. Smartphone’s location service is enabled to allow user shares their location with caretaker in emergency case. The final solution utilizes the Internet-of-Things (IoT) technology to facilitate heart disease management towards telemedicine applications.

Internet of things based electrocardiogram monitoring system using machine learning algorithm

&lt;span&gt;In Bangladesh’s rural regions, almost 30% of the population lives in poverty. Rural residents also have restricted access to nursing and diagnostic services due to obsolete healthcare infrastructure. Consequently, as cardiac failure occurs, they usually fail to call the services and adopt the facilities. The internet of things (IoT) offers a massive advantage in addressing cardiac problems. This study proposed a smart IoT-based electrocardiogram (ECG) monitoring system for heart patients. The system is divided into several parts: ECG sensing network (data acquisition), IoT cloud (data transmission), result analysis (data prediction) and monetization. P, Q, R, S, and T are ECG signal properties fetched, pre-processed, analyzed and predicted to age level for future health management. ECG data are saved in the cloud and accessible via message queuing telemetry transport (MQTT) and hypertext transfer protocol (HTTP) servers. The linear regression method is utilized to determine the impact of electrocardiogram signal characteristics and error rate. The prediction was made to see how much variation there was in PQRST regularity and its sufficiency to be utilized in an ECG monitoring device. Recognizing the quality parameter values, acceptable outcomes are achieved. The proposed system will diminish future medical costs and difficulties for heart patients.&lt;/span&gt;

Conception and realization of an IoT-enabled deep CNN decision support system for automated arrhythmia classification

Abstract Arrhythmias are irregular heartbeats that may be life-threatening. Proper monitoring and the right care at the right time are necessary to keep the heart healthy. Monitoring electrocardiogram (ECG) patterns on continuous monitoring devices is time-consuming. An intense manual inspection by caregivers is not an option. In addition, such an inspection could result in errors and inter-variability. This article proposes an automated ECG beat classification method based on deep neural networks (DNN) to aid in the detection of cardiac arrhythmias. The data collected by an Internet of Things enabled ECG monitoring device are transferred to a server. They are analysed by a deep learning model, and the results are shared with the primary caregiver. The proposed model is trained using the MIT-BIH ECG arrhythmia database to classify into four classes: normal beat (N), left bundle branch block beat (L), right bundle branch block beat (R), and premature ventricular contraction (V). The received data are sampled with an overlapping sliding window and divided into an 80:20 ratio for training and testing, with tenfold cross-validation. The proposed method achieves higher accuracy with a simple model without any preprocessing when compared with previous works. For the train and test sets, we achieved accuracy rates of 99.09 and 99.03%, respectively. A precision, recall, and F1 scores of 0.99 is obtained. The proposed model achieves its goal of developing a simple and accurate ECG monitoring system with improved performance. This simple and efficient deep learning approach for heartbeat classification could be applied in real-time telehealth monitoring systems.

Indications, contraindications, and clinical significance of Holter monitoring device

Holter electrocardiogram monitoring devices are mainly indicated to assess the 24-hour heart rate and rhythm, enabling clinicians and physicians to evaluate the underlying disorders, possibly arrhythmias. The clinical significance of these modalities was apparent in different clinical events, including cardiac and non-cardiac ones. Many applications and clinical advantages were reported since the device was first reported, which changed the way how healthcare settings deal with and monitor cardiac rhythm and associated abnormalities. Evidence shows that Holter monitoring devices are useful in the appropriate patient populace and are very efficacious in diagnosing and following cardiac arrhythmias and other different cardiac conditions, which can significantly enhance the prognosis and management of cardiovascular patients. In this context, it has been shown that primary healthcare physicians should always be aware that many patients might be present with asymptomatic cardiac arrhythmias. However, not many studies have adequately assessed them in the non-cardiac population, which calls for conducting more future investigations. In the present literature review study, we aim to discuss the indications, contraindications, and clinical significance of using the Holter monitoring device based on data from the relevant studies in the literature.

Immobilization of Air-Stable Copper Nanoparticles on Graphene Oxide Flexible Hybrid Films for Smart Clothes.

Through the use of organic/inorganic hybrid dispersants—which are composed of polymeric dispersant and two-dimension nanomaterial graphene oxide (GO)—copper nanoparticles (CuNPs) were found to exhibit nano stability, air-stable characteristics, as well as long-term conductive stability. The polymeric dispersant consists of branched poly(oxyethylene)-segmented esters of trimellitic anhydride adduct (polyethylene glycol−trimethylolpropane−trimellitic anhydride, designated as PTT). PTT acts as a stabilizer for CuNPs, which are synthesized via in situ polymerization and redox reaction of the precursor Cu(CH3COO)2 within an aqueous system, and use graphene oxide to avoid the reduction reaction of CuNPs. The results show that after 30 days of storage the CuNPs/PTT/GO composite film maintains a highly conductive network (9.06 × 10−1 Ω/sq). These results indicate that organic/inorganic PTT/GO hybrid dispersants can effectively maintain the conductivity stability of CuNPs and address the problem of CuNP oxidation. Finally, the new CuNPs/PTT/GO composite film was applied to the electrocardiogram (ECG) smart clothes. This way, a stable and antioxidant-sensing electrode can be produced, which is expected to serve as a long-term ECG monitoring device.

Indications, Contraindications, and Clinical Significance of Holter Monitoring Device

Holter electrocardiogram monitoring devices are mainly indicated to assess the 24-hour heart rate and rhythm, enabling clinicians and physicians to evaluate the underlying disorders, possibly arrhythmias. The clinical significance of these modalities was apparent in different clinical events, including cardiac and non-cardiac ones. Many applications and clinical advantages were reported since the device was first reported, which changed the way how healthcare settings deal with and monitor cardiac rhythm and associated abnormalities. Evidence shows that Holter monitoring devices are useful in the appropriate patient populace and are very efficacious in diagnosing and following cardiac arrhythmias and other different cardiac conditions, which can significantly enhance the prognosis and management of cardiovascular patients. In this context, it has been shown that primary healthcare physicians should always be aware that many patients might be present with asymptomatic cardiac arrhythmias. However, not many studies have adequately assessed them in the non-cardiac population, which calls for conducting more future investigations. In the present literature review study, we aim to discuss the indications, contraindications, and clinical significance of using the Holter monitoring device based on data from the relevant studies in the literature.

The effect of risperidone and electroconvulsive therapy on corrected QT interval in electrocardiogram of psychiatric patients

BACKGROUNDSome types of antidepressants and antipsychotic medications have cardiovascular side effects that can be life-threatening. Electroconvulsive therapy (ECT) is capable of generating physiological stress and may lead to increased QT interval followed by arrhythmias. Risperidone can also increase the risk of arrhythmia by increasing the corrected QT (QTc) interval. Since many patients require co-administration of risperidone and ECT, this study aimed to investigate the concurrent effect of ECT and risperidone administration on the QTc interval.METHODSFor this cross-sectional study, 60 patients (18-65 years) admitted in 22 Bahman Psychiatric Hospital (Qazvin, Iran) that were candidate for treatment with risperidone, ECT, or both methods were concurrently divided into three groups. The groups included patients treated with ECT, risperidone, and combination treatment (risperidone and ECT). At the beginning of the study, electrocardiogram (ECG) was obtained for all patients and QT was performed manually, and finally, QTc interval was measured two times for each group. Required information was collected through medical records. Then, inferential statistics, analysis of variance (ANOVA), was used to determine differences between different variables.RESULTSA significant increase in heart rate (HR) in the third group compared to first and second groups was observed. None of the treatments had a significant effect on QTc interval, but the QTc interval increased slightly in groups treated with the ECT alone and particularly, the ECT plus risperidone in comparison to the baseline values.CONCLUSIONOur study showed that risperidone, ECT, and their concomitant combination did not affect the QTc interval. Therefore, risperidone and ECT are safe and their combination can also be a good option for refractory patients undergoing ECG monitoring and cardiopulmonary devices.

Practical Utility of the Postal Service in Delivering a Self-Fitted, Wearable, Long-Term Electrocardiogram Monitoring Device for Outpatient Care.

Background: Demand is growing for remote electrocardiogram (ECG) monitoring systems in the COVID-19 era in Japan. This study describes initial experiences with a small wireless ECG monitoring device and the utility of delivery via the postal service for outpatient care in Japan.Methods and Results: Long-term ECG monitoring following postal delivery of the small ECG device was evaluated in 25 patients. The patients had no difficulties with either the postal delivery or self-fitting and wearing the devices. A median of 57 h monitoring per patient was performed. Arrhythmic events were detected in 8 patients. Most patients were satisfied with both the ECG devices and postal delivery.Conclusions: Postal delivery of ECG devices could be used in clinical practice to achieve less or no in-person contact during the COVID-19 era.