Efficacy of diagnostic tools for detecting cardiac arrhythmias: systematic literature search

Understanding Arrhythmia-Induced Cardiomyopathy: Symptoms and Treatments

Arrhythmia-induced cardiomyopathy is a complex condition that causes a decline in heart function as a result of irregular heart rhythms. This disorder highlights the link between irregular heart rhythm and heart failure, necessitating prompt identification and intervention. It often occurs due to ongoing fast heart rhythms like atrial fibrillation or tachycardia. Understanding the mechanisms, symptoms, and available treatments is essential for enhancing patient outcomes given the complicated nature of the condition. This article delves into various aspects of arrhythmia-induced cardiomyopathy, including pathogenesis, clinical presentation, diagnostic methods, epidemiology, typical arrhythmias associated with the condition, and management options. It assesses patients' future outlook and necessary follow-up, aiming to provide healthcare providers with a comprehensive understanding of how to handle this intricate condition. The article emphasizes the important effect an integrative approach can have on both patients' lives and the clinical consequences of diagnosing and treating this condition. This extensive understanding enhances the resources at the disposal of physicians, enabling targeted treatments that enhance cardiomyopathy by targeting arrhythmia regulation. More research and development are needed in the field of cardiomyopathy and arrhythmia relationship. The presentation urges the medical field to delve deeper into the complexities of illness by emphasizing the need for continuous research and a multifaceted treatment plan. By combining these understandings, our goal is to enhance patient outcomes and create opportunities for further studies on cardiovascular wellness.

Review of the Requirements for a Wearable, Low-Cost External Cardiac Loop Recorder with WBAN in Resource-Constrained Settings

Cardiovascular disease (CVD) has become the most serious health concern in India and globally. The cost of treatment for CVD is very high and in a country like India, where most of the population belongs to rural area, affording treatment is not possible. Diagnosis and treatment are further hampered due to shortage of medical expertise as well as the unavailability of the wearable device. This makes the condition worst in rural areas. As a result of delay in diagnosis, patients do not receive appropriate treatment on time, thus risking lives. Hence, early detection of physiological abnormalities in patients is the best solution to avoid sudden death. In India, the majority of ECG diagnosis is done using a standard ECG machine or Holter monitor, which are not adequate to detect transient or infrequent arrhythmia as the window of detection is 30 s or up to 48 h. So, for arrhythmia diagnosis or syncope and palpitation, external cardiac loop recorder (ECLR) is preferred. ECLR is a monitoring device which records cardiac activities and detects infrequent arrhythmias with syncope and palpitation of a subject for longer period continuously. Due to recent improvements in technology, such as flexible electronics and wireless body area network (WBAN), wearable medical devices are progressively assisting people to monitor their health status while doing their day-to-day activities and furnishing more information to clinicians for early diagnosis and treatment. Flexible electronics allows to develop an electronic circuit on a flexible substrate hence making the device bendable and stretchable. WBAN is a wireless communication between different nodes like sensors and processors that are located at different points on the body. By incorporating technologies such as miniaturization of electronics, making flexible electronics and WBAN concept in ECLR, the device can be made wearable so as to not interfere with the patient's day-to-day activities. This review paper discusses the limitations of existing standard ECG machines as well as how to make the existing ECLR devices more robust, more advanced, more comfortable and also affordable.

Cardiac monitoring for patients with palpitations

Palpitations are one of the most common reasons for medical consultation. They tend to worry patients and can affect their quality of life. They are often a symptom associated with cardiac rhythm disorders, although there are other etiologies. For diagnosis, it is essential to be able to reliably correlate the symptoms with an electrocardiographic record allowing the identification or ruling out of a possible rhythm disorder. However, reaching a diagnosis is not always simple, given that they tend to be transitory symptoms and the patient is frequently asymptomatic at the time of assessment. In recent years, electrocardiographic monitoring systems have incorporated many technical improvements that solve several of the 24-h Holter monitor limitations. The objective of this review is to provide an update on the different monitoring methods currently available, remarking their indications and limitations, to help healthcare professionals to appropriately select and use them in the work-up of patients with palpitations.

Visualizing and Quantifying Irregular Heart Rate Irregularities to Identify Atrial Fibrillation Events

Background

Screening the general public for atrial fibrillation (AF) may enable early detection and timely intervention, which could potentially decrease the incidence of stroke. Existing screening methods require professional monitoring and involve high costs. AF is characterized by an irregular irregularity of the cardiac rhythm, which may be detectable using an index quantifying and visualizing this type of irregularity, motivating wide screening programs and promoting the research of AF patient subgroups and clinical impact of AF burden.

Methods

We calculated variability, normality and mean of the difference between consecutive RR interval series (denoted as modified entropy scale—MESC) to quantify irregular irregularities. Based on the variability and normality indices calculated for long 1-lead ECG records, we created a plot termed a regularogram (RGG), which provides a visual presentation of irregularly irregular rates and their burden in a given record. To inspect the potency of these indices, they were applied to train and test a machine learning classifier to identify AF episodes in gold-standard, publicly available databases (PhysioNet) that include recordings from both patients with AF and/or other rhythm disturbances, and from healthy volunteers. The classifier was trained and validated on one database and tested on three other databases.

Results

Irregular irregularities were identified using normality, variability and mean MESC indices. The RGG displayed visually distinct differences between patients with vs. without AF and between patients with different levels of AF burden. Training a simple, explainable machine learning tool integrating these three indices enabled AF detection with 99.9% accuracy, when trained on the same person, and 97.8%, when trained on patients from a different database. Comparison to other RR interval-based AF detection methods that utilize signal processing, classic machine learning and deep learning techniques, showed superiority of our suggested method.

Conclusion

Visualizing and quantifying irregular irregularities will be of value for both rapid visual inspection of long Holter recordings for the presence and the burden of AF, and for machine learning classification to identify AF episodes. A free online tool for calculating the indices, drawing RGGs and estimating AF burden, is available.

Extended Segmented Beat Modulation Method for Cardiac Beat Classification and Electrocardiogram Denoising

Beat classification and denoising are two challenging and fundamental operations when processing digital electrocardiograms (ECG). This paper proposes the extended segmented beat modulation method (ESBMM) as a tool for automatic beat classification and ECG denoising. ESBMM includes four main steps: (1) beat identification and segmentation into PQRS and TU segments; (2) wavelet-based time-frequency feature extraction; (3) convolutional neural network-based classification to discriminate among normal (N), supraventricular (S), and ventricular (V) beats; and (4) a template-based denoising procedure. ESBMM was tested using the MIT–BIH arrhythmia database available at Physionet. Overall, the classification accuracy was 91.5% while the positive predictive values were 92.8%, 95.6%, and 83.6%, for N, S, and V classes, respectively. The signal-to-noise ratio improvement after filtering was between 0.15 dB and 2.66 dB, with a median value equal to 0.99 dB, which is significantly higher than 0 (p < 0.05). Thus, ESBMM proved to be a reliable tool to classify cardiac beats into N, S, and V classes and to denoise ECG tracings.

An integrated care solution for the electrocardiogram monitoring

Purpose

This study has demonstrated how technology may contribute to integrated care solutions by comparing conventional ward telemetry (WT) to a wearable ECG monitor (S-Patch) to detect atrial fibrillation (AF) in patients with stroke.

Design/methodology/approach

51 patients admitted for stroke workup were recruited across two major tertiary centres to compare WT monitoring for two days versus S-Patch for four days in the detection of AF. The efficacy to detect AF using both technologies was assessed via data extractions and medical officer review. A matrix was used to measure nursing/patient satisfaction and setup/resource times were assessed.

Findings

Patients (84–94%) and nursing staff (75–95%) preferred the S-Patch wearable technology. Non-parametric tests indicated significant time saving for removal of S-Patch versus WT [2.2 min vs 5.1 min (p = 0.00)]. Efficacy of S-Patch to detect AF following medical officer review was greater than WT, with seven patients identified with AF by S-Patch versus one using WT. The S-patch had a false positive rate of 78%.

Research limitations/implications

The S-Patch is sensitive in the detection of AF; however, it showed a high false-positive rate with automated reporting. This study has provided insight into the details of delivery of integrated healthcare using wearable technology.

Originality/value

The technology and partnership were the first-in-kind in Australia. The S-Patch had a higher detection rate of AF compared to WT which allows patients to be anti-coagulated appropriately for the prevention of further stroke. The results of this study will be ideally placed to inform future policy in integrated healthcare using new technologies.

Comparison of Holter With Zio Patch Electrocardiography Monitoring in Children

The standard for ambulatory arrhythmia detection in children is the Holter monitor. The Zio XT (Zio) patch has been FDA-approved for use in adults. However, its utility in children has not been directly compared with the Holter. We studied the ability to detect arrhythmias and patient comfort of the Zio versus the Holter in children. Patients <22 years old were prospectively enrolled to wear the Holter and Zio simultaneously for 48 hours at our institution. Detection of clinically significant arrhythmias was compared using McNemar's test. Wear-time and artifact time was compared using Wilcoxon sign test. Patient satisfaction ratings were analyzed with paired t tests. Two hundred patients (57% male) were included for analysis. The median age was 13.5 years (range 23 days to 21.7 years), and 40% had heart disease. The Zio and Holter had comparable median wear-times, 48.2 hours (interquartile range [IQR] 45.8 to 50.2]) versus 48.0 (48.0 to 48.0), respectively, p = 0.14, but the Zio had less artifact than the Holter, 2.8% (IQR 1.1 to 8.6) versus 5.6% (2.4 to 15.7), respectively, p <0.001. There was no difference in detection of clinically significant arrhythmias for the Zio versus the Holter (p = 0.23), however 75% of patients preferred the Zio over the Holter (p <0.001) due to lack of wires and the ability to shower. In conclusion, the Zio patch is as good as the Holter monitor in detection of clinically significant arrhythmias in children with less artifact. Patients/parents more often preferred the Zio over the Holter.

Contemporary Yield of 24-hour Holter Monitoring: Role of Inter-Atrial Block Recognition

BACKGROUND

The diagnostic yield of 24-hour ECG Holter monitoring (24H) is currently overcome by alternative ECG monitoring techniquesand it needs to be optimized. The recognition of inter-atrial block (IAB) has emerged as a reliable indicator of patients at risk of atrial fibrillation relapses, and its role enhancing the yield of 24H is yet to be determined. We hypothesized that a presumably low yield of 24H may be ameliorated by means of incorporating the assessment for IAB.

METHODS

We retrospectively analyzed 1017 consecutive 24H registers performed in a Multidisciplinary Integrated Health Care Institution, in which a restrictive definition of diagnostic 24H findings was used. A univariate and multivariate regression analysis served to determine the variables associated with a higher 24H's yield, including the requesting medical specialty, type of indication and a number of clinical, echocardiographic and ECG variables, including IAB.

RESULTS

The mean age of our population was 62 ± 17 years (55% males). The majority of 24H were indicated from the Cardiology department (48%). The overall yield was 12.8%, higher for the assessment of the integrity of the electrical conduction system (26.1%) and poorer for the assessment of syncope (3.2%) and cryptogenic stroke (4.6%). The variables associated with higher diagnostic performance were indication from Cardiology (p < 0.001), IAB (p = 0.004), structural heart disease (p = 0.008) and chronic renal failure (p = 0.009). Patients ≤ 50 years old only retrieved a 7% yield. In the multivariate analysis, indication from Cardiology and IAB remained significant predictors of higher 24H's yield. In a secondary analysis including echocardiographic data, only identification of IAB remained statistically significant.

CONCLUSIONS

The recognition of IAB and the type of indication are major determinants of a higher 24H's diagnostic yield and may help to optimize the selection of candidates.

A randomized trial evaluating the accuracy of AF detection by four external ambulatory ECG monitors compared to permanent pacemaker AF detection

PURPOSE

Several external cardiac monitors (ECMs) have recently been developed. These have never been compared to 'gold standard' monitoring with concurrently implanted DDDRP pacemakers. The accuracy of AF detection of Zio XT Monitor (ZM), NUUBO Vest (NV) and Carnation Ambulatory Monitor (CAM) compared with Novacor 'R' Test 4 (RT) in patients (pts) with DDDRP PPM advanced Holters as the comparator, was evaluated.

METHODS

Twenty-one pts. with AF and a DDDRP PPM, each acting as their own control subject, wore every ECM for 2 weeks in randomized order. PPM downloads were performed at application and removal. Device ECGs were compared for AF burden and individual AF episodes with PPM Holters. Pt acceptability, wear time, costs and time expenditure were evaluated.

RESULTS

RT AF burden was less accurate than the ZM, NV or CAM (p < 0.05). Probability of inaccurate AF diagnosis was higher for RT than ZM or CAM OR 12.31 and 5.85, respectively (p = 0.025 and p = 0.042). ZM wear time was longer than the RT: 307 h vs. 224 h; p = 0.02. Acceptability was greater for CAM than RT (1.86 ± 2.63 compared with 0.57 ± 1.17 for CAM; p = 0.024). All ECMs were more expensive than RT (p < 0.00001).

CONCLUSIONS

All new ECMs were more expensive than the RT system; however, the ZM, NV and CAM are all more accurate than current standard practice RT device in AF burden assessment. The RT is more likely to give inaccurate diagnoses than ZM or CAM. This may have clinical implications.

The multiple arrhythmia dataset evaluation database (M.A.D.A.E.)

The convergence between wearable and medical device technologies is a natural progression. Miniaturization has allowed the design of small, compact monitoring systems that can record physiological signals over longer periods of time. Thus, the potential for these devices to expand the understanding of disease progression and patients' clinical status is very high. The accuracy of these devices, however, is dependent upon the computer algorithms utilized in the analysis of the large volume of physiological data monitored and/or recorded by the devices. Automated interpretation of the data by these new technologies, therefore, necessitates closer examination by regulatory organizations. The current requirements for the validation of novel Ambulatory ECG (A-ECG) annotation algorithms are based on the AAMI/ANSI-EC57 and IEC60601-2-47 Standard. These standards are being updated, but they rely on a very limited set of digitized ECG recordings from a couple of ECG databases built in the first half of the 70's. These reference signals are obsolete. We are developing a validation tool for computerized methods designed to detect and monitor cardiac activities based on body-surface ECGs. We will rely on a set of existing digital high-resolution 12‑lead A-ECG recordings acquired in cardiac patients and healthy individuals. These ECG signals include a large and unique set of electrocardiographic events. This tool is being qualified by the Center for Devices and Radiological Health of the United States Food and Drug Administration (FDA) as a Medical Device Development Tool (MDDT). This document provides insights into the design of the M.A.D.A.E. database, its functionalities, and its ultimate role in enabling the next generations of automatic interpretation of ECG signals.

Arrhythmia Detection in Pediatric Patients: ECG Quality and Diagnostic Yield of a Patient-Triggered Einthoven Lead-I Event Recorder (Zenicor EKG-2™)

Symptoms that may be caused by arrhythmia are common in pediatric outpatient departments, though it remains challenging to reveal paroxysmal tachycardia. This investigation evaluated prospectively the quality and diagnostic yield of a newly available handheld patient-activated event recorder (ER) in children. In 226 children (pts) aged 0-17 years with or without congenital heart defects, pacemaker/ICDs or arrhythmia, a lead-I ER ECG was created. ER ECGs were recorded by pressing the patients' thumbs on the device and were analyzed in comparison with a lead-12 ECG, as gold standard. Event recording and data transmission were possible in all cases. ECG quality of the ER showed a high accordance in measuring heart rate (ICC = 0.962), duration of QRS complexes (κ = 0.686), and PR interval (ICC = 0.750) (p < 0.001) although P wave detection remained challenging (p = 0.120). 36 % (n = 82) of the pts had heart rhythm disturbances. The ER yielded 92 % sensitivity in diagnosing supraventricular tachycardia plus 77 % sensitivity and 92 % specificity in identifying abnormal ECGs. In children, the application of the tested ER was suitable. ECGs of good quality could be performed and transmitted easily, and also complex arrhythmia analysis was possible. This ER is an excellent diagnostic device for the detection and exclusion of tachycardia in children.

Diagnostic yield of patch ambulatory electrocardiogram monitoring in children (from a national registry)

The diagnostic yield of continuous electrocardiographic (ECG) monitoring in children for periods longer than a Holter monitor is unclear. The aim of this study was to characterize diagnostic yield, arrhythmia type, and time to first arrhythmia using a clinical repository of national ambulatory ECG data in children. A cross-sectional study was performed in 3,209 consecutive children receiving a 14-day adhesive patch monitor (Zio Patch) for clinical indications from January 2011 to December 2013. Of the 3,209 children (56% female, mean age 12.5 ± 4.4 years, range 1 month to 17 years), 390 had arrhythmias detected, making the diagnostic yield 12.2%. Of these, 137 patients (4.3%) had arrhythmias deemed clinically significant to warrant urgent physician notification. The most frequent indications for monitoring were palpitations (n = 1,138 [35.5%]), syncope (n = 450 [14.0%]), unspecified tachycardia (n = 291 [9.1%]), supraventricular tachycardia (n = 264 [8.2%]), and chest pain (n = 261 [8.1%]). Arrhythmias were detected in 10.0% of patients with palpitations, 6.7% of patients with syncope, 14.8% of patients with tachycardia, 22.7% of patients with supraventricular tachycardia, and 6.5% of patients with chest pain. The mean times to first detected and first symptom-triggered arrhythmias were 2.7 ± 3.0 and 3.3 ± 3.3 days, respectively. Forty-four percent of first detected arrhythmias and 50.4% of the first symptom-triggered arrhythmias occurred beyond 48 hours of monitoring. In conclusion, the diagnostic yield of continuous ECG patch monitoring in children was substantial beyond 48 hours and should be considered in children who are candidates for longer term ECG monitoring.

Pilot evaluation of an integrated monitor-adhesive patch for long-term cardiac arrhythmia detection in India

Electrocardiographic monitoring represents one of the most reliable and time-tested methods for reducing ambiguity in cardiac arrhythmia diagnosis. In India, the resting ECG is generally the first tool of choice for in-clinic diagnosis. The external loop recorder (ELR) is another useful tool that compounds the advantages of traditional tools by coupling ambulatory monitoring with a long-term window. Thus, the objective was to test the use of a 7-day ELR for arrhythmia diagnosis in India for a broad range of presenting symptoms. In this study set in the Indian healthcare environment, an auto-triggered, wireless patch-type ELR was used with 125 patients (62.5 ± 16.7 years, 76 males) presenting a broad range of symptoms. Eighty percent of the symptoms were related to syncope, presyncope or palpitations. Patients were administered an ELR for 7-28 days depending on the physician's prescription. Prespecified significant arrhythmias included sinus pause >2 s, symptomatic bradycardia <40 b.p.m., second-degree (and higher) AV block, complete heart block, ventricular fibrillation, sustained/nonsustained ventricular tachycardia (>3 beats), atrial fibrillation (chronic or paroxysmal), atrial flutter and supraventricular tachycardia (SVT) >130 b.p.m. Diagnostic yield was 38% when a stringent tabulation methodology considering only clinically significant arrhythmia was used. When first-degree AV block, premature atrial and ventricular beats, couplets (both atrial and ventricular in origin), bigeminy or trigeminy, or sudden changes in rate (noted as sinus arrhythmia) were included in the calculation, diagnostic yield was 80%. Patient compliance was 98%; patients wore the patch for the entire prescribed monitoring period without disruption. Seventy percent of the reported symptoms corresponded with an arrhythmia. Use of the ELR led to therapy change in 24% of patients: 15 patients went on to receive an implantable cardioverter defibrillator or pacemaker, 4 received ablation therapy and 11 altered their medication after diagnosis. This study demonstrates that a high diagnostic yield for clinically significant arrhythmias and high patient compliance can be achieved with a wearable patch monitor in Indian patients suffering from a variety of symptoms.

Patient selection for ambulatory cardiac monitoring in the Indian healthcare environment

Cardiovascular disease (CVD) in India comprises the bulk of non-communicable diseases, resulting in 2 million deaths per year. The incidence of CVD in India is estimated to be up to four times higher than in other countries. Though the quantification of the prevalence of rhythm disorders in India is not available, it can be inferred to be proportionately high. Identification and treatment of arrhythmia is limited by several socioeconomic factors including low health insurance penetration, limited reimbursement and high out-of-pocket expenditures. Thus, there exists a need in India to (1) select an appropriate tool that is both high yielding and cost effective and (2) employ a suitable patient selection method. This paper focuses on these two aspects for cardiac arrhythmia diagnosis using ambulatory monitoring technology, while keeping in mind the dynamics of the Indian healthcare setting.