Comparison of arrhythmia detection by conventional Holter and a novel ambulatory ECG system using patch and Android App, over 24 h period

Karunadas, Chakkalakkal Prabhakaran; Mathew, Cibu

doi:10.1016/j.ipej.2019.12.013

Cited by 22 publications

(20 citation statements)

References 6 publications

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“…All studies showed a higher AF detection rate in the mHealth group compared with the control group except the study by Karunadas, which showed an equal number of events (3; 2.1%) in both groups [ 33 ]. This study used an mHealth patch for 1 day and compared it to Holter monitoring performed on the same day.…”

Section: Resultsmentioning

confidence: 99%

“…Busch et al [ 28 ] used data from a registry, in which the study subjects were volunteers willing to participate in an mHealth study. Karunadas et al [ 33 ] reported no baseline characteristics, and only WebCardio data from the first 24 hours were used. The 24-hour to 72-hour data, although gathered, were not reported.…”

Section: Resultsmentioning

confidence: 99%

“…This systematic review reflects on 14 studies with different populations, interventions, and (primary) outcomes. A total of 13 studies found an increased number of AF diagnoses with the use of an mHealth intervention compared with standard care, with the remaining study by Karunadas et al [ 33 ] showing equal effectiveness. All 4 RCTs showed a statistically significant result in favor of the mHealth intervention.…”

Section: Discussionmentioning

confidence: 99%

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Impact of Mobile Health Devices for the Detection of Atrial Fibrillation: Systematic Review

Biersteker

Schalij

Treskes

2021

JMIR Mhealth Uhealth

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Background Atrial fibrillation (AF) is the most common arrhythmia, and its prevalence is increasing. Early diagnosis is important to reduce the risk of stroke. Mobile health (mHealth) devices, such as single-lead electrocardiogram (ECG) devices, have been introduced to the worldwide consumer market over the past decade. Recent studies have assessed the usability of these devices for detection of AF, but it remains unclear if the use of mHealth devices leads to a higher AF detection rate. Objective The goal of the research was to conduct a systematic review of the diagnostic detection rate of AF by mHealth devices compared with traditional outpatient follow-up. Study participants were aged 16 years or older and had an increased risk for an arrhythmia and an indication for ECG follow-up—for instance, after catheter ablation or presentation to the emergency department with palpitations or (near) syncope. The intervention was the use of an mHealth device, defined as a novel device for the diagnosis of rhythm disturbances, either a handheld electronic device or a patch-like device worn on the patient’s chest. Control was standard (traditional) outpatient care, defined as follow-up via general practitioner or regular outpatient clinic visits with a standard 12-lead ECG or Holter monitoring. The main outcome measures were the odds ratio (OR) of AF detection rates. Methods Two reviewers screened the search results, extracted data, and performed a risk of bias assessment. A heterogeneity analysis was performed, forest plot made to summarize the results of the individual studies, and albatross plot made to allow the P values to be interpreted in the context of the study sample size. Results A total of 3384 articles were identified after a database search, and 14 studies with a 4617 study participants were selected. All studies but one showed a higher AF detection rate in the mHealth group compared with the control group (OR 1.00-35.71), with all RCTs showing statistically significant increases of AF detection (OR 1.54-19.16). Statistical heterogeneity between studies was considerable, with a Q of 34.1 and an I2 of 61.9, and therefore it was decided to not pool the results into a meta-analysis. Conclusions Although the results of 13 of 14 studies support the effectiveness of mHealth interventions compared with standard care, study results could not be pooled due to considerable clinical and statistical heterogeneity. However, smartphone-connectable ECG devices provide patients with the ability to document a rhythm disturbance more easily than with standard care, which may increase empowerment and engagement with regard to their illness. Clinicians must beware of overdiagnosis of AF, as it is not yet clear when an mHealth-detected episode of AF must be deemed significant.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Impact of Mobile Health Devices for the Detection of Atrial Fibrillation: Systematic Review

Biersteker

Schalij

Treskes

2021

JMIR Mhealth Uhealth

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“…One of the widely used patches in South India is the Webcardio monitoring system. The device has been shown to provide good quality diagnostic information comparable to a traditional Holter in adults [ 5 ]. The anecdotal experience of its use in the pediatric population has been reported to be satisfactory.…”

mentioning

confidence: 99%

“…The main challenge in obtaining simultaneous recordings is the inability to place both monitors in the small chest surface of the neonate. We hence have to rely on similar information obtained from older patients which has been deemed satisfactory [ 5 ]. The arrhythmias detected on Webcardio were similar to the ones detected on ECG monitoring lending further credibility to the results.…”

mentioning

confidence: 99%

The utility of patch recorders in neonatal ambulatory electrocardiogram recording

Krishna¹,

Sennaiyan²,

Ramanathan³

2021

Indian Pacing and Electrophysiology Journal

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An efficient priority‐based convolutional auto‐encoder approach for electrocardiogram signal compression in Internet of Things based healthcare system

Mahendran

Parthasarathy

Parthasarathy³

et al. 2020

Trans Emerging Tel Tech

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Due to advancements in healthcare monitoring systems, the Internet of Things concepts are proficiently utilized in the medical field to detect and diagnose the physical health problems. The compression of more substantial medical information is a significant issue that requires ample data storage space and takes longer transmission time. Though several compression algorithms are actualized in past cases, there is an absence of an upgraded approach to achieve improved signal compression without influencing signal quality. Hence a proficient signal compression algorithm is proposed in our work to provide an enhanced electrocardiogram (ECG) signal compression without any data loss and to acquire increased compression ratio (CR) and zero construction error. In this proposed approach, the input ECG signal dataset from the MIT‐BIH arrhythmia database gets influenced by noise because of the electrical measuring gadget. Hence, preprocessing is done by the proposed multi‐scoop notch filter (MSNF) to denoise this signal by removing the specified noise frequency range of around (1‐50) Hz. This proposed MSNF is designed with adaptiveness that has achieved the enhanced denoising by adjusting the notch frequency. In addition, to extricate the sophisticated ECG signal features, Fast Fourier Transform is being utilized, and that performs and decomposes the signal elegantly and obtains characteristics of the signal in the frequency domain. After feature extraction, optimal signal compression is performed by our proposed priority‐based convolutional auto‐encoder (PCAE) that provides better compression with almost zero reconstruction error by encoding the signals into lower‐dimensional vectors in convolutional layers which are again reconstructed using a decoding approach. The experimental results are then assessed using the performance metrics that include signal to noise ratio (SNR), CR, and percentage root‐mean‐square difference (PRD). The attained results are 1.83% as average PRD value, average SNR is about 33 dB, and average CR is about 35.2% whereas the traditional CAE approach has average values of 2.05% PRD, 23.45 dB SNR, and 32.2% CR.

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Comparison of arrhythmia detection by conventional Holter and a novel ambulatory ECG system using patch and Android App, over 24 h period

Cited by 22 publications

References 6 publications

Impact of Mobile Health Devices for the Detection of Atrial Fibrillation: Systematic Review

Impact of Mobile Health Devices for the Detection of Atrial Fibrillation: Systematic Review

The utility of patch recorders in neonatal ambulatory electrocardiogram recording

An efficient priority‐based convolutional auto‐encoder approach for electrocardiogram signal compression in Internet of Things based healthcare system

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