Minimal or No Touch Electrocardiography Recording and Remote Heart Rhythm Monitoring during COVID-19 Pandemic Era

Tondas, Alexander Edo; Halim, Rolando Agustian; Guyanto, Moza

doi:10.30701/ijc.1010

Cited by 7 publications

(6 citation statements)

References 28 publications

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“…By the time this manuscript is published, the Kardia ™ Mobile and Fibricheck ® have not officially entered the Indonesian market yet. However, considering their high specificity, and presumably low false-positive rate, both technologies are very attractive for mass-scale atrial fibrillation screening, especially during imminent pandemics, where direct physical contact should be minimized [16]. Extending this study to some additional health centers including general practitioner clinics, district hospitals or even primary care centers would provide an insight on how to implement this kind of technology in the Indonesian health care system.…”

Section: Discussionmentioning

confidence: 99%

Comparison of two smartphone based atrial fibrillation screening application in Indonesian population

Tondas¹,

Halim²,

Felani³

et al. 2021

IJC

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Background: Due to its adverse outcomes and thromboembolic complications, early detection of atrial fibrillation (AF) is advisable in the general population. This study aims to compare the diagnostic ability of two distinct method in smartphone application format, namely : AliveCor KardiaMobile and FibriCheck. Methods: This study was conducted in Mohammad Hospital General Hospital Palembang with convenience sampling of 170 participants aged 18 years or older. The subjects underwent Fibricheck and KardiaMobile recordings followed by 12 lead electrocardiogram read by board-certified cardiologist as the diagnostic standard. Results: After the exclusion of previous pacemaker implantation (n=7), 163 patients were included in the study. The mean age was 51±15 years with gender distribution of 74.8% men and 25.2% women. Most of the subjects were asymptomatic (87.1%) with mean blood pressure of 130/80 mmHg. The Fibricheck readings showed sensitivity of 73% and specificity of 93%, meanwhile Kardiamobile was able to detect AF with sensitivity of 77% and specificity of 98%. Conclusion: In our study, KardiaMobile demonstrated overall greater sensitivity and specificity when compared to FibriCheck. However, KardiaMobile requires an external metal sensor that must be puchased separately. To the best of our knowledge, this is the first study to directly compare both methods in the Indonesian population.

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Section: Discussionmentioning

confidence: 99%

Comparison of two smartphone based atrial fibrillation screening application in Indonesian population

Tondas¹,

Halim²,

Felani³

et al. 2021

IJC

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“…Furthermore, the global pandemic further complicated cardiology services as COVID-19 eventually emerged as an airborne disease, and physical distancing had been carried out to prevent the spread of infection. Therefore, attempts were made to use portable ECG devices to minimize exposure and disease transmission [18,35].…”

Section: Discussionmentioning

confidence: 99%

“…During the pandemic, efforts are made to minimize physical contact and the risk of infection transmission in routine medical examinations [ 18 , 19 ]. In this context, the use of portable and wearable digital ECG devices (also easily manageable by the patients themselves) may represent a solution.…”

Section: Introductionmentioning

confidence: 99%

Is T-Wave Alternans a Repolarization Abnormality Marker in COVID-19? An Investigation on the Potentialities of Portable Electrocardiogram Device

Tondas¹,

Munawar

Marcantoni

et al. 2023

Cardiol Res

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Background: Cardiac arrhythmias are significantly associated with poor outcomes in coronavirus disease 2019 patients. Microvolt T-wave alternans (TWA) can be automatically quantified and has been recognized as a representation of repolarization heterogeneity and linked to arrhythmogenesis in various cardiovascular diseases. This study aimed to explore the correlation between microvolt TWA and COVID-19 pathology.Methods: Patients suspected of COVID-19 in Mohammad Hoesin General Hospital were consecutively evaluated using Alivecor ® Kardiamobile 6L™ portable electrocardiogram (ECG) device. Severe COVID-19 patients or those who are unable to cooperate in active ECG self-recording were excluded from the study. TWA was detected and its amplitude was quantified using the novel enhanced adaptive match filter (EAMF) method.Results: A total of 175 patients, 114 COVID-19 patients (polymerase chain reaction (PCR)-positive group), and 61 non-COVID-19 pa-tients (PCR-negative group) were enrolled in the study. PCR-positive group was subdivided according to the severity of COVID-19 pathology into mild and moderate severity subgroups. Baseline TWA levels were similar between both groups during admission (42.47 ± 26.52 µV vs. 44.72 ± 38.21 µV), but higher TWA levels were observed during discharge in the PCR-positive compared to the PCR-negative group (53.45 ± 34.42 µV vs. 25.15 ± 17.64 µV, P = 0.03). The correlation between PCR-positive result in COVID-19 and TWA value was significant, after adjustment of other confounding variables (R 2 = 0.081, P = 0.030). There was no significant difference in TWA levels between mild and moderate severity subgroups in patients with COVID-19, both during admission (44.29 ± 27.14 µV vs. 36.75 ± 24.46 µV, P = 0.34) and discharge (49.47 ± 33.62 µV vs. 61.09 ± 35.99 µV, P = 0.33). Conclusions:Higher TWA values can be observed on follow-up ECG obtained during discharge in the PCR-positive COVID-19 patients.

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“…Figure 1 shows a framework for collecting and analyzing ECG device data from a cloud server. Once the ECG data are collected, they are transferred to the mobile terminal via Bluetooth and displayed in real time, then transmitted to the cloud through WIFI or 4G [39]. The DL architecture then validates and analyzes the incoming HRV signals in real time.…”

Section: Methodsmentioning

confidence: 99%

AFibNet: an implementation of atrial fibrillation detection with convolutional neural network

Tutuko

Nurmaini

Tondas³

et al. 2021

BMC Med Inform Decis Mak

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Background Generalization model capacity of deep learning (DL) approach for atrial fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL model formation used only a single frequency sampling of the specific device. Besides, each electrocardiogram (ECG) acquisition dataset produces a different length and sampling frequency to ensure sufficient precision of the R–R intervals to determine the heart rate variability (HRV). An accurate HRV is the gold standard for predicting the AF condition; therefore, a current challenge is to determine whether a DL approach can be used to analyze raw ECG data in a broad range of devices. This paper demonstrates powerful results for end-to-end implementation of AF detection based on a convolutional neural network (AFibNet). The method used a single learning system without considering the variety of signal lengths and frequency samplings. For implementation, the AFibNet is processed with a computational cloud-based DL approach. This study utilized a one-dimension convolutional neural networks (1D-CNNs) model for 11,842 subjects. It was trained and validated with 8232 records based on three datasets and tested with 3610 records based on eight datasets. The predicted results, when compared with the diagnosis results indicated by human practitioners, showed a 99.80% accuracy, sensitivity, and specificity. Result Meanwhile, when tested using unseen data, the AF detection reaches 98.94% accuracy, 98.97% sensitivity, and 98.97% specificity at a sample period of 0.02 seconds using the DL Cloud System. To improve the confidence of the AFibNet model, it also validated with 18 arrhythmias condition defined as Non-AF-class. Thus, the data is increased from 11,842 to 26,349 instances for three-class, i.e., Normal sinus (N), AF and Non-AF. The result found 96.36% accuracy, 93.65% sensitivity, and 96.92% specificity. Conclusion These findings demonstrate that the proposed approach can use unknown data to derive feature maps and reliably detect the AF periods. We have found that our cloud-DL system is suitable for practical deployment

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Minimal or No Touch Electrocardiography Recording and Remote Heart Rhythm Monitoring during COVID-19 Pandemic Era

Cited by 7 publications

References 28 publications

Comparison of two smartphone based atrial fibrillation screening application in Indonesian population

Comparison of two smartphone based atrial fibrillation screening application in Indonesian population

Is T-Wave Alternans a Repolarization Abnormality Marker in COVID-19? An Investigation on the Potentialities of Portable Electrocardiogram Device

AFibNet: an implementation of atrial fibrillation detection with convolutional neural network

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