iKardo: An Intelligent ECG Device for Automatic Critical Beat Identification for Smart Healthcare

Maji, Prasenjit; Mondal, Hemanta Kumar; Roy, Arka; Poddar, Soumyajit; Mohanty, Saraju P.

doi:10.1109/tce.2021.3129316

Cited by 15 publications

(9 citation statements)

References 26 publications

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“…The problem becomes more concerning when the minority class is the class of interest and has a high error cost. This situation has been reported in the literature across a wide variety of problem domains, including medicine [138,139] , oil and gas industry [140] , finance [141] , and banking [142] .…”

Section: Data Biasmentioning

confidence: 71%

Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward

Elyan¹,

Vuttipittayamongkol²,

Johnston³

et al. 2022

Art Int Surg

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The recent development in the areas of deep learning and deep convolutional neural networks has significantly progressed and advanced the field of computer vision (CV) and image analysis and understanding. Complex tasks such as classifying and segmenting medical images and localising and recognising objects of interest have become much less challenging. This progress has the potential of accelerating research and deployment of multitudes of medical applications that utilise CV. However, in reality, there are limited practical examples being physically deployed into front-line health facilities. In this paper, we examine the current state of the art in CV as applied to the medical domain. We discuss the main challenges in CV and intelligent data-driven medical applications and suggest future directions to accelerate research, development, and deployment of CV applications in health practices. First, we critically review existing literature in the CV domain that addresses complex vision tasks, including: medical image classification; shape and object recognition from images; and medical segmentation. Second, we present an in-depth discussion of the various challenges that are considered barriers to accelerating research, development, and deployment of intelligent CV methods in real-life medical applications and hospitals. Finally, we conclude by discussing future directions.

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Section: Data Biasmentioning

confidence: 71%

Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward

Elyan¹,

Vuttipittayamongkol²,

Johnston³

et al. 2022

Art Int Surg

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“…As a consequence, healthcare services with faceto-face settings have been forced to search for a new means of delivery to ensure client and provider safety. Telehealth is a smart healthcare option [1]- [3] that is able to cope with this challenge via well-developed internet and corresponding tools. Telehealth provides healthcare services from professionals using information and communication technologies when distance is a critical factor.…”

Section: Introductionmentioning

confidence: 99%

Remote-Control Sound Pattern Recognition for Auditory Assessment and Therapy in Smart Healthcare

2023

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A remote-control method is proposed for healthcare services involving auditory assessment and therapy through video-conferencing software. Different video-conferencing software platforms have different sound responses, and the user computer also has variations in sound performance because of its hardware and software configuration. These restrictions affect the ability of video-conferencing software to deliver health services that require accurate, reliable, and repeatable sound. In this paper, a remote-control system board is developed wherein sound for auditory assessment or therapy is stored. A low-cost processor is used to remotely deliver sound by sound pattern recognition. Service providers play a sound pattern locally to remotely control the system board. The desired audio files can be delivered to the client side without suffering from sound loss in terms of frequency range and loudness. Experiments were also conducted to verify the performance of the proposed method. The results show that the remote-control method for audio playback can be realized with a low-cost processor, such as a microcontroller unit for sound pattern recognition and that it accurately delivers audio files in terms of frequency and amplitude.INDEX TERMS healthcare, microcontroller unit, pattern recognition, telehealth.

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“…Various power efficiency strategies have been applied to automatic CAs classification systems to enable long-term health monitoring services [10][11][12]. Maji et al proposed an adaptive power management approach to classify critical and non-critical signals before further DL-based ECG classification [13]. Hierarchical classification models can efficiently reduce power consumption [13].…”

mentioning

confidence: 99%

“…Maji et al proposed an adaptive power management approach to classify critical and non-critical signals before further DL-based ECG classification [13]. Hierarchical classification models can efficiently reduce power consumption [13]. Furthermore, they employed typical data augmentation approaches, including SMOTE and BIRCH [14,15], to improve performance in terms of power consumption [13].…”

mentioning

confidence: 99%

“…Hierarchical classification models can efficiently reduce power consumption [13]. Furthermore, they employed typical data augmentation approaches, including SMOTE and BIRCH [14,15], to improve performance in terms of power consumption [13]. Biagetti et al proposed a low-cost wireless system to transmit the acquired surface electromyographic and ECG signals through a 2.4 GHz radio link based on the IEEE 802.15.4 physical layer [16].…”

mentioning

confidence: 99%

See 1 more Smart Citation

SRECG: ECG Signal Super-Resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification

Chen

Tsai²,

Tseng

et al. 2023

IEEE Trans. Consumer Electron.

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A combination of cloud-based deep learning (DL) algorithms with portable/wearable (P/W) devices has been developed as a smart heath care system to support automatic cardiac arrhythmias (CAs) classification using electrocardiography (ECG). However, long-term and continuous ECG monitoring is challenging because of limitations of batteries and transmission bandwidth of P/W devices while incorporated with consumer electronics (CE). A feasible approach to address this challenge is to decrease sampling rates. However, low sampling rates lead to lowresolution signals that hinder the CAs classification performance. In this study, we propose a DL-based ECG signal super-resolution framework (called SRECG) to enhance low-resolution ECG signals by jointly considering the accuracies when applied to the DLbased high-resolution multiclass classifier (HMC) of CAs. In our experiments, we downsampled the ECG signals from the CPSC2018 dataset and evaluated their HMC accuracies with and without the SRECG. Experimental results show that SRECG can well improve the HMC accuracies as compared to traditional interpolation methods. Moreover, approximately half of the CAs classification accuracies of HMC were maintained within the enhanced ECG signals by SRECG. The promising results confirm that SRECG can be suitably used to enhance low-resolution ECG signals from P/W devices with CE to improve their cloud-based HMC performances.

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iKardo: An Intelligent ECG Device for Automatic Critical Beat Identification for Smart Healthcare

Cited by 15 publications

References 26 publications

Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward

Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward

Remote-Control Sound Pattern Recognition for Auditory Assessment and Therapy in Smart Healthcare

SRECG: ECG Signal Super-Resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification

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