Development of an Arrhythmia Monitoring System and Human Study

Lee, Shuenn-Yuh; Huang, Peng-Wei; Liang, Ming-Chun; Hong, Jia-Hua; Chen, Ju‐Yi

doi:10.1109/tce.2018.2875799

Cited by 20 publications

(9 citation statements)

References 17 publications

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“…Several studies related to smart healthcare systems have utilized such devices to support visually impaired individuals and prevent falls in the elderly [69][70][71]. In addition, these devices can provide long-term physiological signal monitoring for clinical diagnosis and evaluation, such as electroencephalogram [72], ECG [73][74][75], and near-infrared spectroscopy [76].…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

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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“…Machine learning techniques can learn from historical energy consumption data and make predictions about future consumption patterns. This enables the development of energy management systems that can optimize energy consumption based on occupancy patterns, weather conditions, and other factors [7][8]. The several machine learning techniques have been proposed and tested for optimizing energy utilization in homes, including deep learning, reinforcement learning, and decision trees.…”

Section: Figure 1smart Homementioning

confidence: 99%

Optimizing Energy Consumption in Smart Homes Using Machine Learning Techniques

Kumar¹,

Sundaram

et al. 2023

E3S Web Conf.

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The increasing demand for energy utilization in smart homes has led to the exploration of machine learning techniques as a means to optimize energy consumption. This review paper explores the merits and demerits of using machine learning techniques for energy optimization in smart homes. Smart homes are becoming increasingly popular due to the potential benefits they offer, including increased energy efficiency, improved comfort, and enhanced security. However, to achieve these benefits, it is essential to optimize the energy utilization in smart homes. This paper presents machine learning techniques that have been used to optimize energy utilization in smart homes. In this paper proposed the using Stochastic Gradient Descent (SGD) algorithm for optimizing energy utilization in smart homes. However, challenges such as data privacy, accuracy of data collection, and cost may hinder the full adoption of these techniques.

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“…Electrocardiography (ECG), Electroencephalography (EEG) and Photoplethysmography (PPG) have all been incorporated into consumer electronics for disease monitoring and management in recent years. An ECG-based consumer electronic device was proposed for the monitoring of arrhythmia disease in [11]. A detailed review of ECG-based cardiovascular disease monitoring was presented in [12].…”

Section: Related Work In Consumer Electronics For Smart Healthcare a Existing Consumer Electronics For Smart Healthcarementioning

confidence: 99%

“…Health Monitoring [11]- [13], [15] Use of Electrocardiography (ECG), Electroencephalography (EEG) and Photoplethysmography (PPG) technologies to design consumer electronic products to monitor and manage diseases such as arrhythmia, epileptic seizures and so on.…”

Section: Categorymentioning

confidence: 99%

EZcap: A Novel Wearable for Real-Time Automated Seizure Detection From EEG Signals

Olokodana

Mohanty

Kougianos

et al. 2021

IEEE Trans. Consumer Electron.

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Epileptic seizures present a serious danger to the lives of their victims, rendering them unconscious, lacking control, and may even result in death only a few seconds after onset. This gives rise to a crucial need for an effective seizure detection method that is fast, accurate, and has the potential for mass market adoption. Kriging methods have a good reputation for high accuracy in spatial prediction, hence, their extensive use in geostatistics. This paper demonstrates the successful application of Kriging methods for an effective seizure detection device in an edge computing environment by modeling the brain as a spatial panorama. We hereby propose a novel wearable for real-time automated seizure detection from EEG signals using three different types of Kriging, namely, Simple Kriging, Ordinary Kriging and Universal Kriging. After multiple experiments with electroencephalogram (EEG) signals obtained from seizure patients as well as those from their healthy counterparts, the results reveal that the three Kriging methods performed very well in accuracy, sensitivity and latency of detection. It was found however, that Simple Kriging outperforms the other Kriging methods with a mean seizure detection latency of 0.81 sec, a perfect specificity, an accuracy of 97.50% and a sensitivity of 94.74%. The results in this paper compare well with other seizure detection models in the literature but their excellent seizure detection latency surpasses the performance of most existing works in seizure detection.

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Development of an Arrhythmia Monitoring System and Human Study

Cited by 20 publications

References 17 publications

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

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

Optimizing Energy Consumption in Smart Homes Using Machine Learning Techniques

EZcap: A Novel Wearable for Real-Time Automated Seizure Detection From EEG Signals

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