ECG Beat Classification on Edge Device

Hou, Dennis; Hou, Mingzhen; Hou, Janpu

doi:10.1109/icce46568.2020.9043116

Cited by 11 publications

(10 citation statements)

References 11 publications

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“…[10] Hou, Yuan Zheng et al used a smartphone camera combined with 3dlicer software to realize the projection positioning of lesions. [11] This method we introduced can be used to mark the scalp directly without additional projection equipment or smartphone. It is simpler and cheaper.…”

Section: Discussionmentioning

confidence: 99%

Application of Computer-aided Image Reconstruction and Image Guild in Parasagittal Meningioma Resection

Zhang

Muhammad

2021

Preprint

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BackgroundIn recent years, due to increased cranial imaging more and more small sized (diameter <2.5cm) meningiomas are diagnosed. If symptomatic then most of them need surgical removal. Exact location of the lesion is extremely important to tailor the craniotomy especially if the neuro-navigation system is not available. Many under developed countries cannot afford high costs of neuro-navigation. Hence, is relevant to discover low cost associated and effective methods to exactly locate the lesions for the surgery.MethodsBy using localization markers, we can acquire preoperative CT images of the patients, reconstruct these images into Three Dimensional (3D) virtual graphs using a computer, measure the spatial distance of the tumor from the markers, and in turn calculate the projection location of the tumor on the scalp by the Triangle Pythagorean theorem. Thereby achieving preoperative precise localization of intracranial microlesions.ResultsThe location of the tumor was consistent with that of the preoperative virtual image, and the craniotomy was exact. The patient was discharged 3 days later without any neurological deficits.ConclusionThis method is simple and reliable, inexpensive, and accurate in location of small sized lesion, which can partially compensate lack of neuro-navigationand is suitable for widespread application in hospitals in third world countries.

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Section: Discussionmentioning

confidence: 99%

Application of Computer-aided Image Reconstruction and Image Guild in Parasagittal Meningioma Resection

Zhang

Muhammad

2021

Preprint

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“…Other works with which we are confronted are [40]- [43]. Table 1 lists and describes the main works we were confronted with.…”

Section: Related Workmentioning

confidence: 99%

“…In [43] they obtain good results in terms of accuracy, although in the work there are not many references to how they were calculated and there are no supporting confusing matrix, making the calculation of the remaining parameters not possible. Here too, a Raspberry is used as a reference platform, which leads to a significantly increasing of power consumption if compared to those obtained in our work.…”

Section: Work Comparisonmentioning

confidence: 99%

An Adaptive Cognitive Sensor Node for ECG Monitoring in the Internet of Medical Things

et al. 2022

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The Internet of Medical Things (IoMT) paradigm is becoming mainstream in multiple clinical trials and healthcare procedures. Cardiovascular diseases monitoring, usually involving electrocardiogram (ECG) traces analysis, is one of the most promising and high-impact applications. Nevertheless, to fully exploit the potential of IoMT in this domain, some steps forward are needed. First, the edgecomputing paradigm must be added to the picture. A certain level of near-sensor processing has to be enabled, to improve the scalability, portability, reliability and responsiveness of the IoMT nodes. Second, novel, increasingly accurate data analysis algorithms, such as those based on artificial intelligence and Deep Learning, must be exploited. To reach these objectives, designers, and programmers of IoMT nodes, have to face challenging optimization tasks, in order to execute fairly complex computing tasks on low-power wearable and portable processing systems, with tight power and battery lifetime budgets. In this work, we explore the implementation of a cognitive data analysis algorithm, based on a convolutional neural network trained to classify ECG waveforms, on a resource-constrained microcontroller-based computing platform. To minimize power consumption, we add an adaptivity layer that dynamically manages the hardware and software configuration of the device to adapt it at runtime to the required operating mode. Our experimental results show that adapting the node setup to the workload at runtime can save up to 50% power consumption. Our optimized and quantized neural network reaches an accuracy value higher than 97% for arrhythmia disorders detection on MIT-BIH Arrhythmia dataset. INDEX TERMSAdaptive system, health information management, Internet of Things, low power electronics, neural network, remote sensing, runtime, wearable sensors.

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“…Biometrics has been widely applied in various consumer electronic products such as mobile phones and wearable devices to authenticate users with high performance [ 1 , 2 ]. For example, authentication systems based on fingerprint, iris, and face recognition are replacing passwords, given their reliability with adequate processing speed and better security than traditional authentication methods [ 3 , 4 , 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning

Kim

Yang

Lee

et al. 2021

Sensors

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Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose an adaptive ECG-based authentication method that performs incremental learning to identify ECG signals from a subject under a variety of measurement conditions. An incremental support vector machine (SVM) is adopted for authentication implementing incremental learning. We collected ECG signals from 11 subjects during 10 min over six days and used the data from days 1 to 5 for incremental learning, and those from day 6 for testing. The authentication results show that the proposed system consistently reduces the false acceptance rate from 6.49% to 4.39% and increases the true acceptance rate from 61.32% to 87.61% per single ECG wave after incremental learning using data from the five days. In addition, the authentication results tested using data obtained a day after the latest training show the false acceptance rate being within reliable range (3.5–5.33%) and improvement of the true acceptance rate (70.05–87.61%) over five days.

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ECG Beat Classification on Edge Device

Cited by 11 publications

References 11 publications

Application of Computer-aided Image Reconstruction and Image Guild in Parasagittal Meningioma Resection

Application of Computer-aided Image Reconstruction and Image Guild in Parasagittal Meningioma Resection

An Adaptive Cognitive Sensor Node for ECG Monitoring in the Internet of Medical Things

Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning

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