A 2.63 μW ECG Processor With Adaptive Arrhythmia Detection and Data Compression for Implantable Cardiac Monitoring Device

Yin, Yue; Abubakar, Syed Muhammad; Tan, Songyao; Yang, Peilin; Yang, Wendi; Jiang, Hanjun; Wang, Zhihua; Wen, Jia; Seng-Pan, U

doi:10.1109/tbcas.2021.3100434

Cited by 31 publications

(7 citation statements)

References 27 publications

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“…e model can objectively and accurately judge the financial risks of enterprises and put forward solutions according to the shortcomings of the model itself. Yin et al [8] studied the financial risks of enterprises by taking cash flow index as the early-warning variable of financial risks and built a model on this basis. e expected cash flow and risk cash flow are used as independent variables to construct a binary early warning model.…”

Section: Literature Reviewmentioning

confidence: 99%

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Using Decision Tree Classification and AdaBoost Classification to Build the Abnormal Data Monitoring System of Financial Accounting in Colleges and Universities

Jiang

2022

Computational Intelligence and Neuroscience

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In order to better solve the problems of low efficiency, large consumption of human resources, and relatively low degree of intelligence in the abnormal data monitoring system of financial accounting in colleges and universities under the background of current accounting computerization, this article takes data mining and neural network algorithm as the technical basis to build the abnormal data monitoring system of financial accounting in colleges and universities. This article uses data mining algorithm and neural network analysis technology to process the original accounting information of colleges and universities, effectively eliminate invalid data, retain valuable data, and improve the detection efficiency of abnormal accounting data. The system test results show that the accuracy of identifying 50 abnormal situations in the original accounting data of colleges and universities is more than 90% by using data mining and neural network model.

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Section: Literature Reviewmentioning

confidence: 99%

“…e expected cash flow and risk cash flow are used as independent variables to construct a binary early warning model. Finally, enterprises and nonenterprises are selected as empirical samples [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Using Decision Tree Classification and AdaBoost Classification to Build the Abnormal Data Monitoring System of Financial Accounting in Colleges and Universities

Jiang

2022

Computational Intelligence and Neuroscience

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“…Methodology SE +P [46] Adaptive derivative-based R-peak detection 98.2% 99.3% [37] Haar DWT-based R-peak detection 99.73% 99.94% [15] Pan-Tompkins algorithm based R-peak detection 99.76% 99.56% Our method Hilbert transform based R-peak detection 99.83% 99.65%…”

Section: Refmentioning

confidence: 99%

An Energy-Efficient ECG Processor Based on HDWT and a Hybrid Classifier for Arrhythmia Detection

Deng,

Ma,

Yang

et al. 2023

Applied Sciences

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Cardiac arrhythmia (CA) is a severe cardiac disorder that results in a significant number of fatalities worldwide each year. Conventional electrocardiography (ECG) devices are often unable to detect arrhythmia symptoms during patients’ hospital visits due to their intermittent nature. This paper presents a wearable ECG processor for cardiac arrhythmia (CA) detection. The processor utilizes a Hilbert transform-based R-peak detection engine for R-peak detection, a Haar discrete wavelet transform (HDWT) unit for feature extraction, and a Hybrid ECG classifier that combines linear methods and Non-Linear Support Vector Machines (NLSVM) classifiers to distinguish between normal and abnormal heartbeats. The processor is fabricated by the CMOS 110 nm process with an area of 1.34 mm2 and validated with the MIT_BIH Database. The whole design consumes 4.08 μW with an average classification accuracy of 97.34%.

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“…The output layer contains five neurons, representing a normal beat (NOR), left bundle branch block beat (LBBB), right bundle branch block beat (RBBB), premature ventricular contraction beat (PVC), and atrial premature beat (APB). The calculation of each neuron is the same as (6). To obtain the probability of each type, Softmax is used as the transfer function, which is defined as:…”

Section: Classification Enginementioning

confidence: 99%

“…However, this SVM classifier can only achieve two-class classification, and the specific types of arrhythmia cannot be obtained. An adaptive derivative-based detection algorithm for potential arrhythmia recording is proposed to detect arrhythmia with the occasional abnormal heart beats [6], while it is also not applicable to the classification with multiple types. Neural network (NN)-based classifiers are widely adopted in ECG processors.…”

Section: Introductionmentioning

confidence: 99%

A Low-Power ECG Processor ASIC Based on an Artificial Neural Network for Arrhythmia Detection

Zhang,

Chang,

Guan

et al. 2023

Applied Sciences

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The early detection of arrhythmia can effectively reduce the risk of serious heart diseases and save time for treatment. Many healthcare devices have been widely used for electrocardiogram (ECG) monitoring. However, most of them can only complete simple two-classes detection and have unacceptable hardware overhead and energy consumption. For achieving accurate and low-power arrhythmia detection, a novel ECG processor application specific integrated circuit (ASIC) is proposed in this paper, which can perform the prediction of five types of cardiac arrhythmias and heart rate monitoring. To realize hardware-efficient R-peak detection, an ECG pre-processing engine based on a first derivative and moving average comparison method is proposed. Efficient arrhythmia detection is realized by the proposed low-power classification engine, which is based on a carefully designed lightweight artificial neural network (ANN) with good prediction accuracy. The hardware reuse strategy is used to implement the hardware logic of ANN, where computations are executed by only one processing unit (PU), which is controlled by a flexible finite state machine (FSM). Also, the weights of ANN are configurable to facilitate model updates. We validate the functionality of the design using real-world ECG data. The proposed ECG processor is implemented using 55 nm CMOS technology, occupying an area of 0.33 mm2. This design consumes 12.88 μW at a 100 kHz clock frequency, achieving a classification accuracy of 96.69%. The comparison results with previous work indicate that our design has advantages in detection performance and power consumption, providing a good solution for low-power and low-cost ECG monitoring.

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A 2.63 μW ECG Processor With Adaptive Arrhythmia Detection and Data Compression for Implantable Cardiac Monitoring Device

Cited by 31 publications

References 27 publications

Using Decision Tree Classification and AdaBoost Classification to Build the Abnormal Data Monitoring System of Financial Accounting in Colleges and Universities

Using Decision Tree Classification and AdaBoost Classification to Build the Abnormal Data Monitoring System of Financial Accounting in Colleges and Universities

An Energy-Efficient ECG Processor Based on HDWT and a Hybrid Classifier for Arrhythmia Detection

A Low-Power ECG Processor ASIC Based on an Artificial Neural Network for Arrhythmia Detection

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