Low-power low-data-loss bio-signal acquisition system for intelligent electrocardiogram detection

Wang, Liang-Hung; Dong, W.; Chen, Jianzhi; Wang, Faxiang; Fan, Min

doi:10.1587/elex.14.20161142

Cited by 23 publications

(6 citation statements)

References 13 publications

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“…(CVDs). CVD is one of the chronic aging diseases and is a leading cause of death, especially in the elderly [1]- [4]. Globally, the aging population is a major driver of this trend in cardiovascular mortality.…”

Section: Electrocardiogram (Ecg) Is a Non-invasive Diagnostic Tool Of...mentioning

confidence: 99%

Three-Heartbeat Multilead ECG Recognition Method for Arrhythmia Classification

Wang

Liu

et al. 2022

IEEE Access

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Electrocardiogram (ECG) is the primary basis for the diagnosis of cardiovascular diseases. However, the amount of ECG data of patients makes manual interpretation time-consuming and onerous. Therefore, the intelligent ECG recognition technology is an important means to decrease the shortage of medical resources. This study proposes a novel classification method for arrhythmia that uses for the very first time a three-heartbeat multi-lead (THML) ECG data in which each fragment contains three complete heartbeat processes of multiple ECG leads. The THML ECG data pre-processing method is formulated which makes use of the MIT-BIH arrhythmia database as training samples. Four arrhythmia classification models are constructed based on one-dimensional convolutional neural network (1D-CNN) combined with a priority model integrated voting method to optimize the integrated classification effect. The experiments followed the recommended inter-patient scheme of the Association for the Advancement of Medical Instrumentation (AAMI) recommendations, and the practicability and effectiveness of THML ECG data are proved with ablation experiments. Results show that the average accuracy of the N, V, S, F, and Q classes is 94.82%, 98.10%, 97.28%, 98.70%, and 99.97%, respectively, with the positive predictive value of the N, V, S, and F classes being 97.0%, 90.5%, 71.9%, and 80.4%, respectively. Compared with current studies, the THML ECG data can effectively improve the morphological integrity and time continuity of ECG information and the 1D-CNN model of ECG sequence has a higher accuracy for arrhythmia classification. The proposed method alleviates the problem of insufficient samples, meets the needs of medical ECG interpretation and contributes to the intelligent dynamic research of cardiac disease.INDEX TERMS Arrhythmia classification, electrocardiogram, one-dimensional convolutional neural network (1D-CNN), priority model integrated voting method, three-heartbeat multi-lead (THML). LU XU was born in Jian, Jiangxi, in 1995. She received the B.S. degree in communication engineering from Yantai University, Shandong, China, in July 2017. She was graduated at Fuzhou University, in 2020. Her research interests include deep learning and arrhythmia classification.

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Section: Electrocardiogram (Ecg) Is a Non-invasive Diagnostic Tool Of...mentioning

confidence: 99%

Three-Heartbeat Multilead ECG Recognition Method for Arrhythmia Classification

Wang

Liu

et al. 2022

IEEE Access

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“…The area of the printed circuit board is 7.2 cm × 6.3 cm. It consists of four parts; the power supply, the front-end circuits of the ECG signal acquisition and amplification, the MCU, and the Bluetooth 4.0 communication module [25].…”

Section: System Architecturementioning

confidence: 99%

A Low-Power High-Data-Transmission Multi-Lead ECG Acquisition Sensor System

Wang

Zhang

Guan

et al. 2019

Sensors

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This study presents a low-power multi-lead wearable electrocardiogram (ECG) signal sensor system design that can simultaneously acquire the electrocardiograms from three leads, I, II, and V1. The sensor system includes two parts, an ECG test clothing with five electrode patches and an acquisition device. Compared with the traditional 12-lead wired ECG detection instrument, which limits patient mobility and needs medical staff assistance to acquire the ECG signal, the proposed vest-type ECG acquisition system is very comfortable and easy to use by patients themselves anytime and anywhere, especially for the elderly. The proposed study incorporates three methods to reduce the power consumption of the system by optimizing the micro control unit (MCU) working mode, adjusting the radio frequency (RF) parameters, and compressing the transmitted data. In addition, Huffman lossless coding is used to compress the transmitted data in order to increase the sampling rate of the acquisition system. It makes the whole system operate continuously for a long period of time and acquire abundant ECG information, which is helpful for clinical diagnosis. Finally, a series of tests were performed on the designed wearable ECG device. The results have demonstrated that the multi-lead wearable ECG device can collect, process, and transmit ECG data through Bluetooth technology. The ECG waveforms collected by the device are clear, complete, and can be displayed in real-time on a mobile phone. The sampling rate of the proposed wearable sensor system is 250 Hz per lead, which is dependent on the lossless compression scheme. The device achieves a compression ratio of 2.31. By implementing a low power design on the device, the resulting overall operational current of the device is reduced by 37.6% to 9.87 mA under a supply voltage of 2.1 V. The proposed vest-type multi-lead ECG acquisition device can be easily employed by medical staff for clinical diagnosis and is a suitable wearable device in monitoring and nursing the off-ward patients.

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“…With the rapid development of modern logistics, retail and production management, the bar code identification system, as an important means of automatic identification technology, has become a key tool to improve the efficiency and accuracy of information management 1 . In this system, photoelectric information technology, as its core component, directly affects the performance and stability of the whole identification system 2 .…”

Section: Introductionmentioning

confidence: 99%

Application of photoelectric information technology in the barcode identification system

Chen

2024

Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023)

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With the rapid development of modern logistics, retail and other fields, the bar code identification system plays a vital role in the automatic identification and tracking of items. This paper discusses the key application of photoelectric information technology in barcode identification system, introduces the technology of photoelectric sensor, image processing and pattern recognition, and its role in improving the efficiency and accuracy of barcode recognition. We prove the feasibility and superiority of photoelectric information technology in the barcode identification system.

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Low-power low-data-loss bio-signal acquisition system for intelligent electrocardiogram detection

Cited by 23 publications

References 13 publications

Three-Heartbeat Multilead ECG Recognition Method for Arrhythmia Classification

Three-Heartbeat Multilead ECG Recognition Method for Arrhythmia Classification

A Low-Power High-Data-Transmission Multi-Lead ECG Acquisition Sensor System

Application of photoelectric information technology in the barcode identification system

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