An Adaptive Threshold Algorithm Combining Shifting Window Difference and Forward-Backward Difference in Real-Time R-Wave Detection

She, Liu; Wang, Guohua; Shi, Z.; Zhao, Jinshuan

doi:10.1109/cisp.2009.5304666

Cited by 10 publications

(10 citation statements)

References 4 publications

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“…In this section, the conventional method [20] and the proposed method are presented. The conventional method calculates a trigger value from the raw data.…”

Section: Detection Methodsmentioning

confidence: 99%

“…As shown in Tables 2-6, we found that the optimum threshold values having the maximum detection probabilities were changed depending on not only the offset values but the detection methods. For example, as shown in Table 7, in the conventional method [20], the optimum threshold value was 0.3 when the offset value was 1%. At 3% offset, the optimal value of threshold was changed to 0.25.…”

Section: Methodsmentioning

confidence: 99%

“…Both signals appear in the form of an impulse signal in a short time. R-wave detection in ECG uses a shifting window difference threshold (SWDT) [20]. Hence, SWDT was selected as a conventional method.…”

Section: Introductionmentioning

confidence: 99%

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A Frame Detection Method for Real-Time Hand Gesture Recognition Systems Using CW-Radar

Kim

Jung

et al. 2020

Sensors

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In this paper, a method to detect frames was described that can be used as hand gesture data when configuring a real-time hand gesture recognition system using continuous wave (CW) radar. Detecting valid frames raises accuracy which recognizes gestures. Therefore, it is essential to detect valid frames in the real-time hand gesture recognition system using CW radar. The conventional research on hand gesture recognition systems has not been conducted on detecting valid frames. We took the R-wave on electrocardiogram (ECG) detection as the conventional method. The detection probability of the conventional method was 85.04%. It has a low accuracy to use the hand gesture recognition system. The proposal consists of 2-stages to improve accuracy. We measured the performance of the detection method of hand gestures provided by the detection probability and the recognition probability. By comparing the performance of each detection method, we proposed an optimal detection method. The proposal detects valid frames with an accuracy of 96.88%, 11.84% higher than the accuracy of the conventional method. Also, the recognition probability of the proposal method was 94.21%, which was 3.71% lower than the ideal method.

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“…In this section, the conventional method [20] and the proposed method are presented. The conventional method calculates a trigger value from the raw data.…”

Section: Detection Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A Frame Detection Method for Real-Time Hand Gesture Recognition Systems Using CW-Radar

Kim

Jung

et al. 2020

Sensors

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“…The packet detection algorithms used on such sensors need to reduce the power consumption as much as possible while ensuring the detection rate. She [30] proposed an adaptive threshold algorithm combining shifting window difference (SWDT) and forward-backward (FBDT) difference in real-time R-wave detection. It can solve the problem of heavily loaded computation caused by the complicated algorithm of the traditional theory.…”

Section: Low Power Consumptionmentioning

confidence: 99%

Research on IEEE 802.11 OFDM Packet Detection Algorithms for Household Wireless Sensor Communication

2022

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Millions of smart home devices equipped with wireless sensors have gradually entered people’s homes and improved the quality of life. Wireless communication with sensors is crucial for remote automatic control of smart devices. Packet detection is one of the key technologies in wireless communication systems and faces the challenges of detection accuracy and power consumption. In households, WiFi devices are widely deployed. Therefore, we focus on IEEE 802.11 OFDM packet detection algorithms for household wireless sensor communication. We first introduce four packet detection algorithms and verify their feasibility by simulation experiments. Then, we identify two important factors that affect detection accuracy and provide suggestions for algorithm improvement. Further, we compare and discuss the performance of the four algorithms from three dimensions in detail. The results show that each algorithm has its own advantages, and the auto-correlation algorithm has the best overall performance. Finally, we point out the open challenges and future research directions in this field.

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“…Each ECG pulse comprises five points, known as P, Q, R, S, and T (Figure 1). Computer algorithms are sometimes used to automatically identify these points (e.g., [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]).…”

Section: Introductionmentioning

confidence: 99%

Accurate ECG R-peak detection for telemedicine

Liao

Rayside

2014

2014 IEEE Canada International Humanitarian Technology Conference - (IHTC)

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Electrocardiograms (ECGs) are usually recorded in a clinical setting by medical professionals using twelve leads attached to the patient. Our industry partner has developed a single-lead ECG machine for use by patients at home. Patients can then send these readings to remote doctors. The goal of the machines is to make medical expertise more accessible, affordable, and convenient.The ECGs recorded by patients with a single-lead suffer greatly from baseline wandering and high frequency noises, as compared to ECGs recorded with twelve-leads in a clinical setting.Accurate R-peak detection is an important step in ECG analysis. A variety of methods have been proposed in the past against standard clinical twelve-lead ECG recordings. In this study, we propose a new R-peak detection algorithm for singlelead mobile ECG recordings. Our area-based approach is built on the understanding that QRS complexes are typically narrow and tall, resulting in large areas over the curve around these locations. Our algorithm is simple to implement, computationally efficient, and does not require any signal pre-processing. This conceptual simplicity is a quality that distinguishes our approach from existing solutions.We evaluated our algorithm against data collected by patients from single-lead portable devices, and yielded 99.4% precision and 99.4% recall. The MIT/BIT Arrhythmia Database of twelvelead clinical ECG recordings was also used to verify our algorithm. On this dataset we obtained a precision of 99.3% and recall of 98.6%.

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An Adaptive Threshold Algorithm Combining Shifting Window Difference and Forward-Backward Difference in Real-Time R-Wave Detection

Cited by 10 publications

References 4 publications

A Frame Detection Method for Real-Time Hand Gesture Recognition Systems Using CW-Radar

A Frame Detection Method for Real-Time Hand Gesture Recognition Systems Using CW-Radar

Research on IEEE 802.11 OFDM Packet Detection Algorithms for Household Wireless Sensor Communication

Accurate ECG R-peak detection for telemedicine

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